{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2025-12-14T01:33:56.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2025-12-14T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":47058,"title":"Determine the Zeckendorf expansion of a number","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 135px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 67.5px; transform-origin: 407px 67.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14px 8.05px; transform-origin: 14px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Goldbach%27s_conjecture\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eGoldbach conjecture\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 294.442px 8.05px; transform-origin: 294.442px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e deals with expressing integers as sums of primes. One might also seek to express integers as sums of other numbers, such as the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Fibonacci_number\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eFibonacci numbers\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 199.892px 8.05px; transform-origin: 199.892px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, which begin 0, 1, 1, 2, 3, 5, 8, 13, 21... For example, 26 can be written as 13+8+5 or 21+3+2. \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Zeckendorf%27s_theorem\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eZeckendorf\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 255.608px 8.05px; transform-origin: 255.608px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (and Lekkerkerker before him) showed that every positive integer can be uniquely expressed as the sum of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 50.9667px 8.05px; transform-origin: 50.9667px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003enon-consecutive\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 196.242px 8.05px; transform-origin: 196.242px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e Fibonacci numbers. The Zeckendorf expansion for 26 is 21+5. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 372.5px 8.05px; transform-origin: 372.5px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to determine the Zeckendorf expansion of the input number. In particular, output in decreasing order the Fibonacci numbers to be used in the sum.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Zeckendorf(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 26;\r\ny_correct = [21 5];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 26;\r\ny_correct = [21 5];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 88;\r\ny_correct = [55 21 8 3 1];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 965;\r\ny_correct = [610 233 89 21 8 3 1];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 4180;\r\ny_correct = [2584 987 377 144 55 21 8 3 1];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 75024;\r\ny_correct = [46368 17711 6765 2584 987 377 144 55 21 8 3 1];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 514228;\r\ny_correct = [317811 121393 46368 17711 6765 2584 987 377 144 55 21 8 3 1];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 514229;\r\ny_correct = 514229;\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 8675309;\r\ny_correct = [5702887 2178309 514229 196418 75025 6765 1597 55 21 3];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 2022243254;\r\ny_correct = [1836311903 165580141 14930352 3524578 1346269 514229 28657 6765 233 89 34 3 1];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 314159265358;\r\ny_correct = [225851433717 86267571272 1836311903 165580141 24157817 9227465 3524578 1346269 75025 28657 6765 1597 144 8];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-24T08:38:37.000Z","updated_at":"2020-10-24T09:11:46.000Z","published_at":"2020-10-24T09:11:46.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Goldbach%27s_conjecture\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGoldbach conjecture\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e deals with expressing integers as sums of primes. One might also seek to express integers as sums of other numbers, such as the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Fibonacci_number\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eFibonacci numbers\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, which begin 0, 1, 1, 2, 3, 5, 8, 13, 21... For example, 26 can be written as 13+8+5 or 21+3+2. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Zeckendorf%27s_theorem\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eZeckendorf\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (and Lekkerkerker before him) showed that every positive integer can be uniquely expressed as the sum of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enon-consecutive\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Fibonacci numbers. The Zeckendorf expansion for 26 is 21+5. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to determine the Zeckendorf expansion of the input number. In particular, output in decreasing order the Fibonacci numbers to be used in the sum.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":49572,"title":"Compute the largest number possible with the Brussels choice","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 235.633px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 117.817px; transform-origin: 407px 117.817px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.208px 7.79167px; transform-origin: 374.208px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Brussels choice (or Choix de Bruxelles) operation changes one number to another by taking a substring of the digits and either doubling it or (if the number is even) halving it. For example, starting with 9 and highlighting substrings, one can generate this sequence:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.89167px 7.79167px; transform-origin: 3.89167px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e9\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.79167px; transform-origin: 3.88333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 7.79167px; transform-origin: 7.78333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e18\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.775px 7.79167px; transform-origin: 7.775px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, 3\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.89167px 7.79167px; transform-origin: 3.89167px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e6\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.79167px; transform-origin: 3.88333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 7.79167px; transform-origin: 7.78333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e31\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 11.6667px 7.79167px; transform-origin: 11.6667px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e2, 6\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 7.79167px; transform-origin: 7.78333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e22\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.79167px; transform-origin: 3.88333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 11.675px 7.79167px; transform-origin: 11.675px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e644\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.775px 7.79167px; transform-origin: 7.775px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, 1\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.89167px 7.79167px; transform-origin: 3.89167px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e2\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 35px 7.79167px; transform-origin: 35px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e88, 1488,...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 65.7417px 7.79167px; transform-origin: 65.7417px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAnother possibility is \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.89167px 7.79167px; transform-origin: 3.89167px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e9\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.775px 7.79167px; transform-origin: 7.775px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, 1\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.89167px 7.79167px; transform-origin: 3.89167px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e8\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.79167px; transform-origin: 3.88333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 11.2917px 7.79167px; transform-origin: 11.2917px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e116\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.79167px; transform-origin: 3.88333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 11.675px 7.79167px; transform-origin: 11.675px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e232\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.79167px; transform-origin: 3.88333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 7.79167px; transform-origin: 7.78333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e46\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.775px 7.79167px; transform-origin: 7.775px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e4, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 7.79167px; transform-origin: 7.78333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e92\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 15.5583px 7.79167px; transform-origin: 15.5583px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e4, 18\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 7.79167px; transform-origin: 7.78333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e44\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 27.2167px 7.79167px; transform-origin: 27.2167px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, 1888,...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 296.767px 7.79167px; transform-origin: 296.767px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBoth of these excerpts have eight entries, but the second ends in a larger number than the first. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 43.6333px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21.8167px; text-align: left; transform-origin: 384px 21.8167px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 129.008px 7.79167px; transform-origin: 129.008px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that takes an initial value \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB8AAAAoCAYAAAAG0SEsAAABPElEQVRYhe2WTRGDMBBGnwccxAAGUFAFdYADHNRCNCABD1hAAxbaQ3aHNCRp2kJ7yZvJgSzsx/4FoFKpVCqVP9ECPTAAnex1ct2eKToBi4h3gAVm4C7LnCF8FecL0AS2u2f7qbAvfjta+OI57yJ288L+MQ2wiuMxcY9mZSWeFcO+OYsY2KJKdfEk9inx/CqiBteYqSB2aNQxx4hTfbkhsGm5em9PS/SyN3KOlYl0ZhbiozfjgsqOpJ/yS8Tes2UmrHfr7YdY9hl5S/yKi2zkuSxWntMmnDN+bU5c316dN7IGtnnXyK0sbSYVyInHbE9oZP4a2VK8BvslAsXi4FI+4GoUNknqY3KY+CfomOXEi+f9XXSeYx+aom7/ltyc507MQ8idcNkxO4ob29ne4KLWsf0JBtdkp/5iVSqVIh6VmIITWgIeSQAAAABJRU5ErkJggg==\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 112.417px 7.79167px; transform-origin: 112.417px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and determines the largest number \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB8AAAAoCAYAAAAG0SEsAAABNklEQVRYhe2WW7GDMBBAjwccYAADUYCCOMABDq4FNCAhHmqhGrDA/WB3ss2E0mGS9idnJh9kyS77DNBoNBqNxo8YgAmYASd7Tp6HmkYD8BTjDliAB7DL6msY9qL8CXSJbDeyrxq2xv9KGx6NcpeR9xfy23TAJorXk3c0Khv5qNxmJnp1VsVB5KGkYYhenyl25uPmkoY/URy4jswtbMjHjHwiRqZqvlPjnqPtVl7TsvAapUHOennuRe65YDDGA4dnnRzWflfPF1m2I/S9nSNKXvRssi4noXpm10oM8Zbsp+jYnYgDSOvkozE8ihdT5sC7y6QjTkXbLbt8VFV0+FgvtYOKj+GUhVgPihZxrnuKosVmU6X5ropeNja3WgOB+B9QBc237XnNd6DCPWDRDkmn3iSr0Wj8hn/n1nOR3s62vwAAAABJRU5ErkJggg==\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 46.2833px 7.79167px; transform-origin: 46.2833px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e resulting after \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 63px 7.79167px; transform-origin: 63px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps. In the above examples, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB8AAAAoCAYAAAAG0SEsAAABPElEQVRYhe2WTRGDMBBGnwccxAAGUFAFdYADHNRCNCABD1hAAxbaQ3aHNCRp2kJ7yZvJgSzsx/4FoFKpVCqVP9ECPTAAnex1ct2eKToBi4h3gAVm4C7LnCF8FecL0AS2u2f7qbAvfjta+OI57yJ288L+MQ2wiuMxcY9mZSWeFcO+OYsY2KJKdfEk9inx/CqiBteYqSB2aNQxx4hTfbkhsGm5em9PS/SyN3KOlYl0ZhbiozfjgsqOpJ/yS8Tes2UmrHfr7YdY9hl5S/yKi2zkuSxWntMmnDN+bU5c316dN7IGtnnXyK0sbSYVyInHbE9oZP4a2VK8BvslAsXi4FI+4GoUNknqY3KY+CfomOXEi+f9XXSeYx+aom7/ltyc507MQ8idcNkxO4ob29ne4KLWsf0JBtdkp/5iVSqVIh6VmIITWgIeSQAAAABJRU5ErkJggg==\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 27.425px 7.79167px; transform-origin: 27.425px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e = 9 and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 36.5px; height: 18px;\" width=\"36.5\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 129.517px 7.79167px; transform-origin: 129.517px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Return the number as a character string.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function a = ChoixdeBruxelles(a0,n)\r\n%  a0 = initial value in the sequence\r\n%  n  = number of steps\r\n  a = f(n);\r\nend","test_suite":"%%\r\na0 = 1;\r\nn = 0;\r\na_correct = '1';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 1;\r\nn = 4;\r\na_correct = '16';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 1;\r\nn = 5;\r\na_correct = '112';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 1;\r\nn = 10;\r\na_correct = '88224';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 1;\r\nn = 20;\r\na_correct = '811281128164416';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 1;\r\nn = 30;\r\na_correct = '8112811281128112811288224';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 1;\r\nn = 50;\r\na_correct = '811281128112811281128112811281128112811288224';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 2;\r\nn = 29;\r\na_correct = '8112811281128112811288224';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 3;\r\nn = 20;\r\na_correct = '11281128112816448';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 7;\r\nn = 4;\r\na_correct = '2112';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 7;\r\nn = 15;\r\na_correct = '1681128164416';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 7;\r\nn = 50;\r\na_correct = '168112811281128112811281128112811281128112816448';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 29;\r\nn = 6;\r\na_correct = '844416';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 11;\r\nn = 37;\r\na_correct = '412316860416';\r\nassert(isequal(num2str(prod(ChoixdeBruxelles(a0,n)-'0')),a_correct))","published":true,"deleted":false,"likes_count":3,"comments_count":2,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":"2021-01-02T23:31:04.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2020-12-23T22:47:46.000Z","updated_at":"2025-12-16T15:11:43.000Z","published_at":"2020-12-23T23:16:59.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Brussels choice (or Choix de Bruxelles) operation changes one number to another by taking a substring of the digits and either doubling it or (if the number is even) halving it. For example, starting with 9 and highlighting substrings, one can generate this sequence:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e9\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e18\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, 3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e6\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e31\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e2, 6\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e22\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e644\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, 1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e88, 1488,...\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAnother possibility is \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e9\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, 1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e8\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e116\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e232\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e46\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e4, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e92\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e4, 18\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e44\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, 1888,...\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBoth of these excerpts have eight entries, but the second ends in a larger number than the first. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that takes an initial value \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and determines the largest number \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e resulting after \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e steps. In the above examples, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e = 9 and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en = 7\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. Return the number as a character string.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58528,"title":"ICFP 2023:Orchestra - Greedy algorithm for Musician Placement","description":"The ICFP 2023 Competition in July was to place musicians on a stage to maximize the attendees net happiness. The musicians played various instruments with attendees having preference values for each instrument type. Musicians could block attendees from seeing musicians behind them. Blocking occurs if a_i to m_j vector touched within 5 of m_k. No musicians allowed within 10 of one another. Vector check is being done with a prior Cody 1446 solution variant.  Volume per musician was allowed but not in this Challenge. Happiness drops off as the square of distance between musician and attendee.\r\nThe ICFP 2023 site will hopefully have contestant writeups. The ICFP 2023 Orchestra Spec shows details of the contest. I modified the problem contents to the sufficient elements regarding the stage. The joys of JSON can be viewed. The musician types were upgraded by 1 to be non-zero based.\r\n\r\nThis Challenge is to place the 16 musicians onto the stage,mxy, of Problem 22 using a Greedy algorithm using provided Happiness tables. Score an Attendee happiness of higher than 46 million; Best known 47221761. The standard inputs are detailed in the function template and include axy-attendee positions, am-attendee happiness factors to each instrument, mu-instruments of each musician, stage limits-[xmin xmax ymin ymax]. This stage is a line of ymin=ymax=10, xmin=10, and xmax=990, seen in the image below. The special performance tables provide Joy values for Musician types vs X. Table mscrit, matrix_score_XvsType, assumes no neighbors and mscrit10 assumes neighbors at +/-10 blocking thus gives less Joy.\r\nIndividual attendee happiness= 1000000*am(i,mu(j))/d2(i,j) for attendee-i and musician-j where d2 is distance squared.\r\nSimplest solution would be mxy(:,1)=10:10:160 where mxy(:,2)=10 but this scores only 13M suffering from 1/d2 and blocks. \r\nPlease submit the template to view the tables and graphs. The graphs show the mscrit and mscrit10 information.\r\n \r\nThis is the Orchestra room problem 22 showing the attendees and the stage. \r\nThe stage is the line on the bottom at Y=10 extending in X 10:990 .","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 862.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 431.25px; transform-origin: 407px 431.25px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 126px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 367px 8px; transform-origin: 367px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe ICFP 2023 Competition in July was to place musicians on a stage to maximize the attendees net happiness. The musicians played various instruments with attendees having preference values for each instrument type. Musicians could block attendees from seeing musicians behind them. Blocking occurs if a_i to m_j vector touched within 5 of m_k. No musicians allowed within 10 of one another. Vector check is being done with a prior Cody 1446 solution variant.  Volume per musician was allowed but not in this Challenge. Happiness drops off as the square of distance between musician and attendee.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 201.5px 8px; transform-origin: 201.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe ICFP 2023 site will hopefully have contestant writeups. The \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://drive.google.com/file/d/16GFrZMudBrNwjMi3tOaP_iiSHh5pUtXL/view?usp=sharing\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP 2023 Orchestra Spec\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 96.5px 8px; transform-origin: 96.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e shows details of the contest. I modified the problem contents to the sufficient elements regarding the stage. The joys of JSON can be viewed. The musician types were upgraded by 1 to be non-zero based.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 377.5px 8px; transform-origin: 377.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis Challenge is to place the 16 musicians onto the stage,mxy, of Problem 22 using a Greedy algorithm using provided Happiness tables. Score an Attendee happiness of higher than 46 million; Best known 47221761. The standard inputs are detailed in the function template and include axy-attendee positions, am-attendee happiness factors to each instrument, mu-instruments of each musician, stage limits-[xmin xmax ymin ymax]. This stage is a line of ymin=ymax=10, xmin=10, and xmax=990, seen in the image below. The special performance tables provide Joy values for Musician types vs X. Table mscrit, matrix_score_XvsType, assumes no neighbors and mscrit10 assumes neighbors at +/-10 blocking thus gives less Joy.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 373px 8px; transform-origin: 373px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIndividual attendee happiness= 1000000*am(i,mu(j))/d2(i,j) for attendee-i and musician-j where d2 is distance squared.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSimplest solution would be mxy(:,1)=10:10:160 where mxy(:,2)=10 but this scores only 13M suffering from 1/d2 and blocks. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 352px 8px; transform-origin: 352px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ePlease submit the template to view the tables and graphs. The graphs show the mscrit and mscrit10 information.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 319.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 159.75px; text-align: left; transform-origin: 384px 159.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 860px;height: 314px\" 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\" data-image-state=\"image-loaded\" width=\"860\" height=\"314\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 244px 8px; transform-origin: 244px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis is the Orchestra room problem 22 showing the attendees and the stage. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 210px 8px; transform-origin: 210px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe stage is the line on the bottom at Y=10 extending in X 10:990 .\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function mxy=Process_orc(d,mu,axy,am,pxyr,mscrit,mscrit10,min_score);\r\n% d [1000 1000 10 990 10 10] room_width room_h xmin xmax ymin ymax\r\n% mu [1 2 3 4 5 6 7 8 9 3 4 7 2 1 2 4]  the 16 musicians who have various instruments 1:9\r\n% axy [400,2]  (x,y) position for the 400 attendees\r\n% am  [400,9]  Each attendee's happiness co-factors per instrument types 1:9 seen in mu\r\n% mscrit [9,1000]  musician types, x-axis-pos   Score assuming no other musicians\r\n% mscrit10 [9,1000]  musician types, x-axis-pos Score assuming musicians at +/-10 blocking\r\n\r\n% No two mu may be within 10 of one another\r\n% If any other mxy is within a distance of 5 from the segment axy_i to mxy_j then Happiness_i_j=0\r\n%  This stage is on a line so there is minimal blocking\r\n%  The min distance from point to a segment is Cody \r\n%   https://www.mathworks.com/matlabcentral/cody/problems/1446\r\n%   The smallest solutions are often very slow\r\n%\r\n% Happiness_i_j=1000000*am(i,mu(j))/d2am(i,j); where d2am(i,j) distance squared axy_i,mxy_j\r\n% Score is sum of Happiness\r\n% Best known solution is 47221761 for problem 22\r\n\r\n% min_score of 46M can be achieved with either mscrit or mscrit10.\r\n\r\n  mxy=ones(length(mu),2)*d(5); % d(5) is ymin. This problem's stage is a line\r\n  mxy(:,1)=0;\r\n  \r\n  %Greedy Algorithm Scheme\r\n  %Place best scoring (Musician_type,X) of table type being used\r\n  % find [mu_type,x-position] of maximum score available in updating Table\r\n  % find musician of mu_type remaining to be placed, mu_loc\r\n  % mxy(mu_loc,1)=x-position\r\n  % clear placed mu \r\n  % if no musician of mu_type remains clear table of mu_type, wT(mu_type,:)=-Inf\r\n  % clear table for x_position +/-9 for all mu_type\r\n  % repeat until all musicians placed\r\n  \r\n  % Next line commented to execute greedy\r\n  mxy(:,1)=10:10:160; % Template weak solution to see Tables and figures.\r\n  \r\n  % Working tables that will be destroyed\r\n  wmu=mu;      % vector of musician types\r\n  wT=mscrit;   % Scores of musician type vs X assuming no neighbors blocking\r\n  wT=mscrit10; % Scores of musician type vs X assuming +/-10 are occupied and block\r\n  \r\n  tic\r\n  while nnz(mxy(:,1)==0)\r\n   if toc\u003e5,break;end % avoiding infinite loop\r\n   [mu_type,x]=find(wT==max(wT(:)),1,'first');\r\n   % 6  more lines here for a  complete greedy algorithm\r\n   %\r\n   % Block X-positions less than 10 of placed musician,assuming integers only, for all types\r\n   wT(:,x-9:x+9)=-Inf; % 0 is not sufficient as values may be negative\r\n  end % while mxy has unfilled cells\r\n  \r\n  %Greedy algorithm may be based on first/last/random max value\r\n  % The threshold may also be varied to .98*max() and then randomly select node to play\r\n  \r\n  \r\n  %mxy(:,2)=ymin;\r\n  %mxy(1,1)=10; %Manual entry\r\n  % ...\r\n  %mxy(16,1)=990;\r\n  \r\n  %mxy(:,1)=[10 40 ... 990];\r\nend","test_suite":"%%\r\n%Google Drive Dowloads need to come from shared files\r\n% Tweak link: file/d/ to uc?export=download\u0026id=   while removing /view?usp=sharing\r\n% https://drive.google.com/file/d/1v3GsGgP3p905wzdvUqypL_-djYmxiyzK/view?usp=sharing\r\n% https://drive.google.com/uc?export=download\u0026id=1v3GsGgP3p905wzdvUqypL_-djYmxiyzK\r\n fname='orc_d_mu_axy_am_pxyr.mat';\r\n %orc is a cell array orc{90} for the 90 Problems in ICFP 2023 Orchestra Competition\r\n \r\n % fn.mat  https://drive.google.com/file/d/10GsOZTIjzMIuO7xAYIqLT1zIq9Cyubl-/view?usp=drive_link\r\n % Google Gives warning thus aborts urlwrite\r\n %\r\n %fname='orc_d_mu_axy_am_pxyr.pdf'\r\n %Fake name of .pdf on GoogleDrive,  write as a mat\r\n \r\n url='https://drive.google.com/file/d/1mgxzsmVQNXgqHEdd61QR2r0STm3N9lgG/view?usp=drive_link';\r\n ptr=strfind(url,'/view'); % Tweaking the url\r\n url(ptr:end)=[];\r\n url=strrep(url,'file/d/','uc?export=download\u0026id=');\r\n \r\n tic\r\n urlwrite(url,fname); %Writing GoogleDrive orc.pdf into orc.mat\r\n fprintf('Download 14MB Time: %.1f  sec\\n\\n',toc); %14MB download Time, about 1-3 sec\r\n \r\ndir_struct=dir;\r\n\r\nfor i=1:size(dir_struct,1)\r\n fprintf('%i %s %i\\n',i,dir_struct(i).name,dir_struct(i).bytes)\r\nend\r\n\r\ntic\r\nload(fname);\r\nfprintf('\\n\\nmat Load Time: %.1f\\n',toc); %Load Time of orc from .mat, 0.1 sec\r\n\r\n%fname='orc_d_mu_axy_am_pxyr.mat'  orc cell array of size 90\r\npid=22; % Stage is a Line [10:990, 10:10] in what they call [X,Y]\r\nmin_score=46000000; %Best known score 47221761\r\nd=orc{pid}.d; %[1000 1000 10 990 10 10] room_width room_h xmin xmax ymin ymax\r\nmu=orc{pid}.mu; %[1 2 3 4 5 6 7 8 9 3 4 7 2 1 2 4]\r\naxy=orc{pid}.axy; %[400,2]\r\nam=orc{pid}.am;  %[400,9]  there are 9 musician types 1:9 seen in mu\r\npxyr=orc{pid}.pxyr; %[0,3] Pillars that do not exist in pid 22\r\nrw=d(1);rh=d(2);xmin=d(3);xmax=d(4);ymin=d(5);ymax=d(6);\r\nfprintf('xmin:%i xmax:%i ymin:%i ymax:%i\\n',d(3:6));\r\n\r\n Lmu=length(mu); % number of musicians\r\n nmut=max(mu); % number of musician types\r\n na=size(axy,1); % number of attendees\r\n mscr=zeros(Lmu,1); % Track individual scores\r\n mscrji=zeros(Lmu,na); % Scores of all attendees to placed musician location\r\n mscrit=zeros(nmut,rw);\r\n mscrit10=zeros(nmut,rw);\r\n d2am=zeros(na,Lmu); \r\n d2ax=ones(na,xmax)*Inf; \r\n \r\n tic\r\n for x=xmin:xmax\r\n  d2ax(:,x)=sum((axy-[x,ymin]).^2,2);\r\n end\r\n \r\n myj=10;\r\n myk=10;\r\n for i=1:na\r\n   ax=axy(i,1);\r\n   ay=axy(i,2);\r\n   for k=1:nmut\r\n    for x=xmin:xmax\r\n     mscrit(k,x)=mscrit(k,x)+1000000*am(i,k)/d2ax(i,x); % Calc k,x assume no issues\r\n     \r\n    %Calc Dist from vector [(mxj,myj),(ax,ay)] to point [mxk,myk]\r\n    %dist2 (x+10,ymin,x,ymin,ax,ay)\u003c=25 % x+10\r\n    mxj=x;\r\n    mxk=x+10; %check if adj blocks\r\n    d2=100; % set to valid\r\n    sL2=(mxj-ax)^2+(myj-ay)^2;\r\n    t=( (mxk-mxj)*(ax-mxj)+(myk-myj)*(ay-myj) )/sL2;\r\n    if t\u003e=0\r\n     if t\u003c=1\r\n      sx=mxj+t*(ax-mxj);\r\n      sy=myj+t*(ay-myj);\r\n      d2=(mxk-sx)^2+(myk-sy)^2;\r\n     end\r\n    end\r\n    if d2\u003c=25,continue;end\r\n    \r\n    %dist2 (x-10,ymin,x,ymin,ax,ay)\u003c=25 % x-10\r\n    mxk=x-10; % check if adj blocks\r\n    d2=100; % set to valid\r\n    sL2=(mxj-ax)^2+(myj-ay)^2;\r\n    t=( (mxk-mxj)*(ax-mxj)+(myk-myj)*(ay-myj) )/sL2;\r\n    if t\u003e=0\r\n     if t\u003c=1\r\n      sx=mxj+t*(ax-mxj);\r\n      sy=myj+t*(ay-myj);\r\n      d2=(mxk-sx)^2+(myk-sy)^2;\r\n     end\r\n    end\r\n    if d2\u003c=25,continue;end\r\n    \r\n     % Calc k,x assumes possible blocks\r\n     mscrit10(k,x)=mscrit10(k,x)+1000000*am(i,k)/d2ax(i,x); \r\n    end % x\r\n  end % k nmut\r\n end % i na\r\n\r\n%Main Function Call\r\nmxy=Process_orc(d,mu,axy,am,pxyr,mscrit,mscrit10,min_score);\r\n\r\n%Fixing out of bounds values\r\nmxy(:,2)=ymin;\r\nmxy(mxy(:,1)\u003cxmin,1)=xmin-10; \r\nmxy(mxy(:,1)\u003exmax,1)=xmin-10;\r\n\r\n%Any players with distance\u003c10 from any other player gets put to xmin-10\r\n\r\ngap_fail=mu\u003eInf; % Logical zero of size mu\r\nfor i=1:length(mu)\r\n mxy_dist=(mxy(:,1)-mxy(i,1)).^2;\r\n mxy_dist(i)=100; % set self distance to 100\r\n if min(mxy_dist)\u003c100, gap_fail(i)=1;end\r\nend\r\nmxy(gap_fail==1,1)=xmin-10; % Throw all gap failers to xmin, No pts will be scored \r\n\r\n% Calc dist squared from each a(attendee) to each m(Musician)\r\n for j=1:Lmu\r\n  d2am(:,j)=sum((axy-mxy(j,:)).^2,2);\r\n end\r\n \r\n scr=0;\r\n \r\n for i=1:na\r\n   ax=axy(i,1);\r\n   ay=axy(i,2);\r\n   \r\n  for j=1:Lmu\r\n   mscrji(j,i)=1000000*am(i,mu(j))/d2am(i,j); % Calc i,j assume no issues\r\n   \r\n   mxj=mxy(j,1);\r\n   if mxj\u003cxmin,continue;end % Skip failed musician placements\r\n   myj=mxy(j,2);\r\n   blocked=0;\r\n   \r\n   for k=1:Lmu %check of musician mxy(k,:) blocks mxy(j,:) from LOS to axy(i,:)\r\n    if k==j,continue;end % can't block self\r\n    mxk=mxy(k,1);\r\n    if mxk\u003cxmin,continue;end % Skip failed musician placements\r\n    myk=mxy(k,2);\r\n    \r\n    %Calc Dist from vector [(mxj,myj),(ax,ay)] to point [mxk,myk]\r\n    d2=100; % set to valid\r\n    sL2=(mxj-ax)^2+(myj-ay)^2;\r\n    t=( (mxk-mxj)*(ax-mxj)+(myk-myj)*(ay-myj) )/sL2;\r\n    if t\u003e=0\r\n     if t\u003c=1\r\n      sx=mxj+t*(ax-mxj);\r\n      sy=myj+t*(ay-myj);\r\n      d2=(mxk-sx)^2+(myk-sy)^2;\r\n     end\r\n    end\r\n    \r\n    if d2\u003c=25  %No one musician adj to vector within 5 or 25 for dist squared\r\n     blocked=1;\r\n     break; %exit upon block determination\r\n    end\r\n \r\n   end % k Lmu\r\n   % Blocked musician-j by any musician scores 0 for this attendee-i\r\n   if blocked==1,continue;end \r\n   \r\n   %musicians play different instruments\r\n   %attendees have like(+) and dislike(-) negative co-factors\r\n   %mu(j) gives instrument of musician\r\n   %am(i,mu(j)) gives attendee Happiness for insturment mu(j)\r\n   mscr(j)=mscr(j)+1000000*am(i,mu(j))/d2am(i,j); %Tracking indiv musician score\r\n   scr=scr+1000000*am(i,mu(j))/d2am(i,j);\r\n  end % j Lmu\r\n end % na\r\n \r\n \r\n for i=1:Lmu\r\n  fprintf('Player %2i xpos: %3i  Instr:%i  scr: %10.0f\\n',i,mxy(i,1),mu(i),mscr(i));\r\n end\r\n fprintf('\\n');\r\n \r\n fprintf('Total scr: %.0f ********************\\n\\n',scr);\r\n \r\n fprintf('Table of max Happiness X per Musician Type\\n');\r\n for i=1:nmut % 9\r\n  fprintf('Musician Type-%i  x: %3i  H: %9.0f\\n', ...\r\n      i,find(mscrit(i,:)==max(mscrit(i,:)),1,'first'),max(mscrit(i,:)));\r\n end\r\n \r\n fprintf('\\nTable of max Happiness per Musician Type; assumes adjacent Musicians blocking\\n');\r\n for i=1:nmut % 9\r\n  fprintf('Musician Type-%i  x: %3i  H: %9.0f\\n', ...\r\n      i,find(mscrit10(i,:)==max(mscrit10(i,:)),1,'first'),max(mscrit10(i,:)));\r\n end\r\n \r\n \r\n fprintf('\\n\\nThe following lines are the titles for the 7 figures lower down\\n\\n');\r\n %Figures/Titles\r\n fprintf ('Problem: %i Orchestra\\n',pid)\r\n \r\n figure(7);\r\n plot(axy(:,1),axy(:,2),'.k');hold on  % Attendees\r\n axis tight; %axis equal\r\n for i=1:size(pxyr,1) % rectangle -no matrix allowed  Pillars fast draw\r\n  rectangle('Position',[pxyr(i,1:2)-pxyr(i,3) 2*pxyr(i,3) 2*pxyr(i,3)],'Curvature',[1 1],'FaceColor',[.9 .4 .8])\r\n end\r\n room= [0 0;rw 0;rw rh;0 rh;0 0];\r\n stage=[xmin ymin;xmax ymin;xmax ymax;xmin ymax;xmin ymin];\r\n plot(stage(:,1),stage(:,2),'k');\r\n plot(room(:,1),room(:,2),'Color',[1 0 1]);\r\n plot(mxy(:,1),mxy(:,2),'.r'); % Musicians\r\n hold off\r\n \r\n \r\n%Plot of Happiness for each Musician Type as a function of X with No blocks\r\n for i=1:3 % 123 456 789 groups \r\n  fprintf('Musician Types-%i:%i rgb Happiness vs X No Blocks\\n', ...\r\n      3*(i-1)+1,3*(i-1)+3);\r\n  figure(i);plot((1:rw)',mscrit(3*(i-1)+1,:)','.r');hold on;\r\n  figure(i);plot((1:rw)',mscrit(3*(i-1)+2,:)','.','Color',[0 .8 0]);\r\n  figure(i);plot((1:rw)',mscrit(3*(i-1)+3,:)','.b');hold off;\r\n end\r\n \r\n %Plot of Happiness for each Musician Type as a function of X with blocks\r\n for i=1:3 % 9\r\n  fprintf('Musician Types-%i:%i rgb Happiness vs X with Blocks\\n', ...\r\n      3*(i-1)+1,3*(i-1)+3);\r\n  figure(i+3);plot((1:rw)',mscrit10(3*(i-1)+1,:)','.r');hold on\r\n  figure(i+3);plot((1:rw)',mscrit10(3*(i-1)+2,:)','.','Color',[0 .8 0]);\r\n  figure(i+3);plot((1:rw)',mscrit10(3*(i-1)+3,:)','.b');hold off\r\n end\r\n \r\nvalid=scr\u003emin_score; % Challenging Threshold 46M   Highest Score 47.2e6\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-07-15T18:43:58.000Z","deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-07-15T03:38:34.000Z","updated_at":"2023-07-15T18:43:58.000Z","published_at":"2023-07-15T18:37:05.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe ICFP 2023 Competition in July was to place musicians on a stage to maximize the attendees net happiness. The musicians played various instruments with attendees having preference values for each instrument type. Musicians could block attendees from seeing musicians behind them. Blocking occurs if a_i to m_j vector touched within 5 of m_k. No musicians allowed within 10 of one another. Vector check is being done with a prior Cody 1446 solution variant.  Volume per musician was allowed but not in this Challenge. Happiness drops off as the square of distance between musician and attendee.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe ICFP 2023 site will hopefully have contestant writeups. The \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://drive.google.com/file/d/16GFrZMudBrNwjMi3tOaP_iiSHh5pUtXL/view?usp=sharing\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP 2023 Orchestra Spec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e shows details of the contest. I modified the problem contents to the sufficient elements regarding the stage. The joys of JSON can be viewed. The musician types were upgraded by 1 to be non-zero based.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to place the 16 musicians onto the stage,mxy, of Problem 22 using a Greedy algorithm using provided Happiness tables. Score an Attendee happiness of higher than 46 million; Best known 47221761. The standard inputs are detailed in the function template and include axy-attendee positions, am-attendee happiness factors to each instrument, mu-instruments of each musician, stage limits-[xmin xmax ymin ymax]. This stage is a line of ymin=ymax=10, xmin=10, and xmax=990, seen in the image below. The special performance tables provide Joy values for Musician types vs X. Table mscrit, matrix_score_XvsType, assumes no neighbors and mscrit10 assumes neighbors at +/-10 blocking thus gives less Joy.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIndividual attendee happiness= 1000000*am(i,mu(j))/d2(i,j) for attendee-i and musician-j where d2 is distance squared.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSimplest solution would be mxy(:,1)=10:10:160 where mxy(:,2)=10 but this scores only 13M suffering from 1/d2 and blocks. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePlease submit the template to view the tables and graphs. The graphs show the mscrit and mscrit10 information.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"314\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"860\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is the Orchestra room problem 22 showing the attendees and the stage. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe stage is the line on the bottom at Y=10 extending in X 10:990 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58493,"title":"ICFP 2022 - Initial to Goal image using block remapping","description":"The ICFP2023 Challenge is Jul 7-10. Start is 0500 PT 7/7/23, Registraion is open. Posted 7/5/23. Updates at TwitICFP2023. These contests are insanely complicated and competitive. The challenge evolves every 12 hours with new files and rule updates.\r\nThe ICFP2022 HomePage has details of the RoboPaint contest. Twit2022, WriteUps, PriorEvents, Spec, ImageSubmissions, Puzzles, SawickiSolutions are useful links. .\r\nThis Challenge is to move the blocks in GI to minimze the error between GI and G based upon RGB distance. The contest had these Initial files but the leaders did not use due to the high Cost for moving small regions. Assuming a low cost remap function this is to find the optimal image score using GI blocks. For Starry Night, 28.png, best score found is 41141 using Robust Hill Climbing where all swaps tried and only the best improvement is implemented. Greedy would start at index 1 and find min error then index 2 would look only from 2 thru N. Max error index swaps to minimize its index error. Many methods are possible. Hill Climbing using the block medians is fast, easily coded, and scores well. Greedy is easiest code.\r\nInput: G-goal image,GI-initial image blocked, irc-row/col for each block, rgbGmed-median of Goal image block, rgbGI-block rgb values, v- remapping of GI blocks, scrmax-max allowed of Score(G,GI(remapped))\r\nOutput: v, [N,1] remapping GI([1:N]) to GI(v([1:N])\r\n   \r\n  ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 860px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 430px; transform-origin: 407px 430px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2023.github.io/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP2023\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 298px 8px; transform-origin: 298px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e Challenge is Jul 7-10. Start is 0500 PT 7/7/23, Registraion is open. Posted 7/5/23. Updates at \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://twitter.com/icfpcontest2023\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eTwitICFP2023\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 339px 8px; transform-origin: 339px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. These contests are insanely complicated and competitive. The challenge evolves every 12 hours with new files and rule updates.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2022.github.io/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP2022 HomePage\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 120px 8px; transform-origin: 120px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e has details of the RoboPaint contest. \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://twitter.com/icfpcontest2022\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eTwit2022\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2022.github.io/writeups/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eWriteUps\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.icfpconference.org/contest.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ePriorEvents\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2022.github.io/Specifications/spec_v2_4.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpec\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://gist.github.com/RafaelBocquet/e464c2d199f2d7a725cafdc22b30df7f\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eImageSubmissions\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://github.com/icfpc-unagi/icfpc2022/tree/main/problems\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ePuzzles\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSawickiSolutions\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.5px 8px; transform-origin: 55.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e are useful links. .\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis Challenge is to move the blocks in GI to minimze the error between GI and G based upon RGB distance. The contest had these Initial files but the leaders did not use due to the high Cost for moving small regions. Assuming a low cost remap function this is to find the optimal image score using GI blocks. For Starry Night, 28.png, best score found is 41141 using Robust Hill Climbing where all swaps tried and only the best improvement is implemented. Greedy would start at index 1 and find min error then index 2 would look only from 2 thru N. Max error index swaps to minimize its index error. Many methods are possible. Hill Climbing using the block medians is fast, easily coded, and scores well. Greedy is easiest code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eInput: G-goal image,GI-initial image blocked, irc-row/col for each block, rgbGmed-median of Goal image block, rgbGI-block rgb values, v- remapping of GI blocks, scrmax-max allowed of Score(G,GI(remapped))\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 153.5px 8px; transform-origin: 153.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOutput: v, [N,1] remapping GI([1:N]) to GI(v([1:N])\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 245.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 122.75px; text-align: left; transform-origin: 384px 122.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 240px;height: 240px\" 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ep+XK3MEnu9I/6nHS/zKB6e491tlhis6dtFsTtLvWB1Qbzqf/wufK3n9d3h8+7USURHQAhNajKVr3CboV4hwuVyKxF0lBchAQADtWcvnbtd89DOaex42BD4UA4E262+NJFbI/HybV738PH7nnTemmesQWyCJ9fHFr+zn5371VsqlPPaRY5Cw6QTVCiV7trf5s3c8ydi2kKgh8VRnEoOlL2QyFoVwbhAlgYLmnTfv4eNfHmWorDPukc01IW1+dJ5NGAmGK5p3v2U/L3rBHGZRpZNbN2GcZG+ZZ2Wss1a8kqExr3jnn0/x2btqVIoGswlJP6lQq41loQ7PPl/yttd7vOLbFARAAyJtexY7q2/ClD1ellRS4vUEFIG65R+/qHn/xyL2HrIMVcSGVT9PzvFD772Jyy7pxEIkdHrh/tOnn+gr1cqR40yRBFAjDT/7ukOM7WgT1h15WCviFWn/ySn5zFqBkta5uULJ299wiPMmWzRDiZT9rZYcJ0M3eVSKhpt//mle9II5onmFAJTMxkZOTh7QkataKxA4ea9uSEolw+/8zDO85sYTnFhUTnm0SZ6Xm+ssnhLUW25i/Kk3evzlewJe8VKFjSx6wcYE05kve/9eLXq/L2WsyDLueAjBa7/L4yO/XeCHXuMRRpZ26M5zvVMrlJIsLIZ8/FNPAJ0gv0y6Cz7x9Cx3fmOGcil3XeUYPKSAxYbiZVfP89Lr5jALCk8tb3X0IjsxKWmJQkF1POTN33mEKEq2ObcT2VaHJD/BuWmMEfzKDx/g4kvqhHMKJW1qdayEOHrRRfrKoiMBWvDL//Eg11xSZ6EhUSnpb9wzS6wAKQUn5i0X75G8/1cD/tObfEol4SZyQKnOd5J+GYNE9/4ESrlzi+Yto6OCt/+kzx/8l4BtI4K5RRvnkqzfQt8YS6nk8YV/3cfR481UxixNfAJf/Mp+Tsy20gbrOXIMEsa61ejrX3EM/KS/TCfJajVwJAI0Jd/xwlnOm2w73/3Az/rshrM+nEz3R//dYV7xkhNEMbG7yWlpwttqsMRyDAWFiuY9P76P8eGIUGelvetPItZapHDjZn7R8j0vV3z43QFXPUcSzbk8lmSidDkgYs0X1olcOOEFTwlMaNHzlhd/m+J//WbAt1+nmF9IXGnrRyKBrzg0U+crdxwAcBVLPCWx1nLr7QcIfBlLI/NVXI7BQQBhJNg53ubKCxrQki457TQmqOz2RgvKNc21l9ZptiViE7lFNjsS11WzJbl4qsmbXn0EGjJOfrNdSXNngl5LJGpKdp/f5P955VEW4+NtxDNLVFbGwmLT8tNv9Hj3zweUKwK96NxE2fjGentk3PFEep5SQrRgmZiQvPeXfd7wasXsYnf3wLVEdv9f/Mp+AFQSA3n8qTkeffwEhYJKmTbHmSFbfGy5QmTnCoSASAvOm2wzVNFYc2bjS4gkSAv4hkvPa3QFb3OcCrYrJvUj33WE0khEFAlkxm01KHRbIkBD8v0vO86l5zVpttY/fpVYHsa4pMBfeovPW37QxzbBRB2J6qmC40mn1mRb92OwRru/z/C97yUHTwl02+Xf/OLbAt76Oo/FxvqQSJKdXix43PvgUY4cbTjXH8Dd90wzN99G5e6rgSCrrhBCnpFS46xATCCTYyF4NjPZn/4u00lOC7aPRBQCizWn/FoOOveu0ZJcfUmD73zRLDRkGthei2GalWhHkaAyHvIDrzxKpOMcu3WKX6XvJtBoWX7xLT7f/70eetEdP8mydue69EYsCV5bSzLwhPQQXhFRGEL4lYG999mmTippOduCn3yzz1u+12Nu0WbKppzRoU4JzxMcn21yzwNHgLiY4j0PHOk6yRxnjoQ8jK5jrT6nlW0CFwMZKpkuAhkIrKBcMC4gO8Ddns1IypS0QsENVy4gqzpN8Fvb43biLrQkN1y5wPA6HbtzDgIpYKFu+ck3eHzfd3vo+e54Ql+XVY9FYhPSUD4iqCKkwjaPYWbuRz/+afRTt2CtGdh7n52fE5KzdfipH/Z5/asUs/NOIbaWJJxYbu22SQnEazQjHnrkmHNf5eqrgcANGoMQPu364/jFPahgDGy00ae2IUiyb+cWFUSDWeEKYcEIkJb5hiTSAn/Q5HQWQxtBtWS4/vKF9JmsVx6NAGwk2LU95MrzG3z1wSrVYlLqZO2Oa62LbZyYt7zx1Yof+wEfU19KHv2+B533GkB4RbAGu3AQc/hBzJGHsLPPYHUTwjre1W9BqAJWtxjUPe0lEWMsIhT8ylt9js3C527XDFcF0Rq13hBCYKwlCCQPPXrM3c+nnpnjyLGm0xYP/JDnJtIVh5CEjb1IVcELtmM5NwmEWIE1fdzrIpAzCdRaG49XATPHfNqhoFwkJ5AVQADtSLB7e4srzm9CKE5b1LDqY8cKI23Aqxiuu2yRL907tOYE5jKqBfN1y3VXSt7xo76rSWVByP7k0U0cLr4hZABSYY48hH7qc9hjj2HbiyCEI5Wohbr4u1DPehm2PY8r+Dl4yW9CIlpbVCB451t9nthn2HvIUiyINUvFsBZ8T/LU3jmOHm8hH39qlnojRMnc8hgkhJBY0yZs7sPoelrc6Vx0YxkLgW955Jki00d9hHdm7qa09pIE2oJ7vlV25rvNVVgrgnDB88nRiIJv111BK4R1NGFhcizC92xPZvpg4bLpBWFoGRsWvOsnfGRRoKPuyrLd3+kmDwDhlbH1aaJ7/5zwrvdipu/FGo3wywi/jDURYmgn6sJXQdggIY+1eueTmEjUsgxvF/zaf/LxfTBmbYVQSkkW6xFPPj2LfPKZecLQpCycY1AQGQJZpE/zx3MKnrIcnfP4+qNlCEw8yFfvcrJxsTqLK7B49KjPPY+VKRZceYxchXVqCFzi4GgtAt9gVpjMOShYK+KTgLGhyPV5WdPHZhESGi146+s8nnWJJKp31FZLz6/bZSWERKgC+qnPEd72O+i9/4qQCuGV4ok6LmilQ9SzXoYojmJNuKbimaw7y1OCaNFyzQsUP/hdHvN1Gwsi1uamSiloNiP2H1xA7juwgO/Jc1slNEAkZREQCh0eQ4fHMNE8CLdEPhfvcWIVSAl//blxzKJKS5hkk6ZOvZ/4H8JVPqVg+Ojnx5g57uOrnDhWCiGcC2nbcDR4UcMKjy8AjGB8OCGQNXRdScFi3fKCqyTf/yqFXSDO9E7OR3Rtn3zmXFYe1hrC+z5M9OBHsLqFCCrxth3ZnzURorIdueNa0C0Qaz+nZvctJdCAN73W45I9gkarW1E2ODjZcBQZ9h1cRB6cXsTzZO47HhCSFYkQHmHrANa0nAViNUli0LmGxNIoFwz3fKvMR28ZhbIh0pD4T051W7J5HpEWeCXDU0+U+OjnxyjHxfncJHTuEfTpIkr6eawzss860msrPXWBX/B9+Kk3eFDMxgf6T/Ad8vCxUZPo63+C2fslhFeOXdM6s511i0PdQm67IrY+1i/WmcZDhECHluq44K2v9+KGa2vhynLPy1OS6Zk68sixRmzKDfg45yg6BCGImgewVqOjeawNOZcnN2vdi1wpGt7795N8454q/nBEGCXuE0cyveMw+ayT+CbwCoawLvm1D04xX89aM/kgXgmsdb25j815EEk2IvxpAaTl2JxHO1qbZ+cCzU6y+/LrFc+/WqHrNs5+7x/36LI8dIvoG+/HzNyLCIawLE0uFIkJLRVi9BLicgjrmr3eCaqDrcOrXqy47kpJPe5jMuhFayJIOHq8iZybD9PGKTkGBYk1LcLWIYQMMOEJZ+Keg+4rB0FSmE9KS6glv/yB3dx/nyORxKpIkJBGdtxr4wjIq2jqi4q3v28P936rTLlo8lLuq4QFpLScWFCgO+S8XovINPalYHZREUZiTUhMxNnmhQBe/yrV9XkvbCwJTPK3LBDd+yHM4QcRQdUFy5eV+RoXDxmaAhOm+1lvJNniFATf929VKkxYi3lHKcGxE02k1oNLdsnh4MzcFlFrGiEDdDQb54Cc2xOcEK7bXdE3zBz3+Yn/fj4f+8R2tBF4VY3wOhOZjru7AQhpUSWNKhq+ft8QP/rbF/D5r9cYqmi0zi2P1cJJMS1PTwecWPDWXd+RyoUNPL6/QKQHLx92bh3XTfCG5yqef5XCNjsr8r6uK3An5RXRj38ac+huRx5xQuCyJ2kNeCVEcQSMc1Wv95uetUJowMteoLjyIkmjZeM44+DeERtLnxcWQ7xzvszGANHJ/1Do8DgmmkMID6sXMbqBkkVcw+RzEYnP2SWMFQJDFAne/b928U9fGeH1Lz/Gi65cYNtohPAs0jeu53YoqC8qHniowsdvHeWzdw1jLNSWkEc+flcD37PMHHcKtpfecAKzqLo6Dq4VkldESYteVNzxUIWCbzNdCgdznOx89m9vlFAA3YZ+1Zq6XFdeCXP0UfTjn0J4ZbAGccqEJQteEaSXPYHBXMhpINKWYERw0w2S+x8zlIqDt0KkEDQaER7kxRMHhY7szyNsHcKaVqziiDDRHCoYj0sinKv3vEMixjh3Vq2sefDJEr/ygSm2jURcdl6TXdvajNc0Cw3JzHGPh58psf9wQDsSVEvaqYhy8jgjSAHtUPC1R8q89NtOpJ+fSXLnqZCQh7UgAsuT+4o8ebBA4CfvxGDdkGFkmRwX3PBcBa0OeSznwhIIrA7Rj/6jYxuv2LE+Tn5lAz3v00VSC0sKC2146XWKP/sHjV6DzHQhoNHUeKfeNMdKkUp4kUStQ1jbQhBgTRsdnSCIfavnNjokYq3AYCkVnFU2v6j4yv1VIi3QxlWGVRIC3xD4hmLg3FrG5ORxZnC+8lLBcOu9Q/zYUZ9aVWN1kluwdiSSWKDSs9zytSEWGopaWcfuysEcNM06X4RXv1gyPikwDdv3mjquHQN+BfPMrZijD7mkwRXL7gWYaJOUQYhdVm3LhedJnvdswVfvtVRLrn/HIIgkWyz23M5uWxNIrG0RtQ4BKg6ohS4OwrmbC9INJ7dNSMAYt3JSylIpGoarmvFaxOiQZqisCeLYSCcoaNN95DgduEoAhcDy+IECf//FUSgYtFm7RMzEwjBG4AWWQ/sL/N0XxygGWQn2YOCUUS7X4wVXSVDESabLK6OEUNiwgdl7KwgXcBcrJgQBUTMNoAMbRibJ9WkDlOCG5ylX8VgMzuuRDXvkBDJgJCVMotYhhFDOILEGHc6m2+SChQQJkXT+bazAGGeBOOWVwKZkkf3JcfqILQ0DpYLhb24Z59h0gFcwqXU3yCGa3ZexQMHwl58Z59AxP3ZfDV4IEWnLUFlwxUUS2iwrZ00zyVUBe/RhzIknESpwn610wo3zQGgeSxOGN/INT7oaEsEVFwkqpZhQBnwMONfrawwQ2QKKOpxFR8fjlYxLNDLhCWyuxMqxiWCsIPAsh475/N7f7IBYaJRM6IMgkSUJoFXNbXfW+LsvjjFU7pS0GeR7IQSEEezZIdizQ8RS5eXOL7lIi5m+x7mixOqmRSEkNmpg5veD9NM8kI1CokAjhEvOk4zWXIXetUBOIANCNgM9ah10JQ/S8iUSHc1hbRh/liPHRsORhOvTovnEV0b44D9MIMoaY7pJ5HSJpF/1gGeeKvJrfzaVussGXcIkid+EEVy0R+AVBUZ3ypP0O0EhFLY1hzn6MKiA1XQmS/JHsAZ7/An33bR678a6sTCWWlVwwZQgjNYmrpXPZgNCJ4CuiFrTWNsmiXmAQocn3Oomt0BybCIkFQKqZc3NH5/g/35+FFWLsJBJ0FwdifStHlA2zJ/w+IWb93Bk1qMQ2IwYYpDWh3N4ag27JwUELFvp1yZ/Kh87txfbmkVI1X/jZY/nyANVwBz5JrZxDCE3hzbJGCCAXdsEUZRYl4MltZxABgoXMHcB9Ni3Cs6FpRcwpklOIDk2DzoVAgSu5P67/nyKD350BzIwqMCkFQJWYo30Eoc27rteLeLhR0u86T0X8MjeIpWiWcMEUCfI8H3YNdGp9dVv9d0pQ+Jh5/a6QPhpegiE9LCNw84Npgouf2SDE7SNBQKY2uEWCYI1yAcZ6N7OcSQZ6GHrEEJkVyECbOgC6UmQLQ+k59gUSFxZTjbte/Dev5vkHX+0h0PTgasQoJwLKlnJZ0vN9JadsXQKJKqSRkjLP31mnLf+3vk8ebDQU3rGHX/QMBZ8D0aGBJhOXKcXqfvJaGzjaPfnq0An/umh996Kbc+7Frd28FngK0VKFBZGhwSeWp0VuVJsDltri6MzQBQmmkOHRzsBdBLZW4SJZp2/dWV77fpfIlQS2PjvzFaxht7azrc6ypbc4jkdrGd1hs1QCSIhESFccuen7hjhzm9W+IFXHuMNrzhGdTyEyFUGsLHsuiOrTla3rkeLLBgIBXd8Y4gP/PN27n64SiEwlAu9lsfaXLONJbyVEpnXaLl3QYAJsa14cXcazyJNIFYBdvZpzNNfQF3yWmjPp/PAej9jlxgJGCgWhOsPsgbHyQlkYLAIoYja0y4DvcsUFlgToqMTdHJB+hl/S0lDSucrbmtJpCXGdKSuSXFCJa1LulOGwNMomZSGcPvMyeTk6Pdy9+sRYTOTUYe4T42OTLnblZIcY7mJZf0mnaybyiV3DpU0i03FH/7tJJ+8bZibXjDHDVcucNUFDfyiQUhHFgjrSs5oAZHgwLTPnd+s8uV7h7j13iqhdtUDjGXNLY8OLJ4UVEuxBcLy9ziNYUSNM4oyp3Jgr0j0+KcRo5cgt12GbS+m1siZPs9sAl9yzJQo+o1f4VaUQ+Xu/ieDRE4gA0CyAkF4LgPdtBCqjJu8k0GqXSA9Rvdg6n6yMn6R25Gi2fbwPc2u8QV2jS1QK7cYrrQYrTbxpOH4YpHZxQJz9QLTx8vsPVJjru5T8CMKvkYKm1bfWotyEVsV2fvfMfcdMTiXjHsmUoo0Ac3zJJ6SiPSz+PeI+DM6C10Lxhi0tmhjMcZgjEVrgzUWYy3G2MxkAElOzHLEsvaE0pngtREoaRkZ0uydKfC+f5jgw5/cxgU7W1y4q0W15BI+A88yuyBZaChmTvg8/HSRo3MeAqiUdE+dq/UgjzisIV0V3pWQvLV2IAKXtJKvbhPd92H8F/wMoroDGzbOmESy1YKT1ABYQUzDQrHQvwbYIOAlJ7fRJvRWhnuoAtBErRmSWaRTRyfOBYl6c0GSla17uYRwWdnzTR9PWabG53nRZfu58fL9XHvxNNVSG6QFT4OKl1aRBO1+WqHiwae38ZWHdvOVb07xxKERmqFHOQjxlMXQm7R1Lj3zbuLMtgS1cX0yKQVKSXzfoxAofN/9eL7CUzIlE/f9dE8nXbgm++5Yg6C1IYoMWmuiyBBFmkgb2m1NFGm0sVhjl5BJr1W0Nu9sxxoBV3Ms8FwZGW3g8f1FHn66lJaUcffNVYFV0hL4zgUGdFkd67lwce8RNNsrO6RbrXucqZOny5XVOEL49ffhX/M2RHUXNlwAoZZYEKfC0t7sFqFcZUib7ONktWeEa+WrDWtSLNPLmkU5iZw+hJAY3SJqT8cB9N7GM07K63oly4xbxL1gUljqLZ/A19x09dN8zw2Pcs1F01SGG84UbXlgBVZLbCS7+ljL+AUtBJprrtjPNVenB2G5AAAgAElEQVTt460LBR56ZhufuOsiPvuN85ldLFIuhF2+7uX9wmcjuidfE0/QSkkKhYByyadY9AkCD6VkamFkJ/7ku+7vdG8rO7oQJGQjpSIIFIIgPa3EGokiQ6sd0W5FtFoRrXbkSEWblFDWh0w6RGKswMYBj0LgyCQbhwMylhtdFsfGWLwCbSyNpj2lTMha66S7XnEgPp5OVd8iduEg4d1/hPfcNyO3XQ5h3XUrzLiv+7qgYku46xkbjVA++GXM3lux7Trqgpuw4WLf4vE2CYgKaLWJEzYH78bylJJke4LkJLJKpE9EYE3DWSCZAHq8ES5D/QTYEISzrZMYhjGC2UbADZcd4D+/9m6uuHDGWRhthW4EQFxhMzmSANUjgbTWSRZN023ve4bnXXaA5112gB955f3c/Mnn8493XEzB0wSeRht51pLIct3mklamvq8olXxKJUccnqfS/tHJ4zRm9bUfsu9Qr/Jm6f9hScwrPrcg8BBDjlSMsbTaEc1GSKPZptkM0XFdivUhk2ypGTpCjVNMRNnvrDeEcHkgrdD935HbcvfFgvQRxWHSJECzkgq8Jzu+iCf8IrZ+hPDu9+Jd9GrknpcgCjWIWlirndubuMDqkpndYuNZX0gPVBnbOIp+5OPoxz+Jd8UbOozQx7ToeD6g0bIucZPBB9K97duqHDw012Uu51g5bPKnUOj2Uaxp0H/ZIzE9fUGUNLRCD08Z3v69d/JDL38AChrT9LB4yNgyyWbsLl+Swf2tpMFaF2A3TR8Q7JyY4zfe9EVecuVefv8frmf6RIVqqU2kzy4SSSaJrHsqSxrlUkClElAq+amVkaz8dZ9SD6t1N2S3O9X2S10TLPkbnFutXPKplAOMKROGmnqjTb3eptEI0brTn7v32s/V91kIaEdwbNZCWk1o6b0QMVkgPURpnKxq8szPIePOMhHRNz+G2H8b6vybkJNXOyIREnToiCv7DgoJUjm3mnUSY3PwbvQzX8IuHAKvgKjujMuu9Hdh2diKQcLRE3Qy0QdtgQwNlWg0I44fX0QpeU4PvNNBVw+Q9iGMaSFlgaVPSuByQY6jgnGUMDTaAeNDDX7rTV/i2uc8A40A3fRj4jB0tOunbrST9cknUNKtqE3bZde+8tse5apnHeEXPvzt3PP4JMOV1tlBIvGpJ+M2IQXfVwxVAyqVAqXY0oAOsfSzAPr2vF4DLHeMXgumYxW5//u+YrRQZmS4RLvtyGRhoUWz4SyTbND/3IRFCkG7Dfum3Qp8OQukkwcSuna0qshqypicCp25QYJfxS5ME93/F4jHP4UcvxQxfjly5ALwKyT5YVjjXF31GWz9iJMFH3kIWz8Cykf4JWcxVSbT7ofLTQ5JPaz9051S9oOe3z1rLdvGK7RaIY1GGylzElkNOqtFQdQ85FxUxP7UzC10q50QHZ5AKcFi3WPX+AJ/+OO3cPEFM0QLRZS0XRbE6T6CzmBx/5DSWTDRQpGdk7P8yU/+Cz/3gZdz+8NT1Mpbl0TScZqJIwAUCh61oSLVagHfd4ogp3rqnhz6WRgbPe57z6MfoSTWUkImw7USrVbE/HyT+YUmYai7Av4ni7GebbBWuI48CvbPWAg5Rb91ASZCDO1GBBVsWGeQ9eo6z0DHVX7BNk+gn/ky7Psqwi+DPwRe4B5U1ICogdVtV+HXWlAFt50Q2LCB3HYBFEfB6pMeW0oBLdg3Y/C9Tkn7QUK6PrqCiYkhlFJLBmyOlSDugd6epmMz994/ibUh1hynFQZMjizyvrf9CxefP0O0WMBTJnVVieUXFatCsp+ESDxl0E2fcrnF//yJz/Kiy/YzVw/wVNba2fzPvXfSN8ZgraVcDti5o8ae3aOMjVXwPOWktPHvs99JsNlX6ycjNkcm7tqKRY+J7UPs2T3K9m1D+J5Ca5OKBZLtz3YkHp3Ag0efskQNi1QdMu7eNv7caERxBDF6setE6NJ1B3xesdQfF7QXQRWhCljdxjYOY+f2Yef3Y5vHHXkIifDKbjuZialajRi71AXpTX8CSa9TwtyC5cn9rsLAWjx+mfiBiwWfie1D6UFyElk5khImWQVWr+oH3LBsNeYoeCG/8yNf5Lw9R4nqhXQCH2RP6O7zS85DoJRBtz38Qsh7fujLnLd9jnrLi91dm3cihaXEkRDDULXE1K5RpnaNUKuVEEKkE2s/bHbSWA79guYJjLFoY/A8xfh4hT17RpmcqFEoeOm9yFo1ZzOsdaVM9h6y7Dvk4iDLXbK7Jy4OIiev7ny+BufVq6oiTigW0kOowP1Iz33mtuoiCWs0wq+gJp6bNq/qNxaEEGkdrG8+aTh2wuKptRnvMhlMWhtqQ0XGRsupyiPHydHVAySaw4Rz9CvG1lFEKNrtE7z9e2/lOZcfTC2PUwXIB4FeEolaPuPb5/nNH/oypSAiShsJrYVW48yQxAF6Yxy1apHdU6Ps3FmjXPa7VuTQueaTTbxbFZ3rWerycrEQyehomd1To0xsH8KLLZJzhUg8JZhftNz/mMlU5F1G7oqEqIXcfhViaBfWhH23HSSWjsWlMblkuzTPTLcQ2y5H1PZgtTvHfs8wjZspeOBbhnpz7RIJZXKS4PzE4+NVKpUCxmx8NcmtgaQHiCvhvpz/VAhLO/I5b+xp/t0LHoZGIY13uN+v/ZlmScRThqge8Nwr9vOfv+dumm0vVXxtJnSUVR1Za7kcMLVrlJ07hymV/PRzoEcTf3aRRj/0U08KskQiGBurpG49KUUaCzpb328bxx+NhTvuNxAlcZDli5haGyEKw6ipG50baxPdm3QBKj3Us17WkVP1WRQl56ykgLrl7gdM6r5ai+uR/bTrkxPOh5rHQ06OrGURtWewpsWymUsWpJTMLSyy2JPgtJ5zXEoiuFIVtl7g+258hKsvnKHe8jMxk4195r0yV60NhYLHzh3DTO0aoVzuIY5kVU23lPdcQnLdNvP/5N4pJZnYPsTuqTGq1WJqxZ2N1ohzy0OpAHc+YDgybZB+//HQEWFIiJrIPTe6FX7k6tlt9H1xZChd8HzHtcjxy7FR3Baiz7mlqt4AHnvact9jhmJhbQLokLFAsmQR+B4Tk7X0AnIS6Y/UtDQRUWu65/OebXFm5NyiZeZoCEpsmDqms4BxWcYUQt580/0Za2gjn3X3xJad/PbsHqVWK2ZkuEvVSucicfQiJZK+JKzYtXOYHZM1kiTi7DZnE3xPMH3Uctu9BgrEXRD7vJ82uX6NCGqoS747lfNu5Hjq1NYKEcVhvEtec9Lzcq6rOP7hw5e/ZphbALVG8Q/oWS4nL6E2hmqlwPh4dcmLmqMbQkiMaaHbM/QrYZKFlIJW27Bvpp0SyEa9tImlIYXFNgO+/XlPc+MV+1hs+qeQPa4dEkLOJviNDJdT90sSHHfnnxPHqZDESLJEklhtw8OllJATFdvZRCLZONi/3Kah5RZw/S4vG2ewUR2181rU+S+L61dtkBWSTaUwIerS/xDHZzqWUf8xb/E8QThrueV2TeCvnfsKMgTSezJaG8ZGy9RqpfSlPVsG12CRKLCOsLSESc+WEhotw/6ZEDyxpN7NRiC1QvyIN377Q3FcJvnt+jxvt3KyXSvlYtFj184RJidrywaAc+I4NbIxkl7LzvMUO3cMs2NyGCVlmrWfbLuVkSiRykXBbfca7rpPI4oideX0u75UlKDdhC23XdlVjn3dYC0WEbuuFlHn34Ta82JsWMelA/QPnCeuO4pwy52ahx43lAqJp2ONVFjZ//Rm4lrr4iEuUJkH1bNw98H5J3V4FGuanKxym7uvEIaW/TNtksI0G3k/s1YIbY/nXXCYXWMLhHr9GlVmiSAZY9u2Vdk9NZqKOXLiOHMs59ZKrJGpqVGKRf+scmlZ6xZtYQR/+2lNXBpqWfLoLGI0Qnp4V/8YYuT8bhJZ43tircUKgZAS255HTj4fddl/cEmFGVHIci4spQQ0LH/zKY2Mm0it5XNcMlP0xkOkFOyYHMbLg+pd6DxIj6g106eJVJ/tLXiecBZIy6xJeeXThdGSaq3B8y6coRErstY6L6TX6iiXC+zZPcq28WpKKMCSiS/H6WM5a6RQUOyeGmF4uHTWuLSccg8qJcGXv6b5xr0aWTq5FZLGHUyIKAzhX/MTyNELUxJZS3VWZ3Ekse0F5MRz8Z73ZpIkxN4Ffu/3jAHK8C9f1dzziKFcXPsCuctOYZ2TshQCj8mJPKieRWqBIGMF1qm149Y6ffr+mTa25SSWG42stBdP88JLD6JkEotYuxfFHVukbpPt24aY2jWybOJbbnUMDv2skeQ57JissX3bEHB2vOvWWqSAdgh//NEIWq5WVnJN2WvrHm+uMZQojeFd9zPIiedi2/O490Jm3v/BnKONkwqttY48dt+Id81PxBnn0bJxj+x1KE/QOGb5wN9HaQvbtX50fQmk98XVxlCtFtg2XsV0rQS37sAaDCTWtonah0mKoZ3sZXMEAvtm2rTadq1zlVYFIZ0b65oLpxkut4j02pxc78o3CBRTu0YYH69gLUv88DlxrB1673Pi0hobq7BjcniJN2IrwsVCLJWS4K4HDJ/4skZUE0VW/yxu6CERv4R37dtQF30nVrewuhWXFxlMyROXeS5jeS54l38//vPejBCqizx6kV0A6Nj6+KtPRnzzSUu5KLrepbXCshZIv6D66GiZka6g+pqe26aHEAJr2uhWosA6uYvF4iR1x+c0R2ejVIm1GSAAjGB0qEm5EKYNqwaF3kB5VglUKgXxmFra8znH2mI5l1atVmTnzmHXJ8VsbRJJCiwGPvzpxyKO7rd4hc4E23tdS0jERAism9ivfZvLE2kvxJ9LeleCyycr9nwu3HetbmOjOnLbZfjX/+eYqEKSSr4nex+cMtHilQSPP2z435/QVEvLE+SgcVIvfL+g+vbtQ5TLwTkdVO9cs+vxEYXHTqnASiAFNNuGvYdiKS/LD7j1QmecCXylGa604lakg9l/b6BcSsmOyRo7JmtIKdOxlB1vOXmsH3rvPTgSqVYK7No5gvLkspPtVoCIqxgUAsFTByzvfn87lckuZ2EtIRFrsVEDOfl8/Bf+PN4Vb0SUtmHDRWxUxxrt9iFkap30/qRWi7VYE2Hbi9ioiahN4T33zfgv+Fnk6CXYqJ6eg7WuY+HycQ/numovWt75vpDZBVf3ar2ek3eqDfoH1Wvs3Xf8HO9kaBFCEbUPxxnoK7t+JQXzde1yQeISA/2a3WwIrOtkOFptYowETl4uekW77JmUKpUCE9uH0lgH5EHyzYJ+JFIuB+zaOcKBgye29PuerNSHq4Jb7jD82d9F/OgP+kRzrm9Ov+tK3PSd+Q9s1EBID3XRdyCnrsdM34s9+jDmxBPY1hy2vRB/WfWcgQWrQSiEV4TiOHLsEuT45YiJ5yCCKoQNLLHLCpa9z0tIrwR/8GcR9zxiGB0SRHr9ns8pCSRBlvEC32NyssaBAye6LmarDarTRaKKQHjoliMQqUqsxAIREtqhZd9025kjdvmBst4wViB9zWi1ibYCQaxHP01kx4a1MD5eZXysghDk5LFJ0fs8tDaUij47dwxz4OCJLktkKz4vbWCoIrj5oxG7tgte/W89onm7LIkQN3Pr3JPYpRQuILyyq02158XOpTV/ADu3F1ufxjaOQ1qV2zriKI8jypOI4T2Iyk5EEDeSiprYcBGQp3RZdbuBQVUFH/lYxEc/HTFcFevmukqwIgLpTALdmeoTEzWmpzvtcLfqoFotsuZh1D6MW6l3pHYn/Z4VSCkcgbRNv+K9G4YkDtIK1RmFB3tdVp5SbJ8YojZUTDOhe8dUjs2Dfu97uRywY3KYg4dmt+yiMetF8RS8830h5SJ8+0s9otmTkUi3S8tBukVk22WrC6+EGL8MJq5ynQJ1u/fgoAJAulLsJoqD5k7JKYQ66fuQJZWUPGqCv//HiN/9cBhLdtd/Mbri6atfUH1kuMTYWGVJeYlzA9IF0MMjJE2kTnX9iRnsezGBtKyrmrlJIIRFR4ojc2WUOj3ro9cFUioG7N49Sm2o2CXPza2OzY0lSkztlJiTE7V4abE1YyJp3EC5us2/9N6QL3xB4w25/JBuq7l/wmGXm0smbZINVjexrXls1HD/z/4YjQ3r2Pa8U3JZg7NuYlfXSWJ/vXJrEKiq4O8/HvFbHwwpFZKk4PUn9FWtf/v5SLeNV87JcifuXjgJr4gD6Cu5couT8j5zqE2zZTaFlDd9ZMISRpJj80WUsKtWaXfGho0XGGWmpkYIArXEZZUHyjc/+i0aa7Wik/PrrSvnTyZizxNEGt7++23+zz9HqKqILa5Tl3TJjl8hkiYCIhNA7/ut9PfZwLhwLpy+3+h2J7ruiqIIH/xIyLvfH+J7Ii7RvzHW4KodKP2UWZMT56IyS2J0Ax2eSANmK3l81jop77E5zbG5zSXlRUCj7bHY8l0f9VV8tTfesX37EJOTQ+nL2k8ummPzo9+icWy0zPBweUvL+VMSUQLfE/zGn4Tc/OEQlEUFLhCdbLecNdKzw25CWeYn+f1yhJGg150VaYsqCXRkec8ft3nvRyIqJYEUbBh5wGkQCPRRZgnBzh3DBIG3peV+K0GagSoUun0E7Oq7lyVS3mcObg4pb1pQ0dM8eWiYetNHyZV1pUwII3khpZTs3DHM+Filq+dE7rLauui3aJzYPrTlG8+lY1a4oos3fzTip97d5uAhg1eLCdOskkjOEL3EoWNFlVcTPPaY4U2/2uZvPqWpVdy8YTZ4QXZGIdxUmWUtniddVU+19bNXT4bEZBfCI2ofxpyiBlY/KCloNA3PHGq7qrx2YyfWtOaVtNzx6C4WmkFczuRU30tcUcQVdH12T40wFMc7IFdZnS3oXTQiYXKihu97W/p9T+YvC9Sqglu/bnjTr7T5h3+KEEKg4tpZHSJhTYikH3FYC6rirKG//GjEW97V5oHHDLVqfM6bwJo/bQLpdUkYYymVfCbP8kZUnesRLoBuIlZb0kBIaIXWEYirgLJh9yk5rJIGUw+4/eFdBJ7GWMHJiin2ujVqQ0V2T3VqWfUq8zZ6oOcYDNL32lh8XzGxfevXzUrOW2vLUEVwfM7yrveF/Pivt7j3fo0qOSKxFnRXapQ9IzLp993EdaYqAlkQfOU2zQ//cov/9uchrbYryaJ1Uqtu49+pFeeB9MMSuZ82DFWLRNsMM4fn0oljMzDlYJHUwDqaZqALZ0ac8puJlNdTgr2HWhtalTcZu9aC8A3P7B/l8YMjFHwdW0WJjr33e91KlbGxCtvGqwA9Lsyz7bmf2+h9301cI298rMLhIwtr2vlurZFd+ftKEFThrgcMP/7rITe9UPOGV3s850rpZswm6MhC3OEwsUqS/aw4ARD37unYjSZ9gVcAWnDHXZq//pTm1q9rrIHhIVdtV29gvKMfzohAgCU3RGvDyEiZSBuOHl1AKXnWkYhbgbXR7aMd99UKry2V8irBM4famLgq70at3ISwaCORvuaWe5/FfCNgqNSOa2Et/xK4eAdMbK8xMlzGWJO64nKX1dmLfu/72GiFZitiYaGJlCfrlrf5kbi00K4MvLHwz1/S3HKH4SXXSl71bYrrr5LUtgnnPWgDkVs4GRv7IgRuQdkDC9jYFSYFTlHlgfQBDUenLbffZ/jkrRG332+IIqiWu2Mhm+2+njGBwFL/qDFO3htFhtnZOkolS+z+K9qth5hAwuOstAZWFta6viD7pts0moZKRQ2iasiqzyEJnntBxJHpGh/710tj68O5r7Jj1U0aneRAP65GUCkHebzjHMOSeIiAbduqNJvhli53kiC1RuLVfq3qFkz/8hXNZ2/TTE0Ibrxact2ViisuEuzcLlBFgQwAg3uXY62N22Hm70Tl24aoYdm71/LgE5a7H9Tcdo/h0FGLEFApCkRAV27KZsRACASWDqpE3qu1YWGhiVLJymRQR1x/dCmwotlTdiFcdj+4/syzC5rpoyEX1jxstDEuH2Nc+ZK//uIV7D865AopatnlvuqNd5RLATt2uABqv/yOHGc/ss876Rk0NlZhemYOJQTiLFgo9iqhhqpu4XX4mOWvP6X52Gc1QxXBs3YKLt7jiGTPDsm2USj4UAgEvud6kbTallYbZo7D3oOGA4ctjz1t2TdjWahbtIZyEYZii8PYjjt4M79TAyMQWEoiQgh27qix/4ChXm9nSGTz3pCVQAiFDo9hbYhYUjRtZZBS0GxZnj7Y5sJLytjmir1gZ4zE+tBG4hVC9u8b5R9uezaVQogx4qTkMVQtMjlZQynRJeE8G55rjtUjsTi1NgzXSiwutlhcbG15V1YWnfHv3lHPEwwH7j1qty0PPW655xH3e0ccceUSmbjEOi6uVtsRiqdceXlPQbUkQLjuiVmLYyvcu4ESSIJeEtm1c5j9B2ZpNNuoLTyw3DUZQKLD41jTRqgyp5OJKyXUWxkpL+vj9knIw1rhpLpG8Hv/53qOLxSpFttok1gfS8ljZKScqm5683224vPMcWZYumCEbeNVGo1wy77jJ0P2chKrREpBsQCloltyOQLoBMc7Xhf3vlVLAlEm7RZobfydTHB8K923get/euW91lqUkuzaOUyx4G/pxKOu4GF4HOJ6NqzyWpJBpXUs5TWdCXstkSUPbQQU27z//17D5+45v4c8emNalm3bqkxO9JdsbqUBn2Ow6HVlFYu+E1Ws05jeKGTHvJP3WrTpTAWJjF3KJAsdwFkjkbZxkm3//W0lrImAtF+OiOc5EikEW5tEnIQ3xIQnSNrYrtb3lCqxPMEzB1vQtCQ6g7W6JVnyiIzAG2rwhTsv4v2fvjqjuoJe8gCYnKy5+kd5ZnmOZZCMB2MsIyMlgiCTYLjFamWdDlbzHrhtz473Zs0yEPqRiO8rdu0aJvC3bskTJ+EN0dHsacc/oKPEevpgi7BllqxoBoXETM5aHn61yd33Pot3/dWL8ZWJdewi3qbzvFwMa5iR4VKutMqxLLKuF2vdez460rFCxIDbI+fYPFjTFLblSWQE31dblEQE2AgdznYskNNAYoHsnY5YbLQhsETaPQ43kZ8ZkWSJIwmYA3jVJp+//RJ+9k9votHy8JTpCpx3ZLqKqWXKkmyVAF+O9UP2PTbGUqsVKRV9RyKu+k+OsxBrngPdj0SCQLFr5/AWJRGXA2K06yB2JvAkzNfhN/73s2kslvErzbjuTme/qyWS3u2NEURaogohQhk++PHr+IUPvZRIS3wvJo9Mfo7WhlIpYPfUKKWin+r6c5lujpUgG/ccHskITPJhc1ZiTVRYvVhaAsFSKPjs2jnMgYOzhKFOs7E39wRlQUh0NA/W1cA6s71JAtXgn79a48Dii/i513yBFz7nAAiLbfloI5DCxi6mjhsKusMuvb+zxPkdwiKDCOkZHn9yO3/w8RfwpQf2MFRqO8WIiZeGGaVVrVZicmIImXFj5S6rHCtBrxUyVC1w4oRPqxVtkfc7x2qhnnv9m961HgfKDpysr7RSLlBvtIkivQW04xYpfMLWARqzd6XKitOHxOgmtdpOZvW1fOLOPTx5YIyJaosdYwvIStupOYzAWBlP+B2SgNjiQGCMSKW5wjPIUhsh4cl94/z5J6/mtz52A986OEqt1MaSFEqM77UFYw3jY5VYabU0kL55n0mOzYasFQKwsNhCrrvbM1GBJe7Zk2xpu3Ofcqwc62KBJOhniQSBYmrXCAcOztJqhZufRITAmgZYDeL0b5+7RmfRtFrHqHoNjDR86u4L+fy9z+Laiw/x4iv2c83Fh3j21HFUuQXSgpZghPuxAqGM+zzp39EIeObQCF97bAd3PLKTf31oN7P1ApVimEp14zPIPA/Yvr3G2Gg5D5bnOCP0WiHVapHiiQat9npYIbaLMAS41sxxroWxApspgCilK2JobCKptTmZrBLrSiDQn0R8X7Fr5wgHD56gmSGRZPvNB3XGaeMd5YprTKV1iBAyldTe9vAUtz64h1q5xfmTs1z/7IPs2T7HtqEm47U6E8N1lLIcni1xZLbM0fkSh45X+Nq3Jnl43zgnFooAVIohw+UWxoo0z8P2FD3cMVljOFda5RgwrHXy/aGhIs0j8yQijTU4Unw8N/k7UoBWKGmGAiWhGBgCzxIUDZEWtENJoyVphwLfsxR8i6csFlcfrmO15OP/ZFh3AoHlSESyK7ZEGo12av5uOmvEWoT0SRt5nNEAi+tqhcewpoVQlbiAG1SLIUI4ZdYj+8a494kJlLQEvibwNAVfI4BWpAgjSStURFoS+JqCp6mVW4BbdSWVdXvJQwjBjh01hqr9lVY5cpwOkjwnsGks5PiJepr/NTh0E4eSFmME8w1JsWA4f2eLa569yHMubDAxGrFtOGTbcMRCQzFz3OPIrM+39hf42iNlHt1bYnZBUQgMgW8xptf9lb8P/bAhBAL9SUR5kqldIxw6NMvCYmuTl4K3DEbEJrGmiW4fRZZraXBeGxGb2pZioCkXotgUd/GLest33xYWTxkCTyNE8vvOdtnMcqArsXPHjuG+1XQ3373OsfXQyUT3A49qtcCJE/UB9gzpJg8hYL6hKAaG73nJcV73suM8+7wmQUWDsqCFq5JrBMMjEVNTLVCWVwI0JAeOBHzq9mE+9oVR9h8JqJRMSkjuHcrdWv2wYQQCS0nEGouUgp27RpiZnmN2rrHJSeTM4a67TdQ+QlC5mMR72wnQW6wRaJH9jkX1BAaNFXFnw2xOh9tHb02rYtFnx45hCoGXynRhE1p7ObYs3DjqxBZqQ0Xm5hoMZiLukIeMJ/lGS/Dy58/xI//uCM+5bMEdIxTohoytbndUIcDq5F1xe1PKsmtHix993TTf85Lj/NXnxvjbz4/TaDlLRuucRJbDhhII9CGRxLUyWUN5kuPH6yR1Zc7OCU7GvUWOpImJ3dcZj/wVDtxeZVgveVQrBSYna3ieXFJS5uy7tzk2EskEndTIKhZ9Go0QKUg1Y/cAACAASURBVM98sZKQh9aCMBK8/Y0H+YHvOgqAbroKEVK4HyF75O8xo8j4c2sFti2wLRgfjfjpHzrIy54/zzv+ZA/7j/hUSjmJLIcNaqbajX4FGA2W7duGmJyoOeVTl6x0qyQdnhxp8qSQRO0jWBMyyMHZ2+RrdKTMrl0jKCXzHI8c6wopBZVKYQDjzU36UtjYzWt5z4/v4we++wimJdFNiRS4StMpEncw6bGThVbnM2eJGC2I5jyuumKRD/zCk1y6p0m9KWM1l0jPIYfDpiAQWEoiWDDGMDJSYmrXCJ6SmSDcVspcXx6Jme+UWIddeXhx5o+kK7YU36eJ7TUm8mq6OdYR2ZI31lgqlSDtCXR66JbpNluCX/qPB3nVy48RLSgn25XZ2MjKxJJumw4xecoSLSp27mrxhz/zDJOjIc22TC2WHB1sGgIBusgh63YplwJ27x6lVOrfPnVrwymxovZRrGlxpo8kW6vKGIPvKaZ2jTI6Ws6r6eZYd6Q5IdZS8D1KJT/97HTfXylgoaH4sdcc5jWvOIae8+LmTXZVxJFFYpUkLi1PWaK6YsdUi//6Y/vxMzXjkiTcHJuMQCBJ8kkmuJ7ifrtG4yqfpmsi3PpEIrGmgQ6PdsVBVoslxFsusHtqlHLetzzHBqGrAkXsxur3u1Oj0wRtoaH4N1fP85Ovm4amQvaQx5mdb3y0hEQWFdc+f56f/b5p6i3nHjtVZvu5hE1HIAk6Ko5seXGYmBhicnIYKTsura1ujaRKrNZhRJzdvtrJfUm8Y7TsXH/e0vuUV9PNsREwxlIuBWlX0tUgcV0ZKyj4hre+dga8pM3sYMgjQXY/SgJ1yetfcZyrLqhTb8nUUsmxiQkElgmuG8vIcIk9u0cplwpofTZYIxJrlyqxVoLe/A4pBZOTw0xsr6W/71W65cixEUjq3xWKq3djJRnmCw3Jd7xwlisvraMbMlZ0DY48OsfrkJbWAlHSvPk7j3SdT+7G2uQEAixZOYNzzwSB61exfdtQxkLZetZIR1UmiVozq1JiLc3v8Ng9NcrIcGmJmw9yl1WOjUF23EkpKJf9VOK7sjHpNtZWUC4YXv/yY5l9r927npCIlEBT8rJr5rn8WQ2a7dwKSbDpCSRB70RojKtbMz5eYWpqJO1dkZ1UtwKRdGpieYTtaVfS5BRKrOx9SKyy0dEyu6dGKRS8PN6RY9Mh+06WSgFKrXyhl1gY7VBw4a4Wzz6vCW2BFGtjffSD1gJV1lx/+SKt+Nh5LGQLEQj0l/pqbSgVfXbvHk2tEW3Mku03N5wSy7SPYXSd5LH0O+9eq8PzXHOuie21Lkssu21OHjk2Gh2XFQS+h6dW3g46cV+12oLrLlvEq2i0WZ8xLRKiiIVXN1y1QLloiNdo5zy2FIFAt0sr+X/Se3l8vMKe3aPUhkppMDnZBjY7kQiMaaLbh3G91rvPtZ/VMRzHgqpDxdxllWNTI/sOKiUoJO1uWdl7aQwUAsvzL6m7D+ImausxxF1+CNCWXHlBg+0jEWGUv1uwBQkkQb8Au9aGQsFj544aU7tGKJcLGGPTgZqdUDcbmQghsaZF2JrG9Rnp7tfRa3Xs3DHMjslhl1W+xGWVWx05NiEymeDFYqeK0krGqrGCom+YGA1B///svXmUJMld5/kxM3ePK8+qrKuv6q5udUtq9SmpGx1ISI1AFxLHaDlWsCBxCS0sYpmBgZl5LDx2YGf37TLDm7fsH7PswjD7lt3hLQM6hkuA0N2tVt93d91VmZVnXB7ubmb7h7l7eERmdl0ZkRFZ8X0vqyIzItzN3c1+X/vd3ZyNocJCybfMTydpTshwTz+K2PVaWFeDfs2iqI1UqwGVSkCz1WFtrUW7HfeYdEZJwLpxSUCjo0WyejtFc1V2XbOzFRb2T+F7qsdUN4mymmDkYbuh+eWSn2elX8qctRaUZ5mp6qEHPzmyAovLDZmb0piuVeuaxlgTSIatCjJmAneqVnZtc1sR6xttWq0IrQ1SbiaRrGrnbozf6eQecedcjyPdpNdVKQfs31ejVguwlk3k0T3OBBOMKgp+kEAhpUDrSxPBJt39z01rMFeWbX61sBaEb5mbSobmgxl17AkCga21kaIfpFYrUasFhGFMvdGh0egQx5rMVNQtsNbFcHf0FiE8ko6LxEIEGONMcvNzVaany3nyZPH6JlrHBOOC4mZHSkkQeLRa0SURgRAQa0GjrZieTrCIodfEFQDGjUFOIrCAPUQgGS5GJOWyT6USMD9XpdWOaDY6tMOYJHFOaClF/r3hC2aJ1Q3iaJmpmZuZnlbMTFfTbHK7iTyK1zvBBOMC50iXBIGi2dxsrt0KMg3jXVlXHDmM6+fBcLUQISxJLFhJa29NKGQPEkiG7YnEJe4pJZmdqTAzXSaONe0wpt2KaHcSklg7E5GlRzMR+T+DGrNEJyFTlXWOHj2INW2sFZvyOsZb6+jtJNfzzgi0EL10m/w4P4PdQ3HzE/hed21d5F4KYYkTwVpTgbIu9VYMpzeHLUxJrQVrDYWUdsIg7GECydA/MYtCOLO/ep5idsZjZrqMMZYoSgjDhHYYEUU61U6MIx9tcv/KIMZqTIyNF5FSEcUGIeSYE4fdRBZCgJQGrCC7lVIAPX/rJ5qdv+6t7uel3t/tPjeez2h4KJYw8X11yfdKSmhHkmdPVHjbAxt0u28OcrQOQqStbT3L8RMBKxsenprwB1wDBFLEqy36jExciKEzc83ZSh4GHMeaKE4wxmedEms7PLaspImQisbGaXQS5o708RRKvQQghXOEai2JEkmiXYkI39POPJFIl+2rLIFn8JRx39lEQFd3H4r3sl9LtbY3kKJ4y4tR390yHP3/bxWYMY7PbvCwlpxALnaPXIl1i68sX3yixsc+IFFq8/Ma1DjBbWqkZ/nykzXWm4rZKY2ZJBNeWwSSYat8kOIu3xZ2wEIIlBJ4nqRS8fC8ClLXODmgMUnp0aqfQusOUvpYa8ZMAPUSh5Kuc1w9VASeYW4q4jXXbXD9/haztYh9Mx08aVlplFirByyul3n21CwX1ss0Y0k5MK4Xg+WqiKQ/K7/7nF19JqUEgS/xPIlSIo3S64ZqOmFlsSaLjAOtLUliSLTNNxqZXV6wmVDyKMHLHv3eQdGEpZREKUmS6It8x93vcmB55niFV86WuPmmEJtXxh28L0RJIJJ8+ckp17RqiBrQKOOaJJAiigu83zldrO6bCRAhBmfCIu1OGIWrdNorVKeuw1ozRrvYXvIQQCP0qJUT3vum0zxwxxK3HqkzXYsgSIVGFg6ZtSBNJGHL5+Xz03zzpX18/rHDLK5VqJYSpLR5Ux8uUQzbNF6nnzikFFTKilJJUS5JfN+ZCmWaWrtdnmnmB8veNybVYI0lijRRZOhEmjguJrAWKihgEcP2/o4ohHAbszjWF7kdjsaVtGy0FJ/+8iw/c6yNCcEbYJdAm2a7ayNQZcMTz1R55Pka1bJ1eSBD8sGMMq55AulHP6FkcEKcgS9850hv06yfZGr2KMZ0cKVNRh1d8pDSkmiBMYJ33nWOD7/lBEdvWHcfixVWS0xL5d8T+b9uUZZLmtcdW+F1t13gO+4/zae/dgN/8cj1hLGk7Ju0F3Z2zlf3RWTBnpkw931JrepRrXr4nmsQVNQw9KtvhjchIwYpnQZDzZ0r0ZZORxOGmnao8342IlVPxmdTMBhkEY+e59oXXEwQWyswWMqB4U/+bp7ve+cqhw5GmEKr2Z28ndkGoUhOv/+ZBVqhZKaq837s1zrGtpTJsDG0xS4kWndorL2MkzSjV3ZlM3rJoxMrfGX42Q89xc995HGOXreBDj106GFTjUMKi5ImrWpK+jolEyMwHYVuB+ybDfno+5/ln/3goxyaC2mGHkraVOBvnQvcn8FvjKVUUizsL3H4YIW52QDfk7nmkLX6zVD0kfRWLsjoqHdjkf1k5xJC4HuS6SmfAwtljhyqMDcX5OHY41YxelBw5mFVENbb34usaVTgW86t+Pz+ZxbAL86D7bXGy0XXx2XRBlRN84WvT/P5b8wwXdET7aOACYGMEGzqFZTSo772IjoJ4SKl3XcfveQRxZLD8y3+xQ89ytvuP4MOPUykkAJnO04hUltQN6O4N7s4IxWjJboZ8JpbVvmNH3mEe44t0+6onmN1x7A5ZLtcUhxYKHP4QIWpmo+UYjNh5GMSPeTRj0wDLZqkMmIpfqefUDxPMj8TcPhghX3zpU1Ecq2RSPHeOg1k89+3+FYaDQUzVc3//Tf7+OsvzKFqGm0ygX71JNJLHgIvsFw4F/Db//4I2aOe9AHpYtSl0zWFrKSJlD7NjVeIOqtI6ayMoyxkiprHwbk2v/bRb3DrzWvoVoAUuJh5uk7Hiylz2fuuCqpFKYMOPWZnOvzzH/om9966TCPXRDb7sDKhvbC/zKGDFWpVDyt6NY2e/IOrTBrNTVNsLQQzTUdKwcy0z+GDFWZnArLabSL1BI/yM95JFK9TKXlZ995aZwKUwvJb//4I58+U8KqGRIurJpF+8lDSgjL81h8e4fi5EuXAFHxwExKBCYGMJIRUxFGT5sYJhOxW5h09dENstRGUfM0nv+sZ5ve1Sdo+Srk4x0sljiKyz7sQTreYdSxBWT75Xc9w44EmnVim5NQ1VwkhmJt1u/2pmpcTCpsidQZXaWCrgp1FcpNSMD8XcOhAmVLgtJGMxPKgjWsgy8BaUPJyfBeZFiIo+ZYL6x6/+G9v5MJ5H6+mSXTXj5aF+F7qODKHOUCiBcqz4Fn+1e9fx189PMNMevyJ36MXEwIZRaQZ6c31E0jp9USDjRqyngydWPHD736BO25bRrd9PGVy7eBq5HRRG8lIZHa+zc988On8HJlgrla93MeRmarcMbo28mGXqBF9xFAkklJJOQ2p5vWM1drdqPQ0PHTvv0XIy7/ObMNSLRmeeKnCj/3WLTzxVA1vJkEIly3ehd2WSPpJxhiX7+HVNO225Bd/5yb+4D8vUC2bPOE1HcFlj3mvYhcJpJhvYft+v3ZRFDSN+gmMjkZc+4B2R/GGo6u8541nsG0/j4qBnQla20Qioc/tx1Z46N6z1NuKwBcc2F/m4EKZwN/aUb07tc2K19AlkszklWlMB/aXmZ0J8jbN15JfRPYR7MXR1TK0EdQqhjMXAj7xP93M//PnB5xWU3OhdNo4UoAuWRR/wEkbbdzvsmRQZcOjj0/x8d++hc99dZaZms4d9RPT1WYMMYy3N6PYZSf3OUKFM1fYgmN2JB/aAIeT2cOl9Gmuv0KStPOEwlEL/cxMBUpavu9tx8HXmIJvYieHmiWMZeclUXzXgyf45itH8MtTlAJ6qgmMaumXrrDsbRg2P+f6hK+sdkDYAtmM1vh3Gld2eWngt7BoLagEhjgR/MbvX8d/+oc5Pv6BJd5xbx1vykAiIM5IRLg5JLJET4tQIAMDWvDyK2X+3acP8LmvzqC1YHZK59rMSMqhEcAQCKQQpSMsCEuiFbFWJNqdXgqDNs6e7asEX2kXgcPVZR8PDAPfHLqSJp32BcLWErWZG7FaMzLXT9dx3go93nz7Be66bRnb8VxexYAydDOfiBQWHSsWDjR53wOLfO6bc1gTObNPn7loFNFPcHmL4mkfLKysdXItZe+TyJVeW4FEjJuL01XNYy9W+NS/uYn7XtPibXfXecudDW67oYOnLMIzCAUYQAusESwueXzl6RpfemKKLz4+xWrdY6qq8T1HTiMne0YMAyaQrilDSUOUeMTaY75W5+YDi9ywb5l9U3UqQcRas8ZKY5qXlw5xenUfjbBMJYiQwmJyErl2dgFCKJK4TWPtZabnbsUkHYQcHZdVlmlugTe+5gJ4GhMrpDAM+hnlmwotef0NZ/n8k8cwVqYa0XgI3H7zWhapNTPjo41lfSPKWwvsZVzdo+qSiEs0hGrZBW48/FyVLz9VY7amufFQxL4ZzXRFM13VhJFgo+mx0VKcWvRZWvex1n13Jq1xZcyEPC4FAySQrslKCkuzU+Lw7BofvP9rvPX2Z5ipNCGI8wqsCAuJh4kDzq3N8ZlH7+evn7qbWCsCL0Ebec2QiLUWISXGxDTWX8aZ90YveznRgtlqxF03r0KicjV/kEPMTFkCQEuum69zcLbBmdUZfHWxkhijh6KWkWki87MBSWJothKk3NtayNX7eoompq7gr5ZN7lB/4VQ594ekAW9IYdNinpbpigbhytJkWsfEZHVpGBCBdMlDCEs9LPOmYy/wyfd8mtn5VYh8rFGYsNzzLfdgDdctLPHx7/xzHrztOX73P3+AlcY0lSC6Zkgk94Mon/r6y+ikjRBqhByrzhEca8mtR+ocnAtBDy9uKCMRYwWqFHPzgRWOL81RyjcaQxrIDqGfRCywb75EFBvixCD3sClr56Z0b9BGRiRCWEqB7c7NTG2mW8LGWFf9YEIcl48B2ESK2caWVqfE++55mF/57j9mdqpB0q5ijauDJFPtpFvGIlVFEw8dVnjDrS/w6x/5I24+sEg7CpCyGxq692HzyrxRuEZWD2tUSEQA2ggOzIUIb2dCdi8X1gpQhrlau6eW1jiin0SUEuyfLyHo/fteQx6ivqPzJtOCXeq4tQKT/ZjuazdnxabPT3DpGIhR3YVbGlqdEg/c+hw/8Z7PAmASD092q9X1l7HIByUsShiSdoWDC0v8/Pv+lNlqk0Sr3N55LYT7CqFIogb1tZeQygdGo7R7dv+NcSYsVH+c/BBhBFPlCG9PbC66ZGGMpVJWzM0GO7hLHw10ibCQqzMR3GOJHSeQLEqmk3gcmVvlE9/+WbACo1WPBrGVHOzJPkbgKU3SrnDdobN8/Nv+0hEI47vLvGykhRXray8BcmQKKwqR+mSA2VoEyjo/1pBJXeDOO13puKTCoZ5959HNd3FXoo1lZtqnUlZ5zsgoPP+rRbeMDGht9sQ1XavYYQJJbYjCkmiPD973Nabn1kgS/6LkUURv4pjBdsp8yx1Pcc/Rl2nHQUEL2bt4tcKKo7TgtJEF3tidZ2Ky+P49gP6aWkLA3FxpTznTi88q0eaqn92g1sMorbNRxUBMWIn2ODCzztvueBqiACUu30be3aW4MF48zUN3PpbvOndVCxnCGu4trHi8p7DibsNa4Z4BsN70YZd6I1gESEujHYyl83w7FP0hxlhKgSsNfyllz8cK1mkg+a8Xua7t3i+GO29VXl9rQ1L40YWf7DOmr1xQsUDmpYzhWsWOSqTc9xF7vP76k0zVGpjEL5TLvrzjFRPHSDxee/0p5msN6mEFJXexIfEQ55CQirizzsbqCxy64e0YE7HbDaYyspDSUm/7oHdTeFtanYDESNJ+f7s1kAEgS8KFmWmfVish0WbPJBgaa4ljU9gs9l5P/zVKIfKll2XwF8vWSFloRZz+7vsS31eoAsmY9HtGm7wfTM9r0+0m2T1mr2a41fiuRewogRR3oa+77iR4GpuanK72PlutmKvVOXbwPF9/6Ta8UmfPm7GA1A8SsbHyLIdueLvzg+x6dV4nwHxlOLFYI4lUobDh4COxekqaaMW5tWmUsGlY5t7oU71VVNbMjM/ySpalvtsjvDJ0n53TruIo2ZRUmcGZ7ejRJlwTKoFSklJJUa34VCs+5bJHuexRChTKkygpkFLmBLB5HLZHo7MWoljT6SSEHU0njAk7mnY7ptGMiWON1qaHUCZkMoA8EGMFvpcwV2t2+11fBbKYf2sFQmn2T9fRVhbDuYePIc0TCwhrkSpgY/lZkriFkGok6jxZwFOWs6tVXjk/xW03rWEjNVTBJqWl3S7x0uI+fE8Xyt7sjYVcdJobY5mq+TRbCWGox9gn0l21mUkpQ5FIMsLI2t5OTQXMzpSp1RxhVCo+ga9yMsm+lxGO+71w1h7zVEYA+V8A1+54qhYghUgTC90YokjTaEasb4SsroXUGxFx5KJJpRJ5QcjsPOP3TK4cAzGqK2Ep+zE7GZxnAaSl5EW7r3kMibnSKYmUHs3GKdrNs9RmbsLqiFEQkkpaNlo+Txyf57ZjK64WVhosMag15DQcF0IslObkhQOsNqv46jKbmY8RilV852YCznfa+d/HDdm1SCGIoqTH15Pt8D1PMj1dYna6xMxMiZnpEuWyh+fJ/LNFE5Yx+rI03+38GFk1cA358YSAUklRqVQ5eKBGkhjaYczqasjqapvV9ZAwTADy8bmjjMIKHTx2nEAEkBhJs1OC1KywEyV9hLBgJK1O+doJ400hhEcSNVi78CQz87dhks6u2zCsFVhh8T3Dw8/v50MPnkAVeoAM0pTV3UBYHjt+GK0lgZcUEsP2DnItJBWc5bKiVvNpNOKx1EJy4S2g1YroRJpS4ExRc3Nl5mbLzM4UCAOBNi5SK0nMtoJ5p+5F9xhdLSZvSoZ7HtWKz/RUiRuunyEME5ZXWpxfarKy0s67YYpNx9ub2HkCEZZYe6w0plx9K65OmBTmG2jFUn0GJfXYx/xfKrKdJ8D6hafRx97fE867W5MzK+VeDjRPnZjj7544xLe9+TSm7bvQ6wFoibn2YQXS15xe3McjL91AyU/IOhfu1bWaNTmwFmanfdrtpNsOd8wghCBJDJ4S3HZsHwv7q0xPBQS+QohewuhHVtYlO07/cXdibP3H6ikzk2s+TuMtlRQ33jDLdUemWVkNOXlqneWVFlo7v9VeN23taBhvN1TX8szZGyDxcm3haqPfhNKsNaY4fuEAvtK7a8Ya4qndpDMoVWJj9Tk67WWk3N0oLIe0FIQFXxn+9Ms3ETVdG9usevJORjx2HZ5ZyI7lb564jTDuNrDaq5qpE15dbcT3JTPT4xfW238Nd915kNfevsC++QpKSRJtiBPd17u+u9iKwn3YXSX7x5LBGEscuzEv7Ktw792Huf/eI1x3ZDonyuy74/KcLgc7SiDZ7rDkxTx39nqarRpSFuO8L+94xR0nXsLTp29krVXD280QXtgV772QHp32MmsXnkSqEtjdL2uSP2/fcGJxij/661vB12nG/M6RSDHqyliBDGK+9uxRvvnKdVSCOJ8ne8181Y+ic3h6yicIJGaM6mR1E4ShWlF4niCKdE82+igQxquh64DfPM4sx2R+rsJddx7k/nuOsH9/xZne+rSYvYKBJBJ6SrO4McvfPn0nBJ1CFd1LJ5HijlMKC7HPXzxxj/vjNZCJXkRmA7fWsLr4KNbq9Pfdn4xOqEO1lPBnX72Rv/zKjchqjDaih0SudJgZObjGQRJVinjl9AH+5Ct3OZ8LXBNzoShMrXXRSXOzpU3vjwOEgErZKziq+7PvR4cwXg3FNsXZ7+CSI5PEMDdX5v57jvC6OxZyDevyW/iONnaYQERuiw5Uwp8+/ADLywfwekqxX1yYFHec2khEuc3nn7yLx0/cTMWP9rS5Yiu48u4GqUqsXXgqNWONQlZ6Zt91CV7lQPP7f3Ebjz19AG8qShO2xGVvHrLPZuRhrcjJY3V1mv/whfuItSq0zt372gcUzSDOoV6tKKaqXu7gzTWUXRzjdsjG5DLrFaWS6hn3OBDGdng1IjHGcvSmOd5033Xsn6sQJTr/zF4gkR3XQLIF73sJy40Z/vVnP4iJPVShKdR2u9Ki0ABItMKrNnnulWP873/7EIGfYDPb+zUIKT3C9hIr5x/NzViwuzbwopDP/v8f/9+7+fPP34AqJ0jfkhT6Sl9MG+m+n+U/uDmjyh2eP3GY3/3s21htVgmU7usad+2gaAqanyvhe7LHRDKKq0MUaG1qyu8JdtgrlXi3I5Io0kxNBdx37xGO3TzfkxQ57iSi7n7gR39tZw+ZCRJJyUs4vbrAieUDvOnml/ArITrx009tD9eeFFSlxXOvHON//syHaXQq+CrBWrmLO05Xm6rdPMfi6S8MddeUCwgTY63h4PVvTeNyHHZjB9e7ULIsYVhZbfOnfxuwsupx9y1tqvMJQguStOnUxYZqrUtIlQJkKUYnir9//Db++Ev3EMY+vqcLbY7hWtA+MmyVoe55klYryc1Bo7ajL+Z6BIFkfi6g+MxGaaw7gX4ikVLkhHFwocrUVMDqWkgca5SUI/e8LgcDIBAHIRwRlPyYlxYP8+jxW7h5/wUW9i+7iW4E1kqnUSCdKBRpk6kgQlj4i2+8md/93AdodsoEXpLvRtMzDGLYF8HuEUjXPuwRti6w7+A9VKoHsTZBiOFPQrfT7S6ULA5/canB6moD37N847kan/vaLJ4R3HFjiD+j02dPqkmm3eAKj1QKi/AtsqIRGh5/+Xr+wxfu56sv3oQnDZ401yx5dNElbGudUBZC0GrrHhv7qAilbiir67RYClTP+HZlnJkDJjUJuvYEFmH7TYCba14UTYkIENZixWY9qt/RnuWTTE+XOLC/xtpaSDtM8LzxJZEBEUgWaunMTWU/Zrkxw989cyf1xjSHpjeYrrQRQYQMYmQQIVWCkJY4Cnj65FF+7y/fx58/+makNPhKjwB5wG4SSAYhFTpuAnDgugcxJu6+N4TxFLWOTAOSUhLHmrPn1qnXw7S0hKBcMtSbis8/MsOXnpxmfdWnpCz7ZjQqMMjAIMvpj0rzXYzg5VMlPv/1Bf7umbv46ydup9EuUQ6S9PzXOnl0NbiiKatSVlhjCTu6Zwe820KpqH3Uqh5zs0Hu+4AhkYcFKyzCpuU2BXn7GiFE+p4zpdlClJX7sX2/d++ryNlGpDxj83WR0cnm7wi0tpRKHgcWqqyth7Ra40si4qOf/PwAjXCZk0wgpcEYSRgHVIMOtxw8z80L56kEEZ7SdGKfjbDK82ev48zaPNooKkGHboLY7gsNaw3Kq7By/lGe+MpvIcRAgtguaRxS+tz7rb9BbeYmjO4MZSw95JG+VkrSbHY4d36DJNFIWexZ4iLohIB2R9KJBdNVw23Xh9x0KGKqopmd0igJaw1Fsy05uxLw5MsB0p/mwTceoew74jCFHCOH8Vpog0Cv38MlUa6ud1jfiPMCgrspgbXRbAAAIABJREFUlHpMbVJw6GClR1AOcmwWXB5zHkpe1NqK5JsK+yvUQNxxbF67xOZE5AiLgrm9uH6MtXhK0okSvvnYOdbWO/i+HLvk0AGH8mQPzeYaRK0Uoo3k6dM38NiJo7lTXKRtbH1P46skL00xKuQxOrAI6RFHG5x95S+4/d6fwuhwyzj6HT1rH3lku6nVtRZLSw2cdpYJh65fxFiBwFIuGaplSLTgqVcqfPPFKta6vuqQmi6lK9CopObwvKYSJLmJazIHNqPfH5I51ZWUrKx18uewGyRSPKe1MD9f6hGQgyWPVNtI2SCbi+QtgzPLlaD7zmWdoPuyRwNJrWJpzyKbkUc2f9MTZ7XAtDaUfI977j7Mo988x/pGZ+w0kSHEghYjcAQmfV32IypB9pkiS4ue2P5rJUTz0uFCepVXYfH0F7n+2AeoTB3C6GhgWkivMOi+Xlyqs7bWehVB1S0tYky6KxOWcslSSS+l+OmUoohjUr9YMUplMge2gqCXRIyxzMz4SCVYXg23fXaDRPE8xljmZgJqabix6HmmO33eVF5kpiqbahaZtpBZRHq/QKZBOM2hf6Ztlj9dE1VRu4EsP81pNDa3k9nCqbJzZ/dIG0Mp8LjnrsM88uhZmq0IpcaHRIZog8mEiYDUcW5s9qPy1zZ9v/dngn4I6RGFK5w78ddI6eMm884mFhbLxmevpXQJUafPrLG62sx7I7z6hO979lZgrMAYgS78GCMwxi22rXo4TLAFtiBvV/rd49ABZzIqFgIcdNhocRzaWKan/F6/R+6/2bnnW7ymzGKR+TaKl2vT8wrb58+wwvlrhXUO8Z4f0fd7qt3YLHQ9PV8ePm3JSUPYHp9KUXMv3qtEGyplj7vecJDAV2MV4rs7RvxxxyjINmuQqsz5k39Hu3k+JZEdPPw2/o5WO+LUqVWarU5PH4ad3i2NSzbyqKCXRNLKvSXF4YOVfPffr43sKIqbDRyJzUz57Jsv5bvugZiuevwpWY5ZUfh2Hd75RijVSlIrFnl5NcfGfU5z+n7PtJSuduMqU5MTSqZqiO4QN23Eis9BCkGcGGanS7zutQfyS+ua3kYXEwK5EozIM5XKJ2wtsnz2a0hV3rHEwq38HVJKVldbnD691hO/PihVe8Idl48u2Xcjn6QUHFgos39fCSXFJiKBq5svNtV8M8FrUqfV/GzA/n2l/PgD83v0+IFEasHIjKGiILRTf2zq6RYij5O67P1gpkwLUiFfcJj3rAmbOVv6os767kXml4kSw+FDUxy9aY44ydby1dycwWNCIGMK67Y1CKFYPP0P6KQFV+kDKe6UsnNk+R3nFzdYXNrAOcs3q+M7CzshkCtEv3kkzz2Y8jl8qJJngW9X8fZSdkdFwikKaWMspZLi0IEKs6nZqjiOnXqoWfi4zcbS61zoMRl1I9RSEhH913v1yLSR/P9c0+kNAc7Hu+meiJzQksRw7OY55ufKaG1G3pQ1IZAxRabeKq/ExuoLrC8/i/LKZLvPy0Vx51o0WUWR5tTpNdbWWkgpB7eTLI6FwUWTXQvIzFj9fhGlJAv7Shw6WGF62kcpkZfVuJxghSJJZd/3PMm++RKHDlQol9Umv4u4gp3+1kgd8WkKcnbc/PceM1F6PaIYcjtYdK+yG+G1lW8yc+sLbK655CHPSnL7bfvHomHYKFTkGz+M0vMUEqObLJ35IvsOumrFlxvSu7XJSrBRD1larJNoPVB/x3YYpds8bsiEZ/+ztRZXzDBQJDMB7XZCu63pRBqtbR4t9GqPOJOFUgqCQFKretSqHkql1QVMv3lmJ59kN1HPBUDZ3Clu08FluR7F+zBsZOcV6S8uJcXmr7PkxpxoCiSitWHfXJmjN87x4isr+J4aWSKZEMiVYJQ0yrRK7/L5bxC2L+CXZrDm0vqDbxYuNq/bs7jYYHW9hYBCfseQHNt2Qh47hX7HbaY1ACgpmJ7ymar5JNrQ6Rji2BAnJu1hAcb2ZtZ5SuD5kpLvKuq6MirkxFE8Z/Z6oMgT+GwurBkxYdtDHoVx2k0Rwt1fEm05etMsixeatFqj2754QiB7AFL6dFpLrC49xpGjD5HoxkX9IdtFWYVhzOJSnVYaj559dqgTV2TBkBPsBPojrzKBn2kkAJ6S+DXXy9tk78GmhyCE0zwE6TGwGNM9zyADKxyKJizSHX3f+UZMyPZoIZkGko/Z5uasLGs+u49BoLjl6ByPP7WIZDSjEic+kDFHliRlreXC2a9hTZIvoO2cb9uZrNbW25w6vUq7vYvkgVtMxXpJE+wMuhpk8Xn2+jN0wfEt03lR/MkirbSxmAIBFX0uOzVfsmOnfuf0ddGE1WfOGnEU4t5yEsy8ODmNFDZ0SWI4dHCK+VnnUIcBhF9fJSYEMuZwi9WgvBLry0/RapzdNidkyxj01GR1/nyd8+c30tBPOTwTxDbodexOsNPoz3PI/pahP3mu+OM+3HUY9x9n58bYzeDueZ0H4Pa+HhcUA4iLLvee6Lb0XioluOnG2U1/HxVMCGSPQAhFFK6zuvQ4yus2m8qwvckq4dTpVdbWW5eYVT4cmAl5DBVFErmkn0LI6qCQR+ja3td7FVutPa0tCws1ZmdGUwuZEMgeQGbGAsvahcfROu4xY21OWhIIKVhdbXHq9CqdTrKrJqt+CAE6MSMxlgl2A7bwv+37297FVtnq1lp8JbjuyHQhsmx01sSEQPYAcjOWKrG+8uymnuk9WoeUJInm7Nl1Fpc2cjPWbpusihAC4tjkztkJrh0UfXrQrcadheaOwPQcOrSxHFioUql4Bd/gaBDqhED2EFyBxVXWl58p9EzvLUeyXm9z8tQq9UY4tMTAy4YQaV6CuSYFxrWM4nx0lbi7ta2uNRNW9toYS7nssbC/is4JZDQWxoRArgSj8ex6kJVFMDphffnpwkpzma1aG86d3+DcuQ20Nig5OiarfghAa0Onk2wKQZ1g72KrpMdsavb/vxex1VosTvuDB2qoEatSPSGQK8EIyjKRbs+U8qmvPk8cNxHSQ0pBvR5y8tQq632O8vx7IwYhXJ+EVjspCI7RG+cEO4ut6z5lBUH2/vPv1Tyge+3OmT47U2ZqKkCb0XGmTwhkT8EipEercZaovYRFcu78OmfOrpMku1OO5EogBBhtaTajPPFqFBbLBINFT+FF+k0618bz796DVNsS3b/7vmTfXBmjbbcS8C7jGiKQzJ7afZ39uCJsBoFJU5LsNp8ffQihMLrF2VOPcfpMMy2COPpaR4au6UJQb0TOD7Lbg5pg4Hi1DUI3ynDvo0se6f99t2V+vuI6Fu7O8DZhjxNIkQQclDQI4Xp1ayNJtKKT+LSjEmEcEGkPbSTGyPzz44LuQjMsLz5HJ3Q9lhl4eYmdQzZGKQX1Roc4NhM/yB7Hq83NodVfGyEIkW1Xe9UQYywz0yWCQO16nlRW02uP1sLqvbkyJQGtFa3ER2CZKod4SlMNOkxX2kyVQjqJz0a7QjMsE2uPVlQiij1KfoynNFJkGspoIlP1hfDR0Vms7SCE3FwraAwgpaDT0WzUOxxYqJIk43cNE1wiCqaqrc1X1x7Sylk90WfGWHxfMTUVsLzcAlVIzx8irE27KGq71wgk5e08dtyFALajEtYK5qoN7rv5Je6/5UXuOHKa2WqTwNMoqVHSYK0gMYo4UbQ6JY4vH+CRl2/l8ZM3c359ljgJmKpKZL9eOVKwIBRJtIRJNlD+PiAZu8WYRWKtrYUcXKgBo++7meDysVWFhOLra/eZi9SHnl27s6T4vmR6KmBpqYnnkZWUHBqyvLEothw5oPYSgWwmjzAKUNJw900v8/bbn+aum46zMLcKKgHtQWqmwnYTlnwV43sx1UqbhYULvPH2Z2g3azx79nq++NztPHL8btpxsIvXeXEIIbEmJO6cxSsdwOoYcZXdCoeJLCRZSsHKWptE600ZuhPsDfRGXvU6y6/159yrXKR5MRZq1SBN/h3+mFwZG4hj+Nkf9PYKgXTJQ0qDNpKwU+Leoy/zjx78B153w0nwI0h8bOJhYt+RTP9RbGpvTB+MSTwEUCl1uPe2Z7n31mdZ3XiEf/v/TfP0VxWBZ4a+A7g0SKxpkYRnYOZ+YLwEb9EP0mhE1BtRWgtofK5hgkvEpiJX3bDdcZqzg0B26cVbZKylWvWdI33I98dai6cE6w3L+79V8Z53ir1AIGmvY5zDux0FlIOIj7/rL3j/fV8HL8FGASas5Kanoglqq/ufzeXMrGWNxHTKGGOZP7DIe+95nv9N2pH0hhRjyJNoEWtjxjWCRQhBHGuWLrSYn62g6TV5TDDmsFkR867e0a26e+1EXr0aumXu3B0y1lIpu+6PSTI8CZSZrsLIcuSA4Bd+xAcz9gSSkYdFSUujU+bWg2f5xLd/lltuPIENy5hOCSnsRUmjiKLamCGPxkp8OrFzxDuz145e0FWj60hXjkBM5ExaYxbBlJdfUYILF1rcfNNcT82uCcYftqh5dE39zggwgmtrN5BrIdkf0t72pZJHFHdQQ7pJ2YYt0fBPfszn4PWCpGHHOYy3K0iUtDQ7ZR689Vl+/R/9EbdcfwrdrqTvdcNwhbj8gIXsO5mWQ+pfyd4bNXTD/xQ6uoA1bcYxWjvvhyAl9UaH5ZUWSnVv+IRIxh+iYJtxSyptEHWNFk3cDo5TRf5aSkEpUEPzgVhrURLWG5bv/06Ph96p0E33t/GTLEDR56GkoR5WeOvtT/FPPvQfKZc76E4pJ45sJ3O1E/LVTF2jhO4wnSM9iS6AUGR+kHFCNlxr4czZ+qRL4R5DsTlt0WBlJ+TRA2dT6M59KQWBr4aS22ytRSlBo225+zWST/2wB51sYy3GlUC65NHslLjv6Ev83Hf+GQA68XLfBQx2JzPKuyRXxTNGR8sIodK/7fKgLhPZeD1PsrLaZm09zMuxuI3rhFDGFambfMuGtGLi+9iEos9PCPB8mSfzDQqZ3yOOLTM1wa9/0qc0JdCJywOx1iK3bFc50nC+ByksUeJxeG6Vn3vvn+EFMVqroZHH6EOCidDxKghJf0b+uCCbk1pbTp/e6IlMmWB80e0CPi5FgnYXRW1cCNEtZzKgjVQxwitK4J/9pM9tr5UkbaeRZOeUsofZxFgQiRBu8hkr+Ng7/5LZuVV0HEzII0W3b7VAxytgNeMa0VLsDb14odWnhUyc6uOK3r1zVq5jEnm1HYqFFcFp5Vlw1mAiEoXze9QtP/bdHu95t0LXnd+jGAkpO5FrZ7opoWckiaSrfbQ6Jd75uie4//ZnMWEZKYZPHiN3e1Lk1UuFQserWBMxzgsz2w0lieHEyfWev08wpiiWLhHdch1ipKs87DLymAOL70nkgHqDuHwPWGtYvuOtip/7qA9tkLJLVnmu1qnTqyxdaLgmQymR9LpsRmehZtniiVbM1xp85MEvumzyIUdGab0rJWguGwKJTjawNhnQLmU4yMbueZLzS01WVtt4Ey1kvNGncHQDssZ3ng4Tg1rPmdO82ba8/hbJb/zXPghXh2ur+mTSGMvycoOTp1ZZXmn2EUmXPEbBTyLSfI52HPD2O57iwMISOvaRYkg5GemEj2LL6AcEWRASk9SxNmGcNRAozENjeenl1by1Z3F+TjBGsAKEdf+nG8NJ7sfFkO7+EQOpxpvXuYosc9OCf/nzPrU5gY5sIQer9wHJzCGTJJqlpTonT61yYblBkjgi2U5NGv6CTZ2pRlIrhbz7zsdBy13ZtUSJdYw81LNeCQTWdjBJM3Wkjy+y/ghKuYisU6c38H01QBvwBBOMGDLzXqq97aQIzirsZqHyv/lzPsdulyStXqd5/1KT2ZeLRHLhQoOTp1ZYXKwThkn+3lZaSfH/QcJaF9wXJR43Lyxy0/4l0F4eAjhoGWLTsgtOAzFsQcYjCIG1GpPUcRHb475LzyM/ePn4Ko1mlE/uiSlrfOCeUzEGaxKPdSkQxY1yn1P9apCbpQS0Qvjlj/u85S2KpO58Ia9WPkhmb7jPdIlEa8PKapOTp1Y5c3aN9fU2xmzWSoZFJlmF3Vgr3nDjcSh10GYXdtVC0AoNegw0ENcnPcHoRq6BjLOQzUgi6xXy4ksr+VycmLLGA0UbevYsr/XeH5eKHkNLxrtXe8ysZJCAjYblpz/i870f9NANF3GVm822eTa5BM4K8BXZxoVLWprNDufOb3Dy5CqLS3XCMM7fzyZANpjs/0EsaGMF1SDinqOvgJFpX2A7FLupE8aAhPW6Jk6Gc96rQ6qB6CZ7ZYdXdKifO9/gzNk6vtdrypqQyGiiX1P0PMlGPSLRZqJBXgJycWOziKirO15OHmmZko99j8cnfsTDti1ScEnEvmkLny9CukQipSOKONGsrDQ5dXqN06fXWFlp0ukkuW1aStlzsu0c8Fc6URKt2D+9wS0HzjvzlehW4h00XLihe73e0BjDwJJ4dg4CrMbodq6B7IVdXnGT89wLy9QbrnXvZBc7uugnD99XnD3X4MmnzgNjYA0eARRljbFXtx3sIY+65aMf9PjUx33opAmel6gVbluNt1hOIAuxc1qHO3CrHdFsdVxlyMCjUvEpl31KZR8v1Uys3d7EtZXgfbVBC2HRRrJ/qk7gJbtSMEcIILGs1TXKlZcaWYGVl3W31mkgtvj30RzzpaLflPX0sxe4/94jPfNnL1znXsBWXQZ9T3H6zAZPPr2I76s9ohsPB5nvVSfmijev/eTxA+9T/NJP+RCBMeQRV5eyfi6pnHu3fIQ7qPuBzOTVDmNa7QghBV5aarhc9ikFPqWS2tLUZQsCrXhh/X8rjsFYwWy1hVAaq4df36lLIImrBTO8U1828ocvRFqRd3yz0TMUJ3X2v++7qKznX1zmdXcsEMfOOOzezkJDxyLiYc9hK/LwPMnxk2s8+/wy2TPR2rr2rKO8oEYA+cYIl5fRTSy89GMUyWOtbvmB9yp+9WcCiN0xL7dlwmX1A+k3T/WTCbg+1s1mh0ajg5SuLanvewS+xPc9fF/lPxmpdI+RHRv69yRSWIxVzFZboBJs4g09a1UKgY0MiyuJKy0+8hPeCU6rO1ird3swl4X+HZAQoKQjbWNcYx1SNV5IycvH16lWA266YZYk0akNF6ywPfOpSygwIZXBoZ88hBAoKXjx5VVeeGkFT0osLhxeawOo/HsTzXFrZFYFCySJKbzBJU3losN8vW75wW3I43Lu/xU3lNqKTLK/F98zxhKGMe120acikELgeQrlSTxPOrOXkqiUdLrk4r7jKUucGGYrDVBmd6rmCIgiy7nl2IW3Dfv8VwIhMCYEmzptxgRSCIR0ZKG1JU6gEztSqJSgHLhaPUKCkmCs5MTxZaZrirm5Ku0OJFqgBHhe2rtA2tRv5s7RLZ0xPvdlHLAVeUgpePq5C5w4uY7npf446/IO2mHCVC3AjMeK2nVobWin6RWXmk6Q5XkgYKNp+dEPe/y3P+5dseaRYUc6EvYz1mZCAeglHG0tScc4p43tPVbX+tL9jqcsK/U2G/e1QF76jdspuJ0u1Fua1Y3E7YbHYL4LBNZ0gNGOdMn8bDLdYUWxJexAKYDZKcGxGwV3HJVcf0hw4yHB4QVBtQyBD6VAYCy0Q0ucrGFos7zhsbIhWVrzOLXks1ZXhB0nuALfImU3DnJCJDuHfvKQUmCN5fEnlzhzro7vdcPJhXClwdvtGCEFVruNwwTbQ6TJfu12fMnm+/w5WGg0LT/1EY9P/qgP4WbyuFztbyAtbbcaxFb2635i6fk8fVEHxkVxLddLLoR3mOSRmktQgvPLMZ3YjlFit8CaCGsNYgQH3U1iddpGveVCCG86InjnmxQP3iW542bJwryACs7SkeBcOrbwA8zOZZOiw1HZcVMrESSRYGnN48R5nydeKvPS2YBWKPE9i++5dkYmL6MxIZIrRXGNG2PxPEkUaZ54apGl5Sa+12umEmkUYztMnD2/7xgTdFHMBI9jQ9hJEJdQTNFai5KCWFuSxCUJ/tD3uFBda7kq8oABEchWeLXBbTVpHLf0Zs4oZVmpVyBRQ/d/2JRAzizFhB1D4I+eMN4aAmtjRs3glvkllHTaw0bDUi7Bu96s+NC7HHHU5tPnHwMadCMLMSyI+MJr5+YRgMz9I1I47fXIwZgjRyIefEOL88sej79Y4eFnK5xb8fCUJfBtGjU48ZFcLvq1DmtdcEOjGfH4k4tsbHQI0rIzvYmEIKSg2XS5IBNsj6I5sNmMSRJz0dmZFUYMO5bAF/zmp3y+/d0K03C+lKslDxgigbwaLm3wAiUsq/USJDIvoDgM5KYfT3DibEQ7NFRKktGf86kT3cTOByI8RoFIinbxZtsS+PDBdyj+i+/0uPcNEnwgBN2yedIUdMtJbzfpcyKxIGR3flgrsAnY2DkQDy0kHDq0wdvvbvLIcxW+9ESVU0s+pcCipMWYTEOeaCMXw1aRVkGgWFpq8uQzS3Q6Cb4v8xpL/c9MCkG9ERHHmiBQuaVhooX0IiulJKVgvR6SJAbfV9t+FgSeEjRalkMLgt/+eZ977nM9PYrr6Grv80gQyKXAJSsaFtdrxInC9zVZQbFBz7U8p0JbTpyLchE8FhNdOA3E9rXw2S1kKnWiLfUW3P9ayX/zUY/773NRCSa02I4jCyncDrWfMC52zzPTaDf8nLRqgfvdJAIbC8olw1vvb/Dm17X4u0drfP4bU7RCSblkJtrIJWA7Z/krx1d5/sWVdM3KvBT4VnDVXzX1RsSBheqrfvZaRX6fcQ70jY1OT0rEVgFNKg3Tves1kn/1Cz7X3yxJ0oZQO5knNT4EAvjKcHp5mudOz3PnbeexHS91hg5+wkkJhIaT5zp4nsizNccCNtn1KKw8DyDtNVCrCH7pxzx+6P0KWRVO28CRhlS9zv6rvc/9IeIZmVgjMKHAV5aH3lLnDcc6/OkXpnnqlTKVkklDxzMSmWgjGfqJI/N3aG148pklTp+uo5S4pLDQTCiurYccXKhtOv4EXQghiGPNRr2T14DrJ4/MJLxWt7z/WxW/9gmfyowgaVg81U3u3ql7Oy6GfAA8Zai3A770zHXgaWeasMOJhhJSEEeG42cjfDUeEVgOIiWP3bO3dR3lgvWG5babJP/rPw/46Pd7SCnQTec4V7J3VzUIASJEL6Go1NSlQ8mhhZif+NAK732wTqIFiRFIWTSVjs1DHxi2M1k1mhEPP3qWU6c3XLvVS9jlFs0yyyst4kRPSGMLZPdEKcHqWkgYJlsWtPWUIIwsUWz51A97/PY/DqhUBbqdkUeWZLtz93h8NBDrmNX3NF959jp+PAxS7WPQ500dt0qwciHh7IU4Z/JxgcXuWiJhf/LSB96h+Bc/7VOdFSQbTqXOyuMM0yTYNW+li1NaTOwydL/z7RscPRTxf/3VHM1QUvIt2kw0kV4HeLeS7slT6zz/4orzY3gqj6C8uKkxE4ySRiNio95hfq6C1hMzVobeSh2wdKGFsVnaZeE54LSOYzdKfvljHm/5FoVtdR3pg9LqxkYDyRLAyr7myRMLvHB6HhEkeT7IIAW6tYAnePFUh1ZoXBb6mMERyHDHXRQiG03Lf/Vhj9/6RZ9qTZC03K4IxEA1jouhSCRO2wAdSl77mpCf+tAK+2c07Ujmmkr66aGPczdRJPfstedJ4tjw5NOLPPn0Ut7J1FyhoEoSw4XlVp4Hkp1nAgcpBJ1OwupaO9fUwWkdcWxptCzf++2KP/jvA97yoEI33GZnJyKtXnVcO37EgSHbKToz1n/84u0uI92KvKnUQM6aZXt6gpdOd2i2TR4VND6waRTWEM+YkQIuwe9nf8jjF3/Kh0Rg4sGp1FeCzKyVOc2VtOhQcuRIxCe+Z5kbD0bXJIkUiaNokvI8ydKFFl9/5Aynzly6yWq7c4DTQpYutOhEya7Ph1FBkUClEiyvtGmn5isXhuvKsM/PCn7rUz7/3acCZmYESdOiVK+zfFD3dKxEoTNjCWqlmE9//RgnTu5HlRKMERRLVOzsObt1L14+1SHRNk+AGhtYy7B9IJnZaqPlMl9/4od9TNtedebrIJGRiLXCkUhHMjub8LEPrHJoPiaM+30iexebczssXhpR9dzzyzz62Dla7ZjAu7oaVtl3pBQ0GhHnF5s9Gv5YrbMBQQiB1pbTZzYQgFKuUkO9aXnv25zW8d73eG59xYNxlm+HsSKQrP+HpwwrjQq/95l7QRosohDzv/PnVRJoG1441SHwRRo6PGZCJLP1DeVULtZ8o+kKtv30R31s0703quSRoWjSUtKiI8nMTMKPvX+VmaohSUQhB2nvCbd+X5RJbeyeJ1lZa/Pwo2d46fhqXq/uSk1W/ed0x4DTZzdcktwIzo1hovgMlBIsXWiyUQ/xPMnahuXQPsFv/pzP//BLAQcPuhDdnUoOvByMFYG42H4XWjldjvjsw8f4/MPHUNVOwcm58yQipCBsG54/HuJ7wgU1jQ2coLO5BjJYoZc57RpNy4N3S37lJ32IU1IZgkq9E9iKRA4cjPmBh9bc3xGIPUYi/cSRvQ58RTtMeOrpJR559CwbG51NJUmuFkVn+sZGh6ULLef4pTfa61pBXnU3v27L6bMb1JuQJPCD7/P4w39Z4oPv9TAdi4kyf+JgIxi3wthEYRVhrUBIi5SG3/7jB3ndDcscOrCBjnyUNHlo79Xew6wsg/AEpxYjllYTpx7uzGUMF0NYhFlESBRbDuwT/NpP++AJdNiNBBll4ihCpIEZOYmEkttvDXnoTQ0+8+VpquXBmEyHja1MVQjwlKtjdfzkGidPbtDuxPie6nmOO/ksi3PjlRNrLOyv9uSRjNPcuVpkplSwBL7kxVcanD7b5qFv8fjYd3vcc7eEhE2Jge67w71HY6aBQK6FGEHJ05xdneKf/h/vIA59lKfRRu6YJiKECx3GFzzzckhzTCOwHAYv7bLJGyfwj3/U4/pbJEnYG0Y4TiiuRSmASPIdb67zuptDwkgW/CHjd20FYWmzAAAgAElEQVT9z6PoIBcIzpyt8/VHzvDcC8tEid5xraMfmRBUSrK+EXL85BpKyZ47O45z6PLRnVOubEnCXGWNf/NPA37nVwPuuVuiC74OGL7WUcQYEohD1uJ2uhLxteeP8Kv/5zsAHInonSGRYhXe50+EtMNxjMAaDjK/R71ped/bFN/xLg/dcDukUfZ5XAzF6CxjAA8+/LY65cDkwRtZKPKoC7hsjNk4i8/F9xTW2pw4Hn9qkUYrwvdUbnrMPjsoZMf2lOTEiXXWN0I8dS31uu+arKSEMBJ8+5sa/O6vwDve7mFii267pNtR8SWOqTjM0vgtiZbM1jp87pFj/MLvPUQY+qhKTKJl/hlrr5xIshImL5zs9EQ3jB8GO2YhBHFiWZgTfOL7PTBZyZDNJRfGDRmJSGnRkeDQ4Yi339WkE7u2AqYwH/qF827j1YhNKYnvKZLEcOLUOg9/4wxPPLWYC24l5dCFVEYWiTY89/wFtO72sdnb/pBs3rg6Vu2O5M6b23zwnS1AkTRcLbthVGu4HIwpgUAxokhrwXSlw18+ejMf/5338dKJ/XjTIRbQJrvEKyMSqQRRS/Pki22CYEwjsIbgOJcSWm34ofcrbjzmTFdX0iJztOFCk4kF33Zfg33TIdpISr7MS0tkWdjAlrv9QWO7c2avpRSpL0NSb3R47sVlvvbwaZ56Zon1jQ5KSZTqbfo0TBRNWcsrbV54aSU3G48aOe80sk1KlAgWZhM+8q4NMGA0OAvi6AWhjDGBQOYPAYE2ThN56sQCP/6v38uf/s3rkUqjKhHgiCRXEC1khLIVMqKxqfnq9GLMmaVxq4HVhwEVU7TWZclGkeXGw4Lve48HaTXdsb1XWyBzqgth0YmgMg+3HlziK18/x5mzdTodjZQuailLrIOtCWWniKX/OFsRhoA8DFcpSaejOXVmg28+fo6vP3KGF19eoR0m+fs9390lIZWd1/cVJ06uc+58Az/tJ1KMEts7sAUzqbu+j3zbOtOzCTrp5h65hNfRII4MYxmF1Ytu+G6iJbVyRDMM+Od/8K189uFb+NGHnuSB157Gq8YQedhEoY3bSWZCYStYINaCUiB5+uUWjZahUpZjKRRtGsY7CHevECAktDvw4Xcp5g8LdH38oq4uBUVTFqHg/W+X/MF/avLwN5tM1zympgJmpkvMzpaZngrwfYXvOTOQ63NRJBPyEvtXKwxdCfRsjFmWsqsCkGhLp6NZWw+5sNxkZTUk7LgOgEqJ3DmejWlUhFRxp/3McxeoVnxmpkvE2uQ+mb0xv7rkIYTze3zvO9Z5zbEQ0+mvfjB62AMEAlmuQ+ZY96RhumL44tM38JVnr+NNt53jvW98mTfffpYbDmw4MkkUJNJ913YdpdkmXUhDqRRDUOax5yPCyFCrSPQYEki6DR7Y4ePYcnhB8N3vVhBlgnYvLO6tIYQgiS2zhwQf+Y6A/+UPYxKtWVpusbjURClJECimpwJmZkpMT5WoVX2CkoeSripxcSdtXUWXtOhl77m20wYyQZ/9n5GUMZYwTNioR9TrHTbqHRrNmChydeOkEnhKbjpHdqxRQXZNWb+Qx548zxvvPUK55KNN1y8y3vOsSx5SQCOUvPu+Bm9/YwMT9bbtHtVL3CMEAkUSMWl9rKlyhLWCrz53HV9+9nrmpkJef+MFHrjjLA/efoZbDq2jlMWTBqks1giSRGIsXNiY4mvPHeGrz9/Ap//q00xXNdr4uN7iI/o0t8VgSplkSYP1Jnzn2ySHrpN5lMheRUaOzhcC735A8u/+RJBo8L10MwJEUS+hKCUIAkW14lMpe/iBIvAVvq8IAun+9xVSOcEhAEGvrTsjG2MsSWKIooSwowk7CWGY0En/b7cTYq0x2uYZ42ob0nDXNJoPLCMJT0mazZjHn1zkvnuOoJTIG0+NL4n0kUdb8sY72nzoWzcgFmkAStd0NarYQwQCxegswNkTBdTKrid4J1J86Znr+fsnb2Sm2mG22mG6GjFdjqiVYzqxR70d0Ah91psBa60qOunQ2jiPlApGoPDf5cItMsMgqvFmhSZ9D971gHIeteFVTNlVCAE2shy9UXL/6wWf/5phuipympZSIBEIldK3sbTbCc1WjC20d+0WxnNk4fsSz5PI1HyTva+NxWhLog1JYjDaYGz2d5MfLzuWkhJP9Sqe40Aa/XC5WBbfk6yuhTz6+DnuvvMQQeCix2RfVNLooxttlZmtmqHkza9t8YPvWQfA2G65nFG/pD1GIBkyO5R7WK7MCUhpmSrHefjvcr3C0noVkxZpdLuBLM5aM1M1RK0TtMwG3Qr84wiLtcmOC3aBK+p242HBA2+QufMcxkdAXQmyna824JXhW+9X/M1XDf1OJps5FSgI95RUii0IMq0CnGaxKcmP7qPrEoD7XUmBkmpLf17mcxk30uhHkURWVtp845tnuesNh6hVfeLY9Jjxss+PJrrkkTnGW6HgHfc0+Z5vWwfrNr3dWmujjz1KIBmKi82t7oxMhLB4yuaLuWftW7DWYKxP2DqN0R2kqsCOu6CHBGvBJuwkg1hrkUrQieCu10hqMwLTsSO/Y9o5uIVOBHffLpmuiU2NkLYTZMZaBMXPueP1v74cbNdLfHSF6eUhJxFfsr7R4RuPnuXO1x9k31yFONF9eSKjpgb3ah1KWjqxC+T54Fs3eOiBBuhezQNG1+9RxJiH8V4uRGGBup+skq/J2+O68iUWAVaThGdw/oNxTmLq70h49dfRdQLDnbdKCCC1zOwZofXqSMkygVuuF9x0WBAll7boB1UKZK9DCJH3X2+HCY88epaXjq/iec70l5mEuuVlRmG9ZpUKnNYhcCarhVnNT35ohYfeUscm6ftjRh6w5zWQK4cQEmNC4vAUQviMo//DQaTRPTHZrqxgWbkqaG2ZrgnuuUNCTBoaPY736PKRm7G0xZ8S3H2H5PEXDNUSJNtoAxNcPdx9tWkkm+XZ55c5dz7kllv2MT0VEPg2DX0tRFa6bw55pP1aB3Ri5zN88PUtPvz2DSpTGhPK3CIybuQBEwLZhK6WodDJGkm0hLNzjcJu5gphNVa3XcIGFiF2RvHUBvbNwNHrBCRXFr7bXw22+HrUhbAQwjnEPaeFAHkJ8gl2El2TlEwtCJ3IEkYwXZPccKDFe+5LqM3M8sUnarRCSaVk8hbFwyOSYiKnWw8qzSyPE8HRwxHvfaDOa28NwQhMxxXkdJ8ffYf5VpgQSB+yqCUhPZLwFFa3EXI8b5MTwhKswSTNvr9f3WwVAhINhxYE5RJ5lPCVkocxBul7oM1YkAek9xEggSMLgkqAK7g4wY6gOw8EUrp722g7Urjlesl7vkXy0LcoXntMQgVINrjvNSF/9fAUT75cJkoE5cBpJCaX7cWN4E7Nsd6IKSEc0WXEcXA+4a1vaPH2u5uoknE5HtDT3XIMpvuWGE/JOEB0nXCSODyNtR0EAcNuCbsTEIUZbUwTdjCUVwjX3Ob6gwIVuD7nl3PkInlobVDVEivnVpmqlQlKHnZMzEBCANrdh3JJEGvXOGuCq0cWwhzFlnYTahV4yz2S73qnxzvfJKntd5ovHdB1Z3a+7lDMD79/lRNnAv7+sSpPvFSm0ZEEnsVXzkdSJJMr004yjahLRpl/QxtBFAu0gUP7Yt5yZ5sHXteiMq0hclrHOJus+jEhkC0hsaZDHJ7Ghe+OZ7JSVrsJJEa3djQXROB2hPtnBfhgO6mF7JLG1SWPJNF4+6b58l9/k89/8Sl++Re/D9OJRyqGZjtk9ngM7JsV+B5EMeMd8b1LKK6vTNtohRat4bqDgnd9u+KD71DcebuEMhCCTtu4uhB9979J3DFuur7Df3l9h5NnAh55tsozJ0osrbkyRr7nfBJSusTjLPnzcszUQjjCMGnobawFxkC1ZLjthoi7j4Xcd3vbEUcscl9HpnWMq8mqHxMCKaAr2CTGtJ0DXY6vA72rgEh0vIY1cY+v4UqRfd1YqJb/f/bePNqy4zrv+1XVGe70pu4G0EA3gG4ADTQxgyQGkqAokpJIyZFjScmK4qXEThzJK8my7BU5kuxkeVlJZFm2pGiKpEWJEiWKpDiblkiKJCiSIDiAAIkZINAYGj3P0xvucE5V5Y864733Dd39ut+7951vrffufMaq+mrv/e1dgFp51xsgj8kGrzz9Gv/on/0Bv/Tzfx8aIWahi+eN0ChsIfBBncd1qFAmDSWdlj61Nuo1uGe35EcfVLz7fsWmrQIM2I7FzDrCkKXV+FyAOlUymZ4EAddeE3HttjO8d0GxZ3/Ai/tD9h4OOHlOsdCRyb7dwJ7Wx0stkrTHu0pA7pUjDEca2oDvWZp1w41bIm68pscbdnS5eksEvh1r4khREcgALEJ4xJ2jmHiW0Vc6WxAKHZ3G2gghQi52mCt2gDDMk2iWI9kB8mjVOPDyIf7bf/q7zC10ePv9u2GhO3pl4JNs/GYdzswO5BNWWARSulyIWFvmOm5gvuYKwTveJXnv2xT3vEFCQzgX1by7olKkyar5VS6Xe0mtmJRIXAwlDAy339Lm9lvaxB3J0VMerx4KOHTS58ys5MycYr6j0AVyMCa3bqQEJSw1zzLV1Ey3DJunYnZs7XH91ojJlnakoQU2FphOXrA1Pa5Rac7ng4pACkgD6AiPqLMfazpI1WD0hwOB1W1MPIsM62BXJ1AtBYS+LYhkFk/g6o95ePWQ00fP8I9//g95+bUj3HPHDq7bvgUiPTrEUYAQjkTsoHu8AmXpeFrqP1NSNQT33yF4z1sVP3hv0doAM+vWmkkl4nk7gmFtrThgu30lM38jsF33nqcs27ZGbNvecwN+BLEWLHQkc21JJ5J0uoJOzy3uVA8NYWCph5aJhib0LUpZRxgAscBqRxoimU/JQpxjBJvzilERSAGpKYzVRJ0D5ElAo1hAMYcQEmsjdHQKP7waS54TcjEwFjrd/PVy+SVZwDzw6Mwu8I//2R/w7Pf3EwQeb7h5O+FEA9Pusp4WzFkpjIFOUom4Ig+HsovKvRfFlrkFt0DSjm2Cd96r+KEHFG+4KYltnIe1sRTyr+XfL7qRbAw2SsrLCIvvW6YCzdSUhrQqd8mHBVhHbFgcIXXKQfBB0hhv8oCKQDIMxj/2JwmEo00eDgJreujeySyn5WJyQYohlG6PZQfMdCAxxqCSCn8/9y//hEcefZErt0xy7MQ57rz1Ogh9zHwnW4FuZJBImjvdJBdmrY9nDTEsrhHHlvlkonHVZsF73iZ59/2K++6Q1Gecksp2z9/aWCmKZJLWDJMiXY0lsVbSumTgiGJgI6WHbLuDaqrxJ40iKgLph1Do3onCYDvayHJB0MS9o+RW1YW7sIozrvmOBb245VGsUSSEgMDjF37pT/nsl77H5pkWnW7ERKvmCCSKR876cImDTjEUr7CUyTihVKwRt94I1sU1FrqgrVPqveUuyQ/eK3nbPYotVwkXWhyipLoQa+N8sdRAf/5ctbEIox8VgSTIEgiFT9Q+gDVthLz4gPNaI5OaCo+oewxrurjVUi4O1jo3xJETQI+ha4AUycMYi5ps8B9+/WN84KNfY9N0C60NWhs2Tbe45aZroBePVADd2iT+o+DYKUs3Gu+1UGAwCVVl5eahF1s6SUXm6QnBm26TvP0eyYNvlGzf5uql0QWTJAP2K6ny7Y75RRwjVASSoFjFM+oecJJXWUsa9lof3cXCIoRCd49iTQ8hg4sOpFvA8+DgUUvUs/i+yNa5gGJJGFzQfKbFh//8IX7zD/+G6almtqpcFGlu3HEV05MNNwoxOqVA0uJ+KDh4zLmw6jUxUFZ9lLEUYWhtWehBbKBZc9n4d++W3LNb8ebbBNdukxACPbCRRXfzfI00kJ5iVO55hTIqAilACIk1HaL2QZAeMA7kkUJh9Cxx7wR+/dqLzkq31gVGj592QdGZGZxypjAqZHLd6SaPPPQEv/z/fJh6zVl1bjVDSbcXcfNNVyOaNfRsO1sgaBRgrXWWnILDxw3dyEl5R3LZYwbJwg30IsmwdoTRjqAXQz10yZO7d0ruvkVwz27FLTsFjUkBPtBNSKNHJmdVJRfVxnb9jCoyoRGu7VcEQnHQUxi9QNw9jBCOQMYFxerCQeMGLN2LCqQDKCWYW7C89Lrh/isVJnIz1HQgirXL9Xj1uX38L7/0p1hj8UKJ1okwIckd2XXDVvBVIV4zOpAC6MLL+1x2s0s6W38uuKWOKZWeCimS1RPdSodx7DLrrYVGHSabgpuvF9yxS3LHLsltN0lmpkVShwrogelYTLtMGpWLavRRqltnLcaAF4iKQHI4N0/cPYLR84x+AmGOvKiiTsqzXHwgHdzgcHYOntljuP8BRXEFPG0MXhgwd3KWn/uF93HsxFkmmnVined5aGOYaNXYncQ/RqUKL+QdSipBe87yzB5D6DyDq3r8Fyt2SIPCIlMcOfl1eq8cWTgVWRy7bPpGDZo1wfarBDddJ7jxWsmN1wp2XSfZPJMQhsG5prRFn0uslYQ0PDm4OuAo3NMKgyi2P2udFeqFAtlQnNjXXT0CGdbQiw04e2/gCWUJHWnjLm39kibklBIIuwfHKIHQIQ2kiyRB0ph2khty4TW+3G8FvoJn9xjo5C4Kay0qcXL//L/+AE8+u5eZ6RZxXE4S1NrQbITctGPryCUQpvEP4cOrL1uOnbL4XlmcMLTtD5GDlr+wyJt9fSSr32T7+g1JCfOEGNKMam3c54HvEh4DDzxf0KjBFTOCq68QbLtScM2VguuvFtywXTIzKZA1XPA7wsltYye3hXz54mFWxijdywpl5B4Zdw+NcdWMvUAg64pzR3p88M8O88cfOXJhBFJuKClR5OavMfnMxph8xpPPfPoCaICQeeKQki5A62rUCKQQ2e+HHcPFIk8gNMSdw4xLAmEZTokVd4+go7N4/mac3+HC4ExZCAN49mXL8ROGK7ZITJTEN6bq/Or//WE+87ePs2lmkDyEEESx5tptm9m0qQVaJ21pNK63ta5TSR8ee1YztwBTLSdfdW6s/K+/3RtTHvzTvrBU8D2v05T/FftLqmjypMisiE1TgpkJmJ4UbJoUbJoWzEzm789MCWYmhKvjFQgX8Ba4ZhHjlllNXFLp/qC/BlVlZYwL+mvkmWTAVTWJDCWnDnT58GcO82cfP8qLr7ap1+TKCKSfMJyf2628FkfOTxprJ+usBVALBY06rtFOCZp1qIX5Z7XAzYSSUBraWObbMLfgHo+fthw46oKz7Y6lG7kZk++Dp1x5AbcE7eDxXSiyGEEh/jFuHUIIhdXzRAuv489sxeroomMOvic4dsryyPcMP/HjkmjBEG5p8YkPf4Xfe/8XnOJKl0vhW2uRUtCLYm6+4Wq8eojpRCMXVPU8Qe+M5fOPmExdFAYiG9DrNWjU3Hop9RDqYf7cWQICTzm3ke+5/mOTAGXmckqee8nn6fc95X6f96u8f022BK1Gsk1PuEinj/PKmuF/NraYXp7XkpLFYi6p/ucVRhP9pJFOeJQE1VIg4PU9C3zwU8f52GdP8Oq+DvWaZNOUR7KW2vJI6/JrbelFrmSDkjDRdGtB33qDYOsVgs1Tgqu3CK69WnDljCtv7flJ0pDCPYrksThuWVxDjt2jjWC+bTl4zLLvsGXP64bnXjHs2Wc5c85yruM6TOCnC83kPrrzbdP5BZQYfQ7dOwZjFkCH9NoIrI3pdfbS4P7k/YuQ8iYXXAj42uOGn/hhTTjT5PGvP8cv/l8fpha6SsaD6h6XLKhjww07roRagFnootSoxJ2StiFcX/gnP+Ex2YKplqBVdwHnWpgTiVJJVnba7tM/MeRvmV0Ofez/M/mjNWC7FttJJ2wkMZHkFAr77K9uW/ysIovxwQBp4KxiKUAGElmTMK959Ftn+ctPH+dzXznF0RMRjXpOHLF2bWSAQIYVPWt3LVEEE0246VrJHbsEt+9S3LFLcO1WgV9PzF8LaDIiSO1zV3emz2fb5ywWFM1zQasluGVKcMut8MMoaMOZc5YX9xoefdrwyBOG1w6aRHvvyKToJji/9p5U4O0exejOWHYWd0rWJUouvIbRFx8HSd1YjZrg28/E7D9cp+Ef4md/4Y/p9WJqNT9XXPXBGEsYeuy49go35Rkh95UbXC1Ww2RL8CPvVvnAnbV7BgZ7q91v+vtAMa6xxC4Xf6s/riLKXy+6vVzZjaXv9+jchworwbBlo1NXqpQF0ogMxw51+cLDZ/jY35zgO0/OMd/WTDRVRhzalCedXv+OhHCz+rhQ9Oy2GyXvul/y1rsVN24XeM3ELO4BMZiuxXRIDjIngyL6A+quMZdPqnTC2gXsbLJdKQXTU4L771Xcf5/if561PLvH8NCjhoe+rTl03BJ4buYnRe53Xq4v5AF0RdxLMrVVnXGzQBzyOEjcO4Zf2wa2l7lLzntrSXtR0jK7oHjfx2bZ8/SfsP/QSWYmGyXFVT+MMTTqIddfewXEo6TASjtgQiIGzBzlQVsMHe/Tj/LP+/sIYJOOcanLeFQYXyxGGFq7vDalBCIQjjS6hqOHujz8nXN84Wun+cbjsxw82kMKaDYccaRijCzoUGibXrpDIQRKiYw4Nk8L3nmfW9DlbXdL1IRwlkUEOi1FUJjZKDGoxFgOIiOTxb5vs9IW4GZvZsGdh+8J7rlbcc8bFf/DP/D44jc1f/M1zQuvGixOhihVErBc4piKiTG6dwxnQo1bAD2HEBKj5+nN7yGo78DSLXjcV45imRJtodXw+OgnPsj86ZeYnmwtSR7gLJBWM+S6bVsgCbCPxvVOZ/DJq6RDFhxEF731kQsGVVhTFPti/l6BMDyB8BLC0BY7r9m3t8PDj57jy988w7e/N8eR4z20sTTqiqkJVwPQZMSRYrBdemlA01o4N2eZmRT89I8qfvq9iq3XyKzoWTybr9glBS7p6BIH1vrVHU4eKjK3i2kDFrZsFvzD/8rjp9+r+Orjmo99QfP4c4YogmbDEePSRCKwpkvczeMfozGYnR/y8xd0F/bQtD9IGow6H+IvW4wGqVrMHv880dyj1GqtRd1WKdIM9a1XTjE14VK3R+dq5xMOh9UjjwoVlsIwonDvu8RPrIu3CSkQvkCGjjD0rGbfvg6PPTXHY0/P8vQLC+zZ2+bEKafCrNclE03l4txm+Ul3EZ6nBPNtl0X7k+9W/I8/4XHdDRIiJ+GzNpcKrrXWu7jfIpmY2GJ7bib4rnd4vOsBxWPPGD7+Rc3XHtPMt6FVF0gphrq2XAmTCB2dRCRroI8jsnwQGRAtvIaOTqO8Kdxa6StEyTzWSNWgM/s0s8f+OqkdlgYBliIQiLXm2m1b8MMAq89j/+sWFYlUuHgMcz9BLve2CVHIJMUBibMugmSyv2DoLmj2vtLle8/M8cTz83z3mTleeb3DmXMxUWwJA0kQCKYmc0vDJNs+37HdOzNrufNmyT//GY/73qxAg57LSywPxjLWRycpHocUYKW7+GbOXdh736S49x7FU88a/vwzMQ9/VxNrRyRWuItWnEUa00bHszBipTQuBEIodHyW7txLNDe9DXsemfdO5pm4+GRI3D3GmUMfTlhZLuv6S7Pio1izbesM1HzMbDxCNbD6LY6KOCosj8Wsh+zz5J/NcoNslucjpEAoAV7ypwR0Dbatac8b9h/u8cLLC3z/lTYvvtLmhZcXOHSsx9lZTRxbaqEk8IWzMmRqYQxaGhcytnv/6n/y+a9/ROG3BHrBuRKUAhgu51tvyE/aHatSzr2lF1y3vusOyW/dFvDodzV/9pmYbz/tNPuNmlvm0iQBdB2ddlKxMR8MsgZjDd2552jMPEB6zsuZreW1PVxplDOHPozunUSqBtYun02euSON5ZqtM+B7hTjUesQwl1XlwtpIWG7wH/6b5DH7hys/mDSVPH6cWBECRwwpUUjhyhy3Db2u4ew5zesHu7x+sMPrB7vsPdDltf3u+ZlzMXPzhnZH4/uS0Bd4nmCy5dxSGWFYsPGgpP5i4P3Dn/Lcwi7ztk8HPlpxgGK9FsiL+um2I8X771Pcf7fkoW8a/vTTMc+8bKgHEARgcWuGu9nzeFsguRsrpLfwMjo6tSI3Vq7mSNqGrHP28Efpzj2HVK0VkUdxW0HgsWXTRGoKruO2tpjFIfq+U2G9YbHgcv/gvjJaSFxLyb/0jhdTD4rNQqR5PwlBkL3GSb0jA5FFR5aoa1loa46eiDlyvMeR4z2OnYg4fLzH/oNd9h7scvRERLtjWGhrOl2DUgLfF/jKxXgbdUmrKQcrHgzJwVpNeHrOJslOeXB6/Xbm5VEMuEOBSOadWOCH3ql4x5sln3pI84HPxBw4apicEAi6YPWGcGFB4saKztCd+z7NTW9f0o1V9stqpGoyf+oR5k7+HVI1ccv+rnzfxlh8z2NmugVJwH39trsqaD4q6CcMi1MiZcFlVXAFJcvtZoP8Sm+nsYVETetq92uLjS06dgl2cWxZaBtm5zXn5mNm53T2d2ZWc+hIl5NnYk6diTl1NuLU6Zizs5pOz9DtWro9Q7dnkNJZEq5igUuvaDYUEy2Vu7sylxdofekJox+eyzK3WU7GuGCASBKC1HNOAvzf/KTHDz0g+eNPRnzmq4L5tl7XrrrVRLGRdWafoTHzFpZTY2XkIet051/m3JGP49aMP/9JR2qBbJ5pjWASYYX1hKGF/4wjDOm7Gk4oYE7TXtAcPhax71CXuXnNfNuwsKCZW9D0ouXtEGOh1zP0IksvskSRYW7eMDsfM7dgmJtPiGJB0+tZtLYZoWSPsc1KhUglUEntMlfzz5WeCQLFlFADNdVgcaJIcbn7kZcmFo1r/12USGZh82bJL/+vPn//nT6/+ns1vvYV4Upyj6cIK0NRjdVbeJm4exQvvBJr4oEGmM/q3HK/Ws9z9vBfuYRLGV5Qvoyxlnrgc+WWKVdEcV3P5qug+XpEfzxCmyToHCZZ1W3DsaM9Hntqliefn7SuuPgAACAASURBVOfZFxd48ZU2J8/GdLuGKLZo4wZ5rfPCgcshdVdlHiuZD/7Fx6IISSlnSYha7gJbqpCmMYmRs8TEbL1MuDbMeiD9ROIpgUmW2bz1jZKf/5kmX/+aHHvyKEIIhYln6cw+y0TtvVh6WCuTzwRll4AAITl76MNE7deRqnlecY8ijLE0GgEz002XA7Iu+kJyjtbmvbz4vHJhrQuU1qfADbaeJ/BaCiLD3lfbPPTIWR565AxPPD/HiVMx7bbGD5wSyUtiBp4n8njGeXiw0v0WX/THUoaNIf1WhBDLt6H1QhJLYcMQSAo3ICYyOZkMBnOaK7ZMUQ89ur0YKcc/DpJ2RCEUndmnaG1+h8uHsfnnOZJkwWOfo332sfMOmhchhMBow6bpFmHgFXrbGg7KaWJQ9pgcT/o8I5EU679jjxtKK+IV16doKLonI7705VN8/G9O8PXHznH8VIQUgnpNUq9Jmo1ycNltr08hdRljcGKgPY0uNhyBQH4Ds0ZjNFuvmGZyosGRY2ezpUnHGaWkwvY+egt7CZs3Y22HtMhiOVnwWWaPfxYp0+XoLnC/uJUIN0038X1V6NHpNPAyE0mWVVokkeRI+zNOK1x29BOHNhYvWZ/i9MEuH/uLI/zlfzrGMy8uYAw065LpSTespfJVrZdvVaMw21+P2JAEkiJzzxhLoxFyw46r2H/oJGHouYzPDQGJNW0Wzj5O2LoF6It7yIC4d4Kzhz8C1oD0LyjukUEkQfSwAdLD6mRRK5FOCUXy/Dz9CueDosWRvnZPxmViOPIoEYe16Dghjprk0CsL/MUnj/NXf32cl1/vEAaSiYZbu8IsUoqjuq2XBhuaQFLE2uBNNXjDrm188atPM9Ea32KKg3Ak0Z19hrh3HOVNY60uJAsazhz8EHH32IqTBZeCQKC14aotTQgUdiFKEhtFHl6wCYkgVtcgySyKRayLojU04MKqcDnQX8oj1hbPF8hJxfHXO/zxR47w5588xsHDvdL6FMalJ2XYGH137bHhCSTNykYIbr15G4Gv1nFOwqWBEB46OkXn3NO0tvxQkhMiXLLgkU8myYIXHjQv78wpZmamGuCrTNKYRSFF4Yv0kch5D+Y2JyNLOc6RbjezOpINl8ircmFdThTJQ2uXn+ZNecwe7fFn7z/E+z5ylL0HOq7M+IxXWOt9Y/XX9YQNTyBZML0bcfftO5hsNYhiPUK1mS4OxWB6++xjNGbeBgikarBw+pvMnfgSUl1c3KO0L8AaS70e5iuWWVm2ONy33UOmni2wR2opiP5BvsA02ddTBVlhG/0ymWI0VRRIpxqULgsGrI7Y4jUVxJaP/tVRfuN9B3lhzwKNhmLTtIfRbr2itSrqWiHH+MuNVgAhBESandddwbXbNxPF8YYZO0qlTdp76Zx7CuVP01t4jbOHP4EQipLg4CL3ZQEhBY16UHh/SMxDkBNEShKi+FykPy7vJJMcp5bLkG3076O4vaxOxQZpAGuI/soXsXbPvRmP55+d46d+9gV+9pdf5rX9HTbNeAS+W6/I3dbq/lwe2CVTGzY8gaQNUWuNP9ngjXfspNOJkFIOJCuNK7LztJb22UfR0RnOHv4I1swjhLeq8SBrLZ6SNBthZgm4MT8d5G1ujVAkg5RkCoQw8Nf3eWp1lCwUcsKwQ6yRCpcFA7JcA96EQmvL7/7uft773z/HQ4+cYWpCUQulIw5bEcdqwhney7X/wSTzlPittRWBQDIzNhY8xf1vvAnPk2wkv2opM739Oidf//+I2gey9T1W+zpIKanV8pR/keVepLGJ5PVAzMNSDJSWrIfMQin+RuQvMsIoEkyfGmuD3O+1Ru42FcTaIn2BmlB89e9O8yP/3XP8H//xdaLYMjWhElXVxumLq4nlJsDO0M6va5EY3J/BGoNJ/tL33c8EQooqBgJpHERCu8db772ZzTMTzLe7qA2QUJgiVV1ZExEtvIq4WLnuovtxbdZLk20yz1Ea30jf64tX9Mc5Fu0bfTGP1JChsK/UtUVFHJcT/bEOnVgd5471+JXf2c8HPn4May0zyTrcqUurIo+lYLF2eCmqYWWJir8rhQoL11lIiZQqIQmZ3APp/qTIXkslKwJJIQTYKGbb9i3cedt1fOWR55lo1ZZdnnVcUKqIK4KEPC7dvpSSDGWBYSqsLABeeF8M+3nfd/tluOnz0j4qXA70K6yUEnhTiq8+dJL//d/t5YWX20xPegjhiMO5Tqr7k2Kp5biLb5dIwpbjRSkZC6mQMiUE6Z5nj6qPtPPt2/I/oFJhAYUZkTZ4U03e8ZZb+dJXn0FssEacn2pan+pSnHsSKE0skKULKRYC6wOOWDHk8ApxkMImhj6vcFkwVGHVUtAx/Nqv7+W3/uQQ1sKmaS8rariR+twArMWKwV5RvCb9lkRePKFoHaiMEKRUSJU+dzMoIdLHbLeUZ2Q2e28pT1hFIAmEEJmc9wffeivTUw1ivQp5DyOFyzPaCsFF1hsbRh5LvV9hLVCyOpJqud60x7PfneWX/v1evvboWaYmPLf4Xrzx3FVDrYoCeSxmTbifOBeSI4eEJKRCqNz9RPL9YcHydGG4lWpIFluzpyKQBNnF6Ubs3nUNb9i1je8+/RqNeogxG8ONdblgLRhz8XklFdYnhloddQkC/uAPD/Dvfv8Ac22dWR16Q1gdFttnbw+3KnK1mUjEIRlJKIXKiEKVvpP8lNyKsIlWZHmGWOmCbsM+rwgkQV46QeNNt3jHW2/lm4/vodWsYe24N+7LCRcY18lCUpWIdrzQL8+11lkdx15v8y9/dS+f/tuTtJqKyaYaa6tjcEAeZlmUyUJIhVIKqTxHFollkbumBq0JmyoKl8BKCOJC70FFIAUIIdxtjjXvfPA2fv/9X0Draqa82rCWwnWtKGRc0J8U6PkCQslnPn2M//M/7uP1g11mplwJklRhNQ4YNjj3rz80SBYSpTxHFp6XkUXZoigTxVLWxHIkcamudUUgBWRlTTo97rrtenbdsJUXXjpIrRZUbqxVghBgrKHXi3KVVIWRxoA8Vzt5bvt0xL/+t6/yZx8/RuALpqec1ZF+d1Sx1LrjxaRc0liFFEjloTwvf1yELJazKJYiirW4phWBFJD7bA3+dJN3PXgbTzyzl0YjrNxYq4S0Gu9Cu5d1no2UtDluGCrPnVE88e2z/ItfeY3vPjPHzLSHteX6VaOE5QmjbF1I5aGKhKEUQsrzIov1RhSLoSKQPjg3lgVtedfbb+eP/vyhyo21mhDOfdXu9ECKqq7RiGIxea5ta37jN/fx2+8/SLtj2TTjAuWjNkkoHu8wwkiTUVNXlPJ8lOeIQ6Q5FiK/ThdCFqNwvTydrEldks4LUWogGwlFN9Yb79jJTTu38uIrh6iFfqVRv0hk1XgtLLS7eT7giA0uGx2DZded1fHcd2f5xV/by1e/fZapCUWzIUcqUD6MNMouKYGUMiELP7Mw5FDrwgzli2xsFeUcqFG4PsPgqZaEGNAWmyTyWJxmu8igG41UdKwJNrV459tu5cnnXqdRD7G2skQuBqmKRCnJqdPzEMUboi2NCxYtux4Zfu/3D/Af/ugAs3OazYnVoUcgUL44aTgrQ0iJVB6e72fEIZPyHstZF4uNmev9mpwP5ENfXuDVV7t02gYhQU0qvCmFrEmEciea1qXJydiWLt44Va0tzZaM5d0/cAfNeli5sVYJqYV35uw8RBstUXN0USyAqLXr796Mx4svzPEP/snz/Ktf30scWyZaoxEo7x/Yi8UDhRB4fkitOUFzcobm5DS15gR+UMuqdPcXFyxuqziGjIr1daHw/um/OUK9Jtg8rdi53eem63xu2Rmw+8aAG7b7NFsSr+WSgIis+zNudmGtRaTW2xhZKpkbq93jTXft5KadV/HSq4crN9YqQSnJqTNzEMVIKcZqAjJuGCi7bsmsjj/6wwP8+z88yOlzMZuSAojr3+oo19lKrQchJcoL8PwALwhRSqUuGPe9IYmvw2IX6/vcVx9e4EMUWQ4eidl7IOILX7f4nqDZkGyaktyyM+DmnQE3Xeeza0fAzu0eE02JaknwRE4qSUapTZaiHOb+GrWLq2NNODPBOx+8jaef31e5sVYB1lqUUhw+doZeLyYI/WUTodYD0va7UrIb9YnUUHdVKJB1xZPfOcev/PY+HvrGGZoNxVRLJQUQ1++5uvMZJA6pPPwwxA9qKM/LSGOYW6oijEF4IJDSrUsdBgIh00Qvy9GTmv1HFvj8w/MZqUy2BDu3+9y8I2DXjoCbd/jsuj5gZkqiGhJ8AT1HKla7ks0CZ6mMipWSH5NrRD/0A7fzxx/8cuXGWgVY60q5nzg5y+xcm82NwC1Jv9YHxvB4nyVptklZCAtZfNB9N/1t8pf8yJKLU4S1FJv5ep5QDRCHtijpssnPHe7yW7+xj/d95AgLbcPM5GgkBRavdXp+SnkEtTp+ECKUWjFprOfzXAtkMt60o9iCaiLwIPQTUjHOwjh1xnL0RIeHH2sjBLQakommZMd25/q6ZWfAnbcE7Lo+oDkhXQ0cbR2pJIG1pYL06+UGldxYd97ATTu3Zm6sEZgwr2tIKej2Ig4dPc3mqzdBL14zOW+/ZZFOnhDgKeHigB7O2lZJo42dzBttwdhEzon7XAm3zmfxe8mjMRajAeHaf5pFuR4mVIsSx4SrnPuJjx/l1//gAM+/3GZqQrlYxwhYHVkJkIQclB8Q1hp4QeByM6wdcE9VpLFyDOSBlDTPDJKK54HvC1oN97nWlrNzhu8+2+Gb32sjhaDVFGyZUdy2K+T2mwJuuznk9psDrphRzvWlEislTqwUDUKsTytFx5pw0wRvf2A3TyVqrMoSuThIKZlf6PLavmPcce/NmAWLdxkX7+onjTQorDyB8ASy5krN29mYUyciDhzpceBwl6MnIk6diTl1JmZuQdPpGBa6BiWg0VA0apJGXVILJVMTim1bQ7ZtDdixLWTTtI9Xk8gpCQboGYgsRlsXN5DOsuk/tkvd9vsnbRlxTDri+PKXTvE7f3qIr33nHL4nRkZhVbI6jEEqRVhv4oe1ocThsr8GA+IVlsaKEgn7L6a1EJsCqSici6uek8qRE5rXD83zn788R6MumWpJdl7rc/uugDfcGHD7zSE3bPdptCTepHSztMRKcTObspVyuQml5MYCHrx/N3/yoa9UVWRXAVIKOp2IV/Yec75Te+lzQfpjF1lAWAnUhAvaxedi9u3r8N2n53jqhXm+9+wcL73WodM1dLqGbtdk7hopQYp8PQVjLdY4g8RYixSCWiip1QT1ULFtq8+uHXVu2lHn1l11bt3V4LprQsIJzxFW10DXoOPEQpd5jkyK1bo+2TaT7WXXwhPO4pjXfP6zJ/njjxzha4+eQxvLZEu5fr/u8zryrPD0PINanbDRQqauqqQPCwqVdPryMiqsDBecib4cqQReElMRoDXMLRi+91yHbz3RRilBqyHZMiO57aaQO24JuOPmkNt2hWyeVm72g4WuhXi42+tyEEoxqfC+e27k6qumOX7iHJ6nLsn+NgJsEihQSvLavmPQ6SWL3FzC/RXahzGAABVKZCiJz8Z85+tn+NuvneHh75xl7/4uZ2djuj1LLZQEviOLWihp1JLsYsiSkVOIwpN0YErX855f0Dz/UsyTzy8QRZZGXdJsSLZdFXDPbS3u3N3gzXdOcMfuBuG050yRtsZEFmMoiVIu1NU7LPifJhGrmkQGks7JiM99/iR/8tGjfOu757AWWk2FEO5c1pOLeRhyl5VL5JNSOfltWCsTR3IN18ptOk5Y1VIm/e4vUzBzlRJ4fVbK4eOavQfn+czfzdGsS6YnJbfsCLj9lpA7bw64a3eNbVepbIZI16wJoZhIM33FNPfdcyMf/+tHmZlsEG+QpW5XG0IIjLWEgcfzLx4gnu/i+Qpr7KoOUGm2b/LCrb+dWhsWXn95gU9/4RSf/sJJXtizwHzbZITRaigmWumgmW0CXciDWuo4+/3nSrnZfT1pr8Y45eOevR2eeXEBa2GypdixPeTBeyf5gfumeMubJtiyNUAGEjrO3RVr6wQphfhJ8br2WyvF1xYXx7TWEZL0hJPnG3htzwKf/NxJPvW3J3luzwJSQKuhQOQkuL6tjkGXlReE1JsTSM8bJI51ToSjBLH73t+7rCHhUucCl0dCnqzYiyy9niUMBVMtyU3XB9xza8jdu0PufkONa65SyOYwQulXx6xOUN5VFzV4MxN88E+/wP/2b/6CqckGxlSR9IuBMYYw8PnCx/41O27YiunGiWrp4u8X5ERlDK6seENhzsV85dtn+ehfn+ChR85w7GREGLiYhVL9hLH6g8ygBNS1WYRzDfUiS7tjCAPB1isC3vLGCd711ineft8U110TwoRyPrKuix+m622kTbF4tGl/kBKETIL7fvLXMcyeivjm92b51OdP8qXkWtRCSb3mYlGjYHGk6J9ABrU69WYLKE8q10NMddxw2QmkH/2NVApHKqk8MIosna4lDARTE5Ibrwt4420hdyWEsu1KhWyVCcXN1FzngYtrPOmMRdYCXnnpID/6079GL4ovcknWCkIIFtpd3vebP8uP/+SDxGfn8NSFuwb7BwptwAsE1BWzR3t84nMn+OCnjvPUC/P0IkOrofA9gbFlK+Nyjy1la8W12dRCWehopBBsmva4/ZYGb3njJG++o8ldb2hy1RU+BBL8RDpvKTOJFC4/q6PRPcvJMzF7XmvzwsttvvW9czz+9BwHjvTo9QytZvlajApxwCB51JoThPVGJsutiOPSYs2r8fbfVFNQfSkp8GrO7WUsdLqWJ553cZScUHzuubWWWCgh117t402pLChvYputx3yhfmQhBPRidl53BbtuuJonnnmNej0c6leusDJIKeh2Ix5/8lV+/Cfe5lQwF+HbL8pP02Dw2cNd/uL9h/nAJ47y0qttfN+ppJp16bKmzeAAfrlRcvta0LFjMc8TzEx6WKDdNTzy2Dm+/I2zNOuSyQmP3TfWufaakE3THjNTHs0kriKFYL6t6XQsx09FHDjcZd/hLvsP9Zhf0MzOa4SAek1Rrw2/FqMy0PYTRL01SVCrD7is0ucVVh9rTiDD0N+p0uC8lIL6AKF0+dYTHcJAMDkh2X1DwFvvrvPA3TXuvCWkMaOQnjPbbZQkNiY6/JXMULIZrTaoyQZvvvsGvvX4SzSbNeK4ioNcKIyx1GsB3/jOi0Rn5/HPMw4yYHEkFRC8KY/OyYgP/OUR3vfhI7z0Wpt6TTIz5WXuHl3g/XV3//oktelEKovLaJhf0Hzre+d4+Dsur0SbpCKucr/XOp00CXzPkZHvOTHApinX5VN3Xdq33K7X2bVYAv3tpN6cqMhjDbAuCaQfKyWUbtfynac6PPJ4m2Zdsm2rx3131njgrhr331Vn29Ue3oR0pn3PutleIpnMtz/Y6IQQWAwIwT137CAMqppYFwtrLUHg8creo7zw4gHufNNNmIUuahnX4DDiEAJUkvD2yY8f5f99/yGefH6eek2yadplS/dbG6OA4rFqYxE2F6Q0PaeOKqq+iurc4nvpn9vOYJB/9FAOmIfNFkG9MUAeo3luo4WRIJB+LEUozbpLctQGDhyO2bP3HH/12Vk2TUnuuDnkbW+s85Z7atx6Y4g/lQxWHUcm1rgkqnzb+VRVCgG9iNt3X0urVSOK9CWVn24EeJ7i1Ok5Hnr4Ge68fzfWdhadNQ7GOBI5cNNp+7/y0Cl+6/2HePg75/CUyIijuAreKA8ow1y9uZ5YMCyFYbFBdJSvA+SxKmsMQb1Brd6qyGONMJIE0o+BmVryOvAFtdAlQC10LF97rM2Xv7XAZEuyc7vPA3fXees9Ne6/q8bkFg8U0M7jJkr2bT/SXLttM1dfOc2rrx8jDD0KHFPhPGCtUxGFgccXvvo0//znfgxPqYG4Upk4yJRHqqFAwne/c47f+dNDfO4rp4l1f8Lb6A+Wy2M4ecB4nnvuds6luv0lYcbxvNcrxoJAiujPRSn6kScaLsko1pYXX4t46vtdPvApwbarPN76xjrvvL/BA3fXmLnCy+ImOnKOcykTc7kRcvNNV/P9lw9Rq/lVIP0CkXb2Wi3g+RcP8viTr3D/g7dh5jooJQdyGrJs6bqrAv3ck3P87gcO8ZkvnWJhwTA5MToJbxUuDCUBjFQJeVxcgmWFi8PYEUg/hlknQghqITRqCmPhyAnNh/7zOT72uVmuvtLjgbtrvPO+Bm9/c52Zrckl6lh6XU3QrLP96k1Esc5mQlWjvXAoKTjX7fHhT3+D+x+8DejL5TAuzuHVXbb0S8/O8Qd/cZhPfP4kZ8/FTE54TE2qkUl4q3DhKAXNG60sSbAij7XD2BNIEQOSyWSSG3iC2qQjkxOnNB/73Byf/MIc27d6vOO+Bj/69gb331UnnJBQ99i2dVPmNaga7cVBG0urUePzDz3Jz7+wjxtv3o7p9BBC5GtQNBSHXl7g9//8CB/6zDFOno6ZbClm+hYxqu7F+CKzMoxxZdjDWhb3qLwAawe1ZduP/du1Poj1AJdZ7gLxjZog8AWzc4bHn+3wN1+Z54uPLHDwYMT26YDZ2RP87VeeqmpirRI8pTh9dh4lJe/+4TfSW+jhBRI54XH2eMTvve8g/+JX9vKlR87gKSdptYjK4tggKLqopOfRmJgeEEZUbWBtsOaZ6OsRRXNYyTznpN3RbJqps3XqVfa+9FGMlWuSfDaOcIUrJZ/90C+y+4GbmNs3y4c/c4I//NAR9rzaptlUhIFA6yrGsVFhraUxMZVZH5Xrau3hYS0WgRB5GeSNjnLcxD3WQkGjpog1vLKvm60y5+yWChcDay1KSc7NtvnV3/5r3vbWn+L9Hz3I919uU6tJNs14GD0KpcQrrDZKrqt6Az+oXFfrCZ5NUo6yx4rRh8IYMFikVATePJGNQXgIqkZ8sRBCoLVholXnq994ks98uUlQv52ZKYWxoiKODYqy68qn1mhRSXbXF6QUyU3oS9Sq2D2HuxbuelgMJjqev66u06rASXBddvqVU09QD04Tm1xlUyXcbDSUyaHWbCGErCS76wxy7uwZdBwjpMxWQMvKBFSdti8fQWHNPCZ6DYRPfyOvcDFILWCF0W2UeRhhI6xIVpHLSKRqkxsBWYVkYwjrDfwgxFoDom+tlwprChnHEfPnTtNdmANRWICe8bVGiueUPl/sL4cB4WN6r2L1KYSoFFirCQtpfQqsCJD2CJ59GHCl+kVGIlTWyJijP9u8Vm/lHpKEOarlZ9cHvJQkOvNzxFGUreJFYQAdxcqWNllztHjsA9/py3YeilQmiMTaCL3wWPpBlUS4inDWLwUSCVH2FawJicXbQFiE1VghXdwpLYhUYaxQintIj3prIrM6KtfV+oOX3RAhiKMuc+diwlqDoFZPfI5m3RNJf1n2/s9SlI9ZZCoq+qwNa0xigRisMRirwYYQPwXxAYQInexgHV2DcUCmaksGDCtCPPscgoiIH8AKH2EjQGCFSIiEikjGBIPre0wgVZVtvp7hlUlBYo2hMz9L1OtQa0zg+QGpOisbZAVrRiTDyKz4fv8xuVLs5CRhLcYYjNEYox1BaI0xJifLgnvL2TIKYY8SmG8gpZdYN9UM+FLARUJyWbkVNZTdA8wR8zasuAJBF2ENeS3zQh3zCiOJMjlYas1JvCAskUeF9QcP8gG3aI3oOGb+3Gn8sEZYa6A8FzR2A2x5I5eSTLKAmU1XOFhsnyKfuSZ/RsdorbFGZyRhjC6RSZbHMbTsNcmnEt8+hpRtLIEbvKrB6pKhTCI2cWcdQdrPEcs3obkFhEIQpX4vIDFGsgBrdX9GBSXhjjGEjRZhLV/fI+31lfWxNljMwyNEXy0skfgQiu6ZqNMm7nXxgpAgrOP5fupjGAg0X6yf0tokG6Vftmmzf/l202NN3Exax+g4dpZF8rgUSaREufRRGiw1PPM9lH0NK8LEP5/GRSpcKuQkQhZYF/TwzSMo8TIxd2HEdhAeECOsRhTbd2WVjAT6B6ew0aLWaDrFFVW+x/liKXf+xW538L1hxRQFpfWphXTa66jTJup28PzAEUkQZINwMYbQ/3jeB5oeGUNcUallYTQ6ihLSiDBaZ58ViUIAJPLkRffX3zgTtY9IArnS7kfZ7znZrq2yzy8nioF1dz8kCIW0RwjsMYzYiuZGjLgOK1ySWdEqyf5XcZJ1iYGYR3OiWlmwDxdCCMO+u/JrKLLBbbFfpPN5IZeoxpuZlORuLYA46hJHPaRSeJ6P8gM8P0BKmXfSZYLZw/ZTPIFc8m8wqSsqjjPCsEm8ojgwLJWpvFQjXJw8PIQ9h2+/jhDGxUFEUvZl+CWrcAmQKXczKS9Y4QMg7SEkB7FMou11GLETw5UgAlKrBCuy32YbrLCmyCanBYKotyZLFXY3AnksFs9d7Hsphl+TwYHfFjw3qWcp/SBVqeYT9v70BZu8nSZQF0IXyXc8Ty5fzl1QIBIEQrhl+ozW9OIYuh2klEjloTzPPSqFkCo70eUG9/SgjE0C2tlfjE4C3f0uCSHkAEWmF8PdkHysWFEjzMjDupmuNfj2YYQ9ixWhI5XUpVLhsiLrEJllabFWYEXgXtsFPJ7F2u9jxZVouwsjrseKBhBlAfeKSNYW5bhlsoS0H1BvTaA8f2zJYzkrYul0gnzMGeblcS58WxAApQpSSqIg97z83lLHQYEwhh21ALqcx3ogGZEUZg/pmbngdJeo1y2RhZASIWTf95NgeMpyxhGHNTkLDpKFGNrh+xtZyX11Pm2vQB4OHp59BGkP5OQxJo15lJHPrNxsKyV1KyQQIrAIexjJIYydRotb0eJmrKghbC9LRhRpG6vu6WXDsAoXQaNJrd4kLZY4IOYZQSxGFuebTmCtdQpRkyhETZpSYDIPzNCikjazM/L59QpFJeU48fKuLMEFLCjV7/IhI4whTKk1oNMvl6lMDDzJ9uVgUQAAHbZJREFUt79CV9SqNLJM5eWO0YoayjyJZ59LguZ22QtZ4fIib+N5rMsm7i1XYkYgmUXab6LsS2h5N1rcAFiEjatkxMuEfi9AOjlUfkCt0SqlCKzXHLPlMIwwhpJFIthJ3UopQWjjPC1GJykFGTmUJ9S5ECj7V0L/flb9PLEIKwZ2fXErEvbd6JJ1Mvjl8xqBh81CVr1hZf49gcBgRQ1pXsGz30n86Na5S6q4x7rF0HmIta6GlvCQ9hTSfBkpXiUWD2DFBML2sMjEiqlIZPXhhrvShNJapPII6g2CWi0R6pRdVunz9YxhcYtBwiinFJgkjUDrGKvLOWhlj0v2r7y9CyCF1bbixCLj96ouabuqB3yZGlIeNA+Q9mgSNBdZToG9RIxeYXVR4g9BZmFY4QECZV9F2hNE8u0YcS3CdpM4iqhIZBVQHrAKrqqEOPywRlCrI5XKXDUC4UQR65w8hh1f+T2RmsHOnR/HaB054U8clYLTZTfR0h6X/mPIxqUVHPPlupYbak30EkpBcw9h5/HMVxCii8XP4x7V4DJycB7JJE5CmtEeIuw8vvkikXwHRtyUkAhQ3ecLQpE0SvGN5FqqhDj8sJYTR1+gPK1Xtx6RDfiF11AY9JOgtI57xFGEjh1pOIVo6vrOr89S57mcxZBd54s9qVXGxiSQYjA1uSOe/TqS01gKQfN13LgrLI2hAXfhIdD45ivEEnRKIqmLsiKRZTGMNNL3sRYhJSoICcIanh8gpBxKHOs5UG4L7SALSBcEQMYYdBQRRT103Mvy0ErXZYVx3BTr9Voshw1IIEVtmk0yzR9H2b1YUaOUaT6a97RCAanCJXdVSoSweOarWOljxPWORIQsqPAqpOiPAPYrqTJrw/PwgxAvCFHKy6y6lDjcb9e3wqp0bH3WhjWGOOoSdbuONNLUgsSyGHZOlyWOu8bYeARii4NJiDR7UfYJSBRXlTdj/JCK7Ip5PkLE+OZhevLHsGImUWdV8ZBBaXy/3z9xOyW5X16SSKw8L7c2FskzKAbW1w2KCicG3VRGa6Jeh6jbQcdxWXnafzJ9bWfcyGIYNhaBDMQ9ZvHtNxOlFbniavzv+4ZDKqEsVRpgHs98m0i+J1dkbSAsN0PutzKklCjPR/k+nhcglcpIAyhZG4ttc72gRBTF3IkicXTb9DodjI6HkkYu4U3eXofneamxgQgkaTCFvA/PfhNBIdO8UlyNNVLr0sW30kWr9mPsHrR4A4KCK2ucBoNF1IT5wG5zgyGTlLqqE9Lz8JSH8n2U5+clixYhjfWeRd4vwx2wOOKYXrdD1G1jtHbXQcrC73O3aB7zuOynsW6wcQikOPukhrJPoeyr5bjHSjVyFUYWRRelm3cqFM+g7Y7EKh1dK2TRgbtAHv1Zy9nnQiKVQiqF8jyU8pyFIRRC5nGBYe6pftJYj+RhC4KZ7DU5ceg4Juos0Ot2sWYYcaQ5bu71+jvDtcHGIJAB19VJPPsELmvZZj7yddjuK1wCOPJIZJhJsqHidbTYjavws34bwkoLgw4rb5EHe0VGFGntOqm87POBzOohrqmRII00XpMFxt1DUVGl44hep03U67rzLBJHwXJbj+e3HrAxCKTk3xZ49jEE7ZJkt2oeGxkCyWtou6sg21obN9ZK8wHS7/b9OP1SNrOW0lkV2WPyXCQDpejbTtGt07/f/oS69TqoLlaHKldU2URR5YgjC4wXLY6kCvn6PMP1g/EnkGwWkaqu9iSS3TD7rJJdbTwISBa5tLiSJ0cR4gyWaQR61Y2QlcYF+r8zrEJq2f+eup9kgSw8hJIoqUoyU6dcL1sWi2ilstLfq1577hJiyRyLTFHVTRRVEWRLVcjB31TUsSKMP4Gk1oeQYLt4PE1qvrolU5fOEK0wxkhiXhaJoIu0R9FiCxBjrVxyTnG+geKliSE5GNufuEbBkpBIKREysSBKr90gmLpaBohiEasi3f6wUh2jMoDagtVVfJ1niyduqm7HuanSwHjmxqrmjxeD8SYQm6ZBGSwByj6LtMdLS9NWDWfjQog0uCoAg+AUzmm+vAGyPCFkn+RWQ7pTigQhnStJJpZEugSClMj0MVE+lQr1uZ0OHMNSCxMtppAaxQnUwHkMS/zrduh1O8RRb6ibKsUInv66wXgTSGJ9WKGyRYdA4awPhprvFTYYRCqhkAjOgo0Ksop+2IEHN/iIPnLI18ARyeCfEoOzGnKSGIgnJH53S+mf21+SyLecUGwpKe0okkWKJa2NhPK1jrPEP6M1rq/LEkusZ5nxqGGMCcQWOqCHtC8h7JlS7KNqQhUyCImw8wihQXgk8p0sxpARRTLDFQVSKC2cViCF1FqAfOAeZjm4t/KkveUmNou5nfq/My5IK/cW3gD6rI24R9TtEBfVVAVigdwKHKdrs9YYXwLJnclgIxR7AMlKXRQVNgby/AiBFF0aE02EauDGpuIA1OfuXMJSKGLYmhHLHtMSFkTxO+OMAWuDMmlgbV5mpNctlxkZ4qaCqs9fCowtgeRJgwGS/Uh7LJlZVqiwGAxKGYQXgI3pH3IWWzp0pTifLO1xJ4jF0J/wN9Ta6PWIum3iqFe2Nio31WXH2I6oxbwPafcBMRYvmaWs5ZFVWJ9w9oS1ESJLtl6aItI6SJkcdCV7qQa1AQy444Yk/OVFDbtovQJro7rOlwXjSSCp20BIhO0gOYw7VVspLiosAYETWSSvVmQppL+rcL7oj20MJvwZ4iiJbUTdZa2NSo57+TGWBJKV5cZDcAxhz4FwA0PVyCoMhwuxChlS6fMuHTKSSF+XEhbdu8bERN0uUa+zaGyjKmq4PjCWBCISuSNIpD2MoIelBrayQCqUkU8oLIgaiICKQFYXw9x8pYTJgrUR9zpEUa+c8LdEbKPqzmuLsSQQIJmexElyWDpDydK5KlQAii4oA7JFmidU4eKRD/ZlJdpQJdUSsY3K2li/GFsCcd7VCGHPkA4KrmzJWh9ZhfWEbJCzGqlmEMLH2h7VROPC4OIaOQUP5qsIrNFJ3kZ3RUqqLJfmsp1FhZVibAnE5XzMIVhInlezlgpDUHSHeJud1Nv21vCARg+DORs5ihVwdRwR9TrEvR7G6EpJNQYYUwJJSlPYs0DMsGqbFSrkDk0LIkB4W8FqoBq8lsPQTPhhhQx17PI2el1XATcljUpJNRYYUwIBl24euVpYa30oFdYl8ix0g5ANpHcl4BIIq0S0YbDJAL+49BZrMcYQ97oZaSzmoiqyRRXbGE2MMYGAsBowgCpONytUANJZrwQbIbyrEbKJtTFQWSBF2ES9mJfr6pPeCjBaJy6qLjpKXFTuWwMuqv7KxBVGF2NMIAI3m0yba2WHVCgjJwmL9K9L4h8R1UyjWLYlcfLZwqt+6W3UHYxr9LmNKxfVeGKMCQTAlXOuUGFRWAOygQx2ZvWvNqr1MbzC76CLKo6jzEVltF42rlHc5ga9tGOLMScQj2o2WWE4nNDC0kN61yK9LQmBbKxCfMNIY+iqflqX4hqLkUbRV7xRruFGxhgTiAV8shLuFSoUkFVhshYZ3grCT+If42+BrIw0wBjtalElcY0sGI4Audg1Gu9rV6GMsSYQKzywyboNVbuukCDLbLYxQm1GhbuS3I/xXWRsWdJIguHWaOJeXzA8WXpxMBhux/iKVVgJxpRAkqC5bWBRCKxjkKqtb3gUl6HFRqjaboSawJoOuJYyNs1kRaSBkzFnwfCo1xfXkKULUoprjM2VqnChGFMCATBYMQGEYNtONVIpQDY8sttvDcgmMrwjSR4cD9fVSt1TJdIYUFAtHwyvUAHGmEAEFouPZQLBPCAr8qiQ5X5Y20HVbkP6V+XWx4gGz/slt8X3+mMaea5GtAxpDBYwrFChH2NLIFgDIsTYzUgOAn6WEFVh48IVTjQgaqj6m9xzRm+QzJZ+LelDhmeF6ygiitKYhllCQZWjKmBYYSUYSwJxoT23JohhG/B8YpFUCekbGSXro34v0t82UtZHnoyXpoTnnw2SRo8o6p0XaVSocL7wYPx070IkQXM0VlyJsRNIZqlKmmxc5INvjJBNvMZ9pcTB3A20vtBPGsXjLJOGJu71XFwjrT+1XOFCqq5Q4cKQtiGvXOpjPJpTat0Lq7GigRHXI+1TrlRFYomMx5lWWCnyCZJBNd+B8K7GmjkgHZRFabBey1pNi5FGdg6F5D4dJaQRRVhbkNyuJCv88p1ShTFCsX16qbtnnBTdWc0dAAxa3ISy30dgsJWcd8PCNfwaYDDRfoSaQcgaTpYUY63OyYRkWnUZrPMiQSxGGulnaXn0OOoVKt2Cs6SWkNyOkYehwtoim2QJgRcGik4nXjyxdEQh0oCijbFiC0Zcj7IvgQgR1lR+4A2GogsrnvuiawdqBhXcgPCvQ/rXIGQLRyYR1pq8+qzbALA6A/Ewwuh/P22f1hiMjol6PeK4h4njMmn0J/dVpFHhMsBaaNR9vDD0aLfHrQJpMeHDIrDE4m6k3Y8gwgo5dlZXhaVRnDVB4AgiPkYcHQLhI9Q00t+BCm9B+NciZD2xSpK6T2mm9gUSyUAAnMWtDGsMcRyho8hZGlq7NUvS71WkUWGNYYyl0fDx6nWf06cX1vp4VhllPTu2B2ITRt2D0t8AUaskvRscbqD1QPiAxeoz6PgxdOdJhLcVFe5GhbsQ6goArO1lRFIkgsUG7KXcUvnv0vIhFmPijDD6g+AAsiKNCusEIplI1esB3hVbWuzbfxrfVxTa98hgmI/aWpt1PqkUnl/HD0KU9wNE545hu3sQslaa1VUYfwzc6zxQhhAqF1nEh4ij/eiFbyGDG1G1u5DBdSAU1vSSdiOTjO484L4SK6MktY0j4ihCxxFax2BM9p1hctvymhpVu62wdtDaMDNdx7vqigl0bNa1lLEfw2ZfJdKQCs8P8IIQz/edyW/dyoRe6z1E8TGsmUcIryKRjYyB+54O9kHirorQnafR3eeR/nWoxn2o4CacYispvpj2myUVU650iNGxI4woItbOyii15T4ro7/2TrWmRoX1ACEEUWy4dvsM3tVbJ4milEDW72C6HGkIKfH8ILE0fKRU6S+dOwCACOltxpv4e0RnP56d77jlwVS4MORVel27cFaAU2mZ6DXM2b3o4Ga85tuR/jVJjMTlkkDZLYW1GK3d4kuJlWFMDCZd2k/k+ygdQyFHo2qTFdYZ0mTcKNJs3zaNt3PH5uzD9dZgFyWNbKao8AI/sTQC5ydOXATWmtK2nJtBYk0HFd4MrfcQzX4WRFCRSAWgUL6j1A4MztIIAYvpfp9etBdVuwOv8RaE2oSgi7VOMaXjmDh2eRlGx64d5iWAhwTAAexAG6xaYoX1CNc+LUoKtl0ziXfjzs00mwFam2V/fDmwJGlY1wGVH+L7gSMNpQqkkbsS0t9nAUyyeSLWtFGNN2Ftm3juywgZViRSYRGkbTCxSpLYmV54FNN7BdX4QTQ3oKMOOu5gjC25nvpzM9y2kuwrkegAq2B4hRGC1pZWK2TXTVfg3bzrShqNgG43GlB6XC4sThqAtY40vCBzUa2UNIrbK+7HzRa7eM0HwfaI57+OkLVMXVB15Ar9yNtl4uYVDaw+Rzz7n9DcQCweANFEiAjEoGIq3cbAehpVU6swYtDaMDlR49bdW5Hbt02zc8cmepG+rMG5Yri+f5C3xnVS5fnUmhM0p2ZoTk4TNppIpbLv5B2zvO1hvuX0/RJBmC5e8514jbdkRfVGSUxQ4fIjazvWIIRCiABPvEzA55EcxxKCdW0zT0AUAxOkChVGEUIIej3NDTs3s3lTE+l5knvu3O6y0aW8xINnwTIovlsgBOV5hI0WzclpWlMzhI0mSnkDpFHexvCA5DAUScTi9P1e64fxGg9gTZuKRCqsBDaxgF1ttRrSnsE3n0faV7GiltRis1iRVoauUGH0IaWg24255+7tSCmQAPe++To8T14y943NOtCgpWGtRSmPsNGkOTlDc3KGWrOF8nz3vX7SKM7kVkga/RiMd0R4rR9G1e+tSKTCiuBCiYlayhqs8BHEBObLKPM0VvhOkJWQyEgmWVWo0AdjDLWax4NvuQFIyrk/+NYb2LK5yfx8D6VWJw4ybO3kgVyNIJfdulyNJOBt+hRU/b7jVSC5YrzDPcb4E+8FDLr9XVfKIpFjVsL7CsOQpHg4K8OarESOb78BRqPl3Qi6uQqraksVRhxRZLjyygnuv/d6AKS1lmu3TXP3HdtodyKkvPCZ9zBLoWhpSKkIag0aE1O0pmeotybx/EQBlXxnWH2g4uPqQZTdWdYCGn/ix1C1u7CmTdH5UM0fKwyDACcQFMJZGwisCPDtoyj7PFaE2WJmDlVLqjB6sNZJd9vtHve/+Xq2bG5ijEVq7Rr0j77nVidB5PwG60VJIwkkCinxa3UarSlaUzPUWxP4YQ0hZEIaZoA0+rd3qVEmEYM3+V8gw92ORITMy1VUqDAEIs0dTEjEvenhmW8i7X5HItYkrqw1PdQKFS4IWXKrFPy9H70VcAUVpUzquP/Ye97A1qsmiCK97MaWJg1XJ8gPatRbk7SmNtFoTeLXaogkSF9WUA2SxuVUqgghSus+WKsRCPzJH0f427G2m5FIhQpLoUgiltSd9XWEPYsVXhUPqTCyEAK63Zgd123ih991CwBKCaQQTtd7zdVT/Mi7dydxkMEBfHHSyGW3nh9Sb07SmpqhMTFFUKtnyq71RBoDKJyPI4sYIer4k/8lQk4kr6ugeoXlkZOISUjjHJ59lCyNtVDAsUKFUYALPwgWFnr80LtuYXqqniWey+LA/Y9+5j4ajYDUrVVErn/vJ42glKsR1BuL5GqsM9LogxggkS7SuxKv9SO4chZUAdAKK0Zqadj/v71zj5GrquP455x7Z3Z3ZunultY+oC/awm4hgbZQW6isbbXQUhCkAj7CQ2ggiBqDxMRofASiYhBDUHwkEKMS/lBjBBMfhKgJCaJVSYwP8AVSqIXusrPbnTsz956ff5y5d+bObtvdZR+zu+fzz27uZqbnNOee7/k9j2rBk3/jmRcQlQUEEWeFOGYPSqmk+vx912xKPdexuhgjbNm8kh296xgaKqEbrihMBEFRLfBrr6bddo6xVqP5RKORdBW8ti1PWs/Gaz0fMYENqrsX33ES4sC6UnHfNo0nf0LJMKI8G1Rv4vfA4YiJg+eDgwGXvrOHzRtXYIxJkq00pNt/3HrzhfgZL+WeqtVqtCe1Gi25djx/dNFI9fZpctFoJJ31pUAq+PntKG8xIhXnynKMCRsvr8ZDlI+mDy0vYDPnxbqy3DpyNDlKKYwI2RafAx/cBsTZ6Haf1PGGGFshOy5ez87edQwOlshkMmRzOfILOsl32AI/38/Yl+MEBX6KkS6r2UQ6vTdCeafg599G3JJxNs/NMY2IFRG7bnw8eR4kGNEry+FoRmLro1AI2Lt7AxdtXVPNvKrtj8lKrj9Z33HbxSzospZGLr8AP5OdoVqNmSPOzAIFpoRu7UH7pxNfJOROj46TERuxtk7EQ9OPllcRqtfozqH3xTH3UEoRRoaOjjY+cecuYGSz2ZQLS2tFFBl6t6/lvddsojBYwhaIzw/RSBHPS9kyMKUy6LaNNeGYq/N2TC7JPQIKiNC8nDxT7hDiaFJEBN/TDAwE3PiBLZzds5QwNHheuuVVypauD3J//I5tLF92CkEpTALqc140GlBJYZiNhXgt61H+IuwtdDgrxHFSaq+KdWNpjtgLqJJXz60hR3NhXVea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VOa7MBplvL0eKGY9QyblnEV/yZBJ2gHfLTS48/wZEsSE7S6DJRZ9BakgGSER7rJ4a5YHkoFYSKSnEh3EEUaaqbohjZmGcYTLrqSxsKzsDMcOupNJSzPRGqYbxt+1a1LQU7AZKNlU8u0mrlaljiz7vTtif4k7hJBuXDxxiU3JtEJIF+EOmO8LCKDICOQ4hbFStTpfd8kIrzYixmshU7UQLzS/S9E9csogEwjHM7qRiRBgJRMNpRmtBozNBhQcSX/Rpr9kk3daa120hbRqM+4yImlB5paDcAD/aN9KF2gQrtGSANqnnV2REchxhrZkJ+aaqZTWTNYi9s4EVL2ISNMsRd55nuzFP3HRnUwEdry57itmfZ+R6YDegsVQxaGS7/CVdBbNFPsXSMcv4id3BpDFU81aIVaRI7YGyAKgdYRw+pDlc+ItYp8RWJARyHGDzlpUncQRRIrx2ZC9VePbAEMatmg/R7fQxAwnNrqNhcS8qbRmbDZkvBZSyRki6StazdySzoS6E3likpRJtwdfZhalssuIJeALMX1iQTSDvexVyNyKZimW/SEjkGMc+yOOeqAYqwaMzxozleyibSQ4UV/sDAeOtFaShAUnvpKCKxkqG19J4nRvH6ewEAft8QTz3OZ57cEr8TZ9HHR0lO/KIAmiAHNvAGgVZ8vv59isFtaxiW7Vb5PvAFUvYs90wFQ9IlQaS3QQxH4ShDJkWDjagyuUNtWUc7ZgoGSzrOKQs09sIml7buVT/clLUbNPIqzCkjBjaa0QMk/50jvmFIPcF5ZYGECG/aGbSQBozgZnvYhnRxs8ubvO+GyI1hpbijn7Z+SRYfHQrJsCxGXnpSCMNLumAjbsqrN9wsMPVXOcttbHMERyvM9jRbOJIoR0ya17H+iAhfgZDieMb8qCcAZn1XUxeUQsNGAm00COEbRXwjWfoaVVzHoRI9MBk7UQpbuZqbIQ3AxHBt1K4kQaXEswWLZZXnFwu2okx3/4b7NttKJ63yuJpn6GsEqGWI7Kcwvjm7F7KV96OzJ/0oKq8CbINJBjAO2ztdZnIQQ1L2LTaIMnYo1DCNEkj/a5wfH7UmZYWugWwWVLQaRaGsmOrhrJXF/e8YRWccoQhCS37gOgQ+DokGaifehgCnfNuw15qJCk0vBCkGkgSxj7mp3V/IiRqYCJLhrHiRzpkmFpotPsmmgkQ2WH5T0OjjXXJHs8aiTpzH0hJPXHfxd/278i3IFYeB+ZZ9VaI6SNDqaw+l9M6XnfNAmOSZvDgkxrGYEsQezrJfJDxe7pgLFqQKRMOKVIHXc8vWwZjj90I5KcLRjucVhWcZBtQSEmkdF8Pz7Wh2lpIcZMpFXA7APXEk3cg3D64gz1w/ugSdiuVg1Ebpjy829BFk4ivX77Qu8hI5AlhH35OZTWjE4HjEwHeJHGEq0XKiOODMcauhFJyZWs7HXpL9nN7XD8aSMtEjECWzW2M/vTV6G87Qi797CSSJM8dABaU3reN7D7X2y0H2kfsCzJCGSJYF8vy/hsyK4pn5qv2sJxM+LIcKwjPYZVvBhZX9FiZa/brAJ83BGJNs9pQnqN4I6mHqL26PWo2iaE0ws6WtT3u3kuYaGjOiApXPAPuCve3EYecGBtmxHIUca+Xo6qF7Fr0meqHiHiMurJMcf0C5QhQwfSYzqKi3kOlh1W9rZHbB0vk6f2995kqCtvhPpjv0G491aEExc0RB3as2qNFoKkLKoOpxC5VRTO/xTO0MvNtbHQxlZ4wNfJCOQoYj7y8EPFzsmA8dmgzUF+rL80GTLsC53jO1Qa1zL+keEeN+UPOT60kfbkQqMFoBX1DR/E3/av5je7jAmWjeJj9u/bbm/HmDiiWdAhzqq3kz/zz5C55XG+h2y25cG0Y0YgRwH7egn2VgN2Tvp4oW6ripuRR4YTBd38I5WcxUkDLuV5zVrHppO9/TmMTwQgHLsdb/OnCMfvAOWBLCKkYxaj0grmraElMKShjZ8jqgNg9TwHd937cVf+Unzd1rUORbZkBHKEMUcNBxCCRqDYPuExWYvi9cUzrSPDiY1uZq3lPQ6rel2knJs7knw+1tD+HCr+bDSHcOwO/B1fIJy4F+3tMmRiFeM6VaaGlSBZrkEbconqzYWhrN6LcVb8Es6KNyGk03b+xWizjECOEPY12EemfXZNBqZmVWauypChhY41RcJIU8xJTup36S3MF611DGojace61pj10luLdil/jGjyXsKxHxKO32nWLdcRWgWANtoJFsLpxeq7FHvwSuyBlyDzJ6UuEZp9FpFwMwI5Augc4OnyI9snfKYbURZdlSHDPtAWraU1aBiqOKzqc3Gs41UbSYhEtJUW0VEDHU6ggyl0MAlECLsP4fQhnH6EVew4XwS0V9ZdrLbJCOQwYr5BrdHsmgzYPeVnTvIMGQ4A6XckVJq8LVjd7zJQcub8fiy/T91khyEC0fRdzH+sMqYsIWmWMDrIKKv9ISOQw4T5yKPuK7aOe7HWkc60PTZnTBkyHGl0aiNaw1DZYc2Ai3Uc+UYSdCeTzr+i7e+RevaMQBYZ7dnk7R04OhOwfcInynwdGTIcMjq1kaIjWTuYay6t28r4PvZJZKkiI5BFxHwznyBSbB33mZgNmxFWGXEsLXSf5bXtYP7QUddY0LmlDVlfH17M8Y0AK3tdVvY5CI4/bWSpISOQRcJ8jvLJWsjWcQ8/zLSOpYDOWWl6O7q1WJeQsv2vaHdkNo/BHJM+HkAphVYqXi9ImGDLVJ9nxLK46NRGevIWawdyFFyZkchhREYgi4BuqrJSmu0TPqMzATTLkOg4kzQbvEcK3QhDK1MeQkqJsCyklFiWjbRtLBuiEIJGgzDwicKAwPfxG3X8Rh2tVDN3x3ZcLNfFcXNYto20rOZ5HMfFydloDVGoiMIAFUUopVAqaiOkTLAtDjrzRiwpOKnfZVml3cGetfXiISOQQ8C+HOWbxxpUGwrbyrSOI4luw1krhUYb4W5ZRvA7kqAR4DVqNGarTIzsZGzndiZGdjIxsovJPbuYmRhndnKcRq0aaxRR6vxGO5HSEIFlO+RLZfKlCoVymZ6BIfqWr6Rv+UoGVqxm+Zp1lPsHcPNF3LxLFCoC30OFIUorpExFzCRXyMbLQSFNFErDYMlm7WCuu4NdiGyptUNARiAHiflMVmPVkG3jXpYUeAQxR8vQGqWU0QhsG8fNIyQ0ajWqE2PsfOZJdjy9gZ1PP8GebZuY2juCX6/hew2CRgMEWLZjNBRpNJTE15HuRR1fK7kHrY3ZKtEyVBhhOQ5uPo+bL9A7NMzwyaey6rSzWHfuRaw642wqfYPYroPf8AgD34RaykwzOVTMcbC7klOGchRdKzNpLSIyAjkIdI0117BtwmNkOsgc5UcAc4SA1iitsCwb23Fxcg5+w2d6bA/bnniczY8/zOafP8SuzU9Tr07j1WaR0sJyHCzLNr4OKQ1ZxNnPxCGi89cd6kBzNiuavg+tdUwqhlDCwEcpRb5YpDIwxMnnPodzXvhSznzeZQyuXI1l2/j1BmHog8aQSRzDnzxvhoWhzcGuNFIK1gy4DJUzk9ZiISOQA8B8Ibp+qNi012O6HmUmq8OMZrtjEjITR7Xj5nByObxajT1bN/H0Q/ex8YEfs3XDo1QnJ/AbNWzHxXZcpGUhpWWOj8mn8xqL0XdzzpM44xEoFRGFIYHXQAhBz9Awp1/0fM5/8cs445IX0T+8ErTGq9eM30XKTNgdJDpNWst7HNb0u3Mmedk7e+DICGSBmE/tnaqHbNnr4UeZyepwonOYKhUhpUWuWAI0o9u28OhdP+CxO3/AjqefoDY9AULi5vItwtCq3Zk+Tz8tRv8t9Nwi1niiMMBv1BFSMrBiNWde8iIuecW1nPHcF+Lm8zRmZ1FRlBHJQaJblNa6wRw5Z26UVtamC0dGIAvAfANs95TPjkkfoLmWczb4Fhedba+VwrJtcsUiXq3Gkz/7CT/7wbfYeP+PmdyzO/Z55JB2XGhPqbZzpftnjjCPI21NvJxRTMy/xJSV+D1oO6Z1rKksIEXreGhXcPZ1D4ZMNGFgyMTN5zn1Oc/n0tf8Ihe85CpKvb0ZkRwCOqO0HEuwbjBHb9HuMkE8BgsyHgVkBLIfzEcem8c8RmeC5podGXksLjrbXakI23HJFQpMj+3lsbtv46c3f51Njz1I4HvkCkUsxzH+i7hk9b7ME4mgV9rYx5UylV7DUBNFGiHAcSSOLXAcEUdb0fRvCREfF5NMFGmCQBEEmiA0x9u2wLLif1IQ+8ZJcdq89yilhVIRXr0GWrPmnAt46Zt+hYtf9moKlR4as9U5pq1s/C0MSVsliYdrB3IsqzgnnHO9a4h7mg1EqkDKPG2SEcg+0I08IqV5drTBZD3KyOMwoJvGIW2bfLHI9Ngo9333Rn78zS+ze9PTSMvCLRTNvmrfpJFoF0rFwj7U+IEJsy4VLYoFi+FlLqtX5BgacOjrsenvdejvtenrdXBdiWODbQlsR2JJ8IOYNEJNra4YnwyYnAqYnA4ZHQsYGfUZGfXYOxFQqynqjQgpBTlXYtuGUNIumPZ7j+85Zh2/XiOKItaefQEvu+5dXHzVq7Edh8ZsFRGHEp8IQm+x0OkXWdnrsLo/N+c3OP7ac847pkHK+Z9RKd3MZetsk4xA5kG3QeQFimdGG8z6KiOPw4D2REyz6lqh3MPs1CT3fvur/PjGL7Nr01PGYZ7Pt2kb6XM0Z/ECEBCGGs9XRJGmWLColC3Wri5w+roC69YUOPXkPCuHcxTzFm5OghuH7UbaVNNWumm20rFtStPSRMzF4gtKYT6HGuUp/EAzXQ15ZnONZ7fWeXZLnQ1PzTKy16fRULiuxHWMlpI2mXWSoIgZ0K/XUEpx9gtewit/9bc563mX4XsNoiBAWoe+wtyJhE6/yFDZZt1grm3CeLyRSOu5zGeZkmMTVZ+xGY+ZekjBtRiouAxWctiWmcQopZum2ma7ZAQyF90Gz0wjYtNoo+ksz+yki4du5io3X8CybB6961a++y9/x5b1D+Pk8ji5fNfIqQSJphGGmoan0Bp6KjZnnVbkeRf2cM6ZRU5eXaCnYmMXpBH4vkJFxoyllJmRJTWv2vo3pdI37735X+uzpmUikzI2fzkCLHOt6emQjU/P8vDjMzz42AzPbqkzW49wHUk+J5uaUmcbNYkEQaNWxbYdXvKmt3P1O3+XSt8A9epM5hs5QLSRSKTpKVicuizfXGPkeGrL9PMkWkfdC7l7wyjrt04xOuURhKqZw2Zbgv6SyxmrKrzkvOUM9eTmWmUyAmlHt0EzPhuyZayB0pmzfLHRrnVECCEpVsrsfOZpvvuvf8eDt94MaHKFEipO1EuIu91nYIRuwzOaRn+vzblnlnjBxb1ccmEPa1bmkAULIo0OFFFsyjLXZs5E4GAERzcbeoLEVyKFMYPJnCEvvxry5LM17n1wip8+OMXTm+tEkaaQl1iWQKl5NBIp0UpRm5lmzVnn8cbf/SPOe9HlNOo1VBgiLeu4EXyHG6ZNAYyJuuBITl2epxBHaB0PYb7d5NrjWyb55k+3s2eygW2Z8SaFSAWRaCKlCUJFueDw8otWcMUFw23nywgkRpJV3NnIe6YDto17barbsTqIlhK6aR25Qoko8Ln7xi/zgy/8I5OjuylWegBTnFDEKkDTPwAIabSNWl2RzwnOPLXE5S/q4yUv7GP1irwxR/mKIFAoNZcsjsQMs5uzUmlAg2UJbFeCLfBmQu5/ZJpbfjjG/Y9MM1uLKBb2TSRSWnj1GkIKrrzuXVzzzt/FzeVp1GexLLtt3wz7RtO5rjS2JTh1WZ5Kfm7m+rHWlp0iXgjBHY+N8O37diAEuLZsat3dIIXRzrww4oVnDvHWXzi5uSZ9RiDMnyC4a8pnx4SPMQEem4NnKaLthYwLG5Z6e9iy/jG+/vGP8sTP7sHNF3CcHCquPzVXcILvKxqeYmjA5UWX9PKKlw5wwTllnJI9L2ksBSHQSSiJRc6yBHZeQqR58pka3/juHn744wlmaxGlohVrXXOJJMlxqVenOe2iF/DWP/wz1p59PrPTU0i5uGtgH+9IR2hJAacuy9NbmBvmeyy1Y5oYpRTcs2GU/7hzC6W83Ywm3NfzNN85AdO1gKsuWsEbLl2Dyghk/jDd7RMeu6eCLDlwkdFmsooiLNvGzRe451tf4cZPfozZyXEK5Z6muaobcQSB8W+sXpHjVVcMcvWVg6xcbZzqYWzCWmqkMR86ySQhPDdvTFwbN1b5yk0j3HnvBEGoKRUttG5/6ZttY1nUqzOU+wZ423//31z8smuozcw0fScZiSwMnX1yylCe/tKxTSKGEAV7phr83U1PEEQKKybKhTxHywcHnh/xG1efwdkn9ZzYBDLfgNg6bmpaZZFWi4t0O0ZhSL5YolGb5cZPfYwf3/hlUxbdcVFRCLT3i5TGZzFbi1g+5PLGa5bz2pcP0b88B77C843XWS4yaXQzP3XDoV6v8/yJSSGXN7WwHnx4is9/ZRcP/XwG15G4bnezlmXZBL6H1oprf+sPefnbfwO/UTfFJWUW6rtQdPb7yYM5Bstzc0WWejsm1hWN8d9+9e6t/HjDKKWcRbQfzWPuuYwG4/kRp62s8NuvPvPEJZD5BsLmvQ1GZ8KsptUiI/1CKhVRqvSy4+mNfOljf8TTD91HqaevWduqzTwjBEJCdTaikJdc87JBrnvdClasKaAaEUHQ0jYSEw8cmoBsv9eUz6XLKTWgjV8/lXTV/je5n4UI785xmTyPW7SIPMX3fjjG576yk917fCplqyuJSCnRSlOvzXDVL/8Gb3r/H5vS8VGUkcgBoDNqae1g94TDpdyO6XudbYT8v/9aT82L9pn3sRAEkeI9rzr9xCSQbgNAA5tGG4zNhpnmschoexGVotjTw0O3f4//+Ms/YWZ8L4VyBRVFbfuB8QkEgcnhuPS5PVz/1lWce14FAo3nKUyh2oX3VaeghfmFQLLNLkiwpfF6h11eFQE4wjCJwrBNnD+SZLgrZY6TcuFmpLlBBibSzCnZjGyv8/ef384P75kgn5PYlmibTZp7N+G8tZkpXvS6t3Ddhz4KmJpbxmeSkchC0BqTxgy0ZiDHcM+xRSKJ7+PRzRN84bZNOLbc/0H7gBQw2wi56qIVJx6BdDrME4fZs6MeE7WMPBYTc2fTmmKlwp1f/yJf/euPIITAcfNEUdjV11GdjRjoc7j+rSt5w9XLQQq8eoQQCxfGcxzWGI1BSPNFdImuSx9zxz0T7BzxTELgTNjSdDBjybEEvT02ji3p7bEol2wG+x36em36Kja5nMQpWKA0gacOeEx1tmGkIJ+TIOGb3x3ln/5tO/WGopCXqbDk1vMIKalNTXLxVa/hHf/r/2E7LqHvZWG+B4A206vSrO53WdnrHhMkorXJtLek4HsP7uQ79++kUrDjMi4Hd78C8ELF2at7sBf1bpc4upGH1vDMngaTtaQUe7bs7GKgM9IKISiUKnz/C//IjZ/8GG6+gJTWHPKQ0kSLzFQVl17Sy/vftYY164oEs6F5Eay5GsR8106QJAdKCa4TZ5oHpgx86Os5L79SkCsz+GLMAAAgAElEQVRLbv/hGH/yl8804+JlXCVRKY2QNMePUhpLtmpn5XISx5GUixYrh3MsG3C49hVDnHt2Gd9TTdJaCDo1JtsS+LG/5/WvG+aMUwp87BOb2bStMcekpc3NUerr56HbbibwG1z/px/HzRcJA6+piSxV4bdUkJYXlhTsmDBrtazsc9vab6m2Y3JLtUZEy3J1CCZejBYy0wiwPvKRj3zkEO/vGMLcjt402mAiXscjm5EtLtLkkSsU+dY//T++/U9/Q75YaibCtTuABX5gHAq/+Sur+f3fPJneXptGLWpmdaf7qJvZKfnNfDfbXVdiFy2kht17PO64a5wvfn03o6MB559d6uo3sSxBtRrx4GMzFAsWA/0OlZJNT8Xm5DV5ykWLYkHSWzHb8nlDHpHS1Bsm23xiKmDHLo+fPjTNbF3xyssHUxFiBzbG0vsn9uugoVi5Ks8Vl/azdUeDpzbVyOfN0rhtJKI1uWKRHU+tZ9uT67noilfhODmi2CfSef4Mc9EW0CEEU3UzJit5u+s+SwrxhPjJHTNsGqmavA8OXgMBiLSmv+SeOCasbrOETaMN9lZjh7nW6Kw0yaKg2dYxQTj5At/4xP/h1n//NIVyD0kNq7RpybYE1VrEsgGHD/3OOl5waT9BNYw1h32T+7z+grwFAnZsq3PPA1M88Mg0T22qMT4ZMFtTVEoW//UvF9JbsQmj9hdfa43jSPaM+tQbxpSWvCiOLYgiY06SEjxPMVONaHiKvRM+09MhY5Mho2M+I3t9Rvf6vOV1w1zz8iH8xoFpIPO1b/K8UaTJuRKlNX/76a1847t7qJSMUFO63Xxn2TazkxNc9LJr+PU/+zsTtBCXhl+Sgm8JotOctXYwx/Il7FhPm7Bue2Q337x3e2zCOvjzSSlo+BEXrOs7MUxY7eRhtm0d89g7m4q2onuUTYYDQ9vMVwjcQpGvf/zPue1Ln6FY6SNZ1KkzRHdqJuTCc8r88QdOYfXaAo3pEEvumzzm+AcijZSQK1soT/HQw9Pc8qMx7n94ij17A6LYlFUqWJyy1uX1r1pGpWTIo9szBIFi2ZCDEG7TEW5+bx8r5ZLF0JBrJnSy3CqoGOhmXkouJwn9g9M+OpH2KSVam2UJPvjedQz2O3z2P3eSz8mmObDZPmFozFm3f5dS74f55f/xf/HrtSVvgllKaDdnGTkiBQzFIb5LrS2FEIj4PVk1UCDnyIMmj+b5MGbb1YPF459AOp2oINg27jGSreWx6GgPe9QUyhVu+oe/4rYv/TPFnr5m1nm7OQCmZ0Je9pIB/vv71lEsWjSq+18auLld62a56VzJhLredvtevn3rGOufrDJbiwDI5SQnrShw8QUVLrmgwgXnlOkbcAk940/odg0hBGGo2yYe+3p28zf+TquYom0ZMkrOuRjjLd3OUhpNRKmI699xEv19Djd8Ziu2Y2aeaRJRUUSpt48f3/hlBleexKvf/X5q09Mki5Vk78L+0T5+NVvGPKQQDKSSDWFptGV67J6+ssJgJcfYjNessHswUBryrsV5a3uPbwLpJA8hBDsmfXZnSYKLjpYAbYXq/uCLn+aWz36SQqWnK3kIATOzEb/02mE+8O61KK3xGmqf/qg55iodJ9spuPenk3zpxt08tqHazA/p67G5+IIeXnn5ABefX6Hc64DAlFqPI7o6hXr6msl97g/JPnOivrRumr4We2baSSJaa7yZkNdfO0zOFfzV328Bi7iWVrtjvVCucPM/f5yBlSdx6WvexOzUFJZtZ071BaKtL7Vm894GUuTpi1c3TPfN0WzLlllX49iSF541xI33bsO15UElElpSMOtFXHL6AKsHi8evD6TzsYQQ7J7y2T7hZ+VJFhltNvkwpNTby33fvZHPf+T3cfMFjFNXzSGP6mzEO35xJb95/UmEDdWMV983eZhjo0g3a0etf3yGL3xtFz97dAbPNyXcVw27XHHZAK946QCnn1oESxB5ypirdHPCvaTMDYeCtNBSCnIVm1tvHeX/fnKzqf7boYlIKYnCENtxee/ffpZTLngujdlqM7z3WG+PI4VO8/jpywvNAoydk9elcJ9eEHHDTU8wMtkg7yycRLQ2Sb2R1jiW5HevPYvhvvzxSSDdHFrjswHPjnoZeRwGJG0ZRSGFcg/PPHw///DBdxIFAZZtm0q6HYJ6thbxq7+0kt/41ZMIGtF+neWtYzVRBLmiRehFfO4ru/j6t/dQa0QoBQN9NldfOcSbXr2M4dUFCBR+vC5IJ2l0u86xjDkk0mPz7W+P8Ff/sIVC3pqzj7Qs/HqN4ZNP5X1/90VKvf2Egd/MVj+e2uZwImkrpTW2FJy1okA+VQo+PfE5qvcXT9Ce2TXDp295GqU0ri3jpQbmNxUDzQlIw4/4tatO5eLTBsyE5HgjkG7kUW1EPDlSn7M9w6GjNTgVjptjcnQ3n3z/OxjbtR03X0BF0RzNo1aPuP6tq/j1t68mqEfNOj37E+rJ707BYsOGKp/67DYe21hFCEHOFbz8FwZ482uWc+oZZfBMfSwh5gZHHM99300T+bcv7eDTX9xBpWS1OVC11iYya2qSi668mnf9n08R+n7b+Y7ntlpMpIV03pWcNVxommKXgszpJJEnd07z5Tu2MDHrU3RNpeduznUhTGxII1BYUvC6F6zmJectb72rxxOB6C6Jgo1A8eRInSDS2WJQi4zOiCvLsvj0h97D4/f8kGJPL1EYtjscpXGYX//WVbz7+jX4s2G86t/++8XYk01Ox7e+P8qnPrcdL15J8KzTS7z3107ioot7IDR+FNElb+REQbsN3tTR+ptPbea/vrOHnood56Kk829sZqcneNMH/iev/LX3GH+IZWfvygGiqYkrTW/B4ozhQtv2o9me6fcgIZGxaY9v3LuNjdumCSOFa0tEqkaW1hCEJvhj7bIir33BSZyxqmLCw2lGeB1PBNLeUZHSPLG7Ts1XzWVosxdicdA2IKOIYk8PN/3DX/Odf/5bSr39TfJI9rUswfRMyBuvWc4H33sygXdgUUnpfd7zofU89PMqQwMOr3vVMt71tlUUyjbebKvMSecxJxrSM87Eif7BP32Khx+foVyymiTSfGeUwrId3veJf2Pt2Rfg1WYzf8hBIGmvUGmWlR3WDXVZBnYJkQjAE9uneWTTBM/snqHWiJopDY4lWbusxAXrerno1IFURF/qWY4XAunG8k+N1JmqRxl5HAak/R7Fci+P3X0bn/nv78F23LbfE/KYqUa8+Pm9/J//cTpaE+dsLLxf0hrIfQ9O8YM7x3n55QO88AV9RA1FGGosC5IsbDixtI5uSE+kcjnJjl0eH/ifTzA5FeA4st2pblk0ZqucftHz+e2/+WybnwiytjwYhEqzus9lVV+qbhZJztkSIJGUJgHgh4q6F+KHCtuSFFyLvGs1j02TTtMkfTwQSLeIh61jrVyPjDwWF22zVsdhdnqSG377bezduRU3V4jXNhfNsL+6pzh5dZ4b/vwseio2QaDmJY/OvoT2l01rjROvJ45mTihu5/4nMtLtEUaafNninnsm+JO/fMbUBOvY17JsqlMTvOF3/hvXvPt3M1PWQSLdXpHSnDLUWktkKYzTTo0o9vEj53GiK21et273Pm82SbKjuUDrX3rbUkDnfQhhlqJNJwoebM2X+Z5xqTz70UDbGAAs2+HGT3yM3ZufJpcvtpFHosq7ruAPfvtkegccfH//5GE+g20LrFROSPK731B4tahrHkcm6FpImxBtS+BVIy578QDXvX6Y6myIlKmxLARKReRLZW7/8r+wbeN6coVSWwRdhoUh3V5SwJYxj5lG+3sBzLsG+ZG4v7Z7lCa7vFPWJ793Rq6m37EmgXQSBMTrGbRFbRhGTT/40SST1thvdcxkLWTnhN+RKHhw559PGHXOiE80iFjYFCsVHrjlm9z3vW9Q7Oltkkeyj5RQryve8/bVnH9hD43ZqI0Q0khvU0rj5iWjYwHTMyGO3R5WKkQyIzr6duWljraXXUJYi3jnW1fz3At6mK21+iMRIJbtUJ0c5+bP/C3Q2p7hwNDmXwI27W3ghx3h7Ee5XdNkkHzu9q9z3zQkdFFpSASAKV+cHJx8T1SdxIYqOFozlHa/hxcotox5TcI4EMHSef9Ka8JIEyqNikkyUmabqanU3mYnAtKmK8fNMb57J9/55xuwHQc6JiAm4iri6isHeNO1w/hVU9uqm/reaWpxyzb33j/Juz+4nj/886cYnwraiKfb4M4wP9oCSyKNnZe899dOopC32iKyTN9GFMo9PHrnD3jotu+QL1eaRTFPlHG+WGj6l4TADzWb9npzfLXHepvaaSGbOEkEMFH12TxSZfdkg4mqb0LTig7LenOsW15m5UABIZjjlYcj80J3doTSpoOCSB+w07zTZikFFBxJT94i55gV36SAMNIEkabqKWa9iCAuzZ0Q6okwE040TsfNcctnP8XI1mco9fQ31/Uw5CFoeIpTT87zvl9fi+6y0FH6fMlvUWynf/jBKf73DZuZroYEoW5Www3Dg9cmT3SkAxq8WsTZ51W47vXD/POXdrSF9pr+MZnqt37x05z7oiuwHMeU5c9wwEj7AqfrEdsmfNYO5IB2c/CxKjfstCNFSsF0LeDWh3fz0LPj1LyIIFRNG1hCMHnX4rSVZa5+7ipOGio2hcqRYtS0yp1g27jPTCNqS95ZyHmgFdYmBAyWbZZXHIqu3Oc5gkgxPhuxZzrAi9voeDanNIk6MnbyJx+8l598+6sUyj1zTFc6rlb7gXevpXfAwUuZrrq1TUIeuYLF5mdq/PnHnzWL30jB71x/EqtXF5r+jgyHhkQ7jGoRb3vDCu59cIqNT9co5FtRWUop3EKRLRsf5d6bv8ZVv/wuZqczh/rBImkv2xLsmQ4ouXKOU/1YbdfYhGXI48kd09xw00bu/PkIfqjI2ZJKwaaQsyjkLMrxZ60167dO8clvP8Fdj+9pc9YdbhJJO/3MH8GemYDRmaBZwXVh5JGcxjh6iznJmcMFThnKU8pZbR3b7Z9jSYZ7HM5ZWWBFjxOvend8qKXzQWuNiGso/eAL/0joe821JJLfzVK0IW9+9XIuuaS3jTy6ni8mb9cRTE8FfPSGTUxMhqgI3v3Lq3jtq5e3OcszHDzSJB9GmlzJ4j3vOCmedHWJdnPz3PGVzzG2ayeOm0PrTAs5WKSd6tvGfWreXKf6sQiZaBUPPzvBp295mulaQClvm6xtWsuBNh3o8UBL4oO/9uOtfOu+7W0kAoc3wiDd8FUvYvu411yqceHCuxUjP1C0OXO4QDlvtZFEcq30dZtHx/vYluCkgRzrhnJNreh4I5G07yNfKvPQD7/D+nt/RL7Ubh+XUuB5inVrCrzjzStRnjJrj8fobL9WP4KwBH/1D1t4alONSGle96oh3v6WVfizc8N0Mxw8Wn0F3mzExRf18IqXDjBbi5pRWUlfu7k8e7Zu4sc3fhk3n88isg4BbX4opdk05jULGR7LMkNKKdg2WuMrd2/BEgInLvPbxDwCNFF3Szmb2x8Z4d6Ne1Pmi8MTYdDp9wgizea9XtPpfyCmq6Qj+woWpy3PN/0mnc+ZfO/msE1rKYNlh3XL8mhotsGxOCDmg9Ym2axeneb2L/0LQkqgnWgFEISad7x5BZVBhyDYf/kYpcAp2Xz5v3Zz570TSCF44cU9vP9da4kai7uGRoYWzJgGIs11rx+mr8eOfUytca6UIlcocu/NX2N0+1acXL5tGeIMB4b0RKvuKzaPec3fOiNgjxVIgJt+uo2Gb/wHCTHsD2kB6diS7zywg4mqf0DZxQeCbg28dcyjHqgDqnGVdroXXcm6Zfm27enP850v+a3zmP6izeo+F9UUqgf/vEsF7dpHiZ/94FtsWf8IuUKxjdClhNl6xAsu7uGVVwwSzLbPaLudM4rNKA/9bIrPfWUnliVYtSLHH753HZZlTIuHazydyGjTGBuKk08r8uqrBqnVo7bcEK3jaLtd27nnpv/EzeXmaOgZDgxpp/r4bMjItD9ncnostaxcv22KZ0dmybumUueBvKiJIHYs43y/d+NegNjktXjNkJwp3dB7ZgImauEBZZp32nlP6nfbjj/QKLK5JAIrel168lasxR15LST9cieflYqIopAoDImiEBVFzQWekv32d07LtpmZGOdHX/0CVkfYriEDyLmS69+yChGXiO7Wjum2dhxBdSrghn/ZShAYc+D737WGZStyeF5Wv+xwIzEfak/zi68dZuVwDt9v1xqVUrj5Avd957/Yu30btusSF+M42rd/zMMSsGMyYLbTH3IMkbN86JmJ5nrPByPsDImAbUvWb5syYbBykQdXh+mq5it2TPgc6GUSORQpoy30FOw5QvRAhVU7iZhzrOhzm/d2pIRfZySHiqKm4M8Xy5R7e6kM9FLu7aVQ6cF2XbRWbXbtzv5vto1S5IpFHrztZrY/td6Uae/QPmr1iGteNsj5F1TwalEz52NfpiuZk3zuP3fx7JY6kdK85dphXvjCPrzq/I73DIuDpF+kFPi+YtnKPG+8erkpgS/T41Zjuy5ju7bz0A+/0/SFpM2XGQ4MbX4Ppdk65rW9T8eS+dt+etcMriPnzM4P+ERxeeA9kw1WDhQWbY7S6ffQWrNlrEGkDjzfI4EQMFS2U98Pbaab7nCtNT15i3LOYrpxZAo5pslPK4UG4+DWisnR3ex+9ilGt2+mVp2mUO6h0j/EmjPPZfnaUwlDn6DRQFrtCw6lIS2L2swM937rq9i20T6SPYQw64b399m85dphCJJcjRahpu8z8T3lihYPPzTNN78/ipSC888qcf1bVxLWVXPhp+T8GQ4fkgmA9iJedcUAN35vD+NTYfua9FpjOy733/JNLnv9dc1w3gwHj7QZseopth+j+SF23QsXZZ0MExqomKj6rBwoLIqW22rI1rbtEz7V1LrZB3rPSmtcS1DMtapMHo6OqhQspuoRcHiFYJo8VBRhuzksx2bDT+/k3pu/xjOP3M/s5AS+VycKQyzLxnZdipVeLrriaq582zsZPvk06jPTCCnnzH60UuQrPTxwyzfZsuFR8sUySTin1iYxrTob8Yarl7F6baFNe5jPhGVJQdCI+My/7yAINHlX8JtvX42ds4z2cpB9m+HAkO5r39cMDue46iUD/NvXd9FTsVGqtY+bz7PjqQ1suPdOLnnFtc3xkuHgkYxv2xKMTgdUchb9pfY11Zc65GJFuAiMaajuR4tzZ8l5Y9OQEIKpWsie6QPL9+iE1lDOW03fx2LeZ/p+yjkL6zC/X2lnv4oiCuUKM+N7+eKff4h//IN38cAt36Q6OY60rFjzGIzNVzkatSo/+trn+fhvXccDt9xEodKD1mrOWBBSEPo+99789diJ1E5aQagZGnB5w9XLUtrH/PeqFNhFi+/ctpfHn6iilObVVw1x4UU9GXkcBSTjRwgg1Fz9skH6e03Wf8eOKKV44JabiMKgLXhkzj+lUFHU9q/bfhnS7xJsm/Dm1Mta6u0kF+tGNcaemnMWR2rODdlVbB33m1rNgd5vWptxOiT7Ygmr9D25tmhbpnWxkT6viiIKlR7W/+RH3PA7b+Mn3/pPbDdHodKDZcWmOq1ARwitQGuktCj19lObmeRzH/4Ad339ixRKPW0lK7RWuPkimx9/mGcevo9csdQWxmmKJUa84qUDrFpbwPPmr7Sb3LPrCMZHPP7jphE0sHpFjl9580p0vPxshiOPxJnueYqT1xV5yQt6qTfaTYlaKXKFAk899FN2PvMkTj4/x2+YjB07lyNfKlPs6aFY6SFfKmPZTts+yXWXuoA83EjLOD/URsbFWGiQy9GEXcrbVOvhITu+tdbYlqS/bBYUOhTzVdJe7SG7frNkyMH5PVr7O4fJxp4mYlPahMMSk9ce/RRSrPTy8A+/x+c//HuEYUCpt78565MSgkDjB4ooAsuCfE4ipSAKQ2w3h7RsvvLXH6Z/xSrOu+xKGtUZY57Qxv9x//dupFGbpdTTh1ItDTOMNH09Dq99+VBL++jyvOmwXVGy+NbXdrFjl4eU8JZrhxkYzuHNhJn2cRQhhEBoDRpe+/Jl3Hb3BJ3lr6RlMzs1waN3fp+1Z5+HX6+3KhFoTaHSg9+oM7L5GSZGdjIzMY6UklJvHytOOYO+5StwXJdGrYYKwzkm0xO139OhvRM1E9o73OMeE1qIvW55mQefGaeQsxacA9INkdIs680x3GfyKg6RjtqYeaxqQnZNTa5DFDCiMz/j8IQkCg5vHkiTPMo9bLzvbr7wp/8fSpuQyyg0EwKlNNNVxfJBh9NOrrBs0GFsIuCxDVXqDUU+L1FRZJYu9TU3feovOeW8i3FyOaLYn7J3+1Z+/uPbcAuFJnk0fR9VxRUv6+fkdcVmuRHdpX9EbPZyXcHeXQ2+e/sYAGeeWuSaqwYJa/PnjGQ4/Gi9axA0Is45s8T5Z5X52WPTFAtWM0ozcaY/+qPvc+V178SyHVQUYVk2lmNz//du5O4bv8TOpzcSeB5hYHIcLNuhUOnh5HMu5OKrXsP5L76Scl8/9WrVaLQxCWX9b0J7d00G9BRsCo5sI5Gl2Db2haf08dCz48DB+hRip2ioOGdND7Ylm4UJDwZzTVeanZN+83xaH5xgbsVYQ6jS2xdP0KdnCpHSc2Zwi3UN40uIyOWLjGx5ln//6IcIAx8nl49faFMN17YEv/KmFbzxmuUsG3SQlgkb3LS1zl//01Z+vrFKqWARRRH5YoltTz7O/bd8kyuvu57qxARupcL6e+9gYmSX8ZGoVma4UkaTuebKQXNfxIsE6rn3CyZsV+QsfnDnCDt2e9i24I1XLydftrOw3SUEUxnA4qWX9nH/I9MIkTJRaY2Ty7Fr09NseuxBzr30crx6jSgM+M+//BN+8u2vIi0Lx80hLYucXWweV5+Z5tG7buWxu25l1RnncPkvvoMXvvrNAISBj5TWCU0iaZkXKs32CY8zlheAdvP7Umsa+ZxT+jl1RZmGH8UCYOEvcfqBKwWHy85ZZn4Qh6aOpu9h56RPI9x/SYz9oZWEaNb+ne96h4L0vfmhbmakLxbSNlEpLQLf58t/8ceM7d5ucjNi8qjVI1YN5/iLPz6d33r3WoaHXKJI43uKINCcenqJj37oNNatydNIVghUCst2eOD736QxO4vlOHj1Og//8HvNaJvWC27s5WefXuT8c8qEnmqSx1ztI440sQW1yYDv/8hoH6esyXP5Zf1Esa39RBUcSwVpvxae4tJLehkaMOVo0pDSwm/UWf+THyGkRFoWX/6LP+aub3yJYqWXXLEUm6ZACE1i0zSBHBXy5Qq7Nz3FFz/63/jch3+fxmwVxzUTnxO9/5Pnt6RgqhYxVg1Scusw2cMPERLgmktWYXeUjtgfWhFA0PAjXvacYQYrOVPy/SDtdmkhIoRgqh6yN7U07WIMMCGg6qlmIbPFQqdDsOpFRGrx7bpJrke+VOLWf/8nNt53N8VKbxyiK6jVFaedXORvPnImFz+3l8ZMiB8kmoP5V58O6R/O8etvXYWKWqTk5HLs3vQUuzc/TbFSZvsT69n8+MO4+ULTeZ70uR8oLjq3glM0a0nsq12UAisv+cmDU2zd0UBKeOXlgxS7RftkOOrwA8WKlXmef1EPdS92psfdpLQpsrjhp3cS+j73f/dG7vvuN+gZHEKpCK2MKTMIFLO1iIan4rVGAK2aRRrLff387Ac38Y9/8G5mpyZwcrmsWCPtUVk7J/14zaGlG5UlldKctrLC2166jijSbet/QPvsPP05IZrZRshVz1nBlReuaM5OD5480iYazY6Jg4+6mg9SCLxQUfNbWsjh6BizBrL5vBjnbrWLWath8+OP8MP/+CyFctn4MaTA9zUDfTYf/uCpLB/O0ZgxCWGdARK2LQhnI15wUS+nrC2Y7GMBUkoatVm2P7mefAnW33sH9ep0K5IrhlJQLFg87zk9EAuHzjDmbvjZI9NMTIX09TpccVk/eEnW84nrQF1KSPpQx27By1/Uj2MLtKLlJtQay3UY37WDn999G3fd+O/YjhubN82B9UbE8iGXy57Xx9mnlygVLWr1qBnZpbUmCiPKfYM8++gDfO7Dv0fg+1iWtWQF5ZFCM8FQCLwwloExlmKb2EKYyroXnzZAIWfzlbu2MD7jkXctZLx8bboWlQCUNlqHY0ne+KI1XHHBcPOEh+bwaalpu6cDZr2DTxjcF7SGsWpAJd/Kvk7+HqwfKP3cs17ETJyFDosfJiyAWz7/99RnpihWzIJOaLPGwwfetYa1pxRpTIfYtkzNaETbOZTSFMsWZ59e5OnNdfI5QEgCz2NiZBdeHTb89C4s2yHJDwGaqw2edWqRc88sEXqqjfi7QQgg0lx6SS8/fWiKN16znBUrcviN5NilZ9s9UZFE7kWe4sJzyqwadtk96uPYrdBFISSRCrntS59hcmQXtttaHKnRULz1dSv41V9aST4niSLN5HTIw4/PcOP3Rnl0Q5Vy0UIIiMKAUu8AG+69k5s//Te8+ff+J41aldgwcsIibcoaqwb0lyx6C/YcObMUIBOThFKas0/q4fdefza/cP5ybEtS9yJmGyF1P6LhR9Ti70przlvby/uuPZMrLhhuc34dzMN1HlvzIkam/WYi3uIJ4OZDMz4bNouYHco1us2Ydk8FJBXxF1P7SNbj2Hjf3fz87tvIlysoZXwY1VrEL75mOZdfPhiHxDKHPNKftQYcyWC/SxjFpUnimadXn2XHU0+x85knTAnvprlSNM0TL35BL265Zb6anzyMBhR4ipde2s8XP3k+b3/TCkJ/4ZpLhiOPKNJUeh0uOq8S5/ekzLTa1Fjb/tQG/EYdIYzPo+Epzj6jyHt+ZTWlollvXUrBskGHV75iGR//0zN599tWEYSqtfphFFKo9HDPt77C9ifX4+aLS0pAHi005YaAHRN+s72WWm6I3RIOht/rrrIAACAASURBVER6ig5vvmwtV1wwzMZtU+wYq9MIIpSGnC0Z6slx9poe1gyVgG5roh+8IE6wbcInUix6Hal2ExlsG/c4c0WhzUF/IDHprcguHUchCUZn0iHHi6t9CCmJAp/b/+NfiYIAJ5cHNJ6vWLM6x6/+0kp0rBHsi8ybzylNJFXzGpg8ALRm430/pj4zTamvvy35K4o0lbLNi57XB0HrWvu67+SafqBwHdn0eyy12VSGVl8qpcESXHx+hZtv29sMkDDdad7zpNuEMBF4nqd4/kW9yIJFYzpoRtaFIUS+Made/2snMTTg8Lef2do0rVqWzez0JA/ddjNrzz4f/wSOxkqQNmXNeord0wGr+ty235YC7PTNJH4NDQxWcrz43OXzHmhmIzQHwWKZf8aqQdP8czgGUbNjpGDGU2wd81g3lJ8jzBIy7Hb5tFDUKfKYqYds61gdcXEc//Fa5OUK63/yI5782T3kSmW0MjNDz9O8+dXL6Rl0m36PfV1ba920FgYdTmwhBVEU8cwj9zeTCdPjw/M0555V5OTVecKg3W81372nTW/NnIKMPJYsEjOW9hQXnlthoM9hJg61FkKDNoMnfkMAY+a2LMFgn9M1CtO2zNIPjamQ1752mA1Pz3LTLXuplC20Vtiuy+M/uYOX/+pvGbOpmltW50RDy5QFe6YDBko2+SWWGyLTN9rURuLPkTKhqInqqlLbIEU4B00eyd9WldZdU8Fht4c3Q0ulYG/VCP108mKL3VvqYmeUVfq+pRDMNCI2jR346oj7Q/O6plO4/3vfIPC8ptbY8BSnrM3zyssHier7L6OeQArAV+zZ65mZYrJdWgSNOjue3tBcB7sz+uq551dwSvs3XyVImwnTJqujPfgz7BthqFg+5HLemSU8PzFjkXKozz0mmSAkaDnmzWxaCCBQvOaqIQp54yPR2iQojm7fwsimZ4xTfgkIx6WApB1CZfLh0tuXApr2i05btBGM7QYpgRE8smO/g+/o9kGyZyY44BUGDxZJB1hSsHsq4MmRBnVfzXmebqQB7S/GyLTPUyN1gujQ81U6kZjIHDfPyNZn2XDfXeSKxTis1pQpec1VQ1QGnHhJ0oU/t1dXPL25jmsb04SKIoqVXqbG9zIzMYa0u0dfPfeC9uirDMcXkj6NFJCTXHR+pU1zTCOeYpliqpGmVo9A0JxIpc9pSARCX3PqyUXWrs4TxGPWLJc8w9OP3Ifj2m2BGycy0g71iVrIdL3db3u026hruEO3GeNizx471TA/VOyZDrCadtXDK5jSjW9bgul6xMbddbaNtypi7uuf0pqJ2ZCNu+psiwugHQ7iSzQ/J+fy6I9+wPTe0WZhuiDQDC9zueolA2hPN2eI+7p+Yse2HMGWHXW27/JwnBaB5IolVBRSr86YsMrUsUGoGF7mcMapRSK/FX2V4fiEFECgOePUYtMp3olYMY4JA0bHAwj3Hc4fKU2+ZHHhOWU8r1VEU0jJMw8/gN8wmemQjS9oz0TfNbW0tJCjGi+XboBdUwF+KmnmSCBtukt8LiPTAet31nl6pM7ItM9kLaTqRdT8iOl6yNhsyNYxjw076zwz2qDmq7hw4uEhDwDLsvBqNX7+49uxbBt0XAm3EXHZ83pZtjKH7y+sbkriu8KVPPjoDDNVk4BoflMUe3pMCXghm1XCEpu452nOPaNEuWIt2HyV4diFEKBCxbqTCvT1OkRxHc3mOBfpiaDJL9o14hHFlQnmni+egMYD68Jzy7iuWcwuSTDcuvFRpvaOmHGeAWhloVvSmMo7M9SPJpEcFQJJC1ohTN7EWHVxM84PBGnhb0mjXUzWI7aO+zwz2uCpkf+fvTOPs+Oq7vz33qp6e29qtdaW1JIsyTaShXebxQsQ7BC2ECAhMAkEJpOQhCGBZBhCliEJCSQhIQkEkgAmA4Rl2AyJQ1gNBi/I+yZ5kWTtvW9vq+3e+eNW1avXm3qVWnb//JHVev1eVb2qe++555zf+Z0aB07VeaKvzqH+Or3jPvWo4NLkgZZGiiO+LtvJcOrwkxx/Yn9Eq1UoZXqQX3NlezIhZxtOlBJ0LeSeh8YNW0xH3P4gYPXGHiojw0noLL4O8zdcuLMEi9DBcgXLG0kYK9S0lCzO68nj+RMMQ2rdig3IsZN16pGs/1RIEvSe4lm7SqxqsxNWnrRtxocGOfzwfWacp1oHPNMR2wghzGY7nETrPTvXddY8kLTVPDHiLWrdxHzQxBaKDIkdF1KmDET69fh6lyoXYLwFhZN1eOzuH1MdG0FaVpLM3rwxx4W7SsmO73T3LgnZ2YLBYZ/HD1bJZCRKY5R9W9sotrVTr5YRltX02TDUlIoWz9pVgpXw1TMGSoHIWZzXUyAINbGa9VSEfUsKRsdDhkZiCu/0xw0CTVdnhvPPixL0AqSQ+F6dgw/cnbxvZYwZpIkINd+E+9O/m5LRcAZwxg3IRO9jqBIwWlv8qu35YLrk+VQ7+7nUiywERrzO5fF770pEDUXEub9kTwuFVnvWelImdwPCkTx+qNoUvgoDn7bONTiZDJWR4aYQgsD0/li9ymHjuizhLJL1Kzj3kYztULNzW4F8TkYK02KKjvcRzdtTnOx1wWpssKY6pooS9HsuLJkEvSCi82Y58uiD1Ctl5IRNzDMdSUJdGMKRO6l74Zm/prMawtLa8JtF6vXlgtlSU5cKSf7Dthkd6Ofoow+Sidx6pSCblVx6UWsyi2cTvtJxttMSPHG4lmgTCSFQQcDqjZsZGxpMtpgJRVuantnnbc1TKFiE0zByVvD0Qvzs8TVbN+eaeoNMhXhj0zfgQeSBTDVGYko4vuaC84oU8laDzms79B05yNhQf8QCXOlamEZCOgpNvrbxOpwNL+SMGpCJeYLhKEEtl4H3sdwghJEucTJZjj32EOXR4SR8ZbyBDBfuLKK8hszEbBDLdR86UmvEqYXpL7K6ewsjfSeRslGs1Ngxanb0FCAb70LPDhISAFP32F5ZbBYPcU1YqDQdbQ6r2p2Gsu4EJBTdQNM/6IOMRQ0mP4847KsCxdbNedrbbMKQBp23Ms6xxx4xdUiLrJp9riPthQxVAup+nCc6O17IWQthLWfvYznAVLhrpCU48uiDeLWqMSCA5yl2bM3T1urMKKU+FaQU1OshTx2rJ/RdrRTZQpF8scTYYF+kvts4rlKQz1n0bM6fsfqPtEFIClmjJl1KaYLQXJfSjT9pavgKFg9KabJZSff6rKk1ojkHkrD1MBuUoREffD0lEwuaE/SlosX2ngK+r0ydmWXh1Wocf/xRszFa8UAmIR7nfqjpGz+7XsgZMyAr3sfcIaWFW6tz/MkDWLZR40SYibdrexGRa3gDs71/JtEZ0D/oJYnOMAgotLSjlKI6NjpFAaEml5Ns3pAzHH8W3+BPNBhgvqeKeqpYliCTk2RLFtlWm1ybTbbVJlu0yBbMn/jzK0ZkcaEUyJxF97pc1J+CuODIvCH+WRs5k8FhH+WrKT2VScfNW+zcWsAP4zxIJNT42CO4tXpSD7KCBpaTF3JGydbpyX1q1GvyPlYMyGRIy6JeKXPiyQPYmUwSvsnlJNt7jDcw2+6PSejHFpzs9RLmCwJUGFBsa0drTa083ty+FrOQd7TZrOpohDAW43lNXOxDpZNFyLYEZAzlJ6iGlCshx0+59PZ7jI0HjJUDajWF6ylqbshzLmvnuZe34/srGkqLiYThI2DD+qypl2Lq5288EMHwqI/v6xnbFCfHDU2hYj4OjQqNncly4sn9VMdGTbO0MFjCb3huYqIXsnlVNvpF/L8zM/7PiAGZuFAMVYKkAG+inMkKGrtwy7YZOnWcSpT/QBstsmLBYvuWfBImmO2CqTUgoX/Qw/OMVwGmSVWpfRXlkSG0Vsk1xMnOINRs3phL+jss9LuloZQxbJYlyOYkWIKgGnCyz+PAkxX2P1HliUNVjhyvM1YO8XxFEOiGoq80U+Xmbw7we2/dwstesga3GiJXxtWiIM5XEGg2rsummFhTvdeEsEbHAoJQY9vT74aTehBfs2NrgWLRolZXWNJsnCqjIwwcP8LW3RcTBCZMs/I8G2jyQsoBa1qclNBis4O4lDhjHkjaiPQ25T7OzBc9lxAn0C3bofepg3i1Gk4uh0ATBpq1qzN0tDlzqgYXQhj9VEvQP+Tj+opCXqK0eSaF1jbGhwZMyCBt8KXADzTr12SRWYlfDpHzCHxONhxmscnkJNgSrxxw732j3HXfGA8fKHP4aJ3xSojrKjKOwLYllgUZR5LLpKInmF1vpRryjW/3c8N1nYmqwAoWERGN23EEvq+nLRSUwjyLWj2ckbUVjy8VatpbbdZ1ZXjiUA07KxBC4taq9B09xHkXXw41nVDYV9BA4oWoZi9E64lZqqXDkhsQM5EbrPEV7+P0SDwQCwaOH8Fza2QLBdAhfmC8gWxW4kWJx9keUwAoGBsPGrm2KJ6dzRfpPXIQIRv6V+kK9DWrM4aaydx2go3nb6BU5G2UJGFd8eiBCnfdO8rtd48aanEtxHEkGUeQy0oKOVPoGLcqiYvY4kuP/xja8dNjLKVrjCbWI52p+qP0OUGjlaat1SabkXie0TRpntnx+yEMYbwcsrrz9BvEUJsE/eaNOR59vEouBwiJ77n0HT2MSCXSV9aKZqS9kMFyQFfJIZ+RZ/RenSEPJNrRAv3jwYr3MQsIKfG9kMETR5DS9IqWmHzEhrVZyErTPGoOGzMTilCMjgVNEu6gsTMZauNjCCmadBFiyZS1XU5TAn1WNScpno5S4DgCp2jhjvvc+t1hvvbNfg48WaFSNUYjm5G0tdqGGRYbDRFpMmkT7gpD8ycItVGLNYcnmxX8/MvXkslbeLXwnB9XyQ5dkYQkNCZkGasALIX+2nRQGnLR8xkZC7BtkRj15us2Ic9KNZzVBlhHBYXd63MmvwYorbEsm4Fjh/Fq3koifQbEzz9Qmv5y2gs5MxuNJTUgE5wPxmqmjewK8+r0kFLi1WsMnToeCShqFGYRXr8221RAOFsIATqEcjVaYKOEm7RsAs8l9D3EBIuktSabkaxZnQU1e7n49AJoDIdkdMDjO//Zzy3fHeCxJ6sIAdmsRWtLw2gkhB5tml15viIINfmspFCw6GizWbM6w9quDKs7MtiWCbHtuaDI5Ze04dfDM0IzXiim8jLMJUfCnBjmmVMQoAArekOgUb7C9zVCNJ7HUhsSrcFxJKvaHA4dqZnXmEzlFUIQJgZETDIwaSSJdKXZtCFLNht5m2gsx6Hv6GE8t5Y0mFrBZKS9kJFKwLpWh4x95ryQJfZAmrcoA+NB459njihwTkIIie/VGOnvTSQdtDbeQFenk9RjzGWgxJO7Vg+JNBRN9W8mi1er4rl1pJxoQMCxBavabIiKvaZk4ExYEMNQJ4ZjuM/jP28e5Ov/1c+R40Y+vlBofCetTDJcKY3nmRa92ayka5XD+ecV2d6TZ+vmPNu3FOhos3EciZ2V4IhoDAnwFF6tsTlZziGP9L2KSQTmvlqYcFGIFAHlsuILnxtheNSjkLfYsDZDz6Y827fkaV1lxoBbVwgafWji4y42lNJksjIqJoyuNzW9RervMNTUaqEpEpjBgqQT9Js35shnpREJlKbN7UjfSSqjI7R3rSNUISsLxvQQQuCGmqFKwLo20/r2THghS2pA0i52LIcu0yNtBdPAJA1r5XEjoCgtNKaQznEEnR0OzFNORClNra5MqIrIgNgOge8TeN6k4yllaMOFgpV0opx0tamBGiojO51tsRgZcLn5SwPc8t0Bjp50yWYErS1Wk6chBbi+wnUVhbzF9p48l+xpZe+FJS7cWaS9zYGsNJTlQEeSF+DVQlSVhicmWfRWwkuBdOgpjAr0sI2X4VfLgEDJVpyuXdxxyxE+9LHbKZXyaAS2bVEqZejqdLjq4hZuvL6T7ecV0Z7CS9Fml+L7xxTw1paZ5UwQZoy5njptfDr2QFSoWduVpVi0GB4NcKQJ4Xr1OoMnjtK5vpvAb3QNXUEz4uctBQxWAta0Ok0biqXEkhmQiRc/MB4QapLk+YoFmR5aGyrj+NAAge8nDBStDQupo91BTyMpMROifHmT+KLWGmlbuNUKYeA3TXoRLQbtrTaOLaekZE40HtmcReCFfO3rvXzh5l4OH6uRz1q0lozhUIqk/qRWDwlD6N6Q5dorO3j+Ve1s35In12J6nihP4fsK5UY1KTK10xXGbZ8qD7BcF5m08dAaskWLxw9U+Mot/Vx0YQs3vvIGVOlSrOIuyK1h78tO8tJ9cPKpI5THx6lVq4wOjzA+Kjl0pMot3xvixdd08EuvWU97Zwa3EmKc1SXKjUhBPmcRaR9OGcMyOQwiAzK7w8babl2rHAaGfBxbJCHc0YFeLMtaWTJmQPycpRDUPMVINWRVsbG0L+WGaskMSHpie4FiuJryPlZGwszQGiklldFhAt/DsmwzMZXpzZDNSGbaBE4L0WB4JU9Aa6S0qFXKhEEwqZGPUprWkj0tpz+tlZXNWzz8yDj/+Klj3P9ImYwjaGux0apB29Xa5GCkgAt3lXjJC1dzzZXttHZlINRoT+FXQ5Mgj+oKYsHH6Vzy5Wow0phYC5UpWHzt6738y7+doq+/ynd+NMq1v/ob5PNOElpcu2E9f/7Pn8St1/Fcl5NHj/LAvp9w5/e/y/133sH4aJkv/nvIT+4f552/tpm9z27FLYdIubgJ9oQCLohqh+JfMGUMy3gg5v3xd5npOrTWZB1JV2eGhw5Uoj2MxHfdKIQbbXTOged81pBiJA2U/SYDspRYkrNM9D4GywFeqJOGUSs4PaSUVMdGCYMA23aImUxtLTb2aXotnA5T5SNVGCbMKa2jlr6Y3WQ+Z2owppKNj3fTmbzFT/aN8Ad/+SS1uqIl8jjC0NQMCAy107IEV1/Wxi+8fC0XX94OUuAO+wz3ukYLSQocW5DPWyZ05Sl8T0XHOTtU1oViovFwspJ/vukon/5KH7YIKeYCfuEtbzLGQwUgZFILhBBkczmyuRwtbW3s3L2bV7/xTdx7x+18+sP/wD0/vo3jJzW/+6dP8K7f3MILruvELceeyOLsPpP5rCGXlVhyutSGRiBQCny/MchOd36lgIygq9MxPUdSGO3vRYVTe5orSCFOpkddC8v1kFLOWvJ7tuQ5kFBpBivBORGfXi7QRBLq9Ro6zlhq0xvdTOCZ2S2zQmrHaIxATH1NPZ+IDZXLSYSMJ/DkQ0kp8Oshn/jcCWp1RaloE0S9CixLUK0ZXaRrr+7gF1+5lgsvKPHkwSqf+tfjHHyqxrFTHmPjYXK8tlaLTeuznNeT54qLW9m+JY+Tl/i1EKV01AXy3KoNSAxtweLj//cY//qlPrK2T2t7O7/93j/j+TfcYN4orMZiGakiN+mEKYW0LC6+6mouvupqPvOPH+FTf/+3qFDz53/3FPms5OqrOyIjsnhx8DhklcvKpAvn5O9IMmb8QCdjbKbnlC5wXb0qk4qIaaRtMzrQi++5K4WEc4DW0F/2KeWa+8ovxVxZcj9npGrEvpZDw6hzAVrHFXPgu3VUSpMKTDdBIUHNR1IkqsSzLJHaQkYJ3SBInaX5enIZi2mlVTEhpmpVMTQSkI0KmSzLVCyXKyEX7ijyq/+tm8sua+Ouu0Z427sPsP/JGpWKb/Iu2getUCpESIkQFg896mDbDp/9apZd23O8+mfW8JzL2wHTc2IpE8aLifj6wlCTLVl8/9ZBPv2lXrK2z+p16/iLf/kkPTt2EIYhUspJO+3470RaJnItwiBAWhav//W3smHzZv7id99BECr+8h+P8KENOTZtzOFFrWUXeo8Suq3W5HNW0oRs6i9s9jt+oGEWaZAknBpq1qzOJD3SQWNZFiP9vfiui7SsM5IUfjpAChiphtR9lZI3OUdyIJPCV5Wg6XfLebIvB6Srv33PNYtqsogYLr4UEM50kGmgMYMrlnGHKIytFL7rTlqs4nNmMtL0d5im8DMINKVWm2uvaueTXzhJqWjh+4o1qzO86Rc28IuvXk9fr8tvv/sA9zxYRqsAFdRoa29l45bz6Nmxk1Wru2jtaKc8Ns5A7ykO7n+UE0eOMDI0xL5ygfseqfD8y1v59V/uZt36HG41TIohl/O4iqm62Yyk/6TLh286jiCkUCrx3g9/lJ4dO/B9H9u2ZwzTpJ8NkKgzB0HA9T/zUirlMh/8/XcxOCz48CeP8hfv3pHQvBcTti2SOh2YXAcSYy6aaUIACjrabDKOSLxMKSMiSeCTdRzjja/gtIgLC4erAeuXmNK76AZkInW34obJ5nW5TvLlhPSE9+q1xkwlCidlDV91unDS6SAlhlEVvyBMziPwvcQ6JEYMszA4jpiWkplQMX3Nr7xuI/m8xUMHymxYm+W1L1vLpt0t3PbNPv7iH55ivKJAVehc3cWLX/UmXvjSl7Ft1/nYjjPpuL7vc3D/o3z75pv59te+wlB/P9+/XfPI41V+/21bePYl7bjjwbI2Io3QE5CRfPEbvfT2+1i4vPl3/pCdu3cT+D6O48zam5po5C3LIgxDXvrzv8ADd93FN7/y/7jzXsGtdwxz7TWroqT6Yn2hqP6j+SVzXamf54IkN6Q07W2GrOH7OgrjStxqBa9aIVcoMb9t0zMLaUrvcCVg7RJTehfVgEy8yJFqSBBipLlXMHsIU1wX+M20Wq0hY4sZw0kzIQ4t5XOyiYmlwhCvXpvSA0E3aLfTHTPOdTmO4E1v2EjoKixbQE7yra+d4v0fPoLWCguXF/3sq3nLO97Jmg0bGsdQChXF94UUSCFxHIddey5i156LeNUvv5FPfPCv+e7Xv8rgkOI97z/Ee96+hauu7Iioq8tTPFEIgdLG+zh1rMZ3bhtBhzUuuvoqXvH6N5jnEfd5YW4brPRGLf7cW97xTn7yw1sZGBjjq//Zz/OvbE+Mx6IYWEFSh3M6gzGXISoE6FDT1uLg2A2tLSEEYRBQGRulfc16gmW4SVhumEjpHa+HtOXNMi+WgP26JJmpeEEZqgRNA3gFs4Mg7hQYTnjk06ugzgYak/BOJLlFYyFSM4QHxGnOGS9OSmm8amjYWlJwx21DfOAjR0CH2Ba8833v591//UHWbNhAGAQN5pcwvH/bcYzuUeqagiBgfXc3v//Bv+F//O/3YNuaaj3gTz/0FI8dKJPNmx7tYgl3WfOFSXoDWckP7xqhf9DDtuFVv/xGwBjuibmOuSD+zlJKwiBgzYYN3PCqV0NYZ/8TNR55rIKTs6LQ42IsHkY2RsUWZKZ3znGcxqHSbFYmBgohUGFAvVpeSaLPAxrjhTT+vfjdHZfsqYzVTRJHLmCCPNNhFvVGwlsD0hLpl+YGDViCYqGRkGzsTNPPp/lZqVBPe8J4B5ze4WYzkoE+l7/7+DHCUCGl5l0f+Cte8prXEoYhSiks204kWtK76KafpcS2bVQYosKQ1/7Km/mdP/1zhPao1kITFhszhWfLMYQFUe2LG3LPQ2UC32Pztu1cec215nfW4tEs42O88OUvp9RaYmzc4677xoyCsl6kDZww+a4ZDxXlR2xbJH3RZwMNWBaUUooHhnwQUK9EBmSZbRCWO6SA0VqIF6gFbVRmPMdiHWjiAE1bvhXMA/GaPSHgvJDnrzVGjqJkE05iyOhJP8fnD2M99RnQ5AFYcNMXTnGiL0QHNd7yjt/j+p95KUEQIKVEpuipM9E7zTVr0wteSsIw4MZX/Ry/+Gu/QeiVeeJwnc9/rRcZFVamKa/LBZYlGBsPeeqYiwpddl96GblCIWHXLRRpyi9Az46dbOzpQYU+B5+qo+sLz4Ek91SAHyimc0BispYQ4KSYfrNZtLQ2oe5iIdWwSghUGOJWK8tyc7DcIYTACzUj1UZ0IaaTLxYWzYCkLZzrK8bq4XxD9SsAEKaYME1vEYJEh2iut9bw7c0HW0pWIqZ4unkpiAq9ZjnopBS4dc2jj40yeOool19zHa9981ui/IvFVKEmpRRhFK4Kw3CS8mojMWiG65t/5x3sveIqhK7zje8McvSpWkQfXl6ertbGY+wbMG14pdBs3bkLoEHPjsN1ShEGQeMepGs/TjPjG2oACsdx2LbzfLTyOdHrUouovAtBMnaAwDeabFMPwKhfjDRswRizNepSioQGHvvEOlT4bn1BG6dnIpK8GjBcnbiZXzwLsiQeyHA1wA+XZ0jhXIDGMKyklKbIKoW5LOZNx0zx7Ve1OzhOzLcXDSMVZ0ZTj02ISNfo9Np45vo0ZJ2At/7a8/jV33s3/+v9H2icP2U8TKiKpP7Bsixs2zZGRkrCKD/QlNiPYv0Ab3r775DJWAwO+Xz3R0OQWcRQzSJBAyQ9whWZTIbWdlPLImg2HlJKLNtu3INo9z2Rvjvj+aL3dK5ZixSacsW0/13oNEx7INW6Mj1Kpn2zqYbOZc24ir/nbM4hBclGIPogSoURxZxJc2EF0yO+55YUVFzDhl2KMNaisLDSi4LWmpFqkFim5RqbXtaIwgDSticYC2GkHua5SMZ8+1XtDplULcgkTaMmr0dQq4fMRnzLyJ8YDaNLX/xzXPqaFwIkC+TEcRJ7JQO9vTy47yf0Hj/Ouu5NXHL11bR2dKDCMMmTQCOcpbXm4quv5oJnX8K+H9/JD+8c5edfsQ7HFjMrxZ4lGJaqws5kaO/sNC+m8kaWZXHs0CEe2LePyvgY3Vu3culznksmm03uwWy8EG3aPVJqbWF0tEznmtWL9h3izcfYeDBjDi4mauRy1qzGTPpzQookiZ68rhW+586fJ7wCQmU6wRaz6cp0sShe3aIYkPTOsuopqt5K8ny+SG6XACeTIz1rhADPNd7AXGNYyTMKNas7J/e2njg/TUW8GWSuq9CnaSbV8DACtNVCaO+MKpHDpKNi+jpi9tD3/v3f+fCf/SkDvSfxPR8n47CuexO/9Qd/xHNf9KJJxgcMucCyba5+wQu5+0c/4mSfz1NHa+zaUSR0bdtY5gAAIABJREFUTb5mOY07IUAKievVGBkcBJo3Vp/+yIf53D99jLHRUVQYYjsOz7rkEt75Z3/O1p07m4zIdN8rNq4A1//Myzlw3x1csP4IpaJNGC483yIEEBgDYs0QEtPaJMMLuTlk0CHZtKSPLRBopQnDYK7DfQUpSGmS6RtTMkCTSz/neewFH4Fm93qsFhIuzrU9MxH39dbgZLMIGUlZ60Y4SSs9r9sb51BWtTuG7ZJaVybO9TjuLaVgvBJGoZDpFy/zfgHaJ9PWg1XchBR6Ss8jNgp3//hH/J+3/QbDA/0UiiXaVq2iUCzRe/w4f/Sbb+WR++5FSplQjOOQVnwdFz77YlraWhgvexx4sgqZqSXnzxYEgNJ0tJmQoed5jI2MACTf6Yuf+Dh//yfvxfc8Si0ttHZ0UCgWue/OO/mDt/4aYyPDSMtCKXVaoyilRKuADZs38d4P/Aqve2UHSpvY5EINqhCgIgMyEyEqrjUqFqy5kz4EzQn/KGxl9OAWcvXPbEghcANF2Z29wOWsj73QA0xcHEZrzeGrFcwNsWy21pDJ5ZGWyYNoDC2v5irDoJrnAIh7nK9ZnTE9qCceJjL+8bOTEsbGgxlj6Q0ar0mjHjmh8bwwer2ZmguY/g7ATR/6EAC5fB6llKHrKkWhVMJzXT75t39rji+bE7LxsTZv20ahWKReDxgZC1JyK8tjtRHCUKDXrcnQ1mqjtODQYwcAyGSzDPUP8NmPfpTW9nakbTfdg47OTp589FG+dNNNwMzJ9GZKtkQBwfBjeG7EmBILn4tCmPDpWDk8bVLejg3IXMOJQkwqCtV6xQNZDCgFo7VmWanFWJ0XbEDSkzUOX8UvLZeJfC7B5BEEWkGuWMKy7GS7J6SgUg3Nwj/P48etSdevzRJM0RZXR5LcELGIpGCsHCbvnQpaa5QGOyv42rfKvO4Nn+Ef3/fH8S8nLQgAA729PPXE42SyOcIJRYxhEJDN5Tj02AHGRkaaxpEQItnW5gp5bNsxRq4cQqAXZbFcTIShpqVks31LDmFleejufYyPjgKw/8H7GRsZxrbtScyzMAzJ5nI8fM89QMPoTkRTFbvWhEqgAxc9/hDCyiJQi5KHlAJ8XzM04hs16Gk9EENdbinZcw+1am36wKSfNyLqyLmC+SC+b1Ka6JBSaZWJhd/VRfNAIApfLWB3vIIGlNbkii1Iy27yBsqVwAjVzfMWKw1kJRvWZhNDNNOhpIC6qxgZDaaV8SY+htY8fqiK62ke3PcTfN+btoI48P0ZF3ohBL7n4fueeWHK95oEtEBTr4dJz/blBNPrQnLJnhKZbJYjTz7Jvtt+CIDnutN/MFr0fd8/bc2IUQFQCCmxLIGljyC8PhBO8vuFIKYjD4/6jFdmritR2ni4paIpCJz1maMknB+oVLMzs2myHOcZm0CfaY7MZqMU30spBPUlCGMtyIBMDl81aj+W0y7wnEPEqCmUWhKtJBPCElSrCs9X81oojXcDKNi4Lksm7mwY5TumOqQQpjnQwJBnesgy07MVZDMC27GoVSqEQXMBU3wNAF3r1tG5Zo2RJU/lSeJksO+6bNzSQ2fXmsaFTDh34Pt4novSglLRgmm6Jp4tCCGiSnTFcy5rZ3WHQ6jgK//6KQDOu+BCMrlc03eP/7YsC8912bZrl5EqmUJqpiEhY3JKo0OD/PUf/Slf/vCfI4ROmF4LQaM4VHCq38Pzpq8riXNsbS3WtB0sp0M8LsNUqNTQ2SW2k3lG2o+myIA2EjIq5dHPVb5HKRirL24Ya0EGJG3Bar6i6oXJw1/xQuYPASgVUmhtx7Kd1IABL9CMjYczegPTIc5T4Cu2dOciTaxGwdFUh5NS4HmagWF/xhyD1oAtWL3KQQiL8bExBnt7k/OmEUYsqp/9b79EpVxGax3pYBk9LBWGeJ7Ha9/85uT9E78HwMmjR3HrdWzb5HRi6YzlMvbi6/R8xZr1WX7q2g6QeR7Ydydf/tdP0d3Tw3U//RKGBvqxoxoQKSVOJkO1UqGlrZ2X/cLrgMl5pKa6mug8H37fn/Hpv/8rPvLRH9I7oHAmssDngTiPhS3o7XOpuzNvXlSoaW8zoohzHZ9Kazw/GqNx6YmUpiPnM4yYk36+oTLtfPOOJGdLpDBagxNrq06HmI2l9OKFsRZE401f+GjUx3pFeXcREHkgxdZ2bMfBd2uARAgIAsXImM9mmQfmVmcjopmpAs2GdVnaW236B33sGUaBFOD6it4+44HoKc4ZHxel6enOUSw6jI2M8sj997Gxp8eo7KZoqDKKgbzi9W+g/+RJPvPRfzSGwHHwPY9Cqciv/+93c91Lfiah+6Ynio4arB946EFGh0cplVrpXp8F3xSjLZfao6RwC9Pr/edftpYf3DHCyVOKf/mrD3D+nov4X+//AEP9/dz27W9B5Hn4vs/qdWv57f/zJ2w7//wpa0Hiuo9QKWzb5ks33cS3v/plOrq6ufziEmu7Mvj+/HNlMdLn7B3wCAJzjwMNYsLiI4SpOVjVbmM7Au80xmbC3UJrM75TJzd1Mo5jNi4L/C7nCiYahvXtDl0lx6ytGgKl6Rv36R3zk3s0mzEvhVEJqbiKllxDh24hmLcBmRiSGKs3vI/lMoHPZWilcLI5Cq1tVMZGsBAQhZOaGUdzP3aoNIW8Rc+mHCd6XTKOlajzxpThmImlMb0FTva5yQI96Vq16VeuPcX5O4p0tGc4fnSMn/zwB/zUK1457c5ZCMFb3vm7XP78a/jRt7/F0OAAq9es5eoXvIC9V1w5rase51XuuvX7ZsHqcNi1vYiKQnvLaeyZCmvjxXWszvCbb+zmD/7yEK7r8ke/+eu8/xM38f5PfJL/+upXePiee6iWx9m4pYcXveIVdPdsnVQDEt+HMAhM5bqUfOtrX+WfPvA+hJWjvU3wW2/qxrIF4SLImEA0xjzFyT4v6b+S/C56g1E6MJ0XV7VnwJHo+uwNSMQBwA8aFHLzusSy434xy+e5LhUmPudtXTnaC6llWkBGCro7shQzFocG6pPm1EwIFYzVgsSALHStXpRCQjdQ1Dy10jhqEaG1xnIcWlZ10fvUQcC4oJ6vGRz2k3zEfO61UuCULHZsLfCDO0bMvBRmAdDRfyKyJlqDbUtO9roEbhw6m7jzNK/5vqarK8OeXXlOnspz163f48SRI2zYvLmpIDA94LVS7L3ySvZeeWXzNYZxe9tm+ZN4QT14YD/3/Pg2hJ3n0j0l2lY5eNVwXgZ1KRFfv2UJ3ErIc57TwZtfV+MfP3WC4YFB3v66n+cdf/YXvPiVP8uLX/mzTZ8NggAr5XnE8vfSspLc2E1/9yE++5F/QGFhWZJ3v20LG7pzTX1SFjofLUvg1hWHjtYi5ePmkKegsegLCatXOXMOJwogCDW1CRtRaUky2ZyRxX8GINE107C+LUN7wZ4032J0FG3KrkPvmI91mo1THAGUAsZd1TwHFzBG5p0DSZ+wXA8J1IrXsZjQSuFksrStXmuKzqIEeBhqBof8xvvmGMNMnpHS7NhWIJeN1U+nyGtAZEAER0+6VKoNBs6URgRACm68vpNczmF4YJDP/fPHku8zkc4rhEh0r8IgMCGZSExwovGIPxP/67Mf/SjjY2Vaig43Xt/ZiJkv4zEoJfjVkF/8ufW8+XXrCbVNuVzjvf/zN3jfO36bQ4891vT+uM1t/Cc2HEII7r39dv7n617LTX/71wTaIZezec/be7j00vZFa7IV32/LEgwOe/T2eTMmx9M1RgQmfDbraxDGgIyXm3sIWbZDobUNpZ45xYRaaywpWN3S2N9PDhkbdJacKTd1ExF/Qgioewo3WBxawoJDWADlqGApfn05T+JzBUornKxD2+o1ppAqSmZKSxgPZJ5MLK01QgKeZvuWPIW8hTsDswbAkjBeDjl+yuXC850piwpNqAb8WsgVz27lkt0l7rxXccsXv8BzX/Rirrz2WgLfT9rXpsdPepctp1DsjcdT/PnvfP1mvveNr4HM89zLW9n9rBa8WjippmW5IB1e0Frju4o3vn4ja1Zn+NinjzM8IvnPL32J27/3HS65+nlcdf317Ny9h65165Iiy3qtxvHDh3lw30+4/bvf5pH77sXzQrRVYkt3lt/99S08a3crbtm0+U2feyHXrUINtuDgUzWq9RDHNqt7Oqcd/6y1bhiQ00jfTIQURmWhUm3IIKE1lpOh0NoW1cksr+e6VNBAxhI4qTk5kZEVI2sLbAnBLLv9CmH6pZfdkFykmHzGQ1hNIQWtKad6Diy3yXsuwhSFAQLautY2aK4abCnoG3QJokV/Xh6I1gShpqszw5ZNOR7eXyGXM7pDcTA6PWBlVMB46EiNC/e0omtTGxxDKdUgBf/99Rt4cH8Fz1N84F2/y1//62fo2bGDwPeTXXT6HFMzu1JMlCDAdhwevf9+PvRHf4gWDqs6HN742g0Y7Zy50xrPJCYaEa8a8pIbu7hwZ5GPf/YEd9xjMTxc51s338z3/uMbtLW342QytHW047ketWqFerXG6MgoCBtp51nVkefF13Twy69dT6nVaTIei2FIE4/PFhw+VqdeV2RbDXNPIJpCWGA8kEJesrYrg442GbO5Bh3Ve4ynFA9isoZlWxRa2yMplwV9nacldJyznNV7Y8khEzVaXXKaXp8P5mVA4oQXQM1TuOninxUsGAm/P9CsWreBTC5vmEwCLFtwstfDdTX5nIjXzjkjDDXZFos955e498Fx8nnZ2ElOuBYpTXL08YM1s1hPsdOPF0bTD0SxY2eJ//GGDfzNPx9jZHCQ//3ff4U/+cjHOO/CC6eNu061+CfJYsfh/jvv5E/e/jaqlTIah99440Y29eRxy4sX719KNBtNjVsO6dmc50/etZ19943xnR8Occ9DFYZHfQaHaihV4fjxwehzgkzGpq1jFevWOFx1SSsveWEn3ZvyKE/hVpvDVot1H6QAXMWhI7Vk0xCTN5ryIFEIam1XhmLBYs76jVIwXg4IwogIgUBrRSZXIJsvoPUzxwMRgB9q3FCTl40N1lSkEjdQUfpgFseNN4TCRI1URPBYSB5knh5I4+fx+gp9d0kgBGEQ0Lm+GyeTIwx8kBIrku0YGfMpFnNG3n2O6rMN2i3sOb/UyINMcwytTb/q/U9W8KphJGUxnRGIkv3VkFe+dC2n+l0+85V++k+d4u2/+PP85nv+kBtf/Zqm6up4QiiljPcDSefCOFn8xU98gk/+7V/hewFe6PA/Xr+en3rhatxyGIn7LU/PYyLSk9+yDNUV4LJL27jskjaGBz0eP1Tl0JE6fQMefpRLaGmxWbPaYdvmPNt78uRbbPA1btUU7y628YiPZVvGM9j/RIVMRpheIOnFjMbGIQg0G9dlyWakKXad5XlM7syM6yCI2uFi2gAUW9uTMfBMgRCmzqNvzGdLZxZoHt/pn/vGfZQGaw7PXAqBFyiqrqK0QDrvgllY5RTXe7nvAM8VxPcwDAPau9aRK5YYHx7EsiyzOHuKE70eG7vz8zp+TLtVEe12VbvDyFgj/DGBzYvW4NiCYydcTvW7bN6Yx/Mm73om7rD9esivvXETji359Jf7GB+v8b7fexdbdu3lgj07UWEkj6JASGuS3lNlfJy7f3QbX/jEx3lo311IOw/C5rfetJHXvHIdXiSrMVsK43JBmv0ShyHdaogA2lttrrisnSuuFsbbi+1stKjiK0LfeC9CmN3kUnz/OBwpM5LHHxmnb8BP8h/NbySRwwlDTff6HGQl2lWIWVB0hBBEKyBDIz6er8g4FppIjaHNFNNOm7l/miF+jpYUDJR9ihnJ6hZnyveeGvUYKjfk9ecyBkIF426YGJAz5oGkdzl+qKm64Qp9d4mglSKTL9C5oZuR/l5sx0FIqFQVx07UufzK9mjHP7/7HkbdCc8/r8Ctd4xQKlqRQm8Uo4xjFZid6Fg54IFHy2zeWkS7akpNpKkSxm/+pW629+T56P89QT6rWSXuBnYiLDtpq1uv1Xhw308YHhhgbHSEQwcO8MBdd3LsqcMEAQiryIa1po7iOVd3JEnzc814xJiYEI29uiDUqCBEV2N6LNH74s+ZP2nDmT7eYsEYOMAWPLy/TLUWGkXhKUNTwmhgZSWbN+YSFd7Z5T900mo58biiHEgYBLR3rcXJZnFrtXPuGc8H6fEsgKcGXcbdkDUtDpnIgNd9Rf+4z3AlSIUVZzcHkmMLKNcVtC3seudsQNKDtuqF+FGZ/QoWH0opsoUiq7t7eOzu2xGiCBhFzWMn68lEnesC2ggZAVnJJRe18v3bRxq/o5Ek1ZjkPdLsSB/eX+GlN8SqtzBV+GyqhPF113Ry6UWtALQEX8F/6A5ky24oXYDV1sPfv/dDfPUz/0Y2m6Feq6O0QFpZnEyejRsyvOB57bzmpWvpWJ1JwjbnqvFIY8o8kjBJ5WYm2tTfdym+e8KIk6BqIQ88Wk7ou2ZPMZmDpZQmFxuQlCrybK5PCsBT9A342FGhYlzz0961DidjU68YNYNnApqMiIDBcsBwpeFphMqoX8/H80jnQaqeUdm2rfnnQebtgYCRb1cK5DPjuZ5xaKVwHElX9xbDkKJRl3HspEtYn9oLmC1ElCC9ZE8LbS02nm/YVXFBIZD8rBTkspIHHi0zNuJTKliEM/R7mJQwroYU82ag+CGI4ASqegjd959YuTx68AmEVSCbcyiWirS2GAn0S3aXeO4V7XSty6JdldQ4mHtxbhuPqXA643Amv69tS/r6XR47VCOTNOuaSLWIw63Gm12/NoOaAwMLjAftu4reATfK5ZjzWI5D+5r15t9zON7TAen5YwyFTuabECKuI573HIjzLFVf0WrFeZC5X+eCciDp3h9Px8l8NhEXj4UhdHVvIRPVAwgBji156lidai00jJdwfrsHIYw0yqYNOXadV2Df/WMU8hM6FaaO60QV6Q/tL/OcqzvwU4v5dN8h/rycMAEQGYTMgtaoUPHWN/Zw7XNXg4auToc1qzOUCnYUTw9NzF8ufrJ4vkh7WBOxlKGlMwWlQGQl9zw0zsioTyFvGe+DZtOhMaIIfqDZutnUFYVzpAYKAa6n6e33iJ0MrRSZbI6ONesJA5Uk68/V+zkfNG/CJvcwX+j9CBXU3JDWJA8ydyMyp/1relKESlN1V5pHLRXiwRH4Huu37iCbL6BUiNam5/ToWMCJUy4y2bHNDfHzUgpk3uLyva1Jh8Kpd4+mANH1FXfdO5pkTmPX93TniQ1i47garUOE0IQh5POSKy9v58rL29nWU6CQs/ADhRvVBkhJQjlsPs6ZRdrV19qEEoQwnfRiheR0055zjT3UmOOAr7jjntGIeRX9rhG7BCEQWkcbEWNA5IQNyOnOFVe6j4z5jJfDpFmVVgo7k6Vj3QYja3MWn/nZxEzfeb73I/2Mq/7CNGLmHQBJ+McLOv0KpoOIVnIVBLSvWUf72g2owFSkSymo1kMOHqkZ0To9v8EUx1jxFJfubaWlZE+/e9TG2OSzFnfdN8booIdjL/zp62gBUsqEudxaiOeqxFuJk8Xpaz7bSO8MswWze6tUQ1xPkclJMlmZGJHpvJTlDseR9PZ5PPBIOUXznuKNQiRNpLZtzifGZbbPKU7UHz1upOKljMa9UuSLJdo6jRLDvOIrK5gSjU1dlIbQ6Q3P3I41pxBWUwI9muTWPBlAK5gdVBiSLZTYsG0Xxx57GCeXQ6LxA82hI/XkffNxZ+PF2/cU27fkuWBHgbsfGI/CWFOLrTmO4Pgpl30PjPPC6zvxU3UYcw+hNRuGmUqJloPhSAo8lU6Svf/vq6f44Z0jnOxzKeSNQOVrX7aWHTuKeLVwziyZ5YA4fLXv/jEGh02+S6V53RMQhppi0WLX9gJ4xlOdPSsIsAVPHa8lTC+tIQx8Vm/qIZPPT2r3u4LFgcD0F/ICTc5pJkXMFvMKYYFxfeJ/nos7rHMFSimcrM2GbTtNNXpEmcw4kscPVQiqM+chZkJTGCtncc2VHUm4YqrJ3/AW4Nbbh2GGPulPR8QG1bJMIdYf//WT/M0/HeH+R8oMjQQcPV7nlu8N8rb3HODb3xsgk6ZFnwOYKnwVJ7B1/MME3QwhSAoIu1ZnjBQJc0igC8DXHDlWT4XDJYHvsXbLNrKFohETXcGiI05FVNyw6bW5YM4hrHgSVd0V+falRpJI9xUbd15ArlBK8iCOLTh8tM7wiN9IPM7TkEsJuIqrLm1j9SoH35/+OLHe0b77x3jyYJVMdu6d584G4nsTe1Tp3M3p8jjpY2gNVkZy0+dP8q0fDNHeZlPISWxLkHEkbS02rq/40MePcvhglWwqnLXcYeY2ZLKSQ0/VuPfBMfJ5mVSfN6c/RLKhcD3F+ecVyJ6GmTcVpBR49ZCnjtVxonBs3Mp27eZtSOvc8t7OFTTGvukmO/H12WLWBiR9YD/U1OepBruC2UNrjZBmN7Zh2y4KrW0mHoxJPI6XA548XEU4Mknmzheer1i3PsulF7VEsWhSfUGaf7Yswdh4wLd+MJTkYOLrXW5Ih+CU0qnF0CyW4QQhxtN9h4wj6T1V5xvfGqC1xSYMSRZNjemoV8hZDA75fONb/ZCJ6yeW371Jo2FUAUfwnduGGBkLEomi6dlmhlZ+wY6icSdmOQ4bBZRQjpSe7YgQosKQXKHI2i3bCX01qSZmBQvHxDxI+rW5YF5J9JqvCFd2BUuOJMQUhhTbO1jXcx6h7yOEREqo1RX7n6waHuU8F6mm5JkUXHtVB5YVUfpinQpIQqNxAWIuZ/Gd24boO1E3NQKL85UXFRNZgxlHkm2xEcIYjkxOki1ZyQJ/uvGsonj94aN1XL8hOz4xlxMqI2v+xOHaaanOyw2OLRgf8vn+7cNko9qPieMqHcgKQ01LyeJZO0sQ0fpnMw5j4yMcyeGjNSqpUGyswLBm81YC319ZZ5YQUhhB3GCeqqyzTqKnE+h1X68UEJ5BqDCk0NrKpvN388jtt5IrlJKd3yOPVVC15kZP89pJSAjrho21bXOew0frZLOyKfadLgzMOIKTvS63fHeAX35DN9pTEaV4eW0sYs8jm7foO+Xy1W/28djBKtVayPo1WZ5/ZTvXPW8Voa+biANTfofoVtRdZQo7ZyKQiElqMMsW8bxWCpyixY9/MMSRY/UkeW6MQsNsxCMipu/u3FZg3ZrMlH1iZjpnnEDf/2SVSjVOoAvCwGfNph5K7R2oMMDUQCzzm3gOQ2nDqrWtuetizSuEVV9AzGwFc0PaQ9hywV6cnCko1FEi/YlDVYaG/UTBdCEIAk2hzeGnruk0aqoCpi5gIvFCvvHtAfpP1MlmZNMCfLYRT4JQaTJ5i/sfGOM3f38/n/z8SfbdP87+x6t889Yh/vAvD/L+vztkhAPlzAZQCCDQnNeTJ+PIKb+nEUg0gpfd67Nk5pEXOFuQElQ95D+/P2CMBo2FfmL+w0jYmNDn3me14BTtpnDgbCCi3NvjBysJW00Iie+6bL7gInLFlpUE+hIjniPpNX0uxnpOIaxEliJYKSA8U4jzIL5bZ/MFeyi1xbsyk4sYHQ848GQVkWkkO+eKRpEf4CmuvbqDrs4MftBIMieGjIaRiL2QL36j11SML6N1MvY8Mo6kr9flz//+ML0DHp0dDvmcJJuVtJYsSkWLL/9HP5/98imsrMklTWcAzY5bsXF9jisubqVcCSe1MbAsox6Qz0le8NxVJidwJr7wPNHID4GTt7jznlHuf7jcqDyfdjyZz+Rzkot3t5ht7DTsvanOCaY5WrkS8NjBGpkkV6SwMxm6dz4rMmLLy6N9eqGRSE+3uJ3LBnDOOZBQEcV/5/rJFSwEYRDQ2tnFhvN24Xtukgepu4qH9peT9813959m1GzozvGcy9qopUJjjTeavwTGCykWjBfy+P4y2fzy8UKSnXNOcvN/9fPUsTotJRs/op/HCXStoa3V5su39NF3yiXjTD8l4rg9Gn7rVzaxY1uBoVGfUDVYXfW6YrwS8EuvWc+ll7UlqsHL2YzEXhO+5su39BvqcfR6w/2Y/Dk/UKxbk+X88wqEEStzts9da5COKSAcHPaSBHoYhuRLLWw+fze+5yHF1J7eChYOE5Y0j7c+z4r0WRmQ9AP0Qk2wUtdzxtCo1QjJFvJs3XMJYdDokZ7JSO5/tIxfbRYZnM95hIh4VhpuvL6TfM5q9mpSa2Ac3rAsQbkS8k+fOQ5pyYt5XsdiIlaTfeJwlUxGTKrJiO+hlIJqTXHwSA3hCKaLOJmFVuD5is5VGf7mj3fy8hevpqVo+rg7tmTr5jzvfec2fum1G/CrDcn5KVfgs4zE+9DG+9j3wBj3PBDpoUXeRyoDBjRyoVKC6yqevbuFUpuTUjCYnaE0bC/JI49XqFQVlhVR1gOfzg2bWbVuI2GwUoG+lGhEkYwBWTI13nT8s+41WiGu4MwgXtxVoDjv2VeQKxRRysSGHVvw1LEax0+69GyeutHTbJFUptdD9lzQwsW7W7jjntFEedcsKCktqEilt1Q0oY+bv9nHy1+2jvp4sCCJ6MWCEKZLXm2aHu6N9xkqbrkSTFUr14TEiHiKjjaHd/3WVkZHfY4er9PaYrN+bRYnb+HXwuQalhuxIA2tIzmiUPOVW/rwA002CyoxthOvWyeG17Ell+9tTQgDs+lLE28q4tqj+x4eT34X5z96LtxLvtRCrTyOkHLZ3runCwSxY6Bxkk3o7Gz3LD2QxpHqgZq1WNoKFgdmATJ5kO4dF7BqQzeB74EQ2LZgbNz0bFiMPEQcD8cWvOKGLiNux+TYtgZElC/QymghfeJzJzl2uEouJxODczahlCZTsFi/NovvTx9iMYuhYMvGfNLLYiqkBRJjT8RzFS0lm93PamFzdx4hBG51+RuPtPeRKVjsu2+MO+4eTdSYkzCkiWUhhGyE8DDqu2tWO1x0YQkV1w3NYfDZlmBwyOeRxxpaW1orbMdh20WXIlK9wFew9FBKU28qIJ5oAO+uAAAgAElEQVTds5xlDqRxMNdvZAXPdojimYJ4EoVhQLGtg+0XXYZfryNTPUPvfnDM8PBTdN75nCcOT/i1kKsubWPPBSVqURy/aTGMtigCUFrjOKYl6Yc+fhStGh32zlY+JA6/ADz/inZs2yyWE3fJtiWo1kJ27yqxbUse35u5QHaiEYlrSry6wvNMGOBcaXYVX6v2FP/2lZMEoW4ysiIOPUgLaTtorRBCJLm3Z+9uoWOVoe/OFvEGRWQljzxWZnikUawYBgGl9lVs3XMJXt1dKSA8Q4g3EvNhYs06iR5PiLofJh9azpPj6QYRJTKlJdlxyVVYjpnQcaOnBx8t0z+wOAq5QpgEuZWTvPLGrkb/h0l8XtDRYhmGmlLR4o67R/nITUexc40itLNhROLF0auGPPfqDl7102sYHvWTxS5OEo+VA1a12/zGm7qxHHEa5pFBHFJM/yyliAxK8++WI5qYVwWL798xzL5YRFPT/Ly0IpMrNWLYNDy2a67sAElyz073fSdW+t//SBk32vTETMNN5++mfc06wsBnORMPni5IS5p482BizYmFpbSJlS3TefG0Rkzn9ep1tl10Ka2dXSbJiCkoHBrxuf+RMiJrzZvOC+nEMgQ1xfOubOeCHUXq9UaVcROlNw6AQ5IP+fzNvXzl671kSs09tM+0EYmpyYGreOsbu3n7WzbTWjJKw15USX753lY++Mc72dZTwKurpBbk6Y6YAOFWQj731VNNqsEQjwOFtLNk8q2Efj0KY5kal80bcjx7d0vSFXNO4StbUBsPuOfBsahY1ageKBVy3t7LyeZzSY5vuRrhpwvSkiZeOPfcxJzk3INIS2gFZw+h79OxZj09uy/mvu/eQr6lFYEiCDR33z/Gi67rbGJCzXsCRqGZbMnilTd28b6/O5xiFDXvDTUidU5BPif58CePsm5Nhquv7qA+Hp7xpHo6hBT//QuvXs+N13dy8Kkao+MBWzfn2bwhh3TEM8Z4JAWWoSbbYvHvN/fyyGNVWkoN5eA456YCn/a15+G7VZQKsaSVUL2vvqyNYpuNW56C6j0N4tocKyPZ/0iZIyfcqCATlArIFVvY/uwrCPwwybms4MxAYDyQuUYMTvvo08fxAo3SMbFvBWcSCZ1XK5ysw85Lrk5+Z8JYFvc+NMbIkLfgqvQkXBZ5IS983ir2nF+iWktpHUU5hpi5lR54cVOgv/j7wzzxWIVc4ezJmqevy6uGtLbYXHJxG9df10nPpjyh0pOMx9N14UrTqx1HMDbo8cVvnCLjNHe1NPcrxMm30L52O9XRXqTlALEas8XzrmhPaNtzCV9pDUi4677RpM5ICEHguazfuoONOy7Ad+tnLXf2jIUwxIi53vJZ7B0aR/TDhqzBCs4OpJB4dY+dl11NqX1VEsZyHMGpfo97HxpHZudflZ5GvFN1Cha/+LPrkm1DImWRVupN7fRNBbhgdDzkz/72EKPDPpmzIHUSL2zxdSUJ75rpse5FKqTPBOMRI859yJzFl/+jL9E8a3o2QhJ4Ndb2XIxXG8d3yxGd1iTPd24rcP55RQJ39uKJMSxLUC+H/OS+sUSsUQiJ73lccOU1FFpaEsXpp/uzWE4QQKANlXcuOK0BST9EP9TTFlmt4AxBmD7pazb10LP72fj1WjK5gxDuuHu0qaBvvot1mpHlVUOee0U7V13aRqUadyCk4Yjq5s/EhqeYN4q0H/jIYaCxUJ/p3eVUCe+4h/nkwsL59wtZzkgb92xWcuJIja99s39SsagQAh0G5IoddG2+iP4jD2A5uYjKa+Tqn39lO3bkVc4WcW7NzkoOPFHhqWORirM27MJ8qYULrrqGMFgJX50taG3W+PS/T4dZhLCaPZCpXl/BmUG6Kt3JZbnwqmuT52D6lUvufnCcwUViYyXn1IAleP3ProtEE5ly4TX/aLwWRFLft94+zL989hh2Pub7L49K9YmGIQx1dH3m30pBEDa79eeyMUlCSI7gC1/vpX/Qw3FE8/MQEt+rsunCF1ArD1Ed60vCV0GgWdXu8JzL2hPp9vmEr35w5wi1eiN85XsuG7bvonvnhSvhq7MEQ+XVTUys2dSCzImF5YcrIorLAVJI/LrH+Vc8j5aOziY2Vv+gx777xxDRznIhaHgU4NZCdu9p4YXP66BcCSYxb2zHQUUtd9OvKwUtJZvPfPkU3/3eINkWm3ARwmsLRTqZH4ZGWSFbssi22mSyEts2vUJyrXayU053FjyXFriG1wfZnOSJxyr8162DFAuTvQ8V+hTb1rL+vCs58diPsOwMRJ5ora644uJWNm3O482h90eMuNfInXePGvaVAoQg9D2edfV15EullfDVWYRSzUys2TyDWUqZREnIqN/xCs4yoqRjV3cP2/Zexv23fpN8ybCxtIbb7hrhhus7E2O/UOaTEAKhNQSa//bq9dx57xjlVKMkpUJyuRYyuQLlkSFTo6JUU0jLsSUf+pcjbNuSp2dLHrdm9I/ORrFdOoymNWRLFrWxgHvuGWX/ExX6Bj3qrqaz3Wbj+hyX7W1hS08BAo3rNq57uRcKppEs9NJ4H+PlcDLzSloEXpldV72W2vgAo32HsDMFQCe1Hy96/qqUdAnMllBjak4k++4Y5tjJOvlI7VeHIYXWdnY/7wUEfrASvjoLSI9jP5z69ekwaxqv1iZLv2JBzi7ScXo747D32hfzwK3fAm3qdHJZyf0Pj3PsRJ3u9Tk8b2YdqNmeT0qB6yo2binw+let40P/coSWkmnpKqVFZWSY7p0XEoYBtfExLNtuirs7jmBkLOAvP/IUH/zjnaYy/HQNnJYAE6m9Tk7yve8P8pkvn+LgkTr1eghRrwulwLYlHe0Oz7+ijTf83HrWb8zhRsbzXDAiTcSGnMXDj4zz/R8PN3sfCW3Xo9SxkQ07n8PDt95kDiDMlK+7ih1b81y8u5Ugqf04PaGmQU4AQs0P7hyJPFBASNxKmfOveB7rt+1qCl8t53v6dEO6FsSfYy3IrENYSmPa2M7t2lawRBBS4tXqnH/58+jc2E3gu4DRxhoeDbh93yikWpIuNOQSJ9SDasgrb+zi2c9qoVoNsaIRJIRg6OQxtl90WRIuEaKhoxWGmlLB4r6Hx/nYp49h5c58PmRiAt92BP/8r8f4o786yBOHq0gR0rkqw6aNrWzqbmP92gLFgqRc9vjaNwd423sOcO99o2SLZ4+WPB807q3m8zf3UptQ/KfBhJICl617byCoVxg4+mBT8twPDJ0722LPPXmOUY0+ccLl3gfHyEfPHkArxZ7nv4hMPrtSPLgMMNfWtrM2IKGeO0d4BUuLwPdoX7ueC666Fq9eQ0pjMCxLcOsdwwTVxenHnY6Rh0pjZy1+9Q0bDTU3GhOW7TA22E+9Ms7F19+IVysjREzXij8LrS02X/6PPr73/UGyLQurmp8PtNaJfPlNnzvBTZ8/QT4naCll2HvRNm74qUu44acu4cUvvJifvuEyrrtmDz1buigWBIPDPn/4gYPs318mm08pFC/TiZF4HxoyeYt7Hxjnxz8ZafI+4qLB0Hdp6dxM9wXXcOzAbfhuFRH1rPYDzeqODNdc1ZHorc0pea6AjORHPxlhYDhI6pRC36dt9RouvOpa/LqLnG1F4gqWBALT72kuHuCsn1gQ6lnqM65gqZGevFop9l57A5lcHqVCdBTG2v9ElYf2l7FjqYhFqAnRWmNJoza756JWXnFDV5JQV0qRLRR58v67sHJrWL3pAnyvipBykpdhW5J/+ORRThyZogZhiZBeTLMFizvvGuFTXzxJsSDJ57O84LqLuOzi7bS2FJBSIqUkk7Hp3riaF1y3l717tpHJwHgl5IMfO0Kt0pCsT3+35YaYBEGo+fzXevF83eR9CGFiVFoF9Oy9Aa01p568K0qeqyR5ftWlbazfaEKisY7YbGFZgqAacuvtw0njKCElbq3KrsufR1f3FnzPYyU+fpYhTFuDuYzk2XsgavnHe59JaGhj1dh+0aVs2rUbr16L6hygXg/5zm1DRuyOxaWfCgnaVbz+VevY3J3DjeS8iRhYD912C1v3/jROtsj/Z+/NoyS5rvPO33ux5J61d1fvezfQDaABkCBIAgRIkCIlSqQo6cgcW5Z05GOdI8u25sxIZxZtHnkWWbJGHlmyfaSxSUriSKRIiTsJkiC4ACT2vYFe0PtS3VVde+Uay3vzx4uIjKzqpbKW7mopP54+TXRlRUZmRLz77v2++10dhgubDF3B2LjPH3/snDmeuDGlrDgABg3Fxz89glYax7F48J37WT/US63uEYYq+a6U0nhegO8H3H1wB/v2bMGxNYffrPLZr4xh5W58BrVYtL5vk3088+IMz740syD7AEHoN+kZ2sHmfe9i9OTzVGcutXWeZxzB+x7sN8dlcdkHtPd+HH6zwtGTNbKZVlnVchwOvvsDiIhQWexxu1gdmAyks0rTNQNI+kChWowquIsbjTAMyBYK3PPIB40EUsSzqi2efnGG8dEmrrNyPSFaG8mr52t6hzL87E9uwA/iEagK28lSnTxLZfoitz/wTwj8Boh2h9ow6g958tlpPvvlUZyCveoLcbyY2jnJUy/McPREDdvW7N2ziY0b+mk0PSxr4eMQCxA8L+CuO7fT21vEdeAzXxrl1PEqmezaGeM7HzFvpX3FZ740akpuLPRJ01qz7a73Y9kuI28+bX7ZuNGYzvNdeQ4eKBryfJHS3QW9H09Pt0YkC0HQbDK8bRd77rkfr96eqXZx86AwQWSxuE4G0jpQ0GFk6mJ1kXRVR1Pc7nzXe+kdXE/o+0BkbTLm8b2npxKHXliZHX68MPnVgA+8Z4C3HixTrYfRDBCF7eY48+o36N+0ny37343fMHzI/P6QXFbyiU9f5MQxM099NTmFpJSj4Knnp/E8RT6fYdfODQRBuKAxcr4NilKabMbh7rt2YNtGUfb//d2lpOqylha/dPbh5C1+8NwML1zJrl0IQr9B3/AeNux5O5MXjzF18RiWnUnI8zDUPPJAP06hM/I8hm0L5qZ8nkr1fshoONqBBx6h1N+y4+ni5kNr6OQyXzOApB+qTlObLlYf8SLge02Gtuzg9rc/RDPZzZmH92uPT9CcC1aETId5TWcapCP5+Z/egJOaJS4th0Z1krOvPcbtD/wTin0bCYJmEkTiBcy2BXPVgD/+2DlCXydDqJLPtsKwLUFl1ufVwxVsWzPQX6ZcyhFG0sUrlWjTzZSeH7B1yxCbNw3iOppv/2CKp5+bwV1QFrr50NrYtQf1kE994VKyFWzLPkw9im13/hBOJs+lE8/iN6st8tzXDA04vOv+XmgunjyPjx/PlHn50BznL0bOu0TWJaUyBx9+P2EQJhlqt3x186G17mijcJ0SVsrmQV/537u4eUgeOkNycN8PfwQ3m0MrlZDpx07WeOr56cRGBJZ//VqlLPDqIQfvLPHgfb0pnyyF7eQ4f+R7BF6DAw/9fGS82N6DEYaaQt7i+Vdm+dQXLmHnV49T0BqkLRi55DE5HSAFDA2WsS0r2Rhd633TP7vzju1kMg5BoPirz11CeZ3PxFgtpLMPOyf57lPTvPpGhVy2JaaINx6BX2dg8wHW77iH2vQol8+8soA8v/+eHoY3ZvH8xZPnrf4YQMETz05HsmdjnOg16my9/S42792f8HZr4bv7h45Ycr2CJawWgrDVRNjdKawdxGR6s15j18H72HHnvSYLETLRtHzl8XHwYqJ75dC6DwQ//aH15FJBSlg2zfos5w5/l/U738r2gz9spKGi/SSUgkLe4pN/e4mjb8ytWilLa8AWXBxr0vQUtmNRLOYW8AFX+5zxwuz7IUODZXbu2IBtaV47XOHxJydx1lAW0so+FH/31bFkvnh79qGR0mbbHe/DzhSYOP861emL7eS5K3jkwT7zuyw++4jhOJKxS02ef2U2mXuOMM4Fdz74XjL5XNL70cXagNYQdNBLuOglRXebCNc0tFJkcjnu/5GfiHaYpvkzl7V45fUKh45WcbLWikh6YV5ppxFyYH+Jd72tlYWgNZZlc+nEM1SnR9n1lg8xsOl2fK+eEKaJNNgS1Gohf/rJC6igfS73ii7GQlCpBvi+wrElhXwGpVvzTBZ5CMJQcfu+zRSLWbQ2zXmNuXZZ783A/Ozje09PcejoVbIPr87QtoMMbD5A0KwyevoltFYJed5sKvbsyHNwf6kj8jxGPPf82ZdnGJ/0E3PPMAgo9Q2y/x0P4zc8ZGRd0t2Urh2oDq7z4gPIkk6li9VGwklISbNW544H38uGHbvxm0b9JCXU6iGPfW9idSS9qQf/xz8w1NppopG2S2VqhLEzL+NmS+x7x3+HewVpbxhqCgWL516e5cvfvLwqqiwNIGG2EhIECikljmMvKgOJfx6fbxAoensK7N61EdvWHDtZ59vfn8JawTLhUhEHZL+m+NuvjLUpreLPgdZYdoatd7wPy3apTl9icuQIltMiz/1A8e4H+nCLnZHn8ftICXiK7z83nfzMuCfU2H33fazbuoPAa8IayNi6aCFeHxaLDjKQpZxOFzcSYeBRHhjk4Lt/GL/ZQAqZSHqffG6GyxGRCSufhfiNkDtvL3L3gaKx6k4d/tKJZ/Hqc/Su38nOez9EGDQXmChpBZmM5C8/e4mxC43INn4FS1mm5QHfN53opp7f2SHaPm8QsmfXBkrFHGjFV741ThAZRN4MXDH7OHKt7OMu+ob3oLXi8plXaNZnEdJY4wWBZqDf4aH7O7Ntb52LKV+dH2lw6GjVbCo0EDUQ3vng+7AdZ9HBu4sbCN2eLFzvGenAC6sbQdYqkodbSALf5573/AiFnj7CsCXpHb3s8cSzU4hMy4NqJS+pUiBcyQffOwhEN2G0052+9CZzk+dRYcCW/Q8zsHk/gddoK2UpbaYYXhxr8uefHQFHdLwbuhZMNzb099q4jkRpzVK9FUzWpCiV8uzYPoztwOvHqvxghcUKnSLOPrxqyGe/Mpr0sMzPPoRls2nfg0jLImjWGD39IlJaES9iyPP7DvawcdPSbNu1BlzJc6/MMj3Tsi4JAo/eoWH2vvWdeA1z/btYe1ixRsIubh0kJG+jwcbdt7Hn3re3SXotS/CtJ+f7Yy1/gYt3j1JC2FC8/d4edm/P04zGnQpp4TdrjJ56wRD70mLPfT+F7WQTy/f4OEpBsWDz6OMTvPDCDJlVIKYdR2JJEwAaDQ/Z4e43nYWEYcjOncMU8hmCQPHVb42DrxE3+Kman3088cwUbxytXkV51WBg0+30b7odrTUzYyeZHT9r1FeRbbttCd79jl5T8lyEQi19HhCVr5qKp1+YMYFbx+WrOrvvuZ++9RsIon6lLtYG0saa6UdNiGs/d4seadsdZbu2ka5127bNve/9UWIzw1jS++bJGkeOV7HdlfHHmo8g0OR6HB56e6+RfQpAKyzbZez0SzTrs6gwoHd4N1vveC+BV29rMEwWZqX52KdG8Ost23Tz82WcnACUply0kJYJIPWGh5BiyccNAkVfT4HNmwZxbHjpUIVDRyq4KyhWWCzi5s6wrvj8o5eN3QxXyD6kxdYD70NG5arR0y8R+k0QZixy01Ns3ZzlnjvLhI3OlHuxdYnjSM6ONHjjWLp8pZGWxf53PIxlW13rkjWGZCPH/BLWta/Pom6PuNzRvdRrH0bSW+e2tz3I+m278COiMibTn3p+GmyzGK8Umd7q3AZ8xUNv76On3CJfpeVQmxllauQotpsl8Opsv+v9lAe3EvjtDYZKafI5i1cPV/jqtybm9YYs/VwFgK/ZtjlLf6+NUoJ8LoNWej4ds6jPa/42pd2dO4bJZGyqtZDHnpg05oSsrFjhaoiDbtx1/tQLM7x2pEouFcTas4/9DG45gAp9vPocE+dfT3o/hADPUzxwXw+FXocg6Ozck7JoRvLcyzNMz6bKV75P3/oN7Ln3/qR81SXP1ybaL8syM5ArH7SLtYww8CkPDHLXQz+E32wmO0PXlTz9wgz11IO9UkgkvZ5ix7Ycdx8oJXMnEAKlQi6fe9VkRCrEzZXYfd9PxL/dthM15yr46y9cYnK0ieuKSG679FKWlAI/UGxYn+UPfms3//SjdzE02EcQLq8PIQhC1g31sG6oB9vSPP3CDFMrOJN+MdA6Khv5mi9+47IJilyp78Ni24H3IoRE2i5TF49FM89NNhKGmmLB4uG390HQGXneVr5qKDNWOV2+atTZc8/b6Vs33C1frXF0wg12IOPtRpC1jhaZLgiDgIMPv59csYQKjc2760hOX2jw+pEqVlRaWKkSQlJCU4AjeejtfRH5SlLGmhw5QqM6jbRdgmaNddvvYXjXfQReHeZlIRnXqHj+5oujiIxljrsMxMf2fM22nTnuur2E5+tkkVz6ccGyJNu2rsO2jf/Ysy/NIlNDk1YLLQECOFmLlw7N8eJrc+Su4HkV+A36N97OwJYDhH4DAVw++wpKBUn5qtFU7N9bZM/OPH5zCeQ54NiSkdHmgvKVZdnc9jZD3HfLV2sbq0OiRzLILtY2EjK92WTDzr1s2LkX3zNZiLF5Vzzz8kxS7FzpMoKUoJsh995ZYrC/VQaR0qZRmTD9BrZr3lcrdt7zQZxsCa2CBYR6Pm/xpccuc/LNyoq43ibNsL7CdfxUfb/z46XLWEGg2Lixn1IpS6gU33tmCjydHH81SzXJZ9Kaz311FM9vt1WJrW6EkGw58AhS2ghp0ahOMXnhcGScqJIy2AP39SKzVir4Lf6h1wrISF56bZapGdNYCaZ5sDy4jl1339ctX/09w6IDyDJL0F3cYKgwIFcssu+t7yTwvIhnML0WL742R2MuTMpYK/0w+75maMjl4P4SjWa6jKUYP/86WiuElAR+k/LgNrbe8Uhk+95OqNuWYHYujFxvl3euZsEXUeAU9JY0tr0ypVmlFMVCjvXr+rCk5o1jVUYuNXFsuaqPTFKazFm8emiOp16YNY676krZx20MbbkD36thOVkmzh+mXplMyPQg0PT12Nx/Tznp/TDvcf3zaAUrwFc898qs+V5Fq3y186576Vu/MXGL7mINo4NEoYMA0k0/bgWky1gqVOx724NkC4VkWqFjS85daPDmqSrWCquxWkowwJa8/d6eaDdMUsaavnQMrzaLkJbp6vYbbN3/CMXejaggDnQtWWohb/Hdp6Z4+dVZ3PmlmaWcI2an3VNS2JZCqVZg6vSY8XcdC0w2bezHdS0mpnxefG0Wssa+fHXMIdvP90vfGE+C9YLsA9h8+8PIWKqb8FHGukRKU7668/Yimzdm8X21qMAxH44jGB/3eO1INeVIAAjBbfc9iOPaqKgW2V1P1i5kBxFkUQEkVth0E5BbA2YBlnjNBlv2HmDd1p1JGcuyoFoPef7VOYjGi964MpZFozLFzNjJSPljMqVsoY9td/4QKvTbdj+xNNXzNZ/+4iUIO/Otmo94zVIasq6ir9xZn8OVEIsHglCxbqiXfN4lVJoXD80lO/nVKNck2UdGcuJkle8/P00+amJsKdYEYdCkZ/1OhrbeReDVsSyX+tx429yP+MF+4L5ecFOGmIv8TuJgL1zJ68eqTE77rbnngU+pb4A9b3kHXnfu+S2BTh6F61/NeDez1LPp4oYj6d0JAgo9Pdx23wP4zUZSxnJsM6Mh3VS40otcEGgGB1z27y0mI2+FkISBx+TIEeI7SghJ4NfZuOcd9KzbSbhA1muykKdfnOWpF6ZNFrLM5kKtBW5GIKlGnEHL52qpUMoMqBoa7IlceqtMpkwEV9ZZODXtzxY8+vgEswtmvphynVaKzbc9hO3m0CrAcjJMXDhMszaNsKK5H4FmsN/hrQfL6Gbnrs2xeScaXj40l3SvC2mGnW257Q76hzdF6qvuSrImkW4ebPvBMvtA4uN2U85bC0kQUZp9b30nmVw+KWO5juDk2Tqjl5vYtljRzLL1vkBGcs8dpbZhRtJ2mBw5Gg0uMrefViF2Js/2uz7Q2rCkOA8RfY6/+8oY2tfLmr2hlMbJS7795DR/+J9f4Nnn3mgLSEsvY2ksS7JuXS+2JZie9Tl+uoZwV1btFr+nBjKuZHSkyePfnySXnT91UhAGHqWBzazbca9RXkkLFfiMn301ebDj8tXdB0qsW+fi+51Jx+Lvy7YE9bmAl1+fM5MHNQgEKgzYc/f9ZHKZxLq9u5asQaQ6ztOXZ9md6Fc6aBdrH/GcEL/ZYPPeA5QH15nRocLYmszNBRw6UgV3der0QgBNxT13lCgXraiMZWZQ1GbHqE1fivoPNAhJ4NVZv+Ne+jfelsxRT8tU8zmLF16d49mXZnCWwYXEu/ZzIw0qNcXo2BQzM9XEN2opSFyFA8Vgf5ls1qFWCzlyvAqWWFG1W5J9RAH6Oz+YZPSyh5Oae9/iwHw27n0nmVwZpULT0Dk3zvTo8aiEGDUIC3jb3WWwZcclvbiUZjmS42dqXBht4tjm38IwIFsoseue+wj8EBFZt3exFnHl67IinehXP3wXax0qDCn09LFl3x1JGcvU7DWvHp5bNq9wNRh5q2bLxiw7t+VaPRdSEng1psdOIi23VTrSGmk5bLvr/SYzSZWU4iwkCDV/99UxmDczpKPzihru9uzIUS7ZeF7I+OQclrV8+5FQKcrlPPl8BkvCkeM1VD1c8UFeYDYBXiXgsScmcRy5wFlAhT750hDDO+8zViWQ9OI067NJ+SqIylf33rn08pXWgCN47XCValQWNQIJj6Et29m4c1/CwXXlu2scgjZ/uOs9Dt0M5O8pklJSGOLmXLbvv9vIZzG7w4wreeNYlXolWHEeJH7vMNTYecmdtxVTyh7zs5mxk6jQXyA3HdxyBwMbb79qFvL8q7O88MosTnZpWYgUoH3N7h15SgUbP1BMTMya72apQSkV6FzXpr+viB8EHD1Ro1YPsZaR3VwJsWni0y/M8OapGtlMS00XuzKHQZP1O95CrjyUfM8qDAz/FJW4Eui6m9wAACAASURBVPXVbUVTvupkFB0Lu88PHamYzxr1nfjNBrsP3ke+3IMKAqBbvlrrEKw0iR4fWKxsrbyL1YcQItrxh2w/cJBsvkgYBmiN6Zq+7HFupIFlL91Q8FrvDYCG/XuLZGJZp1ZIy2Fm7FTEg1it12tT4tp64BGEsKKFqLU4S2l6TD736BjMs+voBEGgGOhz2L0jRxjCxOQcnhe0vdeSPy+wd89mtm0Z4Kd+dIhC3u5oxvS1kMhzoyzqq4+PG/5m3jlrFeK4BTbseQdahYBASJtmfYbp0eNGzqtV9P1G5StHJt3+nS7ytiWYmPI4erxq5rho0FphOxm2H7g71VPSDR63AlYlgNiShHjr2prcGkgm6PkeG3bupWdofcSDCCxLUK2FHD4e8SArTPTGC75uKm7fk6cvZa4Yd0JXpkaiRja9IAvp37hvYRaiTBby7EuzvHF06a63SoHMWdyxt4jWgkqlzsxsDcta+rCt1rTCkIH+Hj7yoXv46Ic3oFW7KGA5SEt3j56o8vLrc+SyKZJeA0IQRt9heXCrGd6FjnpwTtCoTJnZH5iSYF+Pzb13lWEJ5au0fPfI8RrTs61sNgwDCj29bD9wt/Fjk93y1a0AIcDq4F69zi3TOlD6oOI6xEoXawNpOW++3Mu2/QfxvUZbienUmTqERoa5Gg94EGr6ex327MwbyawwASTwG8xePo205k2m02YU7pYDj7Qa4VL3npRQayi+8tj4kkb0Jq7BgWb/vgL5nEWj4TM2No2ULS5hKWiV20IaTRifIlK3LH/xbJfuSh5/cpJKNWyX7ppUBGk5bLrtXYjY0z3S2E6OHI7KWRIpjHX7vt0FhpdQvorPCQApeP1YJWlkNOUrj027b6N33QaCoCvfvVUgAKuDjcQ1X5qWcLXtTrr3wi0FpRRuxmbz3v2RU2vLnffN03Uzd2OF6/RpOa/IWezbVcAPdOre0cxOnE3q80ArC/HqDG25k97h3YR+g/QCrBTks5Innp3m3Nk6mQ6zp8T+3FPs25VneF2GINBcGp0iDMMkfV+KnDf+XFJAEAoqNdnqxF+hh8ZxBNOXmzzxzHTS7d0KeoLQb9I7vLstgxPSwm9UmRw5GvmQmbpXGGruPlBCpCTAnXyPYMj8oBbyxrFqQuYbRZrHjjvuJVvIocKufPdWQLwBsjsYy3zNAJJ+hmwpukT6LQpTxlIMb99NNt/qB7FtwfmRhukcXoWGwmTBCDV7d+ZNuSXiQSzLZfbymWiolNX+O1phORm23P6wKZeK9sXHtgWTUz7f+M6EGX2rF5+FxMcJQk2px+Gu24sEoWByao5qbXmd0mmDxTCEWl0mceN6evrFQCmQWYtnXprlfNt8+1i6a7iHDbvux3aykbuhRlo2lamL1CsTCecUhppC3uLu/UXwW9btncKK+I9TZ+s40bVQKsTN5tm6/y5UaDYs3eBxa8BkICsUQNoe2m4AuWUhpCD0fYa37yaTLyY7QksKqjXFiTN1cMSKT51MeBBfs2dnnlLBmseDTFKbvWwsviNezWQhktBvMLTtbspD2yMZqkhKOFoZU8jHvz/JzITf1gOxGMRcAhLuu7uM60pqNY/R0SnsVC/E0j5z6/9X6yIZ5an18lyEISbPFU88PRV9kPaAr8KAXHmIoW13JVMGdVTSmh49TuDVIg8y032+aUOGndtyhH7nUu4W/yE4crzGXCXEli3+o9jbx5a9+418t+u+e8tghTmQ1AulWLLqpYubh9iFNgwDin39DGzcQhgYxZGUUG+EnD3fgFVUYoURD7JtUw4/iGdwSEKvwdzEOaTltPV9gFESudkim/c9aJREosVfaCDjSM6NNHnq+ek2+/HF3J9xYFNNxd0HiqwfcvF8xfkL45Fl/OKPdbXPbM7TgWwrOC51F56Q547kzLk6L79RScpXyTEj6e7g5jvIFvtRYSybNfYx06MniJ5ghIBmU3H3/hLZok0Qdn5+yXcjBEeOV2l4Lf4j8Dw27r6NYt+AEW0s47N3ceOgMTL3TqrZi1dhWd009FaGUopMrsDwjt0EXjPazhqcG2kkzXmrgTDU2DmZEOlxuUSFAZXpkeR1bX5Uwti9r9t+L/nyelTgJ6/RWseCI775xCS6ubRmvSDQ9Pa73HtHiSAQjI5NMzOzfDWWKQ/CU89f5qknpyI/rKWPt03Ic1fyg+dnmJ65wkTJqCy4fse97b5G0sJvzDE7fjqS7+qE/7rjtmLMry+5fBXWQ46dqOFGGawQJtvddvtBsvlcYl/Sxa0BS4qO6LpFP3aW6DYT3opI5msohe1YbNp1G0ZEZ+wwclmLl1+fY3bKX/Ext5BamKRgx9YsduwAjEZYNtWpkWQWyIJzD32yxX7W73oLYehBZIUR78hzWYvXDlc4fKyKk1m8pDd9DAQ8+LZeshmLWt3j3PnLWNbCzu7FwgQPm0ujk3zhq2/wb/+f05w530gI5k6RJqv9asCTz0wb/7K284t8rwa30ju8x0h3I0WbZTnMXj5DszqTyHfDUFMqWNy+p5AMvupo8mB8ThKmZwNOnaub2Scx/5HLs2Xvfkzi2N143jLQJoCIlbZzh/jAXdyKaO3qScpXYDaqlgWT0wFj4x7SWvkyVqx6wlfs2JqLBh7pSG5qyN3AayTGivH5xn5OWoUM73wbjluImuJakBJq9ZDHnpwEi44W/biMFTYU995ZZtuWLL4PZ89dxvOCZXljSSGoVRs4tiAM4dLlpikRLuFYcaCzXcHxU3WOn66RccW8znOBUgHDO96Ck8knu36NRkiL6bGThEHLxsbzNdu3ZBkacJLy1VLOSTiSU2frzKXkxCoMyRVLbNx9G4Hf7f+4laCJK02L31B0ZGXiWN1u9FsJcUlIK4WTyTJ29ixPfv6vcNxse+ey1jQaKkldV8MXSwWazRuylEsWEYdv+gUac9TnLrc3FCbnEDvKbqFvw75kZ52W9GYzFj94boapeYaCi0UQarIli/e8sw+tjRrrwsgktt3KaDrOQiAVEJdnE5/u/XjmpZnEa6rtNSrAzZYZ3HoXYWD6PCDiP/wGcxNnEdIyAUWA55vZ53Y+1dzZIf+hNOAIjp+qUauFEf8hCAOfoS3bKQ8MRfxHNwNZ60ieOcDpcOO0qACitUYKgbtMhUoXNx7xjtx2Xb7+if/E5XOncDIZtGpNhvN8zcS0D3I1iXQz12Pb5ix+EPMghuCtTl00ASTFgbSsUBTSstmw+20mtZ5XpnIdwchok6dfnJk3y/v655U0FfqaRx7oZ7DfxfMUx0+MoFTn3lit18Y2K+Z9XMc09HW6jM4vXz33ymxSKkoyrajnonf9Lgq9w2YoV/wZpYXfrLYJFbSCbEZyYG8hkYwtZYGXAmgaBZ+Q8TWT+F6TLXsPkC201H5drG2kbYecDnpAoIMMBMBNZSDdOLK2kc4+csUSr3//2zzzlb8lV+xBK5XUOaU0ipzRy15SZlmNHaNSGisr2b7ZKLFkTKQHPrWZMZNZcIUFO1IXDWzab4wBVWuBTL/2Oz+YarPjWGwZS0Qd2Zu2ZnnX/T0EoeDS6BQjI5M4jt1RFmK+N/O9e15AGCpcR9JTtlmqRlprsB3B2QsNTp2t484vX0UvGti8v9UkaP4xKhGO4DXmkqwkCDXlosW+XXn0EviPGJYlaDYUp8/VcZ0WryUtmw0795rjLjP76uLGQgiSAWjmv69/7ToKIE402wBAdFORNYt0GUhaFvXKHF/5f/8DSqvWYpjaXWtgbLxprN1Z2oJyLaSJ9E3DGSwZBQvMzJLa7ChhNA/9ip8nDMkUehnaeufCiYUaclnJa0cqhsztoDM9yXYAAs2H3z9ET8nG80MOHz3fcRYSl93Q0Gh6KK2xbUFv2UaHLXnwYhHx4OBKXj1cYa5ypfKVwskU6N94W1S+ajWESmkzO34m4phM/0cQaLZsytLX6yTlq04QK+AsKZic9rl02UuEESoMyeTyRukXzf/o8h+3DqQwSUKMxVy76waQtmZCK9VM2N1ZrGmYRq+QXLHAd/7mE5x+/SWyuQJKhSaItJwxsS3B6LiP9jo31FsMEiI9al5rdaRjlFgzo4RBc8Gi39plm2BRHtqBlHa73BezG56dC3n6hRnjKqs7bYozWdjuPQUefkcvni+4NDrJmTNji85CWn5e4Achs3N1QNDXYyeGh50iqlCBp3j1cKXtveJgpUKfYv9mCj3r0cpwDuY8JCr0mB0/29ab4vmKPTvyOHlrSfxHol5zBKfP1qnXzUhghAkg+XIP67fuJPT9jo7bxc1F/Ey5druY5Xq4/kjb1EPjWN1u9LWOlnOtIpPNc+7oYb796Y+RyRfaNfmt+IFlCcbGPZqebhsms6KIAsiWDdmoCc7kIFJYNCqT+I1KYrMx//MQcSU963ZiZwpoHX+OlsGgbQt+8Pz0kua8CyGSLOSjH17PYJ9DEGhefu0U9YaHZbUcga93TCkEnuczM1MhDGHnthy51GK9WMTvY9uCmZmAN45FVukqzd+YXpr+jfuimeet62sa+hpUJs9H/IdKvqfd2/PJBrDTRb5F6gtOX2hQb0QEOoZAX7dlh+E/uuNrbzlIQUc+WNBhBuLaArkEVUoXNxYt6wvBox//EypTk1i208aLxLt6rU0AmZzyzdCnVchA4gU6VJreHpvBfiMfFYKEBK7PTWA8sdpluElJRoVk873kSoMROStAt/o5MpHM9eSZOrbT4gkWc26m3CNoNhVbdxb4xx9ZjxfA7GyVl185mSiyrhZEWv8Olm0xPjFLpWKkybftykOm87no8fGkIzl+qsbkFfp0dOQZ1je8tzX3Iw4MUtKsz9CoTieKMKU0+ZzF7u25xP9qKc+yjIQHFy42kw2lEJLA91i/fZfZrHQJ9FsKGpMgdBg/OiXR5ap1K3exfLQR54USh578Ni9/+1GyxZIhzoVEhT6F3mFsJ5cosaQwk+lm50z/w2ptD5TSuK5kw/oMYaCj6Wem1FKvjJtGN31l4lVrheVmKQ1ubU0yRCeZlGUJ5qoBz786u6QyVtwXEtRCfuqD67h7fxE/kBw/McKRYxfIZp2kdBQHkXTPSfxOQsCZs5fxvJDess1bDpahoToewZuWyh45UaV2hdG4KgqqpYEthKHfkjhH/Ed16mJUGjS/GIbQU7LYsD5jTA6X+CxLKfAbIecvNhLvsJgwH9q0HXMZuwT6rQStwbVlx7L1jgKIlMZUsZt/rD0sJM5nefTjf2webFq7eCdTZPdbP2IM7hKPJmOuNzUTmDtilS6wUiAzkuEh1zSwJUqsgEZl0jSdsXChbWUJNqW+DeazxP8uWmUsyxI8//IMqhF2pMZKS4eV0tiu5Fd/aRvloulZee75Y7x5fIRs1r3qMbVSuK7N6Og0585dJlRmlO+OrbnUON/OEI+KPXG6njQ2puW7KvAor9uBky0mmwHz3WiEZVGduUTotxoI/UCxdZNp5lwK/5E+r3pDMXKp2XJxjqxy1m3dTuirjheiLm4OWutGO4G+WCw6gMS9IJklWjJ0sfqIifNsocCTn/8rTh1KE+eSwK+zZf+7Gdxyx4Iyke8rpmZavSCrocTSAJZg/ZDb/kMpqM9NtHXJp9HidQKKfZuxnWyr3q9b5Z5MRnL0RG1J1iFtpayGYvvOPL/2S9sAs3N/+pkjvHHkHI5tYdtWdF6t78l1HRoNj2efP4bnhziO5KMfXg/WFWS3i4QlBbV6yPHTxmtKq/RxzPmWB7fNk++ScCPV6VHzOvMV4wemA93KLb5fJo04UEtLMHrZiwZame/BWLjnWLdlR5sarIu1jfR1cpZgZdRxxTubejC7O4y1gXTpys1kuXzuDN/9m0/gZnORdFcSBE1KA9vYduf7UCrAzZUx8yJEsrhMzwSdWXF2eo4AgWZ4KNMahoRGSovG3Pg1pbwg0GFAoW8DdibftmDGmZRtCWYrIS8fmoPM0r2nLEvQrIQ89HA/v/4r21Fa4/ma554/xneeOMTExCxCCDIZm2zWxbIko6NTPPb4K0zPVKjWFR/5wCB3313Gq4cdcw3pjGr0ssfEpG+I/LYXKWwnS2lgC1oFbZ5TcQd6dfoi0jINmjpS223ZlE06GpdmFglYgoujTZqeSpH5IcW+AYq9/Yb/WELA7OLmQUrIdqjAArAX86J0OpqxW0qs7g2ydhCXNizH5Zuf/FMmLl6gUO4lDAOzKGvFzns+iJMtEXg1MrkylYlzYJP0B8xWgmRM7GpcWyGAULN+nUvGlYQq5kHMjHQV+li2y/waWotzULjZIsXeDUyOHIHI/kToWJZs5KQvHZrjJz64rq2M1QmhDlGD5VzII+8ZxHUlf/inZ7k84XP6zBgXL04yMFCiv7+EZUmmJitcGp3CDxTVuuJdb+vll35uM2Gz5TzcmazYlNJwBKfO1qk1FNl5AVGrEDtToNi30Vi3p97DjJStUp+NeCU0YdSBvml9BoLW/I+OiX002IJLl5s0mopMRqK1IAwC+jdsws5kUEtJb7q4aYirS1lHtv3bYu6NRQWQ9M2fdWRCCHYDyM1Hq7wTkskXOPHKczz76OcTKaWQkqBZZ2j7QYZ3vY2gWUPaLk6mMG8XD7WaSiw3Vvr6xv0cWmmG+l1cV0a+Tq0FL/Dq2G4WHV55AdJKYWfylAa2MH7uELZrlFhaGJ5HKci6kjeOVZmc8unrsTs2C4wXeyEEUmqalZAHH+hn55Y8//WvL/Dks9PMzPnMVSc4fXbcfF9SoJQkk5H81AfX8a9+YYsZ9RqYklin32XyvEkTQJqeIp+VtD6Kud6F3mHcbLHtOhIZKDaqU4Rhy7Zfa20CyIZsEkA6RZJFhppLY17bdxYGAf3Dm3CzORrVVud7F7cGbGlUtjFWNAMhHhqAyUCkjHZIXawJmAfblLAe+8s/pVmrkouUV1oZ9dKue34s2UEKKbGc2FBRRGUkqNZDiMjt1YJSkMtJ+npsZitmroWOavbN2jS50gBGytt+E5uF3ZRMiv2bEJZN3FEfJx8QSZJnfA6/WeWBB/pQV+jevh7SQcSyoFkJ2bghw2//2k5ePTTHt38wxetHq0xM+eioE/7O2wr82A8Nccf+IqGvlxw8WucA+IrLk36ijGtlR0ZNV+zbiOVkCJr1VmOvNhLeRmUSFfhIyzYZZqgZ7HfoKVmEankEum4qxsZNB7ohWIxKrn94k5FQK41chdEAXawOlDYD2qwlxPxFlrBI0mfHEriWoB6VH7q4uYjrz/lSmRe/9RUOff9xcoViItv1mxW23fk+eof3EDSrUa1cYjkZoHUNpRBUq2FiZ7JaiOWCgwMOJ8/WU5/Bp1mLehZSAaH1e5HyKPQpDWw1mUokDoheAYjE2+vQ0QoPPNiX/G6ni2W67GRZAs8zu/y77ixz111lGpUAz29xFcWSBUIknEccPJYKKQWBpxi93EysQpKeGDRCSAo9wwhhXHZl9D0orUwGUpkkDDyk7SJQhIFmw/oMriuXZGGSnJcw5puXJ31j/w+gFI6boWdgnZkBIpYeOLu4cUj3L2VsSSzM6AQdxZz4DduJ9I7er4sVRizbrc7N8M2//FPSK28Y+uRKg2w/+MPGpVXE2YaF7WRaN4smyUCW0x+wGCilsVzBYL9LxLUmgaFZnUkWxCsvPlGwLA+RLfSlHIUhnjmuNTiO5MibVcKa6jj7aHu3eMGO1FlCgFcPadaMyqpYsCgVLfJZiddQSfBIB5+ldHqDuR6VmuLcSDMZIJW8RiksJ0uhbwNKBW19KQJAaeqVSZINgoBAmQxEukuzVUm+E2nMJyen/LaOf9vN0DO0DhWqVc1gu1g5pO/NNP/RyT27pEJlOoAI0Y0gNxNaKbKFAq985+ucfuMVMvl8ZAIoUYHHtjveS6Fn/QKFk+VkUztaswg3mqp9zvYqQGnAlQz2OS0SParpN+szyeJzpV4Q8+8K281SGthKGPhxBCLdWe/YgtPnG0xO+x3bmsxHrDJqLexGtaaUJgyjP9Ec9XSvxlJkuzG0BmkLLlxsGKns/J4WrbGcDIWeYXTY8r8yJywJQy/qq4mCMUZwN9TvGsflJT6yWpvPODXjU0+aI824ZMfN0Ds0bEQb3RkgtxQsCdnULJ1VaSRM3xBpJVZ3u3FzYWSnMHnxAmHgI6WFlBaB36Q0sJVNtz9k3FhTzqixBUa82weziHueWlVuK7mHNPT22Fgy3cEt8RsVdHjlXpDW51VIy6XUv5mFta5WyWm2EnDsVA3hiBUJiumAkPaiSv+Z/7qlvo/WQDTtr1YPzZxq4s8g0Dokk+9tkzO3NUMGPs3aVKS+M0HbcQSD/Q5ECcKSz9ESTE0HyUwXRNREmc1R7BtIJLxd3BpIFFj20kQPHTUSxsg60lhyd+tXNx1CSrxGwL63PUi+VKZRmyUMmqjQZ+c9P2pUOpEDL0TLrdbYdqbFN2Ce+WYUQFbr+U+XWHrLdtLsFxP7Xn3WSFKvsikxnloCrQJKg1uNECBlIBizN1JCo6F482QNlpmB3Ggk5xlqTp6tG74i3UcijOttoWd91EA473NFijyvUYkyTlPWcx1Jf59jOK4O+1Li89IRYT4zF0QzXUSSPRZ6erFsu1vTvsWgMf1Bnc4BibGkDCTnSEPsLfptulgtSClpNursuutefuY3/oC+9XsoDWzlzkf+OcO73krg1ZMegWQHC5HzbUtdJwQmA1nVixplrgp6exycVDlFCInXmEtq+ldCi0gPKPZtxJnXUChiI8aI2D55po5uLPSQWuuwLIFfDzl2oobrtOabxGo7pQLyPetNR75ulzwLBGHoEXj1pGSptcaxBT2l1mCrpYgKAJCC6VmfIJECG/Vfsbcf27lCQOtiTSJtYVLIyCWb5C5Sxtv+xlIK8o6k6atkd9Sted54JLX2aHb4wKaD3Psj/wOWLbHsLIHfANpr+NFvJgOGov80AcTXptyzSuebqPmUpq/HwXEEvq+jedoSrz6HCkMs2zS/Lfz9eQ2FfRuZHDkM0khV9Twe5Mz5Bo2GwnWNffxav0/Tqq+xcY+zI43EWRjSZSpp+ni4QjAQkqBZNZlZwicZG3czGXFpFSato3cTMDsX4sfNiMQZUR+W4xB4Tbpl7bWPFqcIeSe9w+rs2i15b5bPyAU3dhc3HkaFJWhUmxx96ViSZQReSyK7YOHU6QwkyUEIw9iob/XOVwjQcQnLlm0ZSODVUKF33ftJqzCx8QijLuz0rGWNWTDHJjwujnlY9uLt3W8mTEMoCFdw7GQtItBbvBFE/JWdIV9ehw5DU9LTrQ8vhMBvVBN7dzC9N9msUY2pZWQIpvwI1VqY3CLm/c3YZCvuzeniloElIeem5/Csoow3/QDmXXnLlQb+PmH+DIqzb16kVmkibZmUeq6lx5fSatuKxs1mN+L51wpcV5DNxuUZTG1fKfx0U9wV0PosgtLAFizLMduoqBqnRWsXP1cNeeyJCYhndmu9Zhe4tnMTgsNvVmmmZ7yLlADCdsn3rDNDm9KkvY7ECM0qKhIjCAFKa8pFOxqMtfRzFAIINdVqSEKfRdctWyxh2d0RtrcStNbYUpB3l76Qd9wHEiPXJdJvGlpqTo1lSaqzdc4cu4hlS2J32rQq5wpHQAiZ2LzHUArTB7La548h7koFK6X6MuR44FUT8veKv6t1JFX1KQ9uM5P44gmFAuOLpXVia/LYE5OMX2riums7+4hhWQK/GvDSoTlctzXXROgoW4zsXDL5ntbnTqCTAJLOQLQy3fKWXKEAUg8XZLS5QolrXLYu1iCUNs9Iegphp1n6kjMQxxLk0g2FQHS3Q1wv7QaXFUPbDlUrVBgSBiGWLTlzdIR6pYFlLWIHGFV74gwk/XqtdTSnY3UX27gmb0oq0WkJgVYhfrOWyE+veQwVkC32kysOJNPvRGTLEt+nrisYudTkse9NIjItC/O1uOmJs0k7NVnRXTBZ0XxHufIQ0nYXPF9xRhAGflspUmlNLmstOwMB0//SiDMjHZdQJW422ypHLu8tulhtpAj0OPtY6jOxpAwkLovkXWGsEwgROgTMqFRQrf/WamFQWYMP8FpGuqtZKYXluBT7eigP9FCdbXDuzYvYjtVRnf9mXoE4cyoV7ZRs2MhBY9XY1dDWUOjkkgmF7b/TGjLlupKvfXuc6vTCkbBrBfHmQGvAFnz3qUkq6dnu0f9iiW6uNLhgBkgLpk8kPSxMa8jnZHS8ZXAgEUfT9BTpIwlp4bjZpC2n+3SvcUTPihSQT/EfS+EIr6/Cihb+1sGjACAkOddFS4tAWOhIMigI0VjR/zeBROCbv7UiiVmpem8X10ccPDK5HJfPn+WV736DUl8vVmYbnqewrOsfA6DlthvPRW+3MJBxe/EqQmsQFhTyrQzEqPlU1PQYj6u9Mn8TfxfSsikPbDWvSWcyKX4om5GcPNPgse9N8OMfHiaYC6Kd+NpTZDmOYGbc54lnp419u4p7ZwRaxNdNky8NIi2bwNOpgNra/msVmu8uWua1howro+94eY+cUppmU6V4F42UEifKQLpd6LcGEjVtiv9YyjNxzQASN37FTqhgSLqoHEvRmqPPP4KtprFVBVvPIXWdUBQJRIlAFvGtQer2FgJRBiGRuonQPiDRQiC6geSaaNm1m2FRI8eP8mf/yy8xeuYkliUY3Hw79/7Ifw9kiN1qF3VcpVrkcwQhMBPmVuejtN4bQApyGYlu40AUYdBM7oVr9YPEBozloe0RD9LqyE4vqloLXEfwmS+N8e539lMqtsa5roUgEp9DGGqcgsV3vznO2QtNSkUr6aBPgohWWLZDrjRozDJp57riBVyFYdsmwJQsxYrUliIVdvuxhIjm2S//+F3cGGggZwsybVn5CmUg6ZKJacwKESIa49kcRc+8jJp7Hbt6hB3+DIIAqQNMCUsCCo2FFjYaG88apOLcTsW5jYp7B541hNQNhA7oBpLrI7keUvLV//pHjJ45ETmfhkxdepOx0y+x9ugwYwAAIABJREFUZf+78RsVs7W/LgTocIERiOnHuAHfvwaslAqLVlYUBs2E3L/2Am8aCgu9G8gU+qjNXsZ1XcJolkgr8GoyGcnp83W+8PUxfu5nNuOnspCbiXRJ2LYFtWmfzz86ljRYxnb76ddbtku+PJT0ebSyrVTAUEFSwop+EaPaXv61FURDK7vB4pZE67mAYtZqu3eWcnssCCDtwUNFJQ4L7Y2jRr+Mnvgu2puK7qQMCAuNjbqa2kcrMuEY2eA8g/Vv0bTXM579ISZy7yGQPViqhtAKLaQJIsvNsf8eoXWxQ7L5IsdeeIo3nv4uhZ4+At+YIwopI/sPFgSEa0HN46K0JnGcXW3EZG8uY7WtQwJB6DevUttPvS7VUOhkcpQHtzI3eQGnUMCSIY2mnxgbxg9LLmvxuUcv8/6HBxhel8Hz1LLmdawUhBAEUfbx5a+McexknXLJZEnmvEwWBUSft0yuNGgkvFcoFxkF2nx1Fq1+khU55xU7VBc3GPH9IgUUM8vjP2Aeid4ePMysBR3WCS98mvCN/wl18XPosI6wiwirmNrtthPnaUJdC4ESNqEsEsocbjjBpspfsGfqf2Ow/g20kGhhR0GkVVftwiA99OnJz/0VXqOeXOww9MiVBhnadpDQb7LoKXBxnXzeNtKyogCyil9/m4V0VrYZKiIEYdBMyjPXQ2ysWB7YilYmIOzZuZEwbJ9DrrXGcQTjEz5//jcX14Q/VjpDymYkoxfq/M0XR01WpkhW6dZ+ysic8z1DWE7WiFM6QNxrs1wIQTLgKvVhop6U5R+/i9WH1hrbEhSz7fzHUpAcIV6oWsHDQjcuEh75TdSFT6LDGsIuRYuUMq+Btj8I0fb3vC4DQCfBJBuOsHX2v7B95o+QuoESLkKH3SCSQrzTzuRynH79FV7/wbfJ5AuGvxCS0PfYtO9B8uUho0RaFCI/pcBLXXNz/TOOWLg4rDBahoqG4DakvfmZEKkM5DqLUcx1KBVQGtxGJldgamqOfXs2s23LOpOFpAh1paBYsHj0OxN8/+kp3Hw7x3AjkQ5sGsAWfOLTFxkd93AdgYq+o/nlKRWGV/XASiCuTGIrpVdkY2ACyMLPE0upu1i7SO6jSL7rtMn+l5GBpGtgSfCovEl49N+ga6cQTq/JRlKNS4tNedoCTPz/dYgSLqEs0dt8mp0zv4erJlEi2w0iEZK+D62Rls3TX/oM9cocMpJbqdAn37OOTfseJPRbs6+ve0yIdvpeFIjin0EmYyWln9VCWr2TychWR3NUgAsDL1ocr30eSWYWBhT7N5HJlak3GjQaHh/6wNvwvACR+izJPQ782ScvUJsNcBwRyYhvXBBJZ/mhgkzR5vHvTPC1b4+bvpiU9Xx8XpYlkVHjRaG8HmFZVz1fI3pZeC+ozhKWa2C+Uk+0ZSC6S46sWaT9r4pZa97PlnbMpBMwnXmo2dcIjv0O2p9CWHm0CtpOYKmIqyOGNDc9IqEsUfSOsmv6d8mEo1EQSZWz/oFCRAJ+J5vl4qk3eeV7XyeTz6eyjyYbdt/fYfaRHHzBQq0iyaslb1DcjqSlMuqObgtsiyjPxPeiUiFutkhpYAsZR/Diayd5+J13cMft26jVPaSU7eWirOTkmTr/5S/OI1NNVDciiLQFj9Ccy4UzNf7k4+ewrfaGrtZuUbNpwwBSgrQz5HvXJx5Y7UKo1n8Jabf9VAhBEGiWa7VsvicSJ+6osobWmsD3k3e82eKELq4FjSVXhv8AkHGPR5J51E6hTvx7UB5CZqJ/X0ECLvqjhZEVCh0SygLZ4BzbZ/8Dtp5t50T+Ad6MrcYyjeO6PPu1zzE7PoZlO+bnKiCT72HjnnckU/nS8tWrIt7Zpnb66Yc+mxHJgr6qiN40kxFt5K4QRJnR4ont2FywPLQT25KMXJpkcqrCL/7s+4m2K207+riU9flHL/Ppv72IW7RRauHCvdJobwY1nIzvKX7/P59hYsrHdduzISkl9YbHvl2buGv/DqZnKriZHIWe9ShlDCT1FY5vlFpO28+FMNMml+rEm4aUJvDHArF4HojfbCT82c2WRndxdSgNri0X9H8sFbKltrIMYX7yj9BhFSHdFQ8eaSQLlxBREClS8E+wee4TaGwgJe29Ulqs053tCnQY/VHzfnbrBiDbcZkavcgL3/wSbjZH7AMV+E2Gth2k2LcJFXjAInfPqUVGBc22smLcbGbq26v7nYnoLbKuZUpYyblL0weywOPpKseJeZAwoHf9LjLZPPVag2dePMpb33kn73vobuYqjSQLgTg4Qz5n8WefvMD3vjdBpmRH89lXh1yfHzziDvP/8z+e4sXX5ijk06qrdHal+MkfeyfNpk8YBmRyZeOBpVTb65Lvwrwblu2aYWFRE2EcQMIFDRydfg4QsiW/Nm8MWmm8Rj1VDr11n7m/r2jd/2b+R9rHcFkZSHqBVef+wnAeMr+qwSNGOohASCBL9DW+z2D9G4QyD8RZSOqXoqDQ6h1RaOGiZBlllVEya459i6q6kgutFG4uy0uPf43L509ju5kkWNp2hk17H4zsLeK692I5KeOnFPiNFm8SJXq5nIW4ERkImPKcPS9rEqDDMOE3rn8I8zqlfIr9G8kU+tA65OjxC+hqg3/+T9/Plo2DNJvtpSyzwzdqot/7k9McenWWbNmOhiRdaX7Kcj5me9nKtgWWJfh3f3Kax743mTQ2poOXJSVzlRrvfegg77zvNl49fAbXFhT6NpopjFct8UWciZs3dv3Re0spqFTD6H2W8VkALEExnzLBjDKOemUOFS5vMepi9ZC+LuU0/7HM6yU1ptdDTT+Huvx1hF0EVj94xEh2wVEwUMJluPpZssFZlMhckQ+JORQtLJQsYwcXKFa+TGnmM2SaR9DCQclbm5CXlkVtdpZnv/Z32HZsWS4I/Ab9G/fRO7zLkOd0qJqKypVmYp1ISE+lNMW8BTdw0mQiG45PDRbFfySvjxddpbAzBYoDW7EtzfFTlxifmGV4wwC//M8+aApZqeARZwK2LWh4it/+9yd441AriKRfu9RAEv9ufI5BqMnkJM2m4rd//wRfe3yCcsluI7djwrzeaLJ18zp+5Rc/zKWxKSYmZ5ECCr0bsezMtb8jrXDcHDLVUCoFVGohwXIDiAZSJpimYmXGETcqs4RBdx76WobWGscSlHN2+h+XdUxJ5FulLn2x7Y1uJGJORGiNFg6OmmSo9ihamJp/0mCYZB4q4kk8Bsd+i62n38eGC/+M4Yv/gi1nfpiN538G1zuBkvn2ILLGkc4+MvkCh599knNHX2+Vr7TJIDbuexAZz8FYApRSbaaFAlMbLRQssG9UBmLevv3KxJY5nZ2A1grLcikPbsOSkompOc5duEzQ9HnX2w/wj3/iIeYqdSzZ7vullCbjSKZnA379d0/w0gvTZHtstCbhI8zxOz2f9pKVUpAt2Zw/3+BX/+2bPP79qcSqpD0rkgRBiOs6/Oq/+Aj9GwY4evwC9UYTN5Ol0Lcx4q2uwndFhLbtZhFRxhWXnaq1MDWGtnOk36+Qt0iprxFSUq/MEQZXH0fcxc1DWr5bylo4K+jCIIUANfUMeu4NhJVrs8O+0TALvUKJHH3Np8j5p1DChaghsVW2EoDF+ou/wuD47yFVzdimRBlLsfI1Np37aRzvNFrmbj1VlxCoIOD5b3zB6OuFkR6EgUdpcCuDW+4g9ButANDBZxMisk2PZ2ZHCighoJBbGWXG4s+FVB0z/rszIWiLB/HpGdpBJlugWq1x5M3z2BmHSrXBz330vTz0zgPMzNWwUxJYI6XVZF1JpRryv/7ucT7z2Yu4WYmbkcbaHhaVjaSFD9HHIAg1rivJFC2+850JfuU3j/L60UrSad4WPKK/PT/gf/ylj3DvXbug3uTs+XEajSZOJkexf1PiPHzlcxFRAIlKWClHXs9X1BvKPPBLQLp/p5i3jBLLpHaG8K/MEgZ+N4CsQaSvSU+uvXy13OtlKNPxxzGNfjdP9dRG6AobR03R23wuCSBC61aZyyrSM/3nlGc/S2APo1Md8VoIQmsQ1zvJ4OX/g3g8krhJn6sTxIuJm81x/vgRjj33g1TjoFFfbdj9DtxsMeokX9K7mADSqNKyyjDzXYoFyyh1uDFZaPu9Gy28cURbJFo8SEChbyNuvge04uiJCwRNP3mfX/vln2Tvro1Uaw2sKwQRxxEoDX/8sXP8xv91nPMXGmTLZopfmJrUOF+tNT+wKGX+uI4kW7YZudTk3/3hSX7nD08yWwnbCPO24AHU6k1++Rd+lPc/ci/VWgPPCzh9bhRLQibfRzbfe0UCvf0LUTiZAtKyWxyIAN/XTE37YC1DWxKlqr3RPPv4GNKymB2/TOh3A8haRVy+KqX5jxV4xqVujqErR42vFYt3c10NxH0iJhA4FPzDWLpBu+OKRKgmheo3o8ChI5v4NHyULFGofhvHP4eOgtBaRrpx0HZsXnzsy1Rnp7AsU69UYUC2NMjwzrckjYOdkOcxYtWS36wmJLrWZsBTT9mmzV99lRGq9D2cyqY6eOs0D+JkCpQGtmJb8ObJEWYrdRzHwvN8ensK/OavfpShwR7qjeaCIKKUWciLBYvvPT3Nv/7No/z1p0eYqwRkyjZu1kokzkFgylJhqJPPIIQhxzMFi0zR4uJYk4//+Xn+9W8e4cuPTZDNSBxHLCDM487/Wr3JL/7sB/hHP/4glWod27ap1ZucPjeGJTSFvo3X7kCPEBsuutkycZ+PEALPV4xPepA4DXT2PCQqr1Az0GfjOFGJDI2UFpWZKRq1SpT5dLFWML985a7w2GGpZ19FB9MIcf3RIDcOGoVDPjhFJryEEg5JaBEWUs3hesfRwgQ9La5knSIQ2sPxTkRcytoOIDEsx2Fm/DKvfPfrOJksSkeNg0GTddvvIVceajNP7BhC4HtVVOi3LWSOI+gt20mvwKrvIwSpcbYk2t4rjdpdDOJZ4YYHEUxMVTh1dhTHto1KqO6xY+swv/M//wyDfWVq9caCcpbWmjDUlIoWlWrIf/r4Of7VbxzlY584x2uvz9L0FG5Wku2zyZTNn2yPjZszG5lKJeQHT03xB//xNP/y14/w3/76ArNzIeWSdUVexZLSTPhrePzyL3yQn/1H76FaM/Jq27a4ODrJ1EwVKQXF3o3Iqw6RSn8RCmk5ZAo9SeYqJXieZmLKNz5gemklSiFAKxjodyPLlejfpSDwPWYnLiOj77Qr5V0buGr5iqXdA/Nh6/oZYmO2tGrkZiHOQrSwsNUcRe8Qtfx2pG4mP9XCIZR9OPp0MpvkSsJPLSRK9pCU59Yo2sjzYpEXvvklRs+cJFcsoZXCTN/Ls3H3/YmN99KuUzQzu16J3AXixQwcR9JTcpbdrdwJVFsGEmMpC5vZPqjQN/0guQKzczUOHz3LW+7Zg65rLMuiWq2zf+8Wfve3fp7//f/+FCfPXKJczBMq1Xbvh6Hp1SiXTAnqv31qhE99YZThdS779xTYvDFLPmd6WCanfCamA0YuNrhwqcn4lI/nKXI5K1FZzS9ZCSGwLYt600MIwa/+y5/gQx+4n2q1njQ72o7NybOXqNXqZDM5Q6Cr8OoEegSNxrJsMvneVgaCGVU8PuVHr1n6s66UppCzKBVtKrWmmactJIHnMTMxZqx24uaTLtYErli+WiHYunYaZMQzrJGLbtRYEoEiG1xApAKA0AFK9tDMHiBXfwooIbRnSPbkdy2gQWBvxcvsQWiPtRxAYggp8RoNnv/GF1vXQghCv8HQ1oOUh3YQphoHO7leiYpLSLz6LCoMTJ0c87y7tqBcslIjZlcf83epGvMddHqpEh4kDCj0byJT6EfMVTj85nn8RqsHxLIsKtU6O7cN8/v/5hf4vT/+LM++eIxyMY8WrQwh5oaUEoYEz0jCUHPhYpOTZxumn8NqyXMtCbYtsS1huI+MTLiQ9HWKS1YCwcxcjW2bh/iVX/wQ9791XxI80t/GqbNjeJ5PqVg0BHrcgX6Na6+1NhlIvgelwmRDZknBxKQPvmI5dmdmsyEYGnA4f7EJTnzf1pm6NIJlt3idLm4uEsm6hp7MypevAKSun0MIe02ZoOmY3EVi///svXlwXdl93/k559ztrdgXEiS4s5tNspuUWt2SbLeWSLYWjyw7TixnMk5m4mTiiZPK2BnbmfIkcWInNTOVlO2JU4kdx47jpOJxOa5Ynli2pFZL6r1b3ezmToILSJAAARDE8vZ37z3zx7n3vvdAsJsEH4BHEt8qFEHg4S7nnnt+5/f7fX/fny5Gjaea0+x15nv+Or41iAyXohyHBEQSrpJhgVu9f4dQdkd/37mI1QAcL8WV0+9y8d3v4KQi3SsNINiy98NRYnR1qnhxzYeQimp5IWHzCAFBqOnqshoSFesBIajXtQljJbU6YBqXNbJhd3eoeIEOsJ0M+YFdKKk5f3GS+cUiqklnSilFqVyltyfHP/3ff4wvfvYjLBXL1P0AFQlVNhcyxjRcIYxxyGcVvV0WuawiF32fz1mkPJn0XI87HsbXFr+0SilqdZ9CqcKnP3aEf/GLP86zTz9GoVBpMlyYWpBSlbGL17EUePnBqAI9aLnfO40DgJfpRUqFjkgSliW4NlWlXg3vSzAzDDW2p9g65OL7YRTuNMSMm5MT6A1SON7E7WgJX6XbH74CkCKsNpo5dRqEQOkCggbjSAuJDMtU3UPcGP5lfNWHCqaRYQEZllDBTWRY5mbfz7DY9aPIsEDc9bAT0UiemwXm2At/SqW4hJIN1d1M9zD9255oUd291wmQvNACqqWFaHfaWPD6e2ycdTIgkU2kVg+TgrT4N1JZ3HVfkyYkjCZl0TWwC8uyuDVf4PzF6ziObSTSRUPdtlYzBvSnfuKL/Ozf/mG68xkWl4rJ7+903XHiPPYw/EC3sLTia0m+xxiEIAxZXCoy2NfNz/2dH+bnf/pH6OnKUihUUEouMzSSW/MFxidmUUKT79+B5aQTBtYdxzXecYY+6fxg0jckNiDXp6qGynvvw5sg1IAtGBl2k2hnrL81e+0KtXIlklHZRCdAa41rCbpTa5PjtkxmrLNilkkeBIkVLiG0nwgsxiEqGS5RyH+RmnuA/Pzv4lXeRVCnZu+mkPs8xexnEboI0Kgf6aB7XA5l2yzcnOHEi89ju6mm5HmNwR1HcdJd+NXSfd2DEEb+vFZeRAiJxlA8wxD6em2ULahVwrUfpmjhqdW16YmuYg9EIy17VbujZPH0a1EeJMv8/CKnz03w0Q8fNPUuTdRbKSVhGFKu1Pj+zzzD0Sd383t/+G2++s1jLC2V8VIOtqVAm/4cd0JzeKr5umPGVq1Wp1qr09/XxQ99/iP8+e//LgYHuikWK4AxFq2U4hDHtrhweZKlQgnLtsn1jiYepHwP42qMEOgwIN01iLIc02hMCpQ01eiTN6o8ti9j1HnvMWxtmFgaAs2WIRfPjZtfNRmQagXbcQjbpx+/iftAoGEgbUV1O+3fHXYS9WpFaFYOaWihkMEidWc3M0P/BBkWEToklDlMCGuRln7rHWw8dBjiZrK888KfMn31El46E3kmIbabZnjPB+8zeR5DEAR1KsX5pNBMYHbVfT022BJdXgcDYi6FWi3yQES8YTDxe4RcZZ1LY/edyg1QWCpw4uwVysVKslA3FwXGuY7CUpnBvi5+6ie+yOc+9TR/9Kev8cobZ5i9uYiQAtexo54cAjB6982z0TCPzfGDMMQPQmrVOsqSbNvSz8c+eojv/cQH2DE6SK1Sp1AoR+2DWwXtYk8UJTl/8TrlUoXu7jy5gVHCqEjvbnJfOgyxvSxupofirWsITMK/Ugm5OlnhsQM5dOXeQ1nm3ICv2b7Vw3UMi0wA0rJYmLnB4uw0A9t3EgbVqEC1c9+7RwFKQE+6scy3+3lYiffRQYtsoxIhpC67Iu8jWlBig4AxIiKsoIgr0WVkOOBBMB7Ni4fv+7z9jT+JqJdRaKleoX/bIXJ9o6tOnjdDCNM/vVK8ZRoUadACpIL+HsdcE2v70jdCaYJqLSRsyheYMJ4d7bbvDYlhCEMsN03P8D4mxt5mcbFEEIRYSjbJmze8BjBeQK0eUK377NuzlZ/5yT/P1euzvPLGGd56d4yzY9coFCsUSzXCMEQp1TJGWmt8P8CyFCnPYXigm8NP7ORDR/dx9PBuenvz1Gs+hSVjOGJjttKzlFJQq9QYuzSJlBov20s6N5D0QL+7MTaU5nR+kKXZK0jLeJrVWsjEtWpSVnWvcykhF/ia4UGHni4r6aIolaJaKnL94lm27NlPvVqJmmBtYr3RrPeWcSUZV972u3bB0tJBhLWklqJzYEjnvswTYqFoSqSLmJEVsbV0s/R7lDAVdLTxgDj5GGK5LjfGLzD21muN5Hm0LR/c9UGU5RjZ9lXkBpafL6hVqJUWojxDpNPkSAb6HYjE9taSzp0IOEoolkL8MD6nWZyU5SbXdq90rNjwhEGdbU98EiEt/uYP7sDzbKpV/7ap0OyNxCGnSqUGwPBAN3/xh57jBz/3YWZvLXHpyg2uT95kavoWN6bnqdUbHkEq5TLQ18XW4V727BhmdPsg+VwaKSWVSo3CkmFYNRuO+Pwxmo3Z0lKJC5dvoIQ2hZFOmsCvwPtQeBvHMgYk0zWUUH+1NuKRF8ZL6EpwX3mQIKLy7hpNMTFZxW1iYk1eOo9URMn7zmF2PkqIxzwEutPWMlJDmz0QkRpFF04jhEunFNsZwwCCgEDmDLNKl7nt5mOjt8IkFdz+8U5Co0I0xHYcjn/7ayzdukk6bwrAdODjZXsZ2H7ovpLnjXMZBlaldIsgaBgjrY1e01BsQNp2h+91LQalckDcVVdEv1OOF0mRsyo6b1xpn8r28thHfpSe4RvowFTdrzR0y72RmPJb9wOqiyWkFPT15Bge6EZYpsahVq2b3E0UWZVKYjs2CAjqPvV6QLlcRWvjUTSHq96LQRWGIbZtcenqNPOLBZSlyPZtRyoLv67vKjkthABtzpXt3ZYUH2qMvMrY5TKFYkNSZTWLfBiCyCp2bU/xjZduIYTpnCgti+sXzlIrV5GbFekbirj2oyezduErACnSOyC8h6ZEawxTRBi5ysKhokaiOhA6lkl1P1DKolwo8O43v4qyY9l2ie9X6d92kFSuP6k8vy9oItntuSSeDmYx8FzJQJ+NjjyQtd41CoAQyssS9qZg0o0S/Kubi8mCqH0WlwqMTYGKHJr3mt7NNNr4/3GOwPcDSuUqhcUihaUyvh8QhCFBEBKEIfW6T2GpRGGxRKViwlyxx7H8HO913TpahM9fvE6xWMb10uT7dzRUA+7i/dSR1x0GdfL9o1hOKqleVwpuLfhcvVZBrlJ1ObmHULN7RwrXlYQRQcF2HCbOnqS0uGAKCqEj1pRHEaE2fT9cq7WZWrshRfYgRLUTneRuCu3ji24K9hNIXQM6lGq8SjTXflw+eYyJsVM4rhclUkOUshnc9YGWz68+99GoASkvzRL4VUS0Iw8CTX+vTcpTrBdxRggg0BRLQYssOIgohCWS67634zYnpgW2Jbg271L1BVLGL9HdHaf5ePH3Sqk7Jp7j3Mbya7+X5yalxK/WOHN+AgE4qTyZ7i2mgPAe3TEdBniZHtL5AaPojNHqKpYCzl8qGcLEKiRNtNbGea1rHtuTJpsxngwRhXpxbpbrF89h2cbz6aQ15VFDs/cBa+WB5A8h3H50BxTbNS8kUtco2vuoqb6OLwS8VzTXfggpOfHS89TKpcjtN0ypTM8IPcP7TItXcX/eoZHiNsVe5aXZ5OcCU8ewZdA1PbnXyUAbJk/I4pKfLMixKJ/tZpId82rvOWZ1WRJmliwWSuq+qq+XG5I7fS3/7GqgMYakVlmke2hPi/Ly3Ry3EZILsRyPXP9oUjRqfq45f6kMfiPfda8QGEHJgT6H7Vtd6tGxlLKoFAuMn3oHy1HocFMTaz3RCItrUo68TftqLSCFlUPkDkNgVG83+oE3+n2ELDmHoyrzaGv8kO1mlGWxNHeTU698s0k4URD6dQZGn8Txcvch274MQhDUa5SXZhHSivq+mIVg67CLWFcPRFD3dWJATIpGI1RsQPSqBBWbzgCAFJpKXTJxy8FWcX/wzlzQGswzzV/90U/x3d//N9h55AuR8sC9GaUk3yItcn3bE/aU1uA4klPnCpSLftKb/V7e+fg6gkDjZBSP78lQq4WR0dZIJbl88hi1cm0zjLXOaGweoD9rJz1m1hISQA5+FmFlWI8+6HeCpsGqkrpKxRpl3vswUpeBh4sOGLOvnFSaC++8zszVS0nPcx0G2G6aoZ1HE+2j+93VmnNKwqBGaXHGeDq6IXExMuy2XNtaIzZcCwU/YQMZJpSF7UUeyH3mYrRhYRBquDZvE4Tm/1p35iYkNpph4OOk+xk9/DlSuV7CwF/GornLYwlBGNToGdqL5WYiUU6wLcHkjRpXr1Uj3arVhQqjb3hifybpDaLDENvxuHrmBEu3ZpNWBJtYDzTyHI4l6F2WPF+r91pqHSIyexC93wOBWaw3dscgENpnJvVZ6rI3qkLvzJd+NUjCV1H44MSLz+PX61GFsamkzg/sItu3raX2437OByZUVi0tmCr0aNUOQvA8ydZh975CGvcKIUwV+uJS0PBAIAphZVmt3lfrOeJGWZprt2zKdYESa5dMvF/EOXJbwfisYH4x0oBb9S7SGKN015DJowR14jxIoRTw7ukliKRr7vX4xtgD1ZCDj2XpylmJ/peyLBbnZrhy+ji266HDzTzI+iDyDDX0ZqyWtrVrOd+Trb0c/iJYOYi9kHV8yVq9jzIlezdz3vcgdYkkef6QTULLcpifnuLsmy/jeCkj/SBM7Hpg9EmsSMcI2uAVRLv70uI0gV8j1poKtcZzJSPDXmJA1hoxtXV+oU612qQMG4VcTMy/ffL7SsJCRXFjQSFoyLZ3ihFp1IWYMFPd10wu2MSikrGHcO/lhE2xAAAgAElEQVQFfyaRbjsperbsa2JymVfpreNLUA1bPMB7RT3QDPY77NmZTsJYUipqlTIX330zKVvqlLF+2KG1xpKCvqyd/GwtvQ8AKYQ0oStvC3Lo8+ggEh9cs1O2omE8IlK9hqn0DxHINEIHD5X3EcOErzzOvfkyc1PXsGzDgtNhgO3l6B89TODXo9qF+w1dNRhYpYXpRjdDAYGvGep36M5bBOHtxW3tRrKQKGH6ZtQbchpah9heBuV4bVpwTHWJEJqaL5gs5HG7UknDo04yIkIYAoNjSyw3y9Sih6V0VAqzWiKBiP5a0zO8DxXVg4QaXFdy6lyRyRtVbOve3/WEAh5oVErx5IEs9aQI1YSxzn3nVUqLi5t5kHVEqKE7pUjZa0vdbYZs/keNfAnZ93G0v2QSrWt88lbjIVFhicnsjzDvfRgVlsxC9xB5Hy2ieb7PiZeeb1SdC0Hg1+gZ3kumazgKOdz/BGhmYBXnp8z5Maes+5rREc/0u1iXRlJRuEoK5iIDIqKL0TrETXchpUW7Clp1lOdxbclXvvoK//bf/jcKxVKLLtZGIzZktlLcnFvk9778GjfnS0Z6RbPquR/XgwR+na7B3bjZ3kgOxeRBbt6q8+Y7ixALIq7iumM69tFDOdIplfQ/sRyHG+MXmbo8huW4HTPWDzukgP7c+nkfALKxG4uKjXb9HUTuALq+2DAibTYkmtuNhxUuMJv6XqbSP4gKTdX52tdFbwyUbTM3dZ0L77yJ46UaMt1a07/9MMqyk+fRjgkgpMSvVyjMX0cqKzIqhkmzfasHUQOktUdUHqhgdq5Ord4Im+kwwE11IZXd1o2LEIp6ZZ7vfOu/8C9/87/x//3ZG7ieQ9gBFNMWWRMp+Jf/7iv8i1/5bS69/ceJ2OV9byACHy/dTf9IUzuAaE/28psLUFtdGCsWVgxqIft3pxkZcqlHGwKpFOXCIuffeg3bsdA63PCxflgRj2sQarKeIuepdfM+ANM4zEwGE8pC2qg9P4PIPmaMiFD3XYfQjPgocc4DQIVLzHkfZyL3V5BEO2/R2UKIq4GRqwhwPI/zb73KwuwNlGV2DDoMcNJd9I080ZKnuB80PB6JXytRWpiOak00oTYV6KPbPIgUVdeLgYWvuTFTbWoRJiJF4m4zHm1IosfQWmPZKfI9w/R0ZXjz2BjlUrWlydRGw7IUN6Zvcfz0ZfI5z/RqaYPuWYMtBQOjTyVjG2pIeZJjJ5e4cLmE7dx7UWH8WT/QpHIWRw/lqFQbygJSKc6++TLVciWRNemEsX7Y0PzMBppzH+t0fknEuDdGRJl8iNOLevyfIPueQ/tL0YXe/yTQ5gDmfEIhtI/UNSYzX+Jy/icxDKzgoTQeDfVVSeAHnHn9RVMhHIVwgnqVnqG9pLsG2iNd0jgzQlkU56fwa2XizGYQaFIpye7RFNTXj4ElBQTVkKmZGlZEIzXiipJUto846t8uY6ZDHyeVo3vLYwgCLl2Z5uq1GWzbuudFs92IFVMd1+b46XEWl0rYlkPPlscNU+4+Qz9JGKteo3t4H+muoWRuKSVYKvi88MqtpCq92SO62+uPNcs++FQ+6WipwxDb9bh65jizE1ewbGddSTmPCpLCwVCTdRXdTV0H2/kOvRdihb7kghIjIl3Unp9GjvwoOqyhgxImKdkqDXw3SHbCEB0fVFggFB7j+b/FZPZHkLoeGY8479HW++wYWLbN/PQkY8fewEk1ha+EoH/0sOmH0abwVRxfl9Jiae4aft10ixOi0QNksN8hWCcNLIjkyushU9O1pK84WqMsBy/X16Iee79IEslhSO/WAzheisWlAm+9ewHLse55wWw34jCQDjQnz1yhVqsZYze0x1C4RXso9ToMcFI5+rcfbjouuI7khZdvsXSzhm3d+7OP6bxhNeTw41kGBxzqdROgVpbF0vwc5956BcdzTJHsJtqKhG0HDOXtRgHpOm6KEmsgbjMiUU5k5Euox/4xousIOiijg0o0qeVdu9nGe4mkrIMiAs1c6uOc7/mHzHnPocK4c2Cz3tXDZUGS4kHPY+zYGyzMTGHF4avAxP97tx5IXvB2oCWBfut65P2JpA5j7850lPxc+0U0XgiVFCws+szN11Eq8kAi7a9U3PeiTY++kUiu0TW4i1S2H3TIW+9epFapNTHA1t+IJOOhJEuFEifOXMFWmmzfdrxMT1u80CSMJYwRGd7zjKn0D4PEgFy+WuHF1+eRURJ8New039d09dhRGMvU9qBBScVbX/tjKqXyZhirzWjOfeQ8RU+mQXpaTyPSslIJ0RzOinsXhMjcE1j7/wFq788iso+Zz/hLxqDoYFmSrPFSmp8H6KBgDIdQiJ6PYB34RUojf5eiGMHSxrNpCVs9RKEraC4eFIRByNk3XiLwfWMo4gVuaHf7lHebYBLoZZZuXr0tgT464oEnCdZpc6g1YAmuXKtEvbkbFF7LzeBluk0e7h6lO973vNEOvGfrAWylOX1+gqvXZnE2MIwVL9SObXPh0iST07dQUtA9tBdlu20jUcQ1JoFfo2tgB70jjzf01TChrD/++ix+KS7qvLcQlrkPQAmOHsxFrVPNM3VSKcZPv8uV0+8assgmG6ttaB7Hoby94s/XA7dvdZNJET9skRgI2fMs1oF/inr8l1Cj/xOi6whCZRDSNkYlrBujEhr5BSEdhNWF7P0e1M7/BfXE/43a+zOQ3sNw1sdVdUJtDIaIqSEPMZRlsTA7zfm3Xm1lX6Hp33aoreyrBrtHUS0tUFq8gVSGIhtLuO/c7iU9QNZcwl0IQg3YsQGJmhoJgQ4CMl1DKKu9sfLlNMb+7QdxXI/5hQJvvH0ey7E3LIwVU4ylpXj39DjFYgnXy9Cz5bFEvqStz0SbQs2tez9ssp4RuTLlSU6eLfDym/PYaXnPXkgcxtLVkKcOZunKN6rSpVRUikVOvvICylbJOrLphdwflnsf3emN8T7gPXqiN0+ihjcSGC8is9t8Df8A+EvgL4K/hK4vQlgBlUXYObDyYOUQKp0cV2uNRuNYFn1Zm8mFOlZU+P6w2g+jSxTgZLKcePF5bt2YxPY80DoqKsy3lX0Vn1OHIVJZLN2cwK9VIgMSdZRLK/btSpsEulz7iRfH+/E1V69XEjshoj7mme5hlO3hV0ttnQgtieShPaTyA5TKE7z+1jl+8PMfbgljrdeLF79XUgpq1RrHT48jCEnlB8j1bU9kR9pxTcl7LIRpkbz9MLn+HSzdvIKy4iZygt//8g0++nS3MQarWIx8P2Sw3+HAvgwvv7lANq1MszTX5dTL3+TTf/lvtmyQNrF6JMrPwHBXq/ex3sb5PVcr0eSNxB9vDk0JrRFWDuGNILKPI3ueQfY9h+z+ACKzD+EOIVQ6MT6NyWMGYDBn46j1T/ysN5of6vm3X6Veqxrtqyh8lR/YSSrf3vBVbKiFUCzdvNLSA8Qo8Dr09zn4/vpReJUU1MsBF8fLODHzBxMuTXcNETeSWotrMWGsLnq2PI4lNWcvXOPylRu4jr0hYSwN2LbF5NQc5y5cx1LQNbQX20knnmm7rynuF7/j8Kcio2I2bumU5NjJAn/2wk3sjEUQ3v25E3XeEISn+ODhnMmpmVg4tuMydXmM8VPv4Hib2lj3ixbvI6XoSm2c9wF3KXN7e88DSSx30jAoUb4jyYnEXzEDy4gFxsfTWuNYkoGcncTgH0bXNkmWWhbFhXnG3n4dJ5Zuj8KDfVsPJFIT0L6FQwhJUK+wePMKQqpEwr1aC3lsdwZ3vRPoSnBr3ufKtQpWk4Krsj2yPVtXpTz7fmgJBWnNwI4juF6KxaUir37nHMpusLHWa/4Z71BjOzbvnLrMwmIBx3Hp3fp44n21cyFoVs/1a2WGd3+InuH9BLVKVOMFjiP4nT+Y5OZUFdeRhOG9yb1IAdRCjhzKk8u2hrFq5RLHv/21RMRzM4y1ejR7HxuZ+4hxz/ES0TTBG18y+lLRl2z6ipkgjc83T57BnI3nSEIdT9j23VwnIL5fy3G5fuEMsxPjSetaI3aXoXfk8UTsrh0ToREiUdQqBZZuTkT0YG3YMUqwb3caVinnfa+I8x/CFly4UqJYClAtGlhZMj1b0avovHc3aLCxqnQP7SHdvRVJyGvfOUupWL6t9exaolEPJAh9n3dPXiLwfdx0F91Dewj9WrSot/9FiEVSle2w66nvI65KD0PDyLo2WeXf/O4Ewmplp93NtQhhtNW2bXUZ7LeTJlOhDrG9FGdef5HF2Wkz9zexKjR7H91pa8O9D9jARhvJwqoEw3m7yXA8XBYk9tAsWzH29utUSoWoT4LpW53tHYnkts3i2bb6B62j/MdVauXFhEYZ5z8O7M9A3Ahoja22jgwXUnD+YjliYBEl0H2y3VuwnXSi1bRWL4IOAxwvS//2w9gWjF2a4vS5q3iuvf7JR0syO7fE8dOGvts1sBs31ZWMQbvRnAvxa2UGdjzF4M6j+LUSUhqPI5dRfOUbN/nyV6ZxchZB0Ervf69jgyloS3mKPTvS1GOZGm16pU9fvcT5t19PyCObYax7R7zBVgK2dG+89wEbZECWeyH9WZusq+7Zbe50NHsC1VKFsWNvGCotccOfOj3D+6O4d/sWzyT/IS0WZi4T1Bv5j7qv2Trksm3YI1gHCffGGEBQDjh2cimpQI8T6Ln+USynIV/fbjR7wWHgM7jzKG4qR6lc5vkX3zVzjvUJrcTV565rc+b8VWbnFrAsRc/IAWSUZF5LEbzGcTV7P/gFnFQu6bmutWHn/et/P8Hp40t4WYUf6LsyImA8GRxBf69tilNp9v58Trz49UQ8dDOMdW9IvA+t6c/ZpJ1WVekHJoTVTjRuvnMsavuhsWybuRvXmTh3Ett1o+5wIcpy6dm6P8p9tDd8ZSRTqizOXEJI2ch/VEMOPZ5NFgfz2bUdb5MwltyYqTF2qdSQvIjHYHhf0gNkzbyPpnqIXN8oXUN7cZTmte+c4/rUHI69Pt3z4uQ1Gt48NkalUsVN5egd3p+w8NZqYW28bxK/XiU/sINdRz6HX6sghAkjW0pQqoT80//nErPTVTxPRkoF729ENIAUpJoE/WI2oJtKc/bNl7k5OWGkTR6ySMNaIpYl0VrjWoLhLmejLynBhoewwEzKrpRFd7rRl+Jh2J3EL4/tulw6/h2KC7eSNp9hGOBleujq30kQtD/uLaWiWpxncWYcFeU/tDZS3k/sz5is5xrnP5LnGwKO5DvvLjK/6CcSJmEY4GV76BrYtSZj0IxGIj1EKYuhXU9j2RbTswu89PopHNdJCAVrdQ3xcS0lWVwq8c6py9hKk+vfSbprsO1FpO91HUJI6tUSowf/HH3bDlKvlpBSEYSalCcZn6jwC//8IqViYOT+38OINIgyQKAjUcXWeaUsm4WZKc68/iKO56LXpX3AQ4LE+4DhvJ10G9xo7wM6oNl4881v6bZRMq4JefC9kKT6PNRcPP4Wfr1uGGxCEvo1swtO5ZLwVTuQ5D8sh4XpS1TLi5E0uKk+z2YtDsb5D7k+hlpK0JWAb75yqxEyE9L0Pxnah5POt3UM3hNC4vtV+rcfJp0fRBDwjRePU16HZLohE2hc1+HM2ATXp+awlKBny2Mo22s7C+9O19D41zQae+J7/ge8bK/xgKQxFrmM4tjJAv/H/3WBYjHATSt8v8mrWIG5JgTommZ8ooJtR4WjNLwuDbz77a9Sr9ZaGFmbuDMSwkWoyTqGtdr8uzWPQb8POiSEZf5NO8rQeu+B/dGpaFBXLUqL81x89zs4XkzfNb/rHtrT1P+ivSEsgLnJs4RBLcl/GP2rFFsG3aT+Y63QeK5gu5JzF0scP1PAc6VZWLRGSsXAjqeMUdW3V46v2bUFAW6mm/7Rp7AVnLs4ybunx/FcJ2EDrsl5IzKBEII33j5PqVTBTWXpGznQVhbe+6E5lBX4VbI9Wzn43F8xHmAkJe8HmlxW8cY7i/zUPzrH2IUiXpeFFAI/0LexJYMAHE8yPlHm+OklPFcmBiYJY3kpLh1/mxvjF7Ad11THPwQbxbVE4vUBw93ObV7HRo/ehnsgyzGcd/As0UTrfTCNSMIysx2mLo1xc3Ii6sego7BWxvSq9mvJbrBt55YSv1ZmaXY8MVBCQL0ecvRQDpVWCU9/rUNYWgNK8N+ev0mxHKLi8FVQJ9M1TO/IgajR0do/60Yy3TCyhnc/jeOlqVSqfPOl40S/WpMEb2NDISkWy7xz8jKWDMn2jpLtHWmpPl8v6MiI16tFBnY8xcHv+THCwI8S+cYTyWYUZy+W+Lv/4Bz/+feuU/dDvLyF40qkFEhpxtRLK3Sg+Te/M0EhomkvvxVpWRRuzXLy5RewXZtwU9rkPdFK21X0bKBkyZ2w4QZkuRdiKcHWbieZfJ0wSKtBQt91LC4e/w6VwlLUH9owgdJdw6TzAwkDpl3nBJNAr1cKlBZnkhaxYQiZtOKpJ3JG/0qsIV02meSm//bEeJkXXp4j7cVaSyZ8NbDjKdzUOoavkmuLFAD6d5If2IOjQt44Nsbk9C3sNUqmJ0lQx2bs0iRXJmawFPRsfRxrHSjMK11P43uJXy2x7fHnePyjf4nArxojEoWz0p6kVgv5td+e4G///Fn+4A8nOX+hRKUaUquHBIHm3FiRv/ePz/PKW4ukUyaXIoRIZnbsfSnL5sTLz1MuFJN84CZWRjxnlBSMdLstP+8UdMQTXJ5Q78vazJV8FpKdTGdY23uFlJJatcb4qXdaYs9hUKN7eC+Wk060n9p2f1ojlc3S3AT1Wimp//ADTU+Xxa7RFGG9Uf+xlnTRMNTgSP7oz2a4eatOPmcRhiZ57qRybNn7bCLfvu7PV4coy2Fo99PMXTvJ9Ow8b7x9ji989sPUan6ikdW20+lYPFHyxtvnKRRL5LNZereukXjiXaBl8wbUq0V2HP4UUlmcfuk/ov0AZTkEoVFOzmUVY5fL/PKvXyWbUfT32KRTkrqvuX6jRrkSJO0BmkMvJBvERqOpq2dPsvvJD1ItmzqUTbQifi5BCNt6bFKO7JjEeTM66sk1D8q2bveBNR5J7YOyKC0ucPVsRN+N3HWpbLoGdsbC9+27PyEMXVdZFG5dJ6hXkvyHXw/Zsc0jm1E0elitTdV3UuvgSS5fKPInz9+MFhZM8rxeYXDHEfL9O0z4ah1DNw1ZDxP/Hxg9TCrXjw4Dvv3KKWrVetv7hLSErwpl3jh2HiU16e4t5Pt3EAY11jt8FaNlQRICv1pi9OAneOpTP4GTylGvFqMxE4mKcy5rjMT16SrnL5W5PGF6BLUaD0G9VmL3kc+R6x0xfW4wOcFKscDJl7+BslTEDtwMYzWjJXHuyRbBxE4bp44xIMtDWSnHDFzQzOR4QNCc/5i8dJ7F2WmjhKuNqKTtpskP7CQI6rSr6xxgdnoIdOhTXpolDo1JATVfs3NbChXx+tcCzV6kBlCC//Rfpgx1N+54F9V+bHv8OdPKVqxf8nw5wsDHy/bSO3IQW2lOnbvKxfGpSGCxfYZ9efhq/OoMloS+bQeTBk/x5zYCy41IvVJgaOdRnvnCzzG85xmCesX0EEGgaUi+O7bEdSWuIxPChJRGtr1eLbLj0KfY9+wP07v1gKEoCxmFdV1OvfICxYV5pNURQZCOQiNaAdt73NsT5x20oe4YAxKjeTEdyttk3XsXdtto6ChRbjmK8VPvUCkVWvIfma4teJmeNYn9C2mky8uLs+ZlNsu0iaMOu5Hq3dpNQrNzAjejePnVW3ztxTkj7R15H36tzNDuD9K9ZR9BvcJG7LxbDJaGwZ1HsR2PhaUiL712Oupd0V4PZHn4yvXS9G07GIXwNsaANqN5kRJS4ddKeNlenvrU/8yR7/1bdA3uJvCr1CuFKNGuozEyEiZaa8LAp1YpIJXN4x/9UpJP6d/xJJaTgjBAa6PQO3npfMRM3JQ2aUY85/xQM5CzyXqdUXF+J3SUAWm2sFprpBBs63ETqnOnDd57QUpJvepz7fxpYk/A5D/q5Ad3YjmNhlLtvS9JGNSpFG8Zrr2OxPJcyZYh11QjrYH+VbPb7TiC+dka/+q3J8wvE+cjwHYz7Hzy+5LiqI16pg2JjSrdw3vI9GxBCc3rb52jWGjUhNzvON05fLWVfP+oEU/coPDVcjTXdwipCIM6gV9naPfTPP35n+bI9/4tRg9+glSuHyEkYRgki7+UinT3EDuf/D6e+cLPsevIZ83f16t09e8g3z9qPG7MZ+uVCideer6RK9kMYzXeIa1J2ZIt3Z1TcX4ndJz/uDyUlfNMbcjUYh3rAciJNPIfikphqSFfErFQpLTI9W1PqLvtaiAVQwjDMKpXC9GxzYvp2ILebivpQNhOND8vAKEkv/ZbVxmfqJDLGsqwoYsusevI5+ga2Gli61Jt+PPUYYDj5ujbdpj5G5e4PDHDuQvXeerQLsrl6n0neI1RDfFch+OnLt8WvqpXChCpVncCWoxIFF71qyWElAzt+iBDOz9ArVqgtHCDamkRISVKOdhuhlSuD9vLoIMAv1qOKgtDLDfLwOiT3Jo8FxXWmna3p1/7NvPTk6Tz3YTB+jHxOhUNYwpbu52W9W6j35M7oaM8kDthS7dDypYPTG2I1hpl2cxev8L8zA2UFec/QpSTIt83SujX16b2QRi1VRNzbkxISwnyWQuM5FTbJmPz9QcBODmL3/+vU3zlhbkk2RoXrOX6trPryGfx/Rq02XDeKxo1IYJQB/RvP4zrZSgWS7zx9nmkivW67t8DMeErxRvHTPjK6bDw1XLEY5NsCGTDkPi1Mko5dA3sYmjnUQZHn6R36+PkekeQysavlgmCqNI8Hl+/zsCOp7DdbBS2NfnBm9evcv7t13Bb2js/mmgOXfWkLXozrTUfdNgcidGRBuS22hAp2N7rJLV2nfbCNSOmK1quzcS5U1SKhagWwyS3vWwvqVx/xPtvuy+QKNw2Fyaa5KbAtiOWVrvO1hSG8n2Nl7d447Vb/PrvXiOdkhHby3haWmv2P/sXcNNd6KjqeqN3VXFNSOjXyPePkukZQQnNd94Za0sYqzl8VVgq8tpb51BCGzHHgZ0dFb5aCSsZEqL5HfhV/FoZv1Yh8CsEQQ2tw8ggRsSQuOYmqJPp3kL38B6TjI8NSxDw7re+StCy2enMsVhLNIeuXCta6yJ0SsX5ndCRBgRo2p2bf7tSFkN5O1GQ7dSJpiMmFBqmLp0nqNcRMn5hfPK9203+Q68h86bp5W1BG8/VYjwCjZdVjJ0t8Eu/ejkxWHEYpF4tsePQpxnceSQKbcgNNx7xtUOcm0nTO3IAS8H4xAxjlybvm40VLwqe63Dq7FUuXZnGtgR92w5hOal1Lx5cLZpJB/H3QkiEjL6WN49b9jdxzc3gjqPJvkaHIY6X4vxbrzF77Sq28+gq9DaHrkZ7XRyrM2s+VkLHGpBEc69p7EZ6HLJe57OyhJRUSyUmL42hbBsdxr0RQrKRq7821248DKmsKEQUL/KGKVOrhYbme59nuc3zSCuuXa3wj/75RRYWfRzHJNMTmYzRw+x75gcTyZJOeiGSMFYY0DfyBK6XplAs8da7F5DW6tlYSVJYG2bcG8fOU6lUcL0M/dsObVjx4IYgUh7o2/YEbqYHHeU7lG2zeHOa069+C9t1CR/BMFYSugo0g3mb7mVyJZ2OjjUgcHsoSwjBjj63aXfbmUZEKkW5WODG+BiW7RgqrW70/tZhgFir3hdao2wvYWCBMSB+oFkqBOaJ30dsv3lH5PsaL6e4fLnEz/zieSYmq6Ti/hFS4tcrZLqHOfixvxolzMOO21U1h7Fy/aOk8kNIoXn7+EUqpcp9h7EsSzG/UODNY2NG+6p/B9m+uLCuM+fvWiAM6qTyA/Ru3d8IYwECwYmXvk69Wk1UEx6VMWlmLmZcybbuFUJXHfKe3AkdbUDgdiOSdhRbuxsFhp2EeNepLItbU9cpLS6a+o+4LsT2yPZuMzmKNTJ+5vyu6XIYyYNLIahUQ6Zmqqvug95g5pjrDkPw8hYnTyzxs794nutTVdKphvEIgzq2m+HwJ/8GqWxfJBq5ds2SVotGGMsIXHYP78eSmovjN7g6OYsdeSGrOW4YajzX5uSZK1y5NotlSQZGn8JaJ+n2TkBzvY0QksEdR017AR2FsVJpLp14m+sXziVsxYd9TKCZuWhs6Uob4wdhHDregMRoduuG8g49TYJtnbIoCSGMB2DZ3JycoB51ejM7zQA33W1ojmtS/xEhijen8gMR40UgpOlEeOFSuSVZeTfj1mw4gKSxkJu3+NY3b/IzvzjGzFydVGw8hEQHAUIoDn/yr9M9tId6rZxQdtfsvu8DzdfTs3U/juuxuFTk+KlxLLfRZvZeYMbMOIGvvnmWarWGl8rTN/IEgV9vO3274xHV3PRufdyQSII6YLz14sItTr7yApZjxvpRqgkJtGZLt0PGbS0Y7FTW1XI8ELN4JXdutM/FUZ0Vyko8EAVzk1epVSqmjkAYAcFUvj/Kf6xdrNdIRXhku7cQhn6yy7FtyVsnF/FLAc2lDXcat8ZLLJLddFxhrrXmN37zCr/wLy5Sq4V4TsPziKvrD3/ixxkcfZJ6tRjJW3Sm8YAGTTL0a3QN7MJJ5QmDgBOnxwlq/qo9J6UkhUKZd09dQvsl8oO7yPRsTRbPRw2mD0sX/dsPmRCe6WiGsh1OvPh1ykuFR0KhN16zglDTlVJs6XJum1+d95asjAfCgMDtoSzHkoz2uUl4oVOMiKnyhvmZaSK/PfJMQlLZPizbhTUyIGZxNjTLni37kcqJdnRGBO/UuSLHzxSwU4ogbGZ/NFN+W8dQa00QaBxX4mYUx95Z5H/9R+f4978/iW1LLMu8CKZy2bRkPfzJH2fL3meoRcaj9fo6F2EY4Ka7yPXtwFaa0+evMr9YxLJWlwcx7WEdPvXxp+ndup+dT32WuIAUOn882oXl0jEDO46gLAcib8NxPaeUZgoAACAASURBVK6NnebKmeNG2mQVHt+DgmbKrmOZnG6MByl0FeOBMSAxmkNZ3WmL4S4nofZ2CrSGoZ27CYMAHck9+HWfTM820wt6jSZIvJMO/Bo9w/tI5/uTRV0IU+j3W793Hb8S4joC3w9b/jYe29jbAHA8hZtVjF8p83/+8iX+3i+c48SZIvlc3NvdCOgF9QqW7fHUp3+C4T3PUKsUkjBNp78UDUNqhB5Np0jBzVsFLlyexLGte74Hs2kQSOFz+EOf5Jkf+Hl6hveatrGPWviKZdIxg7vJ9mxtkTaplooc//bXEvLHwxjGaplD2uQ93AeIsrsSHqiZvFIoa6THoTvdGfmQWBOoUizyzGd+kO/64l+iXqsR+D4f/cKX+Oxf/THCoBpRXNfuWnUQ4KS7GNr9dLJghaFpDHTsxBK/9CuXqNU0XpeNUo2uclIKLEsYo5GzUFJw6vQSv/yvLvOTP3+WP/6aSQSnmhR9hZTUKgUyXcN88HM/xdDOo5FMiXygXop4lxwGdbqH9kZV6WVOn5tAWOqeFrS4DEcIqPtwYiIW0qxv+BzdaOgwxPay9G8/bEJ5QhDqENvxOPXqNyncummUGx5i+KHJe3SlrBbj8SDigXtSK1F7d/a7nJmsUAtC5AYuWs3XJZXiL/39f8ZH/ru/gBCSnU88heUovJTL298+TblYxXashPvejutNdtIY2uT2Ax/n+vlXqZUWkMomCEMyacXXX5zj+o0qP/z5QT74ZJ50WmEpQb0eslQMmJqucup8kVfeXOD0+SLFkmkUZKRJ4sp2RRDUCf0yW/d9mMc+8iXcTHdL2OpBMR7QROcNfLK9W/EyvSwtFTh74Rq1Su2eNLGE0GgtsJRmZsni+ryDrSINmeQzD8a4tAuNuanRYcDAziOMn/ha1INdYLku01cuMXbsDY584jOUlyIG40OC5rxHT9qKuq4uy3s8gHPigTMgcLsRsZVkZ7/L+RvlDXUHm89rxOEC9h55BoBapYxfrNO3tYdnv/dJ3v72GeZnFnFci7hYrR3Xm5zfN7z7fR/6IY4//xtIZSXJ8GxGcfZCiV/8lUv09dj09tj05C3m5uvMzNWp1UIKxRClwPNUUydBosSn6ffgprvZ8+EfYfvBT6DDgKBW6fiE+Z0QPzutAywnTa5vlLnpcS5cmmJpqUQ2myIIwvd9TubWDXHBkpoL0w6VusSzwyjz8eAY1XajYaRr5Pu2k+/fwfyN8yjLQwqJX6ty4qXnOfLx72v6/IM1j1ZCc97DsyU7H/C8RzMeqBDWcjQbi5ynGOlxCDtAL6t5QlTLJarlEgBSGYn3bFeGD3/6SbbuHKBWjXMU7XFjm2mAfq3EyP6PsvfpH6BeNdcQ97lOeZJ0SrFUCLh4uczrxxa5dLVCuWKS7vmcIp1SCBqGQ2uNXy0ShgEjj303z/zAzzF6+FMEfs0wvqR8oF/6xHu0bLJ9oygpWVgsMj4xg21Z9/R8pNRU6oLz0y5Smp4sWhuSw6MO0ysnxeDOI4bSHrEUHS/Nmddf5ObUNSyn86XM7wYtG1pgV7+LpW6PojyoeGANyPJBj+tD+rIWfrhxUgDL9YCklEn4Q2uT+wjqAZZt8YGPHWTv4VH8umm0I9qYF4kZWX69wp6nf4C9T38Rv14xORGp0NEuWSmB6xpj4joSJWParuk+BwIdBtQrBYQQbNn7LE9//qc5/Im/RirXj18tRud7sI0HNO0UA59c3zYcL02xVObC5UnkXSTSm2s/lNBcn7eZXrKxVTwuD2acu11I3g0hCPw6/dsPY3u5hPpt2Q63blzn3Juv4HjeQ6HQG8+XUMO2Xvf2eo8HHA9kCCvG8lAWGDGyci2kVAs7qqd6I0RiDEUYNeI5+MxeMvk0J18fI/BDLMskvOO/uZ9zxf8G9Sp7n/4i+YEdjL3xhyzdvAqIKKwl0ULQ3DtE6xAdBkavSSq8TDdbH/tuRvZ/lK7BPSDAr1fMuWQjwdwJ43z/EOjAJ9uzFdtNEy4WuXRlGu3777ljbJmHWuC4FhdnPWq+IOXoJLT1qKMRxqqT6R6me2gPM1feMR0Lhfn98W99lWc+88UWmvmDOLfi6/YDzUDeYjDX0MC7bbOljZe63LAIQAtTG9CJY/BAGxC4fbFUUrBnwOPsjTL1QG9oUv1O19p8vfWqz87Ht5LNp3jn5bMUFks4zv3nRW4zIn6FoV0fpGd4H9OX32b26nGW5q5RrxQI/BqBX0UIibRsLCtFKttHrm873cN7DSW4a8jkOfxKfIaWyd4J43u/aDyfEMvNkMoPYc3NcPnKDUplk0i/066xebylhLMXZjg/tRsnko7ZNB4GDSMbopTD4M6jzFx5B2go9I698wbTVy8zsG0Hfq2hm/UgoTlp3p1W7Oj1kt81h7SSdzT5u7ixVrShQ0dlQ51J933gDUiM5sF1bcnufo/z05WOG/TluyqNplb16dvSzUc+c4Tjr5xj6soMtmPf93W3GhHwqyWU5bL9iY+zdf93EdTL1KtFyks3qRTnUJaHl+3By/RgOSksO2VyJvUqfrUUrYHiNkP4sEGHIZabJtszghg/zuT0PEuFEr09OXw/uO2+m3eVnufwH/7fb/Bb//FP2P3UpznwXT9K4JsmSg/jWK0aUZOxvm1P4GV6TIhUKpRlsXRrltOvfpORv/zj1KplpHiw2FjNxiPtSHYPeCa0uSxa0vA4YiamBJrvdXmYPkBr2VEbt4fCgKy0s896ip19LhdnKpiwa+cYEWh1VaUU1GsBbsrh6U8c4uyxS1w4fgUhBUrdX0irZbJJidZh0kZVKgcv65HKDzZCWKEJX2kd4tfKRHrkSUX9w2w4gIgtEyKEJNO9BUspSuUqV6/NMjjQTb3u3/HeLUsxe3OBL//p62gdUlmaeSji+GuFMPRJ5frp3XqA6+dewnIzEFHET7z0PN/9Q//9A0cJb2ZcOUqwe8BrCaU3L/wx609EBlLXZtEL76ArV6A2B9IGZwCR2YfIHUSoVHSOEIRs8WA2Cg+FAYFWI0Ikk92TsRjxHSZu1ZLk8EYPeDOar1lKQRiYvMgTT++hqzfLidfGqJZNvcj9hLSavR5ozluE6CCEoB7xT0XiZUR/yHIdqE4Zu7VCsgCEPumuYWw3RbFU4frUTT5kPYbWxuA3fx5M9X464/D6C8eYnVvEdW36R59E2S5hZLA3YZDMx6jd8cCOI0yOvRrlATSOl+LyyXe4PnaG0ccPU62U77s3/XpgOeNq94CHZ8s7Gg/zvUJXpwknfx9963W0vwjaB2FhSvIDkC7CGUQOfh9i8HMIGYlOio0v1u38p3IPaLCfzP+11gx3OaaTYbhy7HqjcVtIK8qLjOwe4qOfOUL/1t62UX2XM8QaXxIhVVN3ObHiZx8dCHQQkO4awLI9/Hqda1NzsYTVis9ASkG9WuebL58gDHy8TC8Do09FTbTkIzZ+74+Yah74VXq37CedH2hR6C0XFjn58jdQtmUMi+5saZPmRTzUsLPfJevdzrha/n14408ITv1vhDNfRYdVhEojrC6EyiBU1nwvXXRtluDKvyM4/XOEhbMmYqADom3fho3NQ2VAYOVQ1fZel560ofd2In0uXqCT6xZQq/pk8ime+XOH2X9kJ2EQEgZhCyV4E+1Hw6AH2G4GN9ONlILJ6VvUqrUW7yP+vNambe2Z8xMcPz2ObWl6Rw6SatIi23xeKyNW6O3b1qrQa9k2x1/8eqOnTgejeb0JQs22HofezAqMq/izmHkTXPlNgvF/HRmObBMTMgDCpi+NkDbCyqHL4wRn/yHhrddM6EsHG0oyeOgMCKxM793d75JzO0Mz605YnhcJAhM/P/DB3Tz9yUOkMi61av22z26i/dBam74quQGU1ExOzVGr+beFr8yXKRL96jePUSyVsR2P4d0fSjyWR8+De3+0jImGwZ1HUJFStdYa2/WYvHiey6feMQq9YWcq9DYbDz/QDOVthpvk2RMvnoiOS2g8rxt/Qjj1Rwgra8JYCfvqDmFiHZp8ifSAkODyr6FL49Hfhhu2HjyUBgSAZUZESsHeQY+MKzveiEBrSKtW9Rna3s9HPnOU7XuH8f0g8kZul2PfRJsQN+bKDSCFZubmEguLxds8QA24jsXE9Vleev00toJc3w56tuxLWrduPp+V0RzG6ooVev2GQm+tUmoo9NJ587zFeISagZzF9t6VZUpMLUdoFvzKFOG1/wRNSfH3NY5JlCJACAf8RYKrvx2FsBp5l/XGQ2tA4sFsXogtJdg7mCLldL4Raa0rENRrPm7K4ehzB/jAc0/gZdy2y6BswqD5Zc50D2PZDuVyhfGJ6ajFbVP/lFBjuzZf/9Y7TN9cxLIEW/d/l2lbGwYrHX4Ty6DDAMfLMrDjqaTdc6hDbNfj7BsvRgq99kZfZiuaXjc/0PRnLHb2m1qPFUsHmuZMOPOn4C8ihHV3xqMJIvJihMqgl06gF96JQlhh5OGsLx5aAwIr7+ZtJdg3aNgRnWxEYAWWVhji1wO27Rnio585yui+LQR+QLDpjbQV8c44DHxS+UFsJ0W5UmVqej6Rdo9h24qbNxf5sxfexpKadNcWBncexferCfOqE0MvnYAkjBWN9cDok1i2Z6quESjLZnHuJvVaFSE7awx1VNfjh4btuWvgduOxvI5DCIkOa+iFt0A6rLo2KD52WEUvvLHs5+uLh9qAwMpGxLEkewc9XMuo03a6EYFWd7lWNd7Ike95nA987CCZXKrhjchmOvMmVg+jAZbK9qEsh8APmLwx13h5gTAMcT2Xr77wNlevzWIrzdZ9H8HLdKOj5Pkm3hvxQhv4NfL9O+gbOUCtPE8Y1lmam+XAs8+R6+kn8DtnPON30Q813SnFnjsYj1bbEL2P5Ql0baZB010NhDB/K1300hl06Ce1JOuNh6YO5L2wUqGhZ0v2DqY4d6OMH3aW5MlyLK/jiL2RMISR3YP0DXcxdvwK42evE9RDbEfdV93Io47GfDEV6W6mG3XrJlevz1Kt1KJmYODYNjMz8/zRV17HUuDlBtiy76Ob1N17QMvc1pqDz/0YlpujMHeNj//Fv8Ynv/Q/JsWYnTCezVXmeU+xZ3ClKvPlxgNiY6GrNyCsIaTLqg1IfDwhoX4LdI2NWsofCQMCKxuRlCPZN2gkTzrdiMDK91Cr+tiOxaFn9zE82s/Zty8xOzmPsiRKyU1Dch/QOsSyPdLdW1DXxrh8dYZiqUIm5VGr+ziux3/+nW8xMXkTzw4Zeew50rl+6tVCUqy5Oe7vjeY8Xxj4IFM88/1/kz0Ht7Dz8Z1UK2WC4L2FLNf7Wv1Qk3MVewe9ljWjwbx6j4MERUO9bQuEKToMSqDSbTrmveGhD2E1Y6VwVto1uwgljPxAJ4ezoDXBDrE3YooP+4a7efbTT3Ho2X24nkOt6if5E9jMj9wrtNZIZZPrHcWyFHNzi1y8PIWUglw2xWvfOcN//ZPX8BxBumeE7U98DN+vEPdO2TQed0bzRgigXvORSrH30AjPfuoJtu0ZprS0SBgEG/5Oalo9j9h43Emi5D2hMtC2cJM2x5Le+390jfBIGRBY2YhkXcW+IQ9bdn5OJIa5j8YEFlLg18zOZs+h7Xz0c0fZc2g7UsoWthZsGpK7QWyow6BOz5b9OKkMpXKVb7x4HLsnx/jVaX71179sPqw1ez7wBZxUHh3cWStrEwbNczbwAwI/YHh0gI985ghPfGgvlm1Rq9YjZYSmOb4B4xqH1hLPwzNrxfKmUPB+xiOqB3EHQBgpkvuDoQVj92yoAXlkQljNWCkUlHEV+4ZTjN0oU/V1R/USuRNEpFuVTGDZCGt5KZdDz+5j255hxo5fYWp8hlBrLEu9T6x2E9Ca3M31bSfXu4Nq+TTffu00/b/xZb796kmu35jHosrIE59keM+H8GulTe/jPdAcrgr8kCAI6RnMs/fwDoa39ycKDEKQSOdv5Fgur/PoTploxcphq/e7xuj33naE04euTkeJ9FVdmMl/hDVEdj9CWpFBWv9xEvoR3o42P/x4QlTqIWPTFSr1zmpIdTdYfj9ag2WbtrTT1+YYO36Fm1PzCCGw7M38yPsh3n1aTorp8WO8/ZVfRUhFuRrgOhaEFfq2Hebo9/5klPMIW+bSJgwaHgSEgcb3A7L5FLsObmf73mEs28Kv317T1An5DjDGozdt3VGW/W6u03zWiEcGV34zqkLPYQoDV3OfAh1WUHv/PrL7abQOQKh1NyGPtAGBlY1IzTdGpNO6Gt4VtE4E1mia5JZtGSrq5RkunLrKwuzSZqL9fdB4NTTKcpk4/U3Ov/GHhssfavq2HeaJ537MdC4M6ohN76MFzYYjCDSBH+ClXbbvG2bXgW2kMq5p5xxqIwTdicYj0PRljfFo/t29XqeGqBJdosvX8E//LGj/nueM+axCB0VE/knUY7+AWPbOryceeQMCKxsRP9Ccn65QrAaJEYk/8yBg+T3FM8x2THz56vkpLp2eoLhYRlkKpeLOeZvGJMbyxcKyPYqLNyjMXcd2UnQP7QFMX4uHoSd8O9A8d5oNRyrjsm3PMKP7t5DtSichLNFBhgNu9zwGczY7+tyW363mWps3I0JIguu/TzjxHxBWHgjv6p2LjQfaRwPqsV9AZh+LPN+N2bxsGpAIKxqRUHNhusJiJcBqYjJt9CS/F6wU1pJSYNmK0lKFK+euc2VsknKhumlIVkDromFYWTKKOQd+DeisBXCj0DJXBIRBSOCHtxuOICT0w47yOGIsNx7DeTvRtrof47H872NhxXD81wlvfBlh5TB8pvC262h9BxU6rAACtfunkL0fJm5ItVHv6qYBacJKRiTUmkuzVeYKPpZ6MI0IrGxIlJIoS1JcLDN+7joTY1OUi1WUkkhL3l+d00OE5YuHSWICD2Ff+PuCgMA3bQcy+RTbdg+xbe9wYjgCv/M8jhjNzzjUMNLjsKXLyI1o3Z5rbjEiQqCB8PofEF7/PRPOUh4NYmz88sVrjg9BGeEOIXf9JDL/JHFnQjZwLDcNyDKsZEQAJuaqTC3WkVFM90E0InAHQxLlQoqLZa5dvMHExRsU5osgBJalzBzenCWbiLDc20CD7wegNbmeLNv3DTOya4hUxu14wwG0bBYFsKPPpS9r3/au3C9r8fa1BUCgC+cIrv8eeukEBBXz4biDpTby78LqQvR9DLnlhxB2t0mas/EdCTcNyAq4kxGZXqxz9VY1aQjzoBoRWDlHIiOPpFquMXVllqtjU9yaWUQHIcpWiYRH/PcP6r1vYnVYnt8IQ41fD1BK0j2QZ/u+YYZHB3A9OxL51B1tOKDJeIRGrXv3gEfOU3dcA9pxvubjxjkRAF0cI1x4C6o3ICgbI6IyiPRuRNdRhDsYHaPBuNro93DTgNwBtz9o8/2tks/l2Sqh7nzpk7vB8vBMc47ErwfMTt7i6tgUM9fmqNd8lCWRKt4dtR5jEw85okcc5zcc12ZwpJdte4fpG+5O5owpxo3+pEPfkeZrCkJN2pEtPcxhba9dm7hYZARChEkKvc/fxJpgnUPY2DQg74OVkmfFasDFmUpScNj8uQcVKxkSIQz9V2vNws0C1y5MMTk+Q7kYJfIs2eKVbOLhQbMYuRAQak1QN+1V07kUW3YMsG3PEPmeLAgMHVffvgZ24juxPFnelVLs7m+tLm/OfazldUCrN9IY+fi8rT9rTsSvN2V3JWwakLtAY3EFaNSKXJypslQJHujk+nLcNh2iuassE8IqLVWYvnaTyfFZbk0vRBpGJodickON4zzoY/GoYaXcRhgab8OyFb1DXWzdOcjgtj5SGZcwNBTdFmsToVOfffNGKdAwkLX+//bO5cdx5L7j3yoWSb1b3a3px8zs7MCbDYxNAhvrGLksAiNx4CD3IIec8h/lnD8g5yDwIcjNpwQIEPtgwI692Sxm59XTL70lPqoqh2KRRYpSv6clsT6AWmyJpKRiFb/8vYr4tFfLuYMe4+p+IVGjwLpaclZArsmyDK1X5wHOxjEoWd+DfBvSbpFThSxOwmOB4eUY71+d4eS7M4z6UwgulNAkgmpdXOtPuWgoYaCUoN6q4fCTHp6+PEC314bDKOKYQ3KZisY6xzhMisHyZ7seDjte7r1N+B3rhBWQG7AquP76MoAE0riIXm/TKYsFaVeFtkrCeYSLkwHevTrF2dvLxMUFOI4DQklOg/Q+t6FtNpUy0ZBCgnMOgKDW8LB/vIvD5/voHXVRM6wN0021KRdMxXiHxwhe9mrolATL9bLlelgBuSHLOtxozvHtWYAgFlsTFylS2lUkQCiBw9QU1dPxDKdvLvDhzQUuT4cIpiGklEm8hFoxWQcKlgYhBH7dxd7BDg6e76N3vItGuwZCyIK1kdvNBhy3XLyDq3jHy54Pjy0Gy++apltFrIDcAjMWYnbCiAt8exagP9vcyvXrslhcB4CoWVQdRiG4wGwS4Px9H2fvLnFxMsB8GijXiKMyudL7XFtBuXeK7aiFW8c0CFGWxt5hF0+e7mL/qItGqwbqKPekECIX29gUa8OkWBx41HHxfNcHCnVcm/a71gkrIHdgWSd8fRng/SDKxUWAzbhiuymrXFypmAiJYBaifzrExYcBLk76GA+m6X1Kllknev/b2G73zTLBkFJCCAkRc1DmoNbwsNvr4MmzPTx5uoda00+nVxdClLqo9PKmYLaFECpT8sW+j70my72/ib9t3bACckeWxUUuJjG+uwgQ8e1J9b2Ksq6UiQkBdRwQou4+NxnOcHHSx8WHYeLqMqwTSrPYCVBSBV+S9lMhyvrRgmBwDkIomOeg1a5j76iL/cMuuk/a8OvLRSO/z81r46LLqulTvOz5aHg23vEQWAG5B5Z1zCAWeHWuXFrOlmVpXUVZm0AVvCsxoQSUORBcIJiFGJyPcHEywOXpAOPBFFEYg0cchBLl7iIkfUi9o5LP3Ka2XfZ7tLBKoWoVpFAz21JK4HouGq0aur0OdnotdHsdNNt1uD5TLiyuCv1ylgaSNsXmnlBVWwGAyrKCBA46Lp51vaRW6eGLA6uIFZB7ZJlp/H4Q4m0/hEyqvM11q0CpmKDcOokjjmAeYngxTh+DizHCIAKPeHKiVAWMOoZylbDo77CO7b3qe+mXtWUhpYQUUsUnoGYLcD2GZqeOzm4L7d0mdp90UG/V4XoOCKUQcSIYQqdlp3sHNlw0NMUsK59RfLLnoduwLquHxgrIPbPMGhkHHK/OA0wCsVWFh7dhWTaXtk4IIYkrS2UBxWGM2XiOYX+CcX+KUX+C8XCKaB6lk/UJoQLDWli0qICoK2zoa+wb9vabHKMbH09i1BsnIgHthpISkqupKxzmwGEOmOeg0aqhtdNEq9tAZ7eJZqcBz2dgLgNADAsjb2UA2+m6KQbKd5sML/Y8uE5ZltX2/O51wQrIA7Cs4woh8foyxIdRFmDX61e5Y5dW4SaCQohKE6aJqIAQ8ChGHAtEQYTpaIbpeI7ZJMBsPMdsPMd0MkcccnDOIbhQczMBqbCAqPgKMc7gufZPXzaPybJhUjxueaEqWlzQlkTyghRqWU2t74A6FH7NRb1ZQ6NdR71VQ7OtlmsNHw6jYC5TMQwuEjdWtp9tFwxN0epwKMGzroeDjpu+r9NyZaKk29UC64EVkAdkWZbW5STGd5cBglhufbrvbSgVFGQZWiQRXy0sWhiUNaLcXMEsxHQ0w2wSIAoihPMYQRAinEUIgwhxGCuBSdw76YlYZlfv2iJIRUZ/uPFlct9QZoJHjO9lfk/mMrg+g+sxuJ4Lz3dRa/qoN33UmzXUWz5clyUV/6pQU31Hkfr2y6wL9dW2VzA0UspU+KWU4AJo1yhe7NfQ8KzV8bGxAvLALOvQUSzwuh/iYhyr+glrjaxkmaio99SzFhbdnoQSEErTeY60NSK4uoUo5wJRGCMOY8RRjDjiya1Ws2fJk6ym9Eo/yVrSrraCWGgLgTEHzDUfDMxjiYuNpokEellnT5lCpqwwmbOUNFU8QZZZHUcdF0c77kJ7VK1tHgsrIB8BuaTwEAD60xhvLkNMI5GzRsx1LMtZJSxZTD07CZtTeBBdeUx0BXLhOYsz52pcNOnh0e6RZH19vFOXld4+sRzM775KJNSu7cmwaMkLCezUHTzf9VH3aG6dqrfVx8YKyEdk2RUSFxLvBiE+DCMIicrUjTw0K8WldAPzydzGiIiQsnUX188lO12BPfEtp2h1eIzgeMfDk7YZ67Bt+FhYAfnIrLJGJgHH68sQw3lWN6K2sULysbmx+GhU1NYerzti9nkd+9lrMTzrekvmsbLC8RhYAXkkVg2Ak2GId4MIEc+C7HobO0gs20zRXcUF0PApnnc97Bh1HYAVj3XACsgjs5ipBQCqiv39IML5eNGtZVMSLdtGfhwAXEp4DsFB28VBx4VTUk2uly2PhxWQNWDVwBgHHO/6IQYzDoJqVrJbtpdif+dCghKg11LZVR6jC+uZNR6Wx8UKyBqxSkj60xjv+iHGoQC1ab+WDUdZ0lm6gUimWuk2GI52XDR9J1sP1upYV6yArBn5ILtaBrIBdDqKcTKMMI8EVGG2FRLL5lAUAZGkNrd8B8ddFzt1G+fYJKyArCmrBlDMJU6GEc7GEUIubcaWZe1RFeRIi2e0cNQ9isOOi/2mm007kmDdVeuPFZA1Z5WQhLHA6SjC2Ti2QmJZU/S0KwXhcCkOOi72WyznjgWs1bFJWAHZEG4rJHpbOxAtH5PiXF0LwtFkuYQQwArHJmIFZMMwTfoyIfkwinC+VEgWJ+CzWO6T4sWKSKYeqbvaVWWFY5uwArKhXCUkp6MI55MYQSxzWVt6WztQLfeHMWVxAk+yqhoeRa+lXFWOFY6twwrIhrNKSCIucTGJcT6OMA2TmxNRc6AuDnyL5boU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\" 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src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"240\" height=\"240\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function vn=optimize_GI(G,GI,irc,rgbGmed,rgbGI,v,scrmax)\r\n%Create vector vn that places GI regions such that\r\n%the score of created image versus G is good\r\n%vn=[2 1 3:100] would place GI block 2 in top left corner, \r\n%GI block 1 moves to block 2 location of the submission image\r\n% rgbGmed and rgbGI are doubles\r\n% irc contains GI regions r1 r2 c1 c2 of width w for the N regions\r\n% G,GI are uint8 [400,400,3] png arrays\r\n\r\n% rgbGmed and rgbGI are sufficient to create a passing vn\r\n\r\n N=length(v); %rgbGmed and rgbGI are [N,3]\r\n vn=v;\r\n %figure(42);image(GI);\r\n scr=calc_score(G,GI);\r\n \r\n %For seeing the data and flopping blocks [bi bj]\r\n %S=GI;\r\n %treg=S(irc(bi,1):irc(bi,2),irc(bi,3):irc(bi,4),:);\r\n \r\n %S(irc(bi,1):irc(bi,2),irc(bi,3):irc(bi,4),:)= ...\r\n %   S(irc(bj,1):irc(bj,2),irc(bj,3):irc(bj,4),:);\r\n  \r\n % S(irc(bj,1):irc(bj,2),irc(bj,3):irc(bj,4),:)=treg;\r\n %figure(2);image(S);\r\n \r\nend % optimize_GI\r\n \r\nfunction scr=calc_score(G,S) %Scoring function definition, not required\r\n  GmSsq=(double(G)-double(S)).^2;\r\n  dis=sqrt(sum(GmSsq,3));\r\n  scr=round(0.005*sum(dis(:)));\r\nend","test_suite":"%%\r\n%Google Drive Dowloads need to come from shared files\r\n% Tweak link: file/d/ to uc?export=download\u0026id=   while removing /view?usp=sharing\r\n% https://drive.google.com/file/d/1v3GsGgP3p905wzdvUqypL_-djYmxiyzK/view?usp=sharing\r\n% https://drive.google.com/uc?export=download\u0026id=1v3GsGgP3p905wzdvUqypL_-djYmxiyzK\r\n\r\n%28.png Starry Night w=40\r\nztic=tic;\r\nG=imread('https://drive.google.com/uc?export=download\u0026id=1WDYDJJZ3wJ8Fs0-8E2u6bYxlZbOapNJn');\r\nGI=imread('https://drive.google.com/uc?export=download\u0026id=1THVdMv7MENBxBBAaIbkl4qSvPnqnMnpv');\r\ntoc(ztic)\r\n w=1; %determine block width; assumes corner is not 2x2 identical\r\n while isequal(GI(1,1,:),GI(w+1,w+1,:)) \u0026\u0026 ...\r\n       isequal(GI(1,1,:),GI(w+1,1,:)) \u0026\u0026 ...\r\n       isequal(GI(1,1,:),GI(1,w+1,:))\r\n  w=w+1; %Grow along diagonal/Right/Down\r\n end\r\n nr=size(G,1);\r\n N=(nr/w)^2;\r\n v=1:N; %initial vector assignment of GI squares\r\n \r\n irc=zeros(N,4);% idx r1 r2 c1 c2\r\n rgbGmed=zeros(N,3);\r\n rgbGI=zeros(N,3);\r\n \r\n ptr=0;\r\n for c=1:w:nr\r\n  for r=1:w:nr\r\n   ptr=ptr+1;\r\n   irc(ptr,:)=[r r+w-1 c c+w-1]; % indices r1 r2 c1 c2 for each block\r\n  end %r\r\n end %c\r\n \r\n for i=1:N\r\n  % [1 1 3] tuple  %credit Christian Schröder\r\n  rgbTuple=median(G(irc(i,1):irc(i,2),irc(i,3):irc(i,4),:),[1 2]); \r\n  rgb=rgbTuple(:)';\r\n  rgbGmed(i,:)=rgb; % Median of G image block\r\n  rgbGI(i,:)=GI(irc(i,1),irc(i,3),:); %Blocks are singular r,g,b values\r\n end\r\n%Starry Night Best known score 41141, Greedy gets 49K, Hill using rgb blocks 45K\r\n scrmax=52000;\r\n vn=optimize_GI(G,GI,irc,rgbGmed,rgbGI,v,scrmax);\r\n toc(ztic)\r\n \r\n if isequal(sort(vn),v) || isequal(sort(vn),v')\r\n  valid=1;\r\n else\r\n  fprintf('vn fails to contain 1:N\\n')\r\n  valid=0;\r\n end\r\n \r\n S=GI;\r\n for i=1:N % Load S with GI blocks\r\n  j=vn(i);\r\n  S(irc(i,1):irc(i,2),irc(i,3):irc(i,4),:)= ...\r\n  GI(irc(j,1):irc(j,2),irc(j,3):irc(j,4),:); \r\n end\r\n \r\n GmSsq=(double(G)-double(S)).^2; % Scoring Function\r\n dis=sqrt(sum(GmSsq,3));\r\n scr=round(0.005*sum(dis(:)));\r\n fprintf('score:%i\\n',scr); %GI,G scores 99198 for Starry Night vn=1:100\r\n if scr\u003escrmax\r\n   fprintf('Score:%i exceeds Max Score:%i\\n',scr,scrmax);\r\n   valid=0;\r\n end\r\n toc(ztic)\r\nassert(valid)\r\n%%\r\n%33.png Scream w=25\r\nztic=tic;\r\nG=imread('https://drive.google.com/uc?export=download\u0026id=1IxjAN8VmMhM9d5CmTkgHHcVwYEYTWOAj');\r\nGI=imread('https://drive.google.com/uc?export=download\u0026id=1C0A0mBb3YNOGlVnE5-9J3Nyr4X3xG0yy');\r\ntoc(ztic)\r\nw=1; %determine block width; assumes corner is not 2x2 identical\r\n while isequal(GI(1,1,:),GI(w+1,w+1,:)) \u0026\u0026 ...\r\n       isequal(GI(1,1,:),GI(w+1,1,:)) \u0026\u0026 ...\r\n       isequal(GI(1,1,:),GI(1,w+1,:))\r\n  w=w+1; %Grow along diagonal/Right/Down\r\n end\r\n nr=size(G,1);\r\n N=(nr/w)^2;\r\n v=1:N; %initial vector assignment of GI squares\r\n \r\n irc=zeros(N,4);% idx r1 r2 c1 c2\r\n rgbGmed=zeros(N,3);\r\n rgbGI=zeros(N,3);\r\n \r\n ptr=0;\r\n for c=1:w:nr\r\n  for r=1:w:nr\r\n   ptr=ptr+1;\r\n   irc(ptr,:)=[r r+w-1 c c+w-1]; % indices r1 r2 c1 c2 for each block\r\n  end %r\r\n end %c\r\n \r\n for i=1:N\r\n  % [1 1 3] tuple  %credit Christian Schröder\r\n  rgbTuple=median(G(irc(i,1):irc(i,2),irc(i,3):irc(i,4),:),[1 2]); \r\n  rgb=rgbTuple(:)';\r\n  rgbGmed(i,:)=rgb; % Median of G image block\r\n  rgbGI(i,:)=GI(irc(i,1),irc(i,3),:); %Blocks are singular r,g,b values\r\n end\r\n%Scream Best known score 41328, Greedy gets 52K, Hill using rgb blocks 45K\r\n scrmax=55000;\r\n vn=optimize_GI(G,GI,irc,rgbGmed,rgbGI,v,scrmax);\r\n toc(ztic)\r\n \r\n if isequal(sort(vn),v) || isequal(sort(vn),v')\r\n  valid=1;\r\n else\r\n  fprintf('vn fails to contain 1:N\\n')\r\n  valid=0;\r\n end\r\n \r\n S=GI;\r\n for i=1:N % Load S with GI blocks\r\n  j=vn(i);\r\n  S(irc(i,1):irc(i,2),irc(i,3):irc(i,4),:)= ...\r\n  GI(irc(j,1):irc(j,2),irc(j,3):irc(j,4),:); \r\n end\r\n \r\n GmSsq=(double(G)-double(S)).^2; % Scoring Function\r\n dis=sqrt(sum(GmSsq,3));\r\n scr=round(0.005*sum(dis(:)));\r\n fprintf('score:%i\\n',scr); %GI,G scores 99198 for Starry Night vn=1:100\r\n if scr\u003escrmax\r\n   fprintf('Score:%i exceeds Max Score:%i\\n',scr,scrmax);\r\n   valid=0;\r\n end\r\n toc(ztic)\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-07-06T00:33:35.000Z","deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-07-05T22:59:16.000Z","updated_at":"2023-07-06T00:33:37.000Z","published_at":"2023-07-06T00:33:37.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2023.github.io/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP2023\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e Challenge is Jul 7-10. Start is 0500 PT 7/7/23, Registraion is open. Posted 7/5/23. Updates at \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://twitter.com/icfpcontest2023\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eTwitICFP2023\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. These contests are insanely complicated and competitive. The challenge evolves every 12 hours with new files and rule updates.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2022.github.io/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP2022 HomePage\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e has details of the RoboPaint contest. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://twitter.com/icfpcontest2022\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eTwit2022\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2022.github.io/writeups/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWriteUps\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.icfpconference.org/contest.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePriorEvents\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2022.github.io/Specifications/spec_v2_4.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://gist.github.com/RafaelBocquet/e464c2d199f2d7a725cafdc22b30df7f\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eImageSubmissions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://github.com/icfpc-unagi/icfpc2022/tree/main/problems\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePuzzles\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSawickiSolutions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e are useful links. .\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to move the blocks in GI to minimze the error between GI and G based upon RGB distance. The contest had these Initial files but the leaders did not use due to the high Cost for moving small regions. Assuming a low cost remap function this is to find the optimal image score using GI blocks. For Starry Night, 28.png, best score found is 41141 using Robust Hill Climbing where all swaps tried and only the best improvement is implemented. Greedy would start at index 1 and find min error then index 2 would look only from 2 thru N. Max error index swaps to minimize its index error. Many methods are possible. Hill Climbing using the block medians is fast, easily coded, and scores well. Greedy is easiest code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput: G-goal image,GI-initial image blocked, irc-row/col for each block, rgbGmed-median of Goal image block, rgbGI-block rgb values, v- remapping of GI blocks, scrmax-max allowed of Score(G,GI(remapped))\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput: v, [N,1] remapping GI([1:N]) to GI(v([1:N])\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"240\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"240\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"240\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"240\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" 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\",\"relationship\":null},{\"partUri\":\"/media/image2.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAGQCAYAAACAvzbMAAAAAXNSR0IArs4c6QAAG4ZJREFUeF7t17GKVucWgOF/0uQ0pxTkWARHCDYpRdDOKngNTmEtWFqekNYyN2DhJQQkldh4IFimkUCEFAEh6dKYJnPuIS8fe/HNY7/+vfe79rcf5+zyj+8vT4P/3bv4NPjuTqeP75+Nvr/pN/fh3XfTb3H0/Z3feTr6/q7ffj76/v738l+j72/6zZ0BpK0IIK0fQFo/gLR+AGn9ANL6+Qsk9gNICwiQ1g8grR9AWj+AxH4AaQEB0voBpPUDSOsHkNgPIC0gQFo/gLR+AGn9ABL7AaQFBEjrB5DWDyCtH0BiP4C0gABp/QDS+gGk9QNI7AeQFhAgrR9AWj+AtH4Aif0A0gICpPUDSOsHkNYPILEfQFpAgLR+AGn9ANL6AST2A0gLCJDWDyCtH0BaP4DEfgBpAQHS+gGk9QNI6weQ2A8gLSBAWj+AtH4Aaf0AEvsBpAUESOsHkNYPIK0fQGI/gLSAAGn9ANL6AaT1A0jsB5AWECCtH0BaP4C0fgCJ/QDSAgKk9QNI6weQ1g8gsR9AWkCAtH4Aaf0A0voBJPYDSAsIkNYPIK0fQFo/gMR+AGkBAdL6AaT1A0jrB5DYDyAtIEBaP4C0fgBp/QAS+wGkBQRI6weQ1g8grR9AYj+AtIAAaf0A0voBpPUDSOwHkBYQIK0fQFo/gLR+AIn9ANICAqT1A0jrB5DWDyCxH0BaQIC0fgBp/QDS+gEk9gNICwiQ1g8grR9AWj+AxH4AaQEB0voBpPUDSOsHkNgPIC0gQFo/gLR+AGn9ABL7AaQFBEjrB5DWDyCtH0BiP4C0gABp/QDS+gGk9QNI7AeQFhAgrR9AWj+AtH4Aif0A0gICpPUDSOsHkNYPILEfQFpAgLR+AGn9ANL6AST2A0gLCJDWDyCtH0BaP4DEfgBpAQHS+gGk9QNI6weQ2A8gLSBAWj+AtH4Aaf0AEvsBpAUESOsHkNYPIK0fQGI/gLSAAGn9ANL6AaT1A0jsB5AWECCtH0Bav7NHT95ctp8wrcA/L/D21eN/PmzydP/hi9EV7LetZ/p+AdL2azoW8IFpAad/YOx37/0CpO3XdCzgA9MCAqT1mz49fb8Amf4GbX5/AGkLnv6Bsd+99wuQtl/TsYAPTAsIkNZv+vT0/QJk+hu0+f0BpC14+gfGfvfeL0Dafk3HAj4wLSBAWr/p09P3C5Dpb9Dm9weQtuDpHxj73Xu/AGn7NR0L+MC0gABp/aZPT98vQKa/QZvfH0Dagqd/YOx37/0CpO3XdCzgA9MCAqT1mz49fb8Amf4GbX5/AGkLnv6Bsd+99wuQtl/TsYAPTAsIkNZv+vT0/QJk+hu0+f0BpC14+gfGfvfeL0Dafk3HAj4wLSBAWr/p09P3C5Dpb9Dm9weQtuDpHxj73Xu/AGn7NR0L+MC0gABp/aZPT98vQKa/QZvfH0Dagqd/YOx37/0CpO3XdCzgA9MCAqT1mz49fb8Amf4GbX5/AGkLnv6Bsd+99wuQtl/TsYAPTAsIkNZv+vT0/QJk+hu0+f0BpC14+gfGfvfeL0Dafk3HAj4wLSBAWr/p09P3C5Dpb9Dm9weQtuDpHxj73Xu/AGn7NR0L+MC0gABp/aZPT98vQKa/QZvfH0Dagqd/YOx37/0CpO3XdCzgA9MCAqT1mz49fb8Amf4GbX5/AGkLnv6Bsd+99wuQtl/TsYAPTAsIkNZv+vT0/QJk+hu0+f0BpC14+gfGfvfeL0Dafk3HAj4wLSBAWr/p09P3C5Dpb9Dm9weQtuDpHxj73Xu/AGn7NR0L+MC0gABp/aZPT98vQKa/QZvfH0Dagqd/YOx37/0CpO3XdCzgA9MCAqT1mz49fb8Amf4GbX5/AGkLnv6Bsd+99wuQtl/TsYAPTAsIkNZv+vT0/QJk+hu0+f0BpC14+gfGfvfeL0Dafk3HAj4wLSBAWr/p09P3C5Dpb9Dm9weQtuDpHxj73Xu/Zzdv3rxsj7h2evoBefntn2sDxF8/v/M0/sLa8eu3n6+9QPz1W+fX4i+sHZ/+gZ5+fn/58PvaBW3+6wCJCwZICwiQ1g8grR9AWj+AtH4ngLSAAGn9ANL6AaT1A0jrB5DYDyAtIEBaP4C0fgBp/QAS+wGkBQRI6weQ1g8grR9AYj+AtIAAaf0A0voBpPUDSOwHkBYQIK0fQFo/gLR+AIn9ANICAqT1A0jrB5DWDyCxH0BaQIC0fgBp/QDS+gEk9gNICwiQ1g8grR9AWj+AxH4AaQEB0voBpPUDSOsHkNgPIC0gQFo/gLR+AGn9ABL7AaQFBEjrB5DWDyCtH0BiP4C0gABp/QDS+gGk9QNI7AeQFhAgrR9AWj+AtH4Aif0A0gICpPUDSOsHkNYPILEfQFpAgLR+AGn9ANL6AST2A0gLCJDWDyCtH0BaP4DEfgBpAQHS+gGk9QNI6weQ2A8gLSBAWj+AtH4Aaf0AEvsBpAUESOsHkNYPIK0fQGI/gLSAAGn9ANL6AaT1A0jsB5AWECCtH0BaP4C0fgCJ/QDSAgKk9QNI6weQ1g8gsR9AWkCAtH4Aaf0A0voBJPYDSAsIkNYPIK0fQFo/gMR+AGkBAdL6AaT1A0jrB5DYDyAtIEBaP4C0fgBp/QAS+wGkBQRI6weQ1g8grR9AYj+AtIAAaf0A0voBpPUDSOwHkBYQIK0fQFo/gLR+AIn9ANICAqT1A0jrB5DWDyCxH0BaQIC0fgBp/QDS+gEk9gNICwiQ1g8grR9AWj+AxH4AaQEB0voBpPUDSOsHkNgPIC0gQFo/gLR+AGn9ABL7AaQFBEjrB5DWDyCtH0BiP4C0gABp/QDS+gGk9QNI7AeQFhAgrR9AWj+AtH4Aif0A0gICpPUDSOs3HpDpH5iP75+1DVzx6fsPX1zxAu3xp38Ab51faw+4eFq/Fhggrd8JIC0gQFo/H0D9WoE2DZDWDyCxH0BaQIDo1wq0aYC0fgCJ/QDSAgJEv1agTQOk9QNI7AeQFhAg+rUCbRogrR9AYj+AtIAA0a8VaNMAaf0AEvsBpAUEiH6tQJsGSOsHkNgPIC0gQPRrBdo0QFo/gMR+AGkBAaJfK9CmAdL6AST2A0gLCBD9WoE2DZDWDyCxH0BaQIDo1wq0aYC0fgCJ/QDSAgJEv1agTQOk9QNI7AeQFhAg+rUCbRogrR9AYj+AtIAA0a8VaNMAaf0AEvsBpAUEiH6tQJsGSOsHkNgPIC0gQPRrBdo0QFo/gMR+AGkBAaJfK9CmAdL6AST2A0gLCBD9WoE2DZDWDyCxH0BaQIDo1wq0aYC0fgCJ/QDSAgJEv1agTQOk9QNI7AeQFhAg+rUCbRogrR9AYj+AtIAA0a8VaNMAaf0AEvsBpAUEiH6tQJsGSOsHkNgPIC0gQPRrBdo0QFo/gMR+AGkBAaJfK9CmAdL6AST2A0gLCBD9WoE2DZDWDyCxH0BaQIDo1wq0aYC0fgCJ/QDSAgJEv1agTQOk9QNI7AeQFhAg+rUCbRogrR9AYj+AtIAA0a8VaNMAaf0AEvsBpAUEiH6tQJsGSOsHkNgPIC0gQPRrBdo0QFo/gMR+AGkBAaJfK9CmAdL6AST2A0gLCBD9WoE2DZDWDyCxH0BaQIDo1wq0aYC0fgCJ/QDSAgJEv1agTQOk9QNI7AeQFhAg+rUCbRogrR9AYj+AtIAA0a8VaNMAaf0AEvsBpAUEiH6tQJsGSOsHkNgPIC0gQPRrBdr02eUf31+2n7ja0zfuvrvaAeLTf/73y/gLV3v8r88uRgd48PWD0ff39tXj0fc3fb8Aia8PQFpAgLR+0z8wANl7vwBp+z0BpAUESOsHkNbPXyCtH0BaP4DEfgBpAQHS+gGk9QNI6weQ2A8gLSBAWj+AtH4Aaf0AEvsBpAUESOsHkNYPIK0fQGI/gLSAAGn9ANL6AaT1A0jsB5AWECCtH0BaP4C0fgCJ/QDSAgKk9QNI6weQ1g8gsR9AWkCAtH4Aaf0A0voBJPYDSAsIkNYPIK0fQFo/gMR+AGkBAdL6AaT1A0jrB5DYDyAtIEBaP4C0fgBp/QAS+wGkBQRI6weQ1g8grR9AYj+AtIAAaf0A0voBpPUDSOwHkBYQIK0fQFo/gLR+AIn9ANICAqT1A0jrB5DWDyCxH0BaQIC0fgBp/QDS+gEk9gNICwiQ1g8grR9AWj+AxH4AaQEB0voBpPUDSOsHkNgPIC0gQFo/gLR+AGn9ABL7AaQFBEjrB5DWDyCtH0BiP4C0gABp/QDS+gGk9QNI7AeQFhAgrR9AWj+AtH4Aif0A0gICpPUDSOsHkNYPILEfQFpAgLR+AGn9ANL6AST2A0gLCJDWDyCtH0BaP4DEfgBpAQHS+gGk9QNI6weQ2A8gLSBAWj+AtH4Aaf0AEvsBpAUESOsHkNYPIK0fQGI/gLSAAGn9ANL6AaT1A0jsB5AWECCtH0BaP4C0fgCJ/QDSAgKk9QNI6weQ1g8gsR9AWkCAtH4Aaf0A0voBJPYDSAsIkNYPIK0fQFo/gMR+AGkBAdL6AaT1A0jrB5DYDyAtIEBaP4C0fgBp/QAS+wGkBQRI6weQ1g8grR9AYj+AtIAAaf0A0voBpPUDSOwHkBYQIK0fQFq/8YDcu/jUnnDx9K3za4uvsPfPTz/A128/H72AX3/+afT9ffHlV6Pvb3q/3368M7ofQOJ6ANICAqT1m/4BBEjbL0Bav5O/QGLA4eMAaQsCyN79ANL2C5DYb/o4QNqGALJ3P4C0/QIk9ps+DpC2IYDs3Q8gbb8Aif2mjwOkbQgge/cDSNsvQGK/6eMAaRsCyN79ANL2C5DYb/o4QNqGALJ3P4C0/QIk9ps+DpC2IYDs3Q8gbb8Aif2mjwOkbQgge/cDSNsvQGK/6eMAaRsCyN79ANL2C5DYb/o4QNqGALJ3P4C0/QIk9ps+DpC2IYDs3Q8gbb8Aif2mjwOkbQgge/cDSNsvQGK/6eMAaRsCyN79ANL2C5DYb/o4QNqGALJ3P4C0/QIk9ps+DpC2IYDs3Q8gbb8Aif2mjwOkbQgge/cDSNsvQGK/6eMAaRsCyN79ANL2C5DYb/o4QNqGALJ3P4C0/QIk9ps+DpC2IYDs3Q8gbb8Aif2mjwOkbQgge/cDSNsvQGK/6eMAaRsCyN79ANL2C5DYb/o4QNqGALJ3P4C0/QIk9ps+DpC2IYDs3Q8gbb8Aif2mjwOkbQgge/cDSNsvQGK/6eMAaRsCyN79ANL2C5DYb/o4QNqGALJ3P4C0/QIk9ps+DpC2IYDs3Q8gbb8Aif2mjwOkbQgge/cDSNsvQGK/6eMAaRsCyN79ANL2C5DYb/o4QNqGALJ3P4C0/QIk9ps+DpC2IYDs3Q8gbb8Aif2mjwOkbQgge/cDSNsvQGK/6eMAaRsCyN79ANL2C5DYb/o4QNqGALJ3P4C0/QIk9ps+DpC2IYDs3Q8gbb8Aif2mjwOkbQgge/cDSNsvQGK/6eMAaRsCyN79ANL2C5DYb/o4QNqGALJ3v/GA/OfWfy/bCq729PQFX3zz79ELevntn6Pv7/zO09H39+Hdd6Pv797Fp9H3d+v82uj7e/3D69H3dwaQth+AtH4Aaf0A0voBpPUDSOt3AkgLCJDWDyCtH0BaP4C0fgCJ/QDSAgKk9QNI6weQ1g8gsR9AWkCAtH4Aaf0A0voBJPYDSAsIkNYPIK0fQFo/gMR+AGkBAdL6AaT1A0jrB5DYDyAtIEBaP4C0fgBp/QAS+wGkBQRI6weQ1g8grR9AYj+AtIAAaf0A0voBpPUDSOwHkBYQIK0fQFo/gLR+AIn9ANICAqT1A0jrB5DWDyCxH0BaQIC0fgBp/QDS+gEk9gNICwiQ1g8grR9AWj+AxH4AaQEB0voBpPUDSOsHkNgPIC0gQFo/gLR+AGn9ABL7AaQFBEjrB5DWDyCtH0BiP4C0gABp/QDS+gGk9QNI7AeQFhAgrR9AWj+AtH4Aif0A0gICpPUDSOsHkNYPILEfQFpAgLR+AGn9ANL6AST2A0gLCJDWDyCtH0BaP4DEfgBpAQHS+gGk9QNI6weQ2A8gLSBAWj+AtH4Aaf0AEvsBpAUESOsHkNYPIK0fQGI/gLSAAGn9ANL6AaT1A0jsB5AWECCtH0BaP4C0fgCJ/QDSAgKk9QNI6weQ1g8gsR9AWkCAtH4Aaf0A0voBJPYDSAsIkNYPIK0fQFo/gMR+AGkBAdL6AaT1A0jrB5DYDyAtIEBaP4C0fgBp/QAS+wGkBQRI6weQ1g8g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the minimum number of swaps to sort a vector","description":"Cody Problem 1401 asks us to sort a vector with the bubble sort algorithm and count the number of swaps needed. For example, to sort the vector [4 3 2 1] in increasing order, bubble sort requires six swaps. However, the vector can be sorted in only two swaps (4 \u0026 1 and 3 \u0026 2). \r\nWrite a function to determine the minimum number of swaps needed to sort a vector","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 93px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 46.5px; transform-origin: 407px 46.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/1401\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003eCody Problem 1401\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 308.433px 8px; transform-origin: 308.433px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e asks us to sort a vector with the bubble sort algorithm and count the number of swaps needed. For example, to sort the vector [4 3 2 1] in increasing order, bubble sort requires six swaps. However, the vector can be sorted in only two swaps (4 \u0026amp; 1 and 3 \u0026amp; 2). \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 260.083px 8px; transform-origin: 260.083px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to determine the minimum number of swaps needed to sort a vector\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = countSwaps(x)\r\n  y = length(find(x~=sort(x)));\r\nend","test_suite":"%%\r\nx = 1;\r\nassert(isequal(countSwaps(x),0))\r\n\r\n%%\r\nx = [2 1];\r\nassert(isequal(countSwaps(x),1))\r\n\r\n%%\r\nx = [3 1 2];\r\nassert(isequal(countSwaps(x),2))\r\n\r\n%%\r\nx = 1:4;\r\nassert(isequal(countSwaps(x),0))\r\n\r\n%%\r\nx = [4 2 3 1];\r\nassert(isequal(countSwaps(x),1))\r\n\r\n%%\r\nx = [1 4 2 3];\r\nassert(isequal(countSwaps(x),2))\r\n\r\n%%\r\nx = [5 2 1 4 3];\r\nassert(isequal(countSwaps(x),2))\r\n\r\n%%\r\nx = [5 2 4 1 3];\r\nassert(isequal(countSwaps(x),3))\r\n\r\n%%\r\nx = [5 1 4 2 3];\r\nassert(isequal(countSwaps(x),4))\r\n\r\n%%\r\nx = [2 4 3 5 1 6];\r\nassert(isequal(countSwaps(x),3))\r\n\r\n%%\r\nx = [2 5 3 4 1 6];\r\nassert(isequal(countSwaps(x),2))\r\n\r\n%%\r\nx = [10 8 7 6 2 3 1 5 4 9];\r\nassert(isequal(countSwaps(x),8))\r\n\r\n%%\r\nx = [8 1 10 6 3 2 4 5 7 9];\r\nassert(isequal(countSwaps(x),9))\r\n\r\n%%\r\nx = [12 14 13 18 9 2 7 5 16 10 8 4 11 6 3 1 17 15];\r\nassert(isequal(countSwaps(x),13))\r\n\r\n%%\r\nx = [87 79 11 21 140 69 109 126 24 162 10 142 91 7 174 90 157 124 42 147 84 4 96 34 143 95 2 116 101 56 88 134 160 177 85 65 180 35 81 139 179 128 20 137 141 93 151 156 135 28 98 163 45 33 13 37 61 59 19 43 17 166 82 14 123 54 60 120 127 149 80 168 146 30 171 169 119 108 136 75 117 78 18 15 164 175 104 89 118 155 165 145 133 125 58 112 150 5 132 130 38 105 57 97 121 110 26 29 3 115 36 6 70 178 12 74 50 122 39 153 158 83 167 27 66 40 172 138 176 86 152 8 92 129 51 114 159 76 16 113 44 48 99 131 63 62 71 144 72 68 25 67 53 49 170 173 1 100 103 52 9 64 47 94 55 46 111 161 77 31 32 107 73 154 106 41 22 148 23 102];\r\nassert(isequal(countSwaps(x),174))\r\n\r\n%%\r\nx = [169 154 170 136 92 138 108 172 144 94 33 21 23 22 8 146 53 93 145 153 95 31 25 104 90 27 58 71 13 64 65 5 74 137 30 117 125 143 85 51 68 89 157 48 176 11 115 79 91 178 52 39 98 120 107 131 82 18 158 174 142 173 45 50 38 7 105 165 84 97 101 10 81 67 111 109 46 160 139 155 47 114 40 126 110 66 179 20 41 69 86 166 49 4 128 167 124 87 57 56 17 24 132 43 3 149 103 123 100 135 60 121 34 80 2 63 156 118 147 159 28 72 55 16 168 96 127 73 175 164 141 140 76 36 133 19 152 6 113 129 163 9 1 44 29 134 150 177 130 99 12 35 37 112 119 14 32 70 161 180 106 15 42 148 102 88 171 162 116 75 122 54 61 62 78 83 59 151 26 77];\r\nassert(isequal(countSwaps(x),173))\r\n\r\n%%\r\nx = 1:10000;\r\nr = randperm(10000);\r\nn = randi(1000);\r\ni1 = r(1:2:2*n-1);\r\ni2 = r(2:2:2*n);\r\na = x(i1);\r\nx(i1) = x(i2);\r\nx(i2) = a;\r\nassert(isequal(countSwaps(x),n))\r\n\r\n%%\r\nfiletext = fileread('countSwaps.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext, 'regexp'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2023-04-15T15:26:41.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-04-15T15:26:35.000Z","updated_at":"2023-04-15T15:26:41.000Z","published_at":"2023-04-15T15:26:41.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/1401\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCody Problem 1401\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e asks us to sort a vector with the bubble sort algorithm and count the number of swaps needed. For example, to sort the vector [4 3 2 1] in increasing order, bubble sort requires six swaps. However, the vector can be sorted in only two swaps (4 \u0026amp; 1 and 3 \u0026amp; 2). \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to determine the minimum number of swaps needed to sort a vector\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1907,"title":"Capture the flag(s)","description":"Flags are distributed randomly on a large board. Starting from the corner position your goal is to capture as many flags as possible in at most N moves.\r\nDescription:\r\nThe board is described by a matrix B with 1's at the flag positions, and 0's otherwise.\r\nE.g.\r\n B = [0 0 1 1; \r\n      0 0 1 1;\r\n      0 0 1 0];\r\n\r\n N = 6;\r\nYou are starting at the top-left corner (row=1, col=1) and are allowed N steps (steps are up/down/left/right movements, no diagonal movements allowed).\r\nReturn a trajectory attempting to maximize the number of flags captured. The output of your function should be a Nx2 matrix of the form [row, col] (not including the initial [1,1] position) visiting as many flags as possible.\r\nE.g.\r\n path = [1 2;\r\n         1 3;\r\n         1 4;\r\n         2 4;\r\n         2 3;\r\n         3 3];\r\nThis solution captures all 5 flags on the board.\r\nScoring:\r\nYour function will receive a score equal to the number of non-visited flags across all 50 of the testsuite problems. You need to leave at most 10,000 flags univisited (among 50,000 total flags) to pass this problem.\r\nNote:\r\nThe boards and number of movements allowed will be large. Optimizing over all possible trajectories is very likely to time out.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 676px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 401px 338px; transform-origin: 401px 338px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFlags are distributed randomly on a large board. Starting from the corner position your goal is to capture as many flags as possible in at most N moves.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eDescription\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe board is described by a matrix\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eB\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e with 1's at the flag positions, and 0's otherwise.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE.g.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 90px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 397px 45px; transform-origin: 397px 45px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e B = [0 0 1 1; \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e      0 0 1 1;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e      0 0 1 0];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e N = 6;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou are starting at the top-left corner (row=1, col=1) and are allowed\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps (steps are up/down/left/right movements, no diagonal movements allowed).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn a trajectory attempting to maximize the number of flags captured. The output of your function should be a\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eNx2\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matrix of the form\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e[row, col]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (not including the initial [1,1] position) visiting as many flags as possible.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE.g.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 108px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 397px 54px; transform-origin: 397px 54px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e path = [1 2;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         1 3;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         1 4;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         2 4;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         2 3;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         3 3];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis solution captures all 5 flags on the board.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eScoring\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour function will receive a score equal to the number of non-visited flags across all 50 of the testsuite problems. You need to leave at most 10,000 flags univisited (among 50,000 total flags) to pass this problem.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNote\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe boards and number of movements allowed will be large. Optimizing over all possible trajectories is very likely to time out.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function path = capture_the_flag(B,N)\r\npath=[1 2];\r\n","test_suite":"%%\r\n% test cases\r\n\r\nrandn('seed',0);\r\nrand('seed',0);\r\nN=randi([1000 4000],50,1);\r\nS=randi([1,50],50,1);\r\nBoards=arrayfun(@(s)convn(randn(100),ones(s)/s^2,'same'),S,'uni',0); \r\n\r\nFLAGSLEFT=0;\r\nDOPLOT=false;\r\ntic;\r\nfor board=1:50\r\n B=Boards{board};\r\n sB=sort(B(:));\r\n B=double(B\u003esB(round(numel(sB)*.9)));\r\n n=N(board);\r\n path=capture_the_flag(B,n);\r\n assert(size(path,1)\u003c=n,'too many steps');\r\n assert(all(sum(abs(diff([1,1;path])),2)\u003c=1),'no jumping allowed');\r\n if DOPLOT\r\n    imagesc(B);\r\n    hold on;\r\n    plot(path(:,2),path(:,1),'y-');\r\n    hold off;\r\n    axis equal;\r\n    axis off;\r\n    set(gcf,'color',0*[1 1 1]);\r\n    colormap(.5*gray);\r\n    drawnow;\r\n end\r\n B(1)=0;\r\n B((path-1)*[1;size(B,1)]+1)=0;\r\n fprintf('test %d; left %d flags\\n',board,nnz(B));\r\n FLAGSLEFT=FLAGSLEFT+nnz(B);\r\nend\r\ntoc;","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":43,"edited_by":1,"edited_at":"2026-02-11T16:03:17.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2026-02-11T16:03:17.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-10-01T05:38:22.000Z","updated_at":"2026-03-16T11:31:01.000Z","published_at":"2013-10-01T06:27:29.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFlags are distributed randomly on a large board. Starting from the corner position your goal is to capture as many flags as possible in at most N moves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDescription\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe board is described by a matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eB\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e with 1's at the flag positions, and 0's otherwise.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eE.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ B = [0 0 1 1; \\n      0 0 1 1;\\n      0 0 1 0];\\n\\n N = 6;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are starting at the top-left corner (row=1, col=1) and are allowed\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e steps (steps are up/down/left/right movements, no diagonal movements allowed).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn a trajectory attempting to maximize the number of flags captured. The output of your function should be a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNx2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e matrix of the form\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[row, col]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (not including the initial [1,1] position) visiting as many flags as possible.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eE.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ path = [1 2;\\n         1 3;\\n         1 4;\\n         2 4;\\n         2 3;\\n         3 3];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis solution captures all 5 flags on the board.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour function will receive a score equal to the number of non-visited flags across all 50 of the testsuite problems. You need to leave at most 10,000 flags univisited (among 50,000 total flags) to pass this problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe boards and number of movements allowed will be large. Optimizing over all possible trajectories is very likely to time out.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":47058,"title":"Determine the Zeckendorf expansion of a number","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 135px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 67.5px; transform-origin: 407px 67.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14px 8.05px; transform-origin: 14px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Goldbach%27s_conjecture\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eGoldbach conjecture\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 294.442px 8.05px; transform-origin: 294.442px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e deals with expressing integers as sums of primes. One might also seek to express integers as sums of other numbers, such as the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Fibonacci_number\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eFibonacci numbers\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 199.892px 8.05px; transform-origin: 199.892px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, which begin 0, 1, 1, 2, 3, 5, 8, 13, 21... For example, 26 can be written as 13+8+5 or 21+3+2. \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Zeckendorf%27s_theorem\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eZeckendorf\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 255.608px 8.05px; transform-origin: 255.608px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (and Lekkerkerker before him) showed that every positive integer can be uniquely expressed as the sum of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 50.9667px 8.05px; transform-origin: 50.9667px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003enon-consecutive\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 196.242px 8.05px; transform-origin: 196.242px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e Fibonacci numbers. The Zeckendorf expansion for 26 is 21+5. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 372.5px 8.05px; transform-origin: 372.5px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to determine the Zeckendorf expansion of the input number. In particular, output in decreasing order the Fibonacci numbers to be used in the sum.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Zeckendorf(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 26;\r\ny_correct = [21 5];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 26;\r\ny_correct = [21 5];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 88;\r\ny_correct = [55 21 8 3 1];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 965;\r\ny_correct = [610 233 89 21 8 3 1];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 4180;\r\ny_correct = [2584 987 377 144 55 21 8 3 1];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 75024;\r\ny_correct = [46368 17711 6765 2584 987 377 144 55 21 8 3 1];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 514228;\r\ny_correct = [317811 121393 46368 17711 6765 2584 987 377 144 55 21 8 3 1];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 514229;\r\ny_correct = 514229;\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 8675309;\r\ny_correct = [5702887 2178309 514229 196418 75025 6765 1597 55 21 3];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 2022243254;\r\ny_correct = [1836311903 165580141 14930352 3524578 1346269 514229 28657 6765 233 89 34 3 1];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n\r\n%%\r\nx = 314159265358;\r\ny_correct = [225851433717 86267571272 1836311903 165580141 24157817 9227465 3524578 1346269 75025 28657 6765 1597 144 8];\r\nassert(isequal(Zeckendorf(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-24T08:38:37.000Z","updated_at":"2020-10-24T09:11:46.000Z","published_at":"2020-10-24T09:11:46.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Goldbach%27s_conjecture\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGoldbach conjecture\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e deals with expressing integers as sums of primes. One might also seek to express integers as sums of other numbers, such as the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Fibonacci_number\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eFibonacci numbers\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, which begin 0, 1, 1, 2, 3, 5, 8, 13, 21... For example, 26 can be written as 13+8+5 or 21+3+2. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Zeckendorf%27s_theorem\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eZeckendorf\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (and Lekkerkerker before him) showed that every positive integer can be uniquely expressed as the sum of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enon-consecutive\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Fibonacci numbers. The Zeckendorf expansion for 26 is 21+5. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to determine the Zeckendorf expansion of the input number. In particular, output in decreasing order the Fibonacci numbers to be used in the sum.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":49572,"title":"Compute the largest number possible with the Brussels choice","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 235.633px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 117.817px; transform-origin: 407px 117.817px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.208px 7.79167px; transform-origin: 374.208px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Brussels choice (or Choix de Bruxelles) operation changes one number to another by taking a substring of the digits and either doubling it or (if the number is even) halving it. For example, starting with 9 and highlighting substrings, one can generate this sequence:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.89167px 7.79167px; transform-origin: 3.89167px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e9\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.79167px; transform-origin: 3.88333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 7.79167px; transform-origin: 7.78333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e18\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.775px 7.79167px; transform-origin: 7.775px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, 3\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.89167px 7.79167px; transform-origin: 3.89167px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e6\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.79167px; transform-origin: 3.88333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 7.79167px; transform-origin: 7.78333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e31\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 11.6667px 7.79167px; transform-origin: 11.6667px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e2, 6\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 7.79167px; transform-origin: 7.78333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e22\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.79167px; transform-origin: 3.88333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 11.675px 7.79167px; transform-origin: 11.675px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e644\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.775px 7.79167px; transform-origin: 7.775px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, 1\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.89167px 7.79167px; transform-origin: 3.89167px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e2\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 35px 7.79167px; transform-origin: 35px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e88, 1488,...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 65.7417px 7.79167px; transform-origin: 65.7417px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAnother possibility is \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.89167px 7.79167px; transform-origin: 3.89167px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e9\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.775px 7.79167px; transform-origin: 7.775px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, 1\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.89167px 7.79167px; transform-origin: 3.89167px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e8\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.79167px; transform-origin: 3.88333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 11.2917px 7.79167px; transform-origin: 11.2917px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e116\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.79167px; transform-origin: 3.88333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 11.675px 7.79167px; transform-origin: 11.675px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e232\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.79167px; transform-origin: 3.88333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 7.79167px; transform-origin: 7.78333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e46\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.775px 7.79167px; transform-origin: 7.775px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e4, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 7.79167px; transform-origin: 7.78333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e92\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 15.5583px 7.79167px; transform-origin: 15.5583px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e4, 18\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 7.79167px; transform-origin: 7.78333px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e44\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 27.2167px 7.79167px; transform-origin: 27.2167px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, 1888,...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 296.767px 7.79167px; transform-origin: 296.767px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBoth of these excerpts have eight entries, but the second ends in a larger number than the first. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 43.6333px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21.8167px; text-align: left; transform-origin: 384px 21.8167px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 129.008px 7.79167px; transform-origin: 129.008px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that takes an initial value \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB8AAAAoCAYAAAAG0SEsAAABPElEQVRYhe2WTRGDMBBGnwccxAAGUFAFdYADHNRCNCABD1hAAxbaQ3aHNCRp2kJ7yZvJgSzsx/4FoFKpVCqVP9ECPTAAnex1ct2eKToBi4h3gAVm4C7LnCF8FecL0AS2u2f7qbAvfjta+OI57yJ288L+MQ2wiuMxcY9mZSWeFcO+OYsY2KJKdfEk9inx/CqiBteYqSB2aNQxx4hTfbkhsGm5em9PS/SyN3KOlYl0ZhbiozfjgsqOpJ/yS8Tes2UmrHfr7YdY9hl5S/yKi2zkuSxWntMmnDN+bU5c316dN7IGtnnXyK0sbSYVyInHbE9oZP4a2VK8BvslAsXi4FI+4GoUNknqY3KY+CfomOXEi+f9XXSeYx+aom7/ltyc507MQ8idcNkxO4ob29ne4KLWsf0JBtdkp/5iVSqVIh6VmIITWgIeSQAAAABJRU5ErkJggg==\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 112.417px 7.79167px; transform-origin: 112.417px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and determines the largest number \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB8AAAAoCAYAAAAG0SEsAAABNklEQVRYhe2WW7GDMBBAjwccYAADUYCCOMABDq4FNCAhHmqhGrDA/WB3ss2E0mGS9idnJh9kyS77DNBoNBqNxo8YgAmYASd7Tp6HmkYD8BTjDliAB7DL6msY9qL8CXSJbDeyrxq2xv9KGx6NcpeR9xfy23TAJorXk3c0Khv5qNxmJnp1VsVB5KGkYYhenyl25uPmkoY/URy4jswtbMjHjHwiRqZqvlPjnqPtVl7TsvAapUHOennuRe65YDDGA4dnnRzWflfPF1m2I/S9nSNKXvRssi4noXpm10oM8Zbsp+jYnYgDSOvkozE8ihdT5sC7y6QjTkXbLbt8VFV0+FgvtYOKj+GUhVgPihZxrnuKosVmU6X5ropeNja3WgOB+B9QBc237XnNd6DCPWDRDkmn3iSr0Wj8hn/n1nOR3s62vwAAAABJRU5ErkJggg==\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 46.2833px 7.79167px; transform-origin: 46.2833px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e resulting after \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 63px 7.79167px; transform-origin: 63px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps. In the above examples, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB8AAAAoCAYAAAAG0SEsAAABPElEQVRYhe2WTRGDMBBGnwccxAAGUFAFdYADHNRCNCABD1hAAxbaQ3aHNCRp2kJ7yZvJgSzsx/4FoFKpVCqVP9ECPTAAnex1ct2eKToBi4h3gAVm4C7LnCF8FecL0AS2u2f7qbAvfjta+OI57yJ288L+MQ2wiuMxcY9mZSWeFcO+OYsY2KJKdfEk9inx/CqiBteYqSB2aNQxx4hTfbkhsGm5em9PS/SyN3KOlYl0ZhbiozfjgsqOpJ/yS8Tes2UmrHfr7YdY9hl5S/yKi2zkuSxWntMmnDN+bU5c316dN7IGtnnXyK0sbSYVyInHbE9oZP4a2VK8BvslAsXi4FI+4GoUNknqY3KY+CfomOXEi+f9XXSeYx+aom7/ltyc507MQ8idcNkxO4ob29ne4KLWsf0JBtdkp/5iVSqVIh6VmIITWgIeSQAAAABJRU5ErkJggg==\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 27.425px 7.79167px; transform-origin: 27.425px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e = 9 and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 36.5px; height: 18px;\" width=\"36.5\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 129.517px 7.79167px; transform-origin: 129.517px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Return the number as a character string.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function a = ChoixdeBruxelles(a0,n)\r\n%  a0 = initial value in the sequence\r\n%  n  = number of steps\r\n  a = f(n);\r\nend","test_suite":"%%\r\na0 = 1;\r\nn = 0;\r\na_correct = '1';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 1;\r\nn = 4;\r\na_correct = '16';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 1;\r\nn = 5;\r\na_correct = '112';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 1;\r\nn = 10;\r\na_correct = '88224';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 1;\r\nn = 20;\r\na_correct = '811281128164416';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 1;\r\nn = 30;\r\na_correct = '8112811281128112811288224';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 1;\r\nn = 50;\r\na_correct = '811281128112811281128112811281128112811288224';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 2;\r\nn = 29;\r\na_correct = '8112811281128112811288224';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 3;\r\nn = 20;\r\na_correct = '11281128112816448';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 7;\r\nn = 4;\r\na_correct = '2112';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 7;\r\nn = 15;\r\na_correct = '1681128164416';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 7;\r\nn = 50;\r\na_correct = '168112811281128112811281128112811281128112816448';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 29;\r\nn = 6;\r\na_correct = '844416';\r\nassert(isequal(ChoixdeBruxelles(a0,n),a_correct))\r\n\r\n%%\r\na0 = 11;\r\nn = 37;\r\na_correct = '412316860416';\r\nassert(isequal(num2str(prod(ChoixdeBruxelles(a0,n)-'0')),a_correct))","published":true,"deleted":false,"likes_count":3,"comments_count":2,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":"2021-01-02T23:31:04.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2020-12-23T22:47:46.000Z","updated_at":"2025-12-16T15:11:43.000Z","published_at":"2020-12-23T23:16:59.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Brussels choice (or Choix de Bruxelles) operation changes one number to another by taking a substring of the digits and either doubling it or (if the number is even) halving it. For example, starting with 9 and highlighting substrings, one can generate this sequence:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e9\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e18\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, 3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e6\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e31\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e2, 6\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e22\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e644\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, 1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e88, 1488,...\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAnother possibility is \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e9\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, 1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e8\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e116\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e232\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e46\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e4, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e92\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e4, 18\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e44\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, 1888,...\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBoth of these excerpts have eight entries, but the second ends in a larger number than the first. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that takes an initial value \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and determines the largest number \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e resulting after \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e steps. In the above examples, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e = 9 and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en = 7\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. Return the number as a character string.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58528,"title":"ICFP 2023:Orchestra - Greedy algorithm for Musician Placement","description":"The ICFP 2023 Competition in July was to place musicians on a stage to maximize the attendees net happiness. The musicians played various instruments with attendees having preference values for each instrument type. Musicians could block attendees from seeing musicians behind them. Blocking occurs if a_i to m_j vector touched within 5 of m_k. No musicians allowed within 10 of one another. Vector check is being done with a prior Cody 1446 solution variant.  Volume per musician was allowed but not in this Challenge. Happiness drops off as the square of distance between musician and attendee.\r\nThe ICFP 2023 site will hopefully have contestant writeups. The ICFP 2023 Orchestra Spec shows details of the contest. I modified the problem contents to the sufficient elements regarding the stage. The joys of JSON can be viewed. The musician types were upgraded by 1 to be non-zero based.\r\n\r\nThis Challenge is to place the 16 musicians onto the stage,mxy, of Problem 22 using a Greedy algorithm using provided Happiness tables. Score an Attendee happiness of higher than 46 million; Best known 47221761. The standard inputs are detailed in the function template and include axy-attendee positions, am-attendee happiness factors to each instrument, mu-instruments of each musician, stage limits-[xmin xmax ymin ymax]. This stage is a line of ymin=ymax=10, xmin=10, and xmax=990, seen in the image below. The special performance tables provide Joy values for Musician types vs X. Table mscrit, matrix_score_XvsType, assumes no neighbors and mscrit10 assumes neighbors at +/-10 blocking thus gives less Joy.\r\nIndividual attendee happiness= 1000000*am(i,mu(j))/d2(i,j) for attendee-i and musician-j where d2 is distance squared.\r\nSimplest solution would be mxy(:,1)=10:10:160 where mxy(:,2)=10 but this scores only 13M suffering from 1/d2 and blocks. \r\nPlease submit the template to view the tables and graphs. The graphs show the mscrit and mscrit10 information.\r\n \r\nThis is the Orchestra room problem 22 showing the attendees and the stage. \r\nThe stage is the line on the bottom at Y=10 extending in X 10:990 .","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 862.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 431.25px; transform-origin: 407px 431.25px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 126px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 367px 8px; transform-origin: 367px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe ICFP 2023 Competition in July was to place musicians on a stage to maximize the attendees net happiness. The musicians played various instruments with attendees having preference values for each instrument type. Musicians could block attendees from seeing musicians behind them. Blocking occurs if a_i to m_j vector touched within 5 of m_k. No musicians allowed within 10 of one another. Vector check is being done with a prior Cody 1446 solution variant.  Volume per musician was allowed but not in this Challenge. Happiness drops off as the square of distance between musician and attendee.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 201.5px 8px; transform-origin: 201.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe ICFP 2023 site will hopefully have contestant writeups. The \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://drive.google.com/file/d/16GFrZMudBrNwjMi3tOaP_iiSHh5pUtXL/view?usp=sharing\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP 2023 Orchestra Spec\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 96.5px 8px; transform-origin: 96.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e shows details of the contest. I modified the problem contents to the sufficient elements regarding the stage. The joys of JSON can be viewed. The musician types were upgraded by 1 to be non-zero based.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 377.5px 8px; transform-origin: 377.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis Challenge is to place the 16 musicians onto the stage,mxy, of Problem 22 using a Greedy algorithm using provided Happiness tables. Score an Attendee happiness of higher than 46 million; Best known 47221761. The standard inputs are detailed in the function template and include axy-attendee positions, am-attendee happiness factors to each instrument, mu-instruments of each musician, stage limits-[xmin xmax ymin ymax]. This stage is a line of ymin=ymax=10, xmin=10, and xmax=990, seen in the image below. The special performance tables provide Joy values for Musician types vs X. Table mscrit, matrix_score_XvsType, assumes no neighbors and mscrit10 assumes neighbors at +/-10 blocking thus gives less Joy.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 373px 8px; transform-origin: 373px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIndividual attendee happiness= 1000000*am(i,mu(j))/d2(i,j) for attendee-i and musician-j where d2 is distance squared.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSimplest solution would be mxy(:,1)=10:10:160 where mxy(:,2)=10 but this scores only 13M suffering from 1/d2 and blocks. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 352px 8px; transform-origin: 352px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ePlease submit the template to view the tables and graphs. The graphs show the mscrit and mscrit10 information.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 319.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 159.75px; text-align: left; transform-origin: 384px 159.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 860px;height: 314px\" 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\" data-image-state=\"image-loaded\" width=\"860\" height=\"314\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 244px 8px; transform-origin: 244px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis is the Orchestra room problem 22 showing the attendees and the stage. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 210px 8px; transform-origin: 210px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe stage is the line on the bottom at Y=10 extending in X 10:990 .\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function mxy=Process_orc(d,mu,axy,am,pxyr,mscrit,mscrit10,min_score);\r\n% d [1000 1000 10 990 10 10] room_width room_h xmin xmax ymin ymax\r\n% mu [1 2 3 4 5 6 7 8 9 3 4 7 2 1 2 4]  the 16 musicians who have various instruments 1:9\r\n% axy [400,2]  (x,y) position for the 400 attendees\r\n% am  [400,9]  Each attendee's happiness co-factors per instrument types 1:9 seen in mu\r\n% mscrit [9,1000]  musician types, x-axis-pos   Score assuming no other musicians\r\n% mscrit10 [9,1000]  musician types, x-axis-pos Score assuming musicians at +/-10 blocking\r\n\r\n% No two mu may be within 10 of one another\r\n% If any other mxy is within a distance of 5 from the segment axy_i to mxy_j then Happiness_i_j=0\r\n%  This stage is on a line so there is minimal blocking\r\n%  The min distance from point to a segment is Cody \r\n%   https://www.mathworks.com/matlabcentral/cody/problems/1446\r\n%   The smallest solutions are often very slow\r\n%\r\n% Happiness_i_j=1000000*am(i,mu(j))/d2am(i,j); where d2am(i,j) distance squared axy_i,mxy_j\r\n% Score is sum of Happiness\r\n% Best known solution is 47221761 for problem 22\r\n\r\n% min_score of 46M can be achieved with either mscrit or mscrit10.\r\n\r\n  mxy=ones(length(mu),2)*d(5); % d(5) is ymin. This problem's stage is a line\r\n  mxy(:,1)=0;\r\n  \r\n  %Greedy Algorithm Scheme\r\n  %Place best scoring (Musician_type,X) of table type being used\r\n  % find [mu_type,x-position] of maximum score available in updating Table\r\n  % find musician of mu_type remaining to be placed, mu_loc\r\n  % mxy(mu_loc,1)=x-position\r\n  % clear placed mu \r\n  % if no musician of mu_type remains clear table of mu_type, wT(mu_type,:)=-Inf\r\n  % clear table for x_position +/-9 for all mu_type\r\n  % repeat until all musicians placed\r\n  \r\n  % Next line commented to execute greedy\r\n  mxy(:,1)=10:10:160; % Template weak solution to see Tables and figures.\r\n  \r\n  % Working tables that will be destroyed\r\n  wmu=mu;      % vector of musician types\r\n  wT=mscrit;   % Scores of musician type vs X assuming no neighbors blocking\r\n  wT=mscrit10; % Scores of musician type vs X assuming +/-10 are occupied and block\r\n  \r\n  tic\r\n  while nnz(mxy(:,1)==0)\r\n   if toc\u003e5,break;end % avoiding infinite loop\r\n   [mu_type,x]=find(wT==max(wT(:)),1,'first');\r\n   % 6  more lines here for a  complete greedy algorithm\r\n   %\r\n   % Block X-positions less than 10 of placed musician,assuming integers only, for all types\r\n   wT(:,x-9:x+9)=-Inf; % 0 is not sufficient as values may be negative\r\n  end % while mxy has unfilled cells\r\n  \r\n  %Greedy algorithm may be based on first/last/random max value\r\n  % The threshold may also be varied to .98*max() and then randomly select node to play\r\n  \r\n  \r\n  %mxy(:,2)=ymin;\r\n  %mxy(1,1)=10; %Manual entry\r\n  % ...\r\n  %mxy(16,1)=990;\r\n  \r\n  %mxy(:,1)=[10 40 ... 990];\r\nend","test_suite":"%%\r\n%Google Drive Dowloads need to come from shared files\r\n% Tweak link: file/d/ to uc?export=download\u0026id=   while removing /view?usp=sharing\r\n% https://drive.google.com/file/d/1v3GsGgP3p905wzdvUqypL_-djYmxiyzK/view?usp=sharing\r\n% https://drive.google.com/uc?export=download\u0026id=1v3GsGgP3p905wzdvUqypL_-djYmxiyzK\r\n fname='orc_d_mu_axy_am_pxyr.mat';\r\n %orc is a cell array orc{90} for the 90 Problems in ICFP 2023 Orchestra Competition\r\n \r\n % fn.mat  https://drive.google.com/file/d/10GsOZTIjzMIuO7xAYIqLT1zIq9Cyubl-/view?usp=drive_link\r\n % Google Gives warning thus aborts urlwrite\r\n %\r\n %fname='orc_d_mu_axy_am_pxyr.pdf'\r\n %Fake name of .pdf on GoogleDrive,  write as a mat\r\n \r\n url='https://drive.google.com/file/d/1mgxzsmVQNXgqHEdd61QR2r0STm3N9lgG/view?usp=drive_link';\r\n ptr=strfind(url,'/view'); % Tweaking the url\r\n url(ptr:end)=[];\r\n url=strrep(url,'file/d/','uc?export=download\u0026id=');\r\n \r\n tic\r\n urlwrite(url,fname); %Writing GoogleDrive orc.pdf into orc.mat\r\n fprintf('Download 14MB Time: %.1f  sec\\n\\n',toc); %14MB download Time, about 1-3 sec\r\n \r\ndir_struct=dir;\r\n\r\nfor i=1:size(dir_struct,1)\r\n fprintf('%i %s %i\\n',i,dir_struct(i).name,dir_struct(i).bytes)\r\nend\r\n\r\ntic\r\nload(fname);\r\nfprintf('\\n\\nmat Load Time: %.1f\\n',toc); %Load Time of orc from .mat, 0.1 sec\r\n\r\n%fname='orc_d_mu_axy_am_pxyr.mat'  orc cell array of size 90\r\npid=22; % Stage is a Line [10:990, 10:10] in what they call [X,Y]\r\nmin_score=46000000; %Best known score 47221761\r\nd=orc{pid}.d; %[1000 1000 10 990 10 10] room_width room_h xmin xmax ymin ymax\r\nmu=orc{pid}.mu; %[1 2 3 4 5 6 7 8 9 3 4 7 2 1 2 4]\r\naxy=orc{pid}.axy; %[400,2]\r\nam=orc{pid}.am;  %[400,9]  there are 9 musician types 1:9 seen in mu\r\npxyr=orc{pid}.pxyr; %[0,3] Pillars that do not exist in pid 22\r\nrw=d(1);rh=d(2);xmin=d(3);xmax=d(4);ymin=d(5);ymax=d(6);\r\nfprintf('xmin:%i xmax:%i ymin:%i ymax:%i\\n',d(3:6));\r\n\r\n Lmu=length(mu); % number of musicians\r\n nmut=max(mu); % number of musician types\r\n na=size(axy,1); % number of attendees\r\n mscr=zeros(Lmu,1); % Track individual scores\r\n mscrji=zeros(Lmu,na); % Scores of all attendees to placed musician location\r\n mscrit=zeros(nmut,rw);\r\n mscrit10=zeros(nmut,rw);\r\n d2am=zeros(na,Lmu); \r\n d2ax=ones(na,xmax)*Inf; \r\n \r\n tic\r\n for x=xmin:xmax\r\n  d2ax(:,x)=sum((axy-[x,ymin]).^2,2);\r\n end\r\n \r\n myj=10;\r\n myk=10;\r\n for i=1:na\r\n   ax=axy(i,1);\r\n   ay=axy(i,2);\r\n   for k=1:nmut\r\n    for x=xmin:xmax\r\n     mscrit(k,x)=mscrit(k,x)+1000000*am(i,k)/d2ax(i,x); % Calc k,x assume no issues\r\n     \r\n    %Calc Dist from vector [(mxj,myj),(ax,ay)] to point [mxk,myk]\r\n    %dist2 (x+10,ymin,x,ymin,ax,ay)\u003c=25 % x+10\r\n    mxj=x;\r\n    mxk=x+10; %check if adj blocks\r\n    d2=100; % set to valid\r\n    sL2=(mxj-ax)^2+(myj-ay)^2;\r\n    t=( (mxk-mxj)*(ax-mxj)+(myk-myj)*(ay-myj) )/sL2;\r\n    if t\u003e=0\r\n     if t\u003c=1\r\n      sx=mxj+t*(ax-mxj);\r\n      sy=myj+t*(ay-myj);\r\n      d2=(mxk-sx)^2+(myk-sy)^2;\r\n     end\r\n    end\r\n    if d2\u003c=25,continue;end\r\n    \r\n    %dist2 (x-10,ymin,x,ymin,ax,ay)\u003c=25 % x-10\r\n    mxk=x-10; % check if adj blocks\r\n    d2=100; % set to valid\r\n    sL2=(mxj-ax)^2+(myj-ay)^2;\r\n    t=( (mxk-mxj)*(ax-mxj)+(myk-myj)*(ay-myj) )/sL2;\r\n    if t\u003e=0\r\n     if t\u003c=1\r\n      sx=mxj+t*(ax-mxj);\r\n      sy=myj+t*(ay-myj);\r\n      d2=(mxk-sx)^2+(myk-sy)^2;\r\n     end\r\n    end\r\n    if d2\u003c=25,continue;end\r\n    \r\n     % Calc k,x assumes possible blocks\r\n     mscrit10(k,x)=mscrit10(k,x)+1000000*am(i,k)/d2ax(i,x); \r\n    end % x\r\n  end % k nmut\r\n end % i na\r\n\r\n%Main Function Call\r\nmxy=Process_orc(d,mu,axy,am,pxyr,mscrit,mscrit10,min_score);\r\n\r\n%Fixing out of bounds values\r\nmxy(:,2)=ymin;\r\nmxy(mxy(:,1)\u003cxmin,1)=xmin-10; \r\nmxy(mxy(:,1)\u003exmax,1)=xmin-10;\r\n\r\n%Any players with distance\u003c10 from any other player gets put to xmin-10\r\n\r\ngap_fail=mu\u003eInf; % Logical zero of size mu\r\nfor i=1:length(mu)\r\n mxy_dist=(mxy(:,1)-mxy(i,1)).^2;\r\n mxy_dist(i)=100; % set self distance to 100\r\n if min(mxy_dist)\u003c100, gap_fail(i)=1;end\r\nend\r\nmxy(gap_fail==1,1)=xmin-10; % Throw all gap failers to xmin, No pts will be scored \r\n\r\n% Calc dist squared from each a(attendee) to each m(Musician)\r\n for j=1:Lmu\r\n  d2am(:,j)=sum((axy-mxy(j,:)).^2,2);\r\n end\r\n \r\n scr=0;\r\n \r\n for i=1:na\r\n   ax=axy(i,1);\r\n   ay=axy(i,2);\r\n   \r\n  for j=1:Lmu\r\n   mscrji(j,i)=1000000*am(i,mu(j))/d2am(i,j); % Calc i,j assume no issues\r\n   \r\n   mxj=mxy(j,1);\r\n   if mxj\u003cxmin,continue;end % Skip failed musician placements\r\n   myj=mxy(j,2);\r\n   blocked=0;\r\n   \r\n   for k=1:Lmu %check of musician mxy(k,:) blocks mxy(j,:) from LOS to axy(i,:)\r\n    if k==j,continue;end % can't block self\r\n    mxk=mxy(k,1);\r\n    if mxk\u003cxmin,continue;end % Skip failed musician placements\r\n    myk=mxy(k,2);\r\n    \r\n    %Calc Dist from vector [(mxj,myj),(ax,ay)] to point [mxk,myk]\r\n    d2=100; % set to valid\r\n    sL2=(mxj-ax)^2+(myj-ay)^2;\r\n    t=( (mxk-mxj)*(ax-mxj)+(myk-myj)*(ay-myj) )/sL2;\r\n    if t\u003e=0\r\n     if t\u003c=1\r\n      sx=mxj+t*(ax-mxj);\r\n      sy=myj+t*(ay-myj);\r\n      d2=(mxk-sx)^2+(myk-sy)^2;\r\n     end\r\n    end\r\n    \r\n    if d2\u003c=25  %No one musician adj to vector within 5 or 25 for dist squared\r\n     blocked=1;\r\n     break; %exit upon block determination\r\n    end\r\n \r\n   end % k Lmu\r\n   % Blocked musician-j by any musician scores 0 for this attendee-i\r\n   if blocked==1,continue;end \r\n   \r\n   %musicians play different instruments\r\n   %attendees have like(+) and dislike(-) negative co-factors\r\n   %mu(j) gives instrument of musician\r\n   %am(i,mu(j)) gives attendee Happiness for insturment mu(j)\r\n   mscr(j)=mscr(j)+1000000*am(i,mu(j))/d2am(i,j); %Tracking indiv musician score\r\n   scr=scr+1000000*am(i,mu(j))/d2am(i,j);\r\n  end % j Lmu\r\n end % na\r\n \r\n \r\n for i=1:Lmu\r\n  fprintf('Player %2i xpos: %3i  Instr:%i  scr: %10.0f\\n',i,mxy(i,1),mu(i),mscr(i));\r\n end\r\n fprintf('\\n');\r\n \r\n fprintf('Total scr: %.0f ********************\\n\\n',scr);\r\n \r\n fprintf('Table of max Happiness X per Musician Type\\n');\r\n for i=1:nmut % 9\r\n  fprintf('Musician Type-%i  x: %3i  H: %9.0f\\n', ...\r\n      i,find(mscrit(i,:)==max(mscrit(i,:)),1,'first'),max(mscrit(i,:)));\r\n end\r\n \r\n fprintf('\\nTable of max Happiness per Musician Type; assumes adjacent Musicians blocking\\n');\r\n for i=1:nmut % 9\r\n  fprintf('Musician Type-%i  x: %3i  H: %9.0f\\n', ...\r\n      i,find(mscrit10(i,:)==max(mscrit10(i,:)),1,'first'),max(mscrit10(i,:)));\r\n end\r\n \r\n \r\n fprintf('\\n\\nThe following lines are the titles for the 7 figures lower down\\n\\n');\r\n %Figures/Titles\r\n fprintf ('Problem: %i Orchestra\\n',pid)\r\n \r\n figure(7);\r\n plot(axy(:,1),axy(:,2),'.k');hold on  % Attendees\r\n axis tight; %axis equal\r\n for i=1:size(pxyr,1) % rectangle -no matrix allowed  Pillars fast draw\r\n  rectangle('Position',[pxyr(i,1:2)-pxyr(i,3) 2*pxyr(i,3) 2*pxyr(i,3)],'Curvature',[1 1],'FaceColor',[.9 .4 .8])\r\n end\r\n room= [0 0;rw 0;rw rh;0 rh;0 0];\r\n stage=[xmin ymin;xmax ymin;xmax ymax;xmin ymax;xmin ymin];\r\n plot(stage(:,1),stage(:,2),'k');\r\n plot(room(:,1),room(:,2),'Color',[1 0 1]);\r\n plot(mxy(:,1),mxy(:,2),'.r'); % Musicians\r\n hold off\r\n \r\n \r\n%Plot of Happiness for each Musician Type as a function of X with No blocks\r\n for i=1:3 % 123 456 789 groups \r\n  fprintf('Musician Types-%i:%i rgb Happiness vs X No Blocks\\n', ...\r\n      3*(i-1)+1,3*(i-1)+3);\r\n  figure(i);plot((1:rw)',mscrit(3*(i-1)+1,:)','.r');hold on;\r\n  figure(i);plot((1:rw)',mscrit(3*(i-1)+2,:)','.','Color',[0 .8 0]);\r\n  figure(i);plot((1:rw)',mscrit(3*(i-1)+3,:)','.b');hold off;\r\n end\r\n \r\n %Plot of Happiness for each Musician Type as a function of X with blocks\r\n for i=1:3 % 9\r\n  fprintf('Musician Types-%i:%i rgb Happiness vs X with Blocks\\n', ...\r\n      3*(i-1)+1,3*(i-1)+3);\r\n  figure(i+3);plot((1:rw)',mscrit10(3*(i-1)+1,:)','.r');hold on\r\n  figure(i+3);plot((1:rw)',mscrit10(3*(i-1)+2,:)','.','Color',[0 .8 0]);\r\n  figure(i+3);plot((1:rw)',mscrit10(3*(i-1)+3,:)','.b');hold off\r\n end\r\n \r\nvalid=scr\u003emin_score; % Challenging Threshold 46M   Highest Score 47.2e6\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-07-15T18:43:58.000Z","deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-07-15T03:38:34.000Z","updated_at":"2023-07-15T18:43:58.000Z","published_at":"2023-07-15T18:37:05.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe ICFP 2023 Competition in July was to place musicians on a stage to maximize the attendees net happiness. The musicians played various instruments with attendees having preference values for each instrument type. Musicians could block attendees from seeing musicians behind them. Blocking occurs if a_i to m_j vector touched within 5 of m_k. No musicians allowed within 10 of one another. Vector check is being done with a prior Cody 1446 solution variant.  Volume per musician was allowed but not in this Challenge. Happiness drops off as the square of distance between musician and attendee.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe ICFP 2023 site will hopefully have contestant writeups. The \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://drive.google.com/file/d/16GFrZMudBrNwjMi3tOaP_iiSHh5pUtXL/view?usp=sharing\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP 2023 Orchestra Spec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e shows details of the contest. I modified the problem contents to the sufficient elements regarding the stage. The joys of JSON can be viewed. The musician types were upgraded by 1 to be non-zero based.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to place the 16 musicians onto the stage,mxy, of Problem 22 using a Greedy algorithm using provided Happiness tables. Score an Attendee happiness of higher than 46 million; Best known 47221761. The standard inputs are detailed in the function template and include axy-attendee positions, am-attendee happiness factors to each instrument, mu-instruments of each musician, stage limits-[xmin xmax ymin ymax]. This stage is a line of ymin=ymax=10, xmin=10, and xmax=990, seen in the image below. The special performance tables provide Joy values for Musician types vs X. Table mscrit, matrix_score_XvsType, assumes no neighbors and mscrit10 assumes neighbors at +/-10 blocking thus gives less Joy.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIndividual attendee happiness= 1000000*am(i,mu(j))/d2(i,j) for attendee-i and musician-j where d2 is distance squared.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSimplest solution would be mxy(:,1)=10:10:160 where mxy(:,2)=10 but this scores only 13M suffering from 1/d2 and blocks. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePlease submit the template to view the tables and graphs. The graphs show the mscrit and mscrit10 information.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"314\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"860\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is the Orchestra room problem 22 showing the attendees and the stage. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe stage is the line on the bottom at Y=10 extending in X 10:990 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58493,"title":"ICFP 2022 - Initial to Goal image using block remapping","description":"The ICFP2023 Challenge is Jul 7-10. Start is 0500 PT 7/7/23, Registraion is open. Posted 7/5/23. Updates at TwitICFP2023. These contests are insanely complicated and competitive. The challenge evolves every 12 hours with new files and rule updates.\r\nThe ICFP2022 HomePage has details of the RoboPaint contest. Twit2022, WriteUps, PriorEvents, Spec, ImageSubmissions, Puzzles, SawickiSolutions are useful links. .\r\nThis Challenge is to move the blocks in GI to minimze the error between GI and G based upon RGB distance. The contest had these Initial files but the leaders did not use due to the high Cost for moving small regions. Assuming a low cost remap function this is to find the optimal image score using GI blocks. For Starry Night, 28.png, best score found is 41141 using Robust Hill Climbing where all swaps tried and only the best improvement is implemented. Greedy would start at index 1 and find min error then index 2 would look only from 2 thru N. Max error index swaps to minimize its index error. Many methods are possible. Hill Climbing using the block medians is fast, easily coded, and scores well. Greedy is easiest code.\r\nInput: G-goal image,GI-initial image blocked, irc-row/col for each block, rgbGmed-median of Goal image block, rgbGI-block rgb values, v- remapping of GI blocks, scrmax-max allowed of Score(G,GI(remapped))\r\nOutput: v, [N,1] remapping GI([1:N]) to GI(v([1:N])\r\n   \r\n  ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 860px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 430px; transform-origin: 407px 430px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2023.github.io/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP2023\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 298px 8px; transform-origin: 298px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e Challenge is Jul 7-10. Start is 0500 PT 7/7/23, Registraion is open. Posted 7/5/23. Updates at \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://twitter.com/icfpcontest2023\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eTwitICFP2023\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 339px 8px; transform-origin: 339px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. These contests are insanely complicated and competitive. The challenge evolves every 12 hours with new files and rule updates.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2022.github.io/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP2022 HomePage\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 120px 8px; transform-origin: 120px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e has details of the RoboPaint contest. \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://twitter.com/icfpcontest2022\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eTwit2022\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2022.github.io/writeups/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eWriteUps\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.icfpconference.org/contest.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ePriorEvents\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2022.github.io/Specifications/spec_v2_4.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpec\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://gist.github.com/RafaelBocquet/e464c2d199f2d7a725cafdc22b30df7f\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eImageSubmissions\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://github.com/icfpc-unagi/icfpc2022/tree/main/problems\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ePuzzles\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSawickiSolutions\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.5px 8px; transform-origin: 55.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e are useful links. .\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis Challenge is to move the blocks in GI to minimze the error between GI and G based upon RGB distance. The contest had these Initial files but the leaders did not use due to the high Cost for moving small regions. Assuming a low cost remap function this is to find the optimal image score using GI blocks. For Starry Night, 28.png, best score found is 41141 using Robust Hill Climbing where all swaps tried and only the best improvement is implemented. Greedy would start at index 1 and find min error then index 2 would look only from 2 thru N. Max error index swaps to minimize its index error. Many methods are possible. Hill Climbing using the block medians is fast, easily coded, and scores well. Greedy is easiest code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eInput: G-goal image,GI-initial image blocked, irc-row/col for each block, rgbGmed-median of Goal image block, rgbGI-block rgb values, v- remapping of GI blocks, scrmax-max allowed of Score(G,GI(remapped))\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 153.5px 8px; transform-origin: 153.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOutput: v, [N,1] remapping GI([1:N]) to GI(v([1:N])\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 245.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 122.75px; text-align: left; transform-origin: 384px 122.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 240px;height: 240px\" 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ep+XK3MEnu9I/6nHS/zKB6e491tlhis6dtFsTtLvWB1Qbzqf/wufK3n9d3h8+7USURHQAhNajKVr3CboV4hwuVyKxF0lBchAQADtWcvnbtd89DOaex42BD4UA4E262+NJFbI/HybV738PH7nnTemmesQWyCJ9fHFr+zn5371VsqlPPaRY5Cw6QTVCiV7trf5s3c8ydi2kKgh8VRnEoOlL2QyFoVwbhAlgYLmnTfv4eNfHmWorDPukc01IW1+dJ5NGAmGK5p3v2U/L3rBHGZRpZNbN2GcZG+ZZ2Wss1a8kqExr3jnn0/x2btqVIoGswlJP6lQq41loQ7PPl/yttd7vOLbFARAAyJtexY7q2/ClD1ellRS4vUEFIG65R+/qHn/xyL2HrIMVcSGVT9PzvFD772Jyy7pxEIkdHrh/tOnn+gr1cqR40yRBFAjDT/7ukOM7WgT1h15WCviFWn/ySn5zFqBkta5uULJ299wiPMmWzRDiZT9rZYcJ0M3eVSKhpt//mle9II5onmFAJTMxkZOTh7QkataKxA4ea9uSEolw+/8zDO85sYTnFhUTnm0SZ6Xm+ssnhLUW25i/Kk3evzlewJe8VKFjSx6wcYE05kve/9eLXq/L2WsyDLueAjBa7/L4yO/XeCHXuMRRpZ26M5zvVMrlJIsLIZ8/FNPAJ0gv0y6Cz7x9Cx3fmOGcil3XeUYPKSAxYbiZVfP89Lr5jALCk8tb3X0IjsxKWmJQkF1POTN33mEKEq2ObcT2VaHJD/BuWmMEfzKDx/g4kvqhHMKJW1qdayEOHrRRfrKoiMBWvDL//Eg11xSZ6EhUSnpb9wzS6wAKQUn5i0X75G8/1cD/tObfEol4SZyQKnOd5J+GYNE9/4ESrlzi+Yto6OCt/+kzx/8l4BtI4K5RRvnkqzfQt8YS6nk8YV/3cfR481UxixNfAJf/Mp+Tsy20gbrOXIMEsa61ejrX3EM/KS/TCfJajVwJAI0Jd/xwlnOm2w73/3Az/rshrM+nEz3R//dYV7xkhNEMbG7yWlpwttqsMRyDAWFiuY9P76P8eGIUGelvetPItZapHDjZn7R8j0vV3z43QFXPUcSzbk8lmSidDkgYs0X1olcOOEFTwlMaNHzlhd/m+J//WbAt1+nmF9IXGnrRyKBrzg0U+crdxwAcBVLPCWx1nLr7QcIfBlLI/NVXI7BQQBhJNg53ubKCxrQki457TQmqOz2RgvKNc21l9ZptiViE7lFNjsS11WzJbl4qsmbXn0EGjJOfrNdSXNngl5LJGpKdp/f5P955VEW4+NtxDNLVFbGwmLT8tNv9Hj3zweUKwK96NxE2fjGentk3PFEep5SQrRgmZiQvPeXfd7wasXsYnf3wLVEdv9f/Mp+AFQSA3n8qTkeffwEhYJKmTbHmSFbfGy5QmTnCoSASAvOm2wzVNFYc2bjS4gkSAv4hkvPa3QFb3OcCrYrJvUj33WE0khEFAlkxm01KHRbIkBD8v0vO86l5zVpttY/fpVYHsa4pMBfeovPW37QxzbBRB2J6qmC40mn1mRb92OwRru/z/C97yUHTwl02+Xf/OLbAt76Oo/FxvqQSJKdXix43PvgUY4cbTjXH8Dd90wzN99G5e6rgSCrrhBCnpFS46xATCCTYyF4NjPZn/4u00lOC7aPRBQCizWn/FoOOveu0ZJcfUmD73zRLDRkGthei2GalWhHkaAyHvIDrzxKpOMcu3WKX6XvJtBoWX7xLT7f/70eetEdP8mydue69EYsCV5bSzLwhPQQXhFRGEL4lYG999mmTippOduCn3yzz1u+12Nu0WbKppzRoU4JzxMcn21yzwNHgLiY4j0PHOk6yRxnjoQ8jK5jrT6nlW0CFwMZKpkuAhkIrKBcMC4gO8Ddns1IypS0QsENVy4gqzpN8Fvb43biLrQkN1y5wPA6HbtzDgIpYKFu+ck3eHzfd3vo+e54Ql+XVY9FYhPSUD4iqCKkwjaPYWbuRz/+afRTt2CtGdh7n52fE5KzdfipH/Z5/asUs/NOIbaWJJxYbu22SQnEazQjHnrkmHNf5eqrgcANGoMQPu364/jFPahgDGy00ae2IUiyb+cWFUSDWeEKYcEIkJb5hiTSAn/Q5HQWQxtBtWS4/vKF9JmsVx6NAGwk2LU95MrzG3z1wSrVYlLqZO2Oa62LbZyYt7zx1Yof+wEfU19KHv2+B533GkB4RbAGu3AQc/hBzJGHsLPPYHUTwjre1W9BqAJWtxjUPe0lEWMsIhT8ylt9js3C527XDFcF0Rq13hBCYKwlCCQPPXrM3c+nnpnjyLGm0xYP/JDnJtIVh5CEjb1IVcELtmM5NwmEWIE1fdzrIpAzCdRaG49XATPHfNqhoFwkJ5AVQADtSLB7e4srzm9CKE5b1LDqY8cKI23Aqxiuu2yRL907tOYE5jKqBfN1y3VXSt7xo76rSWVByP7k0U0cLr4hZABSYY48hH7qc9hjj2HbiyCEI5Wohbr4u1DPehm2PY8r+Dl4yW9CIlpbVCB451t9nthn2HvIUiyINUvFsBZ8T/LU3jmOHm8hH39qlnojRMnc8hgkhJBY0yZs7sPoelrc6Vx0YxkLgW955Jki00d9hHdm7qa09pIE2oJ7vlV25rvNVVgrgnDB88nRiIJv111BK4R1NGFhcizC92xPZvpg4bLpBWFoGRsWvOsnfGRRoKPuyrLd3+kmDwDhlbH1aaJ7/5zwrvdipu/FGo3wywi/jDURYmgn6sJXQdggIY+1eueTmEjUsgxvF/zaf/LxfTBmbYVQSkkW6xFPPj2LfPKZecLQpCycY1AQGQJZpE/zx3MKnrIcnfP4+qNlCEw8yFfvcrJxsTqLK7B49KjPPY+VKRZceYxchXVqCFzi4GgtAt9gVpjMOShYK+KTgLGhyPV5WdPHZhESGi146+s8nnWJJKp31FZLz6/bZSWERKgC+qnPEd72O+i9/4qQCuGV4ok6LmilQ9SzXoYojmJNuKbimaw7y1OCaNFyzQsUP/hdHvN1Gwsi1uamSiloNiP2H1xA7juwgO/Jc1slNEAkZREQCh0eQ4fHMNE8CLdEPhfvcWIVSAl//blxzKJKS5hkk6ZOvZ/4H8JVPqVg+Ojnx5g57uOrnDhWCiGcC2nbcDR4UcMKjy8AjGB8OCGQNXRdScFi3fKCqyTf/yqFXSDO9E7OR3Rtn3zmXFYe1hrC+z5M9OBHsLqFCCrxth3ZnzURorIdueNa0C0Qaz+nZvctJdCAN73W45I9gkarW1E2ODjZcBQZ9h1cRB6cXsTzZO47HhCSFYkQHmHrANa0nAViNUli0LmGxNIoFwz3fKvMR28ZhbIh0pD4T051W7J5HpEWeCXDU0+U+OjnxyjHxfncJHTuEfTpIkr6eawzss860msrPXWBX/B9+Kk3eFDMxgf6T/Ad8vCxUZPo63+C2fslhFeOXdM6s511i0PdQm67IrY+1i/WmcZDhECHluq44K2v9+KGa2vhynLPy1OS6Zk68sixRmzKDfg45yg6BCGImgewVqOjeawNOZcnN2vdi1wpGt7795N8454q/nBEGCXuE0cyveMw+ayT+CbwCoawLvm1D04xX89aM/kgXgmsdb25j815EEk2IvxpAaTl2JxHO1qbZ+cCzU6y+/LrFc+/WqHrNs5+7x/36LI8dIvoG+/HzNyLCIawLE0uFIkJLRVi9BLicgjrmr3eCaqDrcOrXqy47kpJPe5jMuhFayJIOHq8iZybD9PGKTkGBYk1LcLWIYQMMOEJZ+Keg+4rB0FSmE9KS6glv/yB3dx/nyORxKpIkJBGdtxr4wjIq2jqi4q3v28P936rTLlo8lLuq4QFpLScWFCgO+S8XovINPalYHZREUZiTUhMxNnmhQBe/yrV9XkvbCwJTPK3LBDd+yHM4QcRQdUFy5eV+RoXDxmaAhOm+1lvJNniFATf929VKkxYi3lHKcGxE02k1oNLdsnh4MzcFlFrGiEDdDQb54Cc2xOcEK7bXdE3zBz3+Yn/fj4f+8R2tBF4VY3wOhOZjru7AQhpUSWNKhq+ft8QP/rbF/D5r9cYqmi0zi2P1cJJMS1PTwecWPDWXd+RyoUNPL6/QKQHLx92bh3XTfCG5yqef5XCNjsr8r6uK3An5RXRj38ac+huRx5xQuCyJ2kNeCVEcQSMc1Wv95uetUJowMteoLjyIkmjZeM44+DeERtLnxcWQ7xzvszGANHJ/1Do8DgmmkMID6sXMbqBkkVcw+RzEYnP2SWMFQJDFAne/b928U9fGeH1Lz/Gi65cYNtohPAs0jeu53YoqC8qHniowsdvHeWzdw1jLNSWkEc+flcD37PMHHcKtpfecAKzqLo6Dq4VkldESYteVNzxUIWCbzNdCgdznOx89m9vlFAA3YZ+1Zq6XFdeCXP0UfTjn0J4ZbAGccqEJQteEaSXPYHBXMhpINKWYERw0w2S+x8zlIqDt0KkEDQaER7kxRMHhY7szyNsHcKaVqziiDDRHCoYj0sinKv3vEMixjh3Vq2sefDJEr/ygSm2jURcdl6TXdvajNc0Cw3JzHGPh58psf9wQDsSVEvaqYhy8jgjSAHtUPC1R8q89NtOpJ+fSXLnqZCQh7UgAsuT+4o8ebBA4CfvxGDdkGFkmRwX3PBcBa0OeSznwhIIrA7Rj/6jYxuv2LE+Tn5lAz3v00VSC0sKC2146XWKP/sHjV6DzHQhoNHUeKfeNMdKkUp4kUStQ1jbQhBgTRsdnSCIfavnNjokYq3AYCkVnFU2v6j4yv1VIi3QxlWGVRIC3xD4hmLg3FrG5ORxZnC+8lLBcOu9Q/zYUZ9aVWN1kluwdiSSWKDSs9zytSEWGopaWcfuysEcNM06X4RXv1gyPikwDdv3mjquHQN+BfPMrZijD7mkwRXL7gWYaJOUQYhdVm3LhedJnvdswVfvtVRLrn/HIIgkWyz23M5uWxNIrG0RtQ4BKg6ohS4OwrmbC9INJ7dNSMAYt3JSylIpGoarmvFaxOiQZqisCeLYSCcoaNN95DgduEoAhcDy+IECf//FUSgYtFm7RMzEwjBG4AWWQ/sL/N0XxygGWQn2YOCUUS7X4wVXSVDESabLK6OEUNiwgdl7KwgXcBcrJgQBUTMNoAMbRibJ9WkDlOCG5ylX8VgMzuuRDXvkBDJgJCVMotYhhFDOILEGHc6m2+SChQQJkXT+bazAGGeBOOWVwKZkkf3JcfqILQ0DpYLhb24Z59h0gFcwqXU3yCGa3ZexQMHwl58Z59AxP3ZfDV4IEWnLUFlwxUUS2iwrZ00zyVUBe/RhzIknESpwn610wo3zQGgeSxOGN/INT7oaEsEVFwkqpZhQBnwMONfrawwQ2QKKOpxFR8fjlYxLNDLhCWyuxMqxiWCsIPAsh475/N7f7IBYaJRM6IMgkSUJoFXNbXfW+LsvjjFU7pS0GeR7IQSEEezZIdizQ8RS5eXOL7lIi5m+x7mixOqmRSEkNmpg5veD9NM8kI1CokAjhEvOk4zWXIXetUBOIANCNgM9ah10JQ/S8iUSHc1hbRh/liPHRsORhOvTovnEV0b44D9MIMoaY7pJ5HSJpF/1gGeeKvJrfzaVussGXcIkid+EEVy0R+AVBUZ3ypP0O0EhFLY1hzn6MKiA1XQmS/JHsAZ7/An33bR678a6sTCWWlVwwZQgjNYmrpXPZgNCJ4CuiFrTWNsmiXmAQocn3Oomt0BybCIkFQKqZc3NH5/g/35+FFWLsJBJ0FwdifStHlA2zJ/w+IWb93Bk1qMQ2IwYYpDWh3N4ag27JwUELFvp1yZ/Kh87txfbmkVI1X/jZY/nyANVwBz5JrZxDCE3hzbJGCCAXdsEUZRYl4MltZxABgoXMHcB9Ni3Cs6FpRcwpklOIDk2DzoVAgSu5P67/nyKD350BzIwqMCkFQJWYo30Eoc27rteLeLhR0u86T0X8MjeIpWiWcMEUCfI8H3YNdGp9dVv9d0pQ+Jh5/a6QPhpegiE9LCNw84Npgouf2SDE7SNBQKY2uEWCYI1yAcZ6N7OcSQZ6GHrEEJkVyECbOgC6UmQLQ+k59gUSFxZTjbte/Dev5vkHX+0h0PTgasQoJwLKlnJZ0vN9JadsXQKJKqSRkjLP31mnLf+3vk8ebDQU3rGHX/QMBZ8D0aGBJhOXKcXqfvJaGzjaPfnq0An/umh996Kbc+7Frd28FngK0VKFBZGhwSeWp0VuVJsDltri6MzQBQmmkOHRzsBdBLZW4SJZp2/dWV77fpfIlQS2PjvzFaxht7azrc6ypbc4jkdrGd1hs1QCSIhESFccuen7hjhzm9W+IFXHuMNrzhGdTyEyFUGsLHsuiOrTla3rkeLLBgIBXd8Y4gP/PN27n64SiEwlAu9lsfaXLONJbyVEpnXaLl3QYAJsa14cXcazyJNIFYBdvZpzNNfQF3yWmjPp/PAej9jlxgJGCgWhOsPsgbHyQlkYLAIoYja0y4DvcsUFlgToqMTdHJB+hl/S0lDSucrbmtJpCXGdKSuSXFCJa1LulOGwNMomZSGcPvMyeTk6Pdy9+sRYTOTUYe4T42OTLnblZIcY7mJZf0mnaybyiV3DpU0i03FH/7tJJ+8bZibXjDHDVcucNUFDfyiQUhHFgjrSs5oAZHgwLTPnd+s8uV7h7j13iqhdtUDjGXNLY8OLJ4UVEuxBcLy9ziNYUSNM4oyp3Jgr0j0+KcRo5cgt12GbS+m1siZPs9sAl9yzJQo+o1f4VaUQ+Xu/ieDRE4gA0CyAkF4LgPdtBCqjJu8k0GqXSA9Rvdg6n6yMn6R25Gi2fbwPc2u8QV2jS1QK7cYrrQYrTbxpOH4YpHZxQJz9QLTx8vsPVJjru5T8CMKvkYKm1bfWotyEVsV2fvfMfcdMTiXjHsmUoo0Ac3zJJ6SiPSz+PeI+DM6C10Lxhi0tmhjMcZgjEVrgzUWYy3G2MxkAElOzHLEsvaE0pngtREoaRkZ0uydKfC+f5jgw5/cxgU7W1y4q0W15BI+A88yuyBZaChmTvg8/HSRo3MeAqiUdE+dq/UgjzisIV0V3pWQvLV2IAKXtJKvbhPd92H8F/wMoroDGzbOmESy1YKT1ABYQUzDQrHQvwbYIOAlJ7fRJvRWhnuoAtBErRmSWaRTRyfOBYl6c0GSla17uYRwWdnzTR9PWabG53nRZfu58fL9XHvxNNVSG6QFT4OKl1aRBO1+WqHiwae38ZWHdvOVb07xxKERmqFHOQjxlMXQm7R1Lj3zbuLMtgS1cX0yKQVKSXzfoxAofN/9eL7CUzIlE/f9dE8nXbgm++5Yg6C1IYoMWmuiyBBFmkgb2m1NFGm0sVhjl5BJr1W0Nu9sxxoBV3Ms8FwZGW3g8f1FHn66lJaUcffNVYFV0hL4zgUGdFkd67lwce8RNNsrO6RbrXucqZOny5XVOEL49ffhX/M2RHUXNlwAoZZYEKfC0t7sFqFcZUib7ONktWeEa+WrDWtSLNPLmkU5iZw+hJAY3SJqT8cB9N7GM07K63oly4xbxL1gUljqLZ/A19x09dN8zw2Pcs1F01SGG84UbXlgBVZLbCS7+ljL+AUtBJprrtjPNVenB2G5AAAgAElEQVTt460LBR56ZhufuOsiPvuN85ldLFIuhF2+7uX9wmcjuidfE0/QSkkKhYByyadY9AkCD6VkamFkJ/7ku+7vdG8rO7oQJGQjpSIIFIIgPa3EGokiQ6sd0W5FtFoRrXbkSEWblFDWh0w6RGKswMYBj0LgyCQbhwMylhtdFsfGWLwCbSyNpj2lTMha66S7XnEgPp5OVd8iduEg4d1/hPfcNyO3XQ5h3XUrzLiv+7qgYku46xkbjVA++GXM3lux7Trqgpuw4WLf4vE2CYgKaLWJEzYH78bylJJke4LkJLJKpE9EYE3DWSCZAHq8ES5D/QTYEISzrZMYhjGC2UbADZcd4D+/9m6uuHDGWRhthW4EQFxhMzmSANUjgbTWSRZN023ve4bnXXaA5112gB955f3c/Mnn8493XEzB0wSeRht51pLIct3mklamvq8olXxKJUccnqfS/tHJ4zRm9bUfsu9Qr/Jm6f9hScwrPrcg8BBDjlSMsbTaEc1GSKPZptkM0XFdivUhk2ypGTpCjVNMRNnvrDeEcHkgrdD935HbcvfFgvQRxWHSJECzkgq8Jzu+iCf8IrZ+hPDu9+Jd9GrknpcgCjWIWlirndubuMDqkpndYuNZX0gPVBnbOIp+5OPoxz+Jd8UbOozQx7ToeD6g0bIucZPBB9K97duqHDw012Uu51g5bPKnUOj2Uaxp0H/ZIzE9fUGUNLRCD08Z3v69d/JDL38AChrT9LB4yNgyyWbsLl+Swf2tpMFaF2A3TR8Q7JyY4zfe9EVecuVefv8frmf6RIVqqU2kzy4SSSaJrHsqSxrlUkClElAq+amVkaz8dZ9SD6t1N2S3O9X2S10TLPkbnFutXPKplAOMKROGmnqjTb3eptEI0brTn7v32s/V91kIaEdwbNZCWk1o6b0QMVkgPURpnKxq8szPIePOMhHRNz+G2H8b6vybkJNXOyIREnToiCv7DgoJUjm3mnUSY3PwbvQzX8IuHAKvgKjujMuu9Hdh2diKQcLRE3Qy0QdtgQwNlWg0I44fX0QpeU4PvNNBVw+Q9iGMaSFlgaVPSuByQY6jgnGUMDTaAeNDDX7rTV/i2uc8A40A3fRj4jB0tOunbrST9cknUNKtqE3bZde+8tse5apnHeEXPvzt3PP4JMOV1tlBIvGpJ+M2IQXfVwxVAyqVAqXY0oAOsfSzAPr2vF4DLHeMXgumYxW5//u+YrRQZmS4RLvtyGRhoUWz4SyTbND/3IRFCkG7Dfum3Qp8OQukkwcSuna0qshqypicCp25QYJfxS5ME93/F4jHP4UcvxQxfjly5ALwKyT5YVjjXF31GWz9iJMFH3kIWz8Cykf4JWcxVSbT7ofLTQ5JPaz9051S9oOe3z1rLdvGK7RaIY1GGylzElkNOqtFQdQ85FxUxP7UzC10q50QHZ5AKcFi3WPX+AJ/+OO3cPEFM0QLRZS0XRbE6T6CzmBx/5DSWTDRQpGdk7P8yU/+Cz/3gZdz+8NT1Mpbl0TScZqJIwAUCh61oSLVagHfd4ogp3rqnhz6WRgbPe57z6MfoSTWUkImw7USrVbE/HyT+YUmYai7Av4ni7GebbBWuI48CvbPWAg5Rb91ASZCDO1GBBVsWGeQ9eo6z0DHVX7BNk+gn/ky7Psqwi+DPwRe4B5U1ICogdVtV+HXWlAFt50Q2LCB3HYBFEfB6pMeW0oBLdg3Y/C9Tkn7QUK6PrqCiYkhlFJLBmyOlSDugd6epmMz994/ibUh1hynFQZMjizyvrf9CxefP0O0WMBTJnVVieUXFatCsp+ESDxl0E2fcrnF//yJz/Kiy/YzVw/wVNba2fzPvXfSN8ZgraVcDti5o8ae3aOMjVXwPOWktPHvs99JsNlX6ycjNkcm7tqKRY+J7UPs2T3K9m1D+J5Ca5OKBZLtz3YkHp3Ag0efskQNi1QdMu7eNv7caERxBDF6setE6NJ1B3xesdQfF7QXQRWhCljdxjYOY+f2Yef3Y5vHHXkIifDKbjuZialajRi71AXpTX8CSa9TwtyC5cn9rsLAWjx+mfiBiwWfie1D6UFyElk5khImWQVWr+oH3LBsNeYoeCG/8yNf5Lw9R4nqhXQCH2RP6O7zS85DoJRBtz38Qsh7fujLnLd9jnrLi91dm3cihaXEkRDDULXE1K5RpnaNUKuVEEKkE2s/bHbSWA79guYJjLFoY/A8xfh4hT17RpmcqFEoeOm9yFo1ZzOsdaVM9h6y7Dvk4iDLXbK7Jy4OIiev7ny+BufVq6oiTigW0kOowP1Iz33mtuoiCWs0wq+gJp6bNq/qNxaEEGkdrG8+aTh2wuKptRnvMhlMWhtqQ0XGRsupyiPHydHVAySaw4Rz9CvG1lFEKNrtE7z9e2/lOZcfTC2PUwXIB4FeEolaPuPb5/nNH/oypSAiShsJrYVW48yQxAF6Yxy1apHdU6Ps3FmjXPa7VuTQueaTTbxbFZ3rWerycrEQyehomd1To0xsH8KLLZJzhUg8JZhftNz/mMlU5F1G7oqEqIXcfhViaBfWhH23HSSWjsWlMblkuzTPTLcQ2y5H1PZgtTvHfs8wjZspeOBbhnpz7RIJZXKS4PzE4+NVKpUCxmx8NcmtgaQHiCvhvpz/VAhLO/I5b+xp/t0LHoZGIY13uN+v/ZlmScRThqge8Nwr9vOfv+dumm0vVXxtJnSUVR1Za7kcMLVrlJ07hymV/PRzoEcTf3aRRj/0U08KskQiGBurpG49KUUaCzpb328bxx+NhTvuNxAlcZDli5haGyEKw6ipG50baxPdm3QBKj3Us17WkVP1WRQl56ykgLrl7gdM6r5ai+uR/bTrkxPOh5rHQ06OrGURtWewpsWymUsWpJTMLSyy2JPgtJ5zXEoiuFIVtl7g+258hKsvnKHe8jMxk4195r0yV60NhYLHzh3DTO0aoVzuIY5kVU23lPdcQnLdNvP/5N4pJZnYPsTuqTGq1WJqxZ2N1ohzy0OpAHc+YDgybZB+//HQEWFIiJrIPTe6FX7k6tlt9H1xZChd8HzHtcjxy7FR3Baiz7mlqt4AHnvact9jhmJhbQLokLFAsmQR+B4Tk7X0AnIS6Y/UtDQRUWu65/OebXFm5NyiZeZoCEpsmDqms4BxWcYUQt580/0Za2gjn3X3xJad/PbsHqVWK2ZkuEvVSucicfQiJZK+JKzYtXOYHZM1kiTi7DZnE3xPMH3Uctu9BgrEXRD7vJ82uX6NCGqoS747lfNu5Hjq1NYKEcVhvEtec9Lzcq6rOP7hw5e/ZphbALVG8Q/oWS4nL6E2hmqlwPh4dcmLmqMbQkiMaaHbM/QrYZKFlIJW27Bvpp0SyEa9tImlIYXFNgO+/XlPc+MV+1hs+qeQPa4dEkLOJviNDJdT90sSHHfnnxPHqZDESLJEklhtw8OllJATFdvZRCLZONi/3Kah5RZw/S4vG2ewUR2181rU+S+L61dtkBWSTaUwIerS/xDHZzqWUf8xb/E8QThrueV2TeCvnfsKMgTSezJaG8ZGy9RqpfSlPVsG12CRKLCOsLSESc+WEhotw/6ZEDyxpN7NRiC1QvyIN377Q3FcJvnt+jxvt3KyXSvlYtFj184RJidrywaAc+I4NbIxkl7LzvMUO3cMs2NyGCVlmrWfbLuVkSiRykXBbfca7rpPI4oideX0u75UlKDdhC23XdlVjn3dYC0WEbuuFlHn34Ta82JsWMelA/QPnCeuO4pwy52ahx43lAqJp2ONVFjZ//Rm4lrr4iEuUJkH1bNw98H5J3V4FGuanKxym7uvEIaW/TNtksI0G3k/s1YIbY/nXXCYXWMLhHr9GlVmiSAZY9u2Vdk9NZqKOXLiOHMs59ZKrJGpqVGKRf+scmlZ6xZtYQR/+2lNXBpqWfLoLGI0Qnp4V/8YYuT8bhJZ43tircUKgZAS255HTj4fddl/cEmFGVHIci4spQQ0LH/zKY2Mm0it5XNcMlP0xkOkFOyYHMbLg+pd6DxIj6g106eJVJ/tLXiecBZIy6xJeeXThdGSaq3B8y6coRErstY6L6TX6iiXC+zZPcq28WpKKMCSiS/H6WM5a6RQUOyeGmF4uHTWuLSccg8qJcGXv6b5xr0aWTq5FZLGHUyIKAzhX/MTyNELUxJZS3VWZ3Ekse0F5MRz8Z73ZpIkxN4Ffu/3jAHK8C9f1dzziKFcXPsCuctOYZ2TshQCj8mJPKieRWqBIGMF1qm149Y6ffr+mTa25SSWG42stBdP88JLD6JkEotYuxfFHVukbpPt24aY2jWybOJbbnUMDv2skeQ57JissX3bEHB2vOvWWqSAdgh//NEIWq5WVnJN2WvrHm+uMZQojeFd9zPIiedi2/O490Jm3v/BnKONkwqttY48dt+Id81PxBnn0bJxj+x1KE/QOGb5wN9HaQvbtX50fQmk98XVxlCtFtg2XsV0rQS37sAaDCTWtonah0mKoZ3sZXMEAvtm2rTadq1zlVYFIZ0b65oLpxkut4j02pxc78o3CBRTu0YYH69gLUv88DlxrB1673Pi0hobq7BjcniJN2IrwsVCLJWS4K4HDJ/4skZUE0VW/yxu6CERv4R37dtQF30nVrewuhWXFxlMyROXeS5jeS54l38//vPejBCqizx6kV0A6Nj6+KtPRnzzSUu5KLrepbXCshZIv6D66GiZka6g+pqe26aHEAJr2uhWosA6uYvF4iR1x+c0R2ejVIm1GSAAjGB0qEm5EKYNqwaF3kB5VglUKgXxmFra8znH2mI5l1atVmTnzmHXJ8VsbRJJCiwGPvzpxyKO7rd4hc4E23tdS0jERAism9ivfZvLE2kvxJ9LeleCyycr9nwu3HetbmOjOnLbZfjX/+eYqEKSSr4nex+cMtHilQSPP2z435/QVEvLE+SgcVIvfL+g+vbtQ5TLwTkdVO9cs+vxEYXHTqnASiAFNNuGvYdiKS/LD7j1QmecCXylGa604lakg9l/b6BcSsmOyRo7JmtIKdOxlB1vOXmsH3rvPTgSqVYK7No5gvLkspPtVoCIqxgUAsFTByzvfn87lckuZ2EtIRFrsVEDOfl8/Bf+PN4Vb0SUtmHDRWxUxxrt9iFkap30/qRWi7VYE2Hbi9ioiahN4T33zfgv+Fnk6CXYqJ6eg7WuY+HycQ/numovWt75vpDZBVf3ar2ek3eqDfoH1Wvs3Xf8HO9kaBFCEbUPxxnoK7t+JQXzde1yQeISA/2a3WwIrOtkOFptYowETl4uekW77JmUKpUCE9uH0lgH5EHyzYJ+JFIuB+zaOcKBgye29PuerNSHq4Jb7jD82d9F/OgP+kRzrm9Ov+tK3PSd+Q9s1EBID3XRdyCnrsdM34s9+jDmxBPY1hy2vRB/WfWcgQWrQSiEV4TiOHLsEuT45YiJ5yCCKoQNLLHLCpa9z0tIrwR/8GcR9zxiGB0SRHr9ns8pCSRBlvEC32NyssaBAye6LmarDarTRaKKQHjoliMQqUqsxAIREtqhZd9025kjdvmBst4wViB9zWi1ibYCQaxHP01kx4a1MD5eZXysghDk5LFJ0fs8tDaUij47dwxz4OCJLktkKz4vbWCoIrj5oxG7tgte/W89onm7LIkQN3Pr3JPYpRQuILyyq02158XOpTV/ADu3F1ufxjaOQ1qV2zriKI8jypOI4T2Iyk5EEDeSiprYcBGQp3RZdbuBQVUFH/lYxEc/HTFcFevmukqwIgLpTALdmeoTEzWmpzvtcLfqoFotsuZh1D6MW6l3pHYn/Z4VSCkcgbRNv+K9G4YkDtIK1RmFB3tdVp5SbJ8YojZUTDOhe8dUjs2Dfu97uRywY3KYg4dmt+yiMetF8RS8830h5SJ8+0s9otmTkUi3S8tBukVk22WrC6+EGL8MJq5ynQJ1u/fgoAJAulLsJoqD5k7JKYQ66fuQJZWUPGqCv//HiN/9cBhLdtd/Mbri6atfUH1kuMTYWGVJeYlzA9IF0MMjJE2kTnX9iRnsezGBtKyrmrlJIIRFR4ojc2WUOj3ro9cFUioG7N49Sm2o2CXPza2OzY0lSkztlJiTE7V4abE1YyJp3EC5us2/9N6QL3xB4w25/JBuq7l/wmGXm0smbZINVjexrXls1HD/z/4YjQ3r2Pa8U3JZg7NuYlfXSWJ/vXJrEKiq4O8/HvFbHwwpFZKk4PUn9FWtf/v5SLeNV87JcifuXjgJr4gD6Cu5couT8j5zqE2zZTaFlDd9ZMISRpJj80WUsKtWaXfGho0XGGWmpkYIArXEZZUHyjc/+i0aa7Wik/PrrSvnTyZizxNEGt7++23+zz9HqKqILa5Tl3TJjl8hkiYCIhNA7/ut9PfZwLhwLpy+3+h2J7ruiqIIH/xIyLvfH+J7Ii7RvzHW4KodKP2UWZMT56IyS2J0Ax2eSANmK3l81jop77E5zbG5zSXlRUCj7bHY8l0f9VV8tTfesX37EJOTQ+nL2k8ummPzo9+icWy0zPBweUvL+VMSUQLfE/zGn4Tc/OEQlEUFLhCdbLecNdKzw25CWeYn+f1yhJGg150VaYsqCXRkec8ft3nvRyIqJYEUbBh5wGkQCPRRZgnBzh3DBIG3peV+K0GagSoUun0E7Oq7lyVS3mcObg4pb1pQ0dM8eWiYetNHyZV1pUwII3khpZTs3DHM+Filq+dE7rLauui3aJzYPrTlG8+lY1a4oos3fzTip97d5uAhg1eLCdOskkjOEL3EoWNFlVcTPPaY4U2/2uZvPqWpVdy8YTZ4QXZGIdxUmWUtniddVU+19bNXT4bEZBfCI2ofxpyiBlY/KCloNA3PHGq7qrx2YyfWtOaVtNzx6C4WmkFczuRU30tcUcQVdH12T40wFMc7IFdZnS3oXTQiYXKihu97W/p9T+YvC9Sqglu/bnjTr7T5h3+KEEKg4tpZHSJhTYikH3FYC6rirKG//GjEW97V5oHHDLVqfM6bwJo/bQLpdUkYYymVfCbP8kZUnesRLoBuIlZb0kBIaIXWEYirgLJh9yk5rJIGUw+4/eFdBJ7GWMHJiin2ujVqQ0V2T3VqWfUq8zZ6oOcYDNL32lh8XzGxfevXzUrOW2vLUEVwfM7yrveF/Pivt7j3fo0qOSKxFnRXapQ9IzLp993EdaYqAlkQfOU2zQ//cov/9uchrbYryaJ1Uqtu49+pFeeB9MMSuZ82DFWLRNsMM4fn0oljMzDlYJHUwDqaZqALZ0ac8puJlNdTgr2HWhtalTcZu9aC8A3P7B/l8YMjFHwdW0WJjr33e91KlbGxCtvGqwA9Lsyz7bmf2+h9301cI298rMLhIwtr2vlurZFd+ftKEFThrgcMP/7rITe9UPOGV3s850rpZswm6MhC3OEwsUqS/aw4ARD37unYjSZ9gVcAWnDHXZq//pTm1q9rrIHhIVdtV29gvKMfzohAgCU3RGvDyEiZSBuOHl1AKXnWkYhbgbXR7aMd99UKry2V8irBM4famLgq70at3ISwaCORvuaWe5/FfCNgqNSOa2Et/xK4eAdMbK8xMlzGWJO64nKX1dmLfu/72GiFZitiYaGJlCfrlrf5kbi00K4MvLHwz1/S3HKH4SXXSl71bYrrr5LUtgnnPWgDkVs4GRv7IgRuQdkDC9jYFSYFTlHlgfQBDUenLbffZ/jkrRG332+IIqiWu2Mhm+2+njGBwFL/qDFO3htFhtnZOkolS+z+K9qth5hAwuOstAZWFta6viD7pts0moZKRQ2iasiqzyEJnntBxJHpGh/710tj68O5r7Jj1U0aneRAP65GUCkHebzjHMOSeIiAbduqNJvhli53kiC1RuLVfq3qFkz/8hXNZ2/TTE0Ibrxact2ViisuEuzcLlBFgQwAg3uXY62N22Hm70Tl24aoYdm71/LgE5a7H9Tcdo/h0FGLEFApCkRAV27KZsRACASWDqpE3qu1YWGhiVLJymRQR1x/dCmwotlTdiFcdj+4/syzC5rpoyEX1jxstDEuH2Nc+ZK//uIV7D865AopatnlvuqNd5RLATt2uABqv/yOHGc/ss876Rk0NlZhemYOJQTiLFgo9iqhhqpu4XX4mOWvP6X52Gc1QxXBs3YKLt7jiGTPDsm2USj4UAgEvud6kbTallYbZo7D3oOGA4ctjz1t2TdjWahbtIZyEYZii8PYjjt4M79TAyMQWEoiQgh27qix/4ChXm9nSGTz3pCVQAiFDo9hbYhYUjRtZZBS0GxZnj7Y5sJLytjmir1gZ4zE+tBG4hVC9u8b5R9uezaVQogx4qTkMVQtMjlZQynRJeE8G55rjtUjsTi1NgzXSiwutlhcbG15V1YWnfHv3lHPEwwH7j1qty0PPW655xH3e0ccceUSmbjEOi6uVtsRiqdceXlPQbUkQLjuiVmLYyvcu4ESSIJeEtm1c5j9B2ZpNNuoLTyw3DUZQKLD41jTRqgyp5OJKyXUWxkpL+vj9knIw1rhpLpG8Hv/53qOLxSpFttok1gfS8ljZKScqm5683224vPMcWZYumCEbeNVGo1wy77jJ0P2chKrREpBsQCloltyOQLoBMc7Xhf3vlVLAlEm7RZobfydTHB8K923get/euW91lqUkuzaOUyx4G/pxKOu4GF4HOJ6NqzyWpJBpXUs5TWdCXstkSUPbQQU27z//17D5+45v4c8emNalm3bqkxO9JdsbqUBn2Ow6HVlFYu+E1Ws05jeKGTHvJP3WrTpTAWJjF3KJAsdwFkjkbZxkm3//W0lrImAtF+OiOc5EikEW5tEnIQ3xIQnSNrYrtb3lCqxPMEzB1vQtCQ6g7W6JVnyiIzAG2rwhTsv4v2fvjqjuoJe8gCYnKy5+kd5ZnmOZZCMB2MsIyMlgiCTYLjFamWdDlbzHrhtz473Zs0yEPqRiO8rdu0aJvC3bskTJ+EN0dHsacc/oKPEevpgi7BllqxoBoXETM5aHn61yd33Pot3/dWL8ZWJdewi3qbzvFwMa5iR4VKutMqxLLKuF2vdez460rFCxIDbI+fYPFjTFLblSWQE31dblEQE2AgdznYskNNAYoHsnY5YbLQhsETaPQ43kZ8ZkWSJIwmYA3jVJp+//RJ+9k9votHy8JTpCpx3ZLqKqWXKkmyVAF+O9UP2PTbGUqsVKRV9RyKu+k+OsxBrngPdj0SCQLFr5/AWJRGXA2K06yB2JvAkzNfhN/73s2kslvErzbjuTme/qyWS3u2NEURaogohQhk++PHr+IUPvZRIS3wvJo9Mfo7WhlIpYPfUKKWin+r6c5lujpUgG/ccHskITPJhc1ZiTVRYvVhaAsFSKPjs2jnMgYOzhKFOs7E39wRlQUh0NA/W1cA6s71JAtXgn79a48Dii/i513yBFz7nAAiLbfloI5DCxi6mjhsKusMuvb+zxPkdwiKDCOkZHn9yO3/w8RfwpQf2MFRqO8WIiZeGGaVVrVZicmIImXFj5S6rHCtBrxUyVC1w4oRPqxVtkfc7x2qhnnv9m961HgfKDpysr7RSLlBvtIkivQW04xYpfMLWARqzd6XKitOHxOgmtdpOZvW1fOLOPTx5YIyJaosdYwvIStupOYzAWBlP+B2SgNjiQGCMSKW5wjPIUhsh4cl94/z5J6/mtz52A986OEqt1MaSFEqM77UFYw3jY5VYabU0kL55n0mOzYasFQKwsNhCrrvbM1GBJe7Zk2xpu3Ofcqwc62KBJOhniQSBYmrXCAcOztJqhZufRITAmgZYDeL0b5+7RmfRtFrHqHoNjDR86u4L+fy9z+Laiw/x4iv2c83Fh3j21HFUuQXSgpZghPuxAqGM+zzp39EIeObQCF97bAd3PLKTf31oN7P1ApVimEp14zPIPA/Yvr3G2Gg5D5bnOCP0WiHVapHiiQat9npYIbaLMAS41sxxroWxApspgCilK2JobCKptTmZrBLrSiDQn0R8X7Fr5wgHD56gmSGRZPvNB3XGaeMd5YprTKV1iBAyldTe9vAUtz64h1q5xfmTs1z/7IPs2T7HtqEm47U6E8N1lLIcni1xZLbM0fkSh45X+Nq3Jnl43zgnFooAVIohw+UWxoo0z8P2FD3cMVljOFda5RgwrHXy/aGhIs0j8yQijTU4Unw8N/k7UoBWKGmGAiWhGBgCzxIUDZEWtENJoyVphwLfsxR8i6csFlcfrmO15OP/ZFh3AoHlSESyK7ZEGo12av5uOmvEWoT0SRt5nNEAi+tqhcewpoVQlbiAG1SLIUI4ZdYj+8a494kJlLQEvibwNAVfI4BWpAgjSStURFoS+JqCp6mVW4BbdSWVdXvJQwjBjh01hqr9lVY5cpwOkjwnsGks5PiJepr/NTh0E4eSFmME8w1JsWA4f2eLa569yHMubDAxGrFtOGTbcMRCQzFz3OPIrM+39hf42iNlHt1bYnZBUQgMgW8xptf9lb8P/bAhBAL9SUR5kqldIxw6NMvCYmuTl4K3DEbEJrGmiW4fRZZraXBeGxGb2pZioCkXotgUd/GLest33xYWTxkCTyNE8vvOdtnMcqArsXPHjuG+1XQ3373OsfXQyUT3A49qtcCJE/UB9gzpJg8hYL6hKAaG73nJcV73suM8+7wmQUWDsqCFq5JrBMMjEVNTLVCWVwI0JAeOBHzq9mE+9oVR9h8JqJRMSkjuHcrdWv2wYQQCS0nEGouUgp27RpiZnmN2rrHJSeTM4a67TdQ+QlC5mMR72wnQW6wRaJH9jkX1BAaNFXFnw2xOh9tHb02rYtFnx45hCoGXynRhE1p7ObYs3DjqxBZqQ0Xm5hoMZiLukIeMJ/lGS/Dy58/xI//uCM+5bMEdIxTohoytbndUIcDq5F1xe1PKsmtHix993TTf85Lj/NXnxvjbz4/TaDlLRuucRJbDhhII9CGRxLUyWUN5kuPH6yR1Zc7OCU7GvUWOpImJ3dcZj/wVDtxeZVgveVQrBSYna3ieXFJS5uy7tzk2EskEndTIKhZ9Go0QKUg1Y/cAACAASURBVM98sZKQh9aCMBK8/Y0H+YHvOgqAbroKEVK4HyF75O8xo8j4c2sFti2wLRgfjfjpHzrIy54/zzv+ZA/7j/hUSjmJLIcNaqbajX4FGA2W7duGmJyoOeVTl6x0qyQdnhxp8qSQRO0jWBMyyMHZ2+RrdKTMrl0jKCXzHI8c6wopBZVKYQDjzU36UtjYzWt5z4/v4we++wimJdFNiRS4StMpEncw6bGThVbnM2eJGC2I5jyuumKRD/zCk1y6p0m9KWM1l0jPIYfDpiAQWEoiWDDGMDJSYmrXCJ6SmSDcVspcXx6Jme+UWIddeXhx5o+kK7YU36eJ7TUm8mq6OdYR2ZI31lgqlSDtCXR66JbpNluCX/qPB3nVy48RLSgn25XZ2MjKxJJumw4xecoSLSp27mrxhz/zDJOjIc22TC2WHB1sGgIBusgh63YplwJ27x6lVOrfPnVrwymxovZRrGlxpo8kW6vKGIPvKaZ2jTI6Ws6r6eZYd6Q5IdZS8D1KJT/97HTfXylgoaH4sdcc5jWvOIae8+LmTXZVxJFFYpUkLi1PWaK6YsdUi//6Y/vxMzXjkiTcHJuMQCBJ8kkmuJ7ifrtG4yqfpmsi3PpEIrGmgQ6PdsVBVoslxFsusHtqlHLetzzHBqGrAkXsxur3u1Oj0wRtoaH4N1fP85Ovm4amQvaQx5mdb3y0hEQWFdc+f56f/b5p6i3nHjtVZvu5hE1HIAk6Ko5seXGYmBhicnIYKTsura1ujaRKrNZhRJzdvtrJfUm8Y7TsXH/e0vuUV9PNsREwxlIuBWlX0tUgcV0ZKyj4hre+dga8pM3sYMgjQXY/SgJ1yetfcZyrLqhTb8nUUsmxiQkElgmuG8vIcIk9u0cplwpofTZYIxJrlyqxVoLe/A4pBZOTw0xsr6W/71W65cixEUjq3xWKq3djJRnmCw3Jd7xwlisvraMbMlZ0DY48OsfrkJbWAlHSvPk7j3SdT+7G2uQEAixZOYNzzwSB61exfdtQxkLZetZIR1UmiVozq1JiLc3v8Ng9NcrIcGmJmw9yl1WOjUF23EkpKJf9VOK7sjHpNtZWUC4YXv/yY5l9r927npCIlEBT8rJr5rn8WQ2a7dwKSbDpCSRB70RojKtbMz5eYWpqJO1dkZ1UtwKRdGpieYTtaVfS5BRKrOx9SKyy0dEyu6dGKRS8PN6RY9Mh+06WSgFKrXyhl1gY7VBw4a4Wzz6vCW2BFGtjffSD1gJV1lx/+SKt+Nh5LGQLEQj0l/pqbSgVfXbvHk2tEW3Mku03N5wSy7SPYXSd5LH0O+9eq8PzXHOuie21Lkssu21OHjk2Gh2XFQS+h6dW3g46cV+12oLrLlvEq2i0WZ8xLRKiiIVXN1y1QLloiNdo5zy2FIFAt0sr+X/Se3l8vMKe3aPUhkppMDnZBjY7kQiMaaLbh3G91rvPtZ/VMRzHgqpDxdxllWNTI/sOKiUoJO1uWdl7aQwUAsvzL6m7D+ImausxxF1+CNCWXHlBg+0jEWGUv1uwBQkkQb8Au9aGQsFj544aU7tGKJcLGGPTgZqdUDcbmQghsaZF2JrG9Rnp7tfRa3Xs3DHMjslhl1W+xGWVWx05NiEymeDFYqeK0krGqrGCom+YGA1B///svXmUJMld5/kxM3ePK8+qrKuv6q5udUtq9SmpGx1ISI1AFxLHaDlWsCBxCS0sYpmBgZl5LDx2YGf37TLDm7fsH7PswjD7lt3hLQM6hkuA0N2tVt93d91VmZVnXB7ubmb7h7l7eERmdl0ZkRFZ8X0vqyIzItzN3c1+X/vd3ZyNocJCybfMTydpTshwTz+K2PVaWFeDfs2iqI1UqwGVSkCz1WFtrUW7HfeYdEZJwLpxSUCjo0WyejtFc1V2XbOzFRb2T+F7qsdUN4mymmDkYbuh+eWSn2elX8qctRaUZ5mp6qEHPzmyAovLDZmb0piuVeuaxlgTSIatCjJmAneqVnZtc1sR6xttWq0IrQ1SbiaRrGrnbozf6eQecedcjyPdpNdVKQfs31ejVguwlk3k0T3OBBOMKgp+kEAhpUDrSxPBJt39z01rMFeWbX61sBaEb5mbSobmgxl17AkCga21kaIfpFYrUasFhGFMvdGh0egQx5rMVNQtsNbFcHf0FiE8ko6LxEIEGONMcvNzVaany3nyZPH6JlrHBOOC4mZHSkkQeLRa0SURgRAQa0GjrZieTrCIodfEFQDGjUFOIrCAPUQgGS5GJOWyT6USMD9XpdWOaDY6tMOYJHFOaClF/r3hC2aJ1Q3iaJmpmZuZnlbMTFfTbHK7iTyK1zvBBOMC50iXBIGi2dxsrt0KMg3jXVlXHDmM6+fBcLUQISxJLFhJa29NKGQPEkiG7YnEJe4pJZmdqTAzXSaONe0wpt2KaHcSklg7E5GlRzMR+T+DGrNEJyFTlXWOHj2INW2sFZvyOsZb6+jtJNfzzgi0EL10m/w4P4PdQ3HzE/hed21d5F4KYYkTwVpTgbIu9VYMpzeHLUxJrQVrDYWUdsIg7GECydA/MYtCOLO/ep5idsZjZrqMMZYoSgjDhHYYEUU61U6MIx9tcv/KIMZqTIyNF5FSEcUGIeSYE4fdRBZCgJQGrCC7lVIAPX/rJ5qdv+6t7uel3t/tPjeez2h4KJYw8X11yfdKSmhHkmdPVHjbAxt0u28OcrQOQqStbT3L8RMBKxsenprwB1wDBFLEqy36jExciKEzc83ZSh4GHMeaKE4wxmedEms7PLaspImQisbGaXQS5o708RRKvQQghXOEai2JEkmiXYkI39POPJFIl+2rLIFn8JRx39lEQFd3H4r3sl9LtbY3kKJ4y4tR390yHP3/bxWYMY7PbvCwlpxALnaPXIl1i68sX3yixsc+IFFq8/Ma1DjBbWqkZ/nykzXWm4rZKY2ZJBNeWwSSYat8kOIu3xZ2wEIIlBJ4nqRS8fC8ClLXODmgMUnp0aqfQusOUvpYa8ZMAPUSh5Kuc1w9VASeYW4q4jXXbXD9/haztYh9Mx08aVlplFirByyul3n21CwX1ss0Y0k5MK4Xg+WqiKQ/K7/7nF19JqUEgS/xPIlSIo3S64ZqOmFlsSaLjAOtLUliSLTNNxqZXV6wmVDyKMHLHv3eQdGEpZREKUmS6It8x93vcmB55niFV86WuPmmEJtXxh28L0RJIJJ8+ckp17RqiBrQKOOaJJAiigu83zldrO6bCRAhBmfCIu1OGIWrdNorVKeuw1ozRrvYXvIQQCP0qJUT3vum0zxwxxK3HqkzXYsgSIVGFg6ZtSBNJGHL5+Xz03zzpX18/rHDLK5VqJYSpLR5Ux8uUQzbNF6nnzikFFTKilJJUS5JfN+ZCmWaWrtdnmnmB8veNybVYI0lijRRZOhEmjguJrAWKihgEcP2/o4ohHAbszjWF7kdjsaVtGy0FJ/+8iw/c6yNCcEbYJdAm2a7ayNQZcMTz1R55Pka1bJ1eSBD8sGMMq55AulHP6FkcEKcgS9850hv06yfZGr2KMZ0cKVNRh1d8pDSkmiBMYJ33nWOD7/lBEdvWHcfixVWS0xL5d8T+b9uUZZLmtcdW+F1t13gO+4/zae/dgN/8cj1hLGk7Ju0F3Z2zlf3RWTBnpkw931JrepRrXr4nmsQVNQw9KtvhjchIwYpnQZDzZ0r0ZZORxOGmnao8342IlVPxmdTMBhkEY+e59oXXEwQWyswWMqB4U/+bp7ve+cqhw5GmEKr2Z28ndkGoUhOv/+ZBVqhZKaq837s1zrGtpTJsDG0xS4kWndorL2MkzSjV3ZlM3rJoxMrfGX42Q89xc995HGOXreBDj106GFTjUMKi5ImrWpK+jolEyMwHYVuB+ybDfno+5/ln/3goxyaC2mGHkraVOBvnQvcn8FvjKVUUizsL3H4YIW52QDfk7nmkLX6zVD0kfRWLsjoqHdjkf1k5xJC4HuS6SmfAwtljhyqMDcX5OHY41YxelBw5mFVENbb34usaVTgW86t+Pz+ZxbAL86D7bXGy0XXx2XRBlRN84WvT/P5b8wwXdET7aOACYGMEGzqFZTSo772IjoJ4SKl3XcfveQRxZLD8y3+xQ89ytvuP4MOPUykkAJnO04hUltQN6O4N7s4IxWjJboZ8JpbVvmNH3mEe44t0+6onmN1x7A5ZLtcUhxYKHP4QIWpmo+UYjNh5GMSPeTRj0wDLZqkMmIpfqefUDxPMj8TcPhghX3zpU1Ecq2RSPHeOg1k89+3+FYaDQUzVc3//Tf7+OsvzKFqGm0ygX71JNJLHgIvsFw4F/Db//4I2aOe9AHpYtSl0zWFrKSJlD7NjVeIOqtI6ayMoyxkiprHwbk2v/bRb3DrzWvoVoAUuJh5uk7Hiylz2fuuCqpFKYMOPWZnOvzzH/om9966TCPXRDb7sDKhvbC/zKGDFWpVDyt6NY2e/IOrTBrNTVNsLQQzTUdKwcy0z+GDFWZnArLabSL1BI/yM95JFK9TKXlZ995aZwKUwvJb//4I58+U8KqGRIurJpF+8lDSgjL81h8e4fi5EuXAFHxwExKBCYGMJIRUxFGT5sYJhOxW5h09dENstRGUfM0nv+sZ5ve1Sdo+Srk4x0sljiKyz7sQTreYdSxBWT75Xc9w44EmnVim5NQ1VwkhmJt1u/2pmpcTCpsidQZXaWCrgp1FcpNSMD8XcOhAmVLgtJGMxPKgjWsgy8BaUPJyfBeZFiIo+ZYL6x6/+G9v5MJ5H6+mSXTXj5aF+F7qODKHOUCiBcqz4Fn+1e9fx189PMNMevyJ36MXEwIZRaQZ6c31E0jp9USDjRqyngydWPHD736BO25bRrd9PGVy7eBq5HRRG8lIZHa+zc988On8HJlgrla93MeRmarcMbo28mGXqBF9xFAkklJJOQ2p5vWM1drdqPQ0PHTvv0XIy7/ObMNSLRmeeKnCj/3WLTzxVA1vJkEIly3ehd2WSPpJxhiX7+HVNO225Bd/5yb+4D8vUC2bPOE1HcFlj3mvYhcJpJhvYft+v3ZRFDSN+gmMjkZc+4B2R/GGo6u8541nsG0/j4qBnQla20Qioc/tx1Z46N6z1NuKwBcc2F/m4EKZwN/aUb07tc2K19AlkszklWlMB/aXmZ0J8jbN15JfRPYR7MXR1TK0EdQqhjMXAj7xP93M//PnB5xWU3OhdNo4UoAuWRR/wEkbbdzvsmRQZcOjj0/x8d++hc99dZaZms4d9RPT1WYMMYy3N6PYZSf3OUKFM1fYgmN2JB/aAIeT2cOl9Gmuv0KStPOEwlEL/cxMBUpavu9tx8HXmIJvYieHmiWMZeclUXzXgyf45itH8MtTlAJ6qgmMaumXrrDsbRg2P+f6hK+sdkDYAtmM1vh3Gld2eWngt7BoLagEhjgR/MbvX8d/+oc5Pv6BJd5xbx1vykAiIM5IRLg5JLJET4tQIAMDWvDyK2X+3acP8LmvzqC1YHZK59rMSMqhEcAQCKQQpSMsCEuiFbFWJNqdXgqDNs6e7asEX2kXgcPVZR8PDAPfHLqSJp32BcLWErWZG7FaMzLXT9dx3go93nz7Be66bRnb8VxexYAydDOfiBQWHSsWDjR53wOLfO6bc1gTObNPn7loFNFPcHmL4mkfLKysdXItZe+TyJVeW4FEjJuL01XNYy9W+NS/uYn7XtPibXfXecudDW67oYOnLMIzCAUYQAusESwueXzl6RpfemKKLz4+xWrdY6qq8T1HTiMne0YMAyaQrilDSUOUeMTaY75W5+YDi9ywb5l9U3UqQcRas8ZKY5qXlw5xenUfjbBMJYiQwmJyErl2dgFCKJK4TWPtZabnbsUkHYQcHZdVlmlugTe+5gJ4GhMrpDAM+hnlmwotef0NZ/n8k8cwVqYa0XgI3H7zWhapNTPjo41lfSPKWwvsZVzdo+qSiEs0hGrZBW48/FyVLz9VY7amufFQxL4ZzXRFM13VhJFgo+mx0VKcWvRZWvex1n13Jq1xZcyEPC4FAySQrslKCkuzU+Lw7BofvP9rvPX2Z5ipNCGI8wqsCAuJh4kDzq3N8ZlH7+evn7qbWCsCL0Ebec2QiLUWISXGxDTWX8aZ90YveznRgtlqxF03r0KicjV/kEPMTFkCQEuum69zcLbBmdUZfHWxkhijh6KWkWki87MBSWJothKk3NtayNX7eoompq7gr5ZN7lB/4VQ594ekAW9IYdNinpbpigbhytJkWsfEZHVpGBCBdMlDCEs9LPOmYy/wyfd8mtn5VYh8rFGYsNzzLfdgDdctLPHx7/xzHrztOX73P3+AlcY0lSC6Zkgk94Mon/r6y+ikjRBqhByrzhEca8mtR+ocnAtBDy9uKCMRYwWqFHPzgRWOL81RyjcaQxrIDqGfRCywb75EFBvixCD3sClr56Z0b9BGRiRCWEqB7c7NTG2mW8LGWFf9YEIcl48B2ESK2caWVqfE++55mF/57j9mdqpB0q5ijauDJFPtpFvGIlVFEw8dVnjDrS/w6x/5I24+sEg7CpCyGxq692HzyrxRuEZWD2tUSEQA2ggOzIUIb2dCdi8X1gpQhrlau6eW1jiin0SUEuyfLyHo/fteQx6ivqPzJtOCXeq4tQKT/ZjuazdnxabPT3DpGIhR3YVbGlqdEg/c+hw/8Z7PAmASD092q9X1l7HIByUsShiSdoWDC0v8/Pv+lNlqk0Sr3N55LYT7CqFIogb1tZeQygdGo7R7dv+NcSYsVH+c/BBhBFPlCG9PbC66ZGGMpVJWzM0GO7hLHw10ibCQqzMR3GOJHSeQLEqmk3gcmVvlE9/+WbACo1WPBrGVHOzJPkbgKU3SrnDdobN8/Nv+0hEI47vLvGykhRXray8BcmQKKwqR+mSA2VoEyjo/1pBJXeDOO13puKTCoZ5959HNd3FXoo1lZtqnUlZ5zsgoPP+rRbeMDGht9sQ1XavYYQJJbYjCkmiPD973Nabn1kgS/6LkUURv4pjBdsp8yx1Pcc/Rl2nHQUEL2bt4tcKKo7TgtJEF3tidZ2Ky+P49gP6aWkLA3FxpTznTi88q0eaqn92g1sMorbNRxUBMWIn2ODCzztvueBqiACUu30be3aW4MF48zUN3PpbvOndVCxnCGu4trHi8p7DibsNa4Z4BsN70YZd6I1gESEujHYyl83w7FP0hxlhKgSsNfyllz8cK1mkg+a8Xua7t3i+GO29VXl9rQ1L40YWf7DOmr1xQsUDmpYzhWsWOSqTc9xF7vP76k0zVGpjEL5TLvrzjFRPHSDxee/0p5msN6mEFJXexIfEQ55CQirizzsbqCxy64e0YE7HbDaYyspDSUm/7oHdTeFtanYDESNJ+f7s1kAEgS8KFmWmfVish0WbPJBgaa4ljU9gs9l5P/zVKIfKll2XwF8vWSFloRZz+7vsS31eoAsmY9HtGm7wfTM9r0+0m2T1mr2a41fiuRewogRR3oa+77iR4GpuanK72PlutmKvVOXbwPF9/6Ta8UmfPm7GA1A8SsbHyLIdueLvzg+x6dV4nwHxlOLFYI4lUobDh4COxekqaaMW5tWmUsGlY5t7oU71VVNbMjM/ySpalvtsjvDJ0n53TruIo2ZRUmcGZ7ejRJlwTKoFSklJJUa34VCs+5bJHuexRChTKkygpkFLmBLB5HLZHo7MWoljT6SSEHU0njAk7mnY7ptGMiWON1qaHUCZkMoA8EGMFvpcwV2t2+11fBbKYf2sFQmn2T9fRVhbDuYePIc0TCwhrkSpgY/lZkriFkGok6jxZwFOWs6tVXjk/xW03rWEjNVTBJqWl3S7x0uI+fE8Xyt7sjYVcdJobY5mq+TRbCWGox9gn0l21mUkpQ5FIMsLI2t5OTQXMzpSp1RxhVCo+ga9yMsm+lxGO+71w1h7zVEYA+V8A1+54qhYghUgTC90YokjTaEasb4SsroXUGxFx5KJJpRJ5QcjsPOP3TK4cAzGqK2Ep+zE7GZxnAaSl5EW7r3kMibnSKYmUHs3GKdrNs9RmbsLqiFEQkkpaNlo+Txyf57ZjK64WVhosMag15DQcF0IslObkhQOsNqv46jKbmY8RilV852YCznfa+d/HDdm1SCGIoqTH15Pt8D1PMj1dYna6xMxMiZnpEuWyh+fJ/LNFE5Yx+rI03+38GFk1cA358YSAUklRqVQ5eKBGkhjaYczqasjqapvV9ZAwTADy8bmjjMIKHTx2nEAEkBhJs1OC1KywEyV9hLBgJK1O+doJ400hhEcSNVi78CQz87dhks6u2zCsFVhh8T3Dw8/v50MPnkAVeoAM0pTV3UBYHjt+GK0lgZcUEsP2DnItJBWc5bKiVvNpNOKx1EJy4S2g1YroRJpS4ExRc3Nl5mbLzM4UCAOBNi5SK0nMtoJ5p+5F9xhdLSZvSoZ7HtWKz/RUiRuunyEME5ZXWpxfarKy0s67YYpNx9ub2HkCEZZYe6w0plx9K65OmBTmG2jFUn0GJfXYx/xfKrKdJ8D6hafRx97fE867W5MzK+VeDjRPnZjj7544xLe9+TSm7bvQ6wFoibn2YQXS15xe3McjL91AyU/IOhfu1bWaNTmwFmanfdrtpNsOd8wghCBJDJ4S3HZsHwv7q0xPBQS+QohewuhHVtYlO07/cXdibP3H6ikzk2s+TuMtlRQ33jDLdUemWVkNOXlqneWVFlo7v9VeN23taBhvN1TX8szZGyDxcm3haqPfhNKsNaY4fuEAvtK7a8Ya4qndpDMoVWJj9Tk67WWk3N0oLIe0FIQFXxn+9Ms3ETVdG9usevJORjx2HZ5ZyI7lb564jTDuNrDaq5qpE15dbcT3JTPT4xfW238Nd915kNfevsC++QpKSRJtiBPd17u+u9iKwn3YXSX7x5LBGEscuzEv7Ktw792Huf/eI1x3ZDonyuy74/KcLgc7SiDZ7rDkxTx39nqarRpSFuO8L+94xR0nXsLTp29krVXD280QXtgV772QHp32MmsXnkSqEtjdL2uSP2/fcGJxij/661vB12nG/M6RSDHqyliBDGK+9uxRvvnKdVSCOJ8ne8181Y+ic3h6yicIJGaM6mR1E4ShWlF4niCKdE82+igQxquh64DfPM4sx2R+rsJddx7k/nuOsH9/xZne+rSYvYKBJBJ6SrO4McvfPn0nBJ1CFd1LJ5HijlMKC7HPXzxxj/vjNZCJXkRmA7fWsLr4KNbq9Pfdn4xOqEO1lPBnX72Rv/zKjchqjDaih0SudJgZObjGQRJVinjl9AH+5Ct3OZ8LXBNzoShMrXXRSXOzpU3vjwOEgErZKziq+7PvR4cwXg3FNsXZ7+CSI5PEMDdX5v57jvC6OxZyDevyW/iONnaYQERuiw5Uwp8+/ADLywfwekqxX1yYFHec2khEuc3nn7yLx0/cTMWP9rS5Yiu48u4GqUqsXXgqNWONQlZ6Zt91CV7lQPP7f3Ebjz19AG8qShO2xGVvHrLPZuRhrcjJY3V1mv/whfuItSq0zt372gcUzSDOoV6tKKaqXu7gzTWUXRzjdsjG5DLrFaWS6hn3OBDGdng1IjHGcvSmOd5033Xsn6sQJTr/zF4gkR3XQLIF73sJy40Z/vVnP4iJPVShKdR2u9Ki0ABItMKrNnnulWP873/7EIGfYDPb+zUIKT3C9hIr5x/NzViwuzbwopDP/v8f/9+7+fPP34AqJ0jfkhT6Sl9MG+m+n+U/uDmjyh2eP3GY3/3s21htVgmU7usad+2gaAqanyvhe7LHRDKKq0MUaG1qyu8JdtgrlXi3I5Io0kxNBdx37xGO3TzfkxQ57iSi7n7gR39tZw+ZCRJJyUs4vbrAieUDvOnml/ArITrx009tD9eeFFSlxXOvHON//syHaXQq+CrBWrmLO05Xm6rdPMfi6S8MddeUCwgTY63h4PVvTeNyHHZjB9e7ULIsYVhZbfOnfxuwsupx9y1tqvMJQguStOnUxYZqrUtIlQJkKUYnir9//Db++Ev3EMY+vqcLbY7hWtA+MmyVoe55klYryc1Bo7ajL+Z6BIFkfi6g+MxGaaw7gX4ikVLkhHFwocrUVMDqWkgca5SUI/e8LgcDIBAHIRwRlPyYlxYP8+jxW7h5/wUW9i+7iW4E1kqnUSCdKBRpk6kgQlj4i2+8md/93AdodsoEXpLvRtMzDGLYF8HuEUjXPuwRti6w7+A9VKoHsTZBiOFPQrfT7S6ULA5/canB6moD37N847kan/vaLJ4R3HFjiD+j02dPqkmm3eAKj1QKi/AtsqIRGh5/+Xr+wxfu56sv3oQnDZ401yx5dNElbGudUBZC0GrrHhv7qAilbiir67RYClTP+HZlnJkDJjUJuvYEFmH7TYCba14UTYkIENZixWY9qt/RnuWTTE+XOLC/xtpaSDtM8LzxJZEBEUgWaunMTWU/Zrkxw989cyf1xjSHpjeYrrQRQYQMYmQQIVWCkJY4Cnj65FF+7y/fx58/+makNPhKjwB5wG4SSAYhFTpuAnDgugcxJu6+N4TxFLWOTAOSUhLHmrPn1qnXw7S0hKBcMtSbis8/MsOXnpxmfdWnpCz7ZjQqMMjAIMvpj0rzXYzg5VMlPv/1Bf7umbv46ydup9EuUQ6S9PzXOnl0NbiiKatSVlhjCTu6Zwe820KpqH3Uqh5zs0Hu+4AhkYcFKyzCpuU2BXn7GiFE+p4zpdlClJX7sX2/d++ryNlGpDxj83WR0cnm7wi0tpRKHgcWqqyth7Ra40si4qOf/PwAjXCZk0wgpcEYSRgHVIMOtxw8z80L56kEEZ7SdGKfjbDK82ev48zaPNooKkGHboLY7gsNaw3Kq7By/lGe+MpvIcRAgtguaRxS+tz7rb9BbeYmjO4MZSw95JG+VkrSbHY4d36DJNFIWexZ4iLohIB2R9KJBdNVw23Xh9x0KGKqopmd0igJaw1Fsy05uxLw5MsB0p/mwTceoew74jCFHCOH8Vpog0Cv38MlUa6ud1jfiPMCgrspgbXRbAAAIABJREFUlHpMbVJw6GClR1AOcmwWXB5zHkpe1NqK5JsK+yvUQNxxbF67xOZE5AiLgrm9uH6MtXhK0okSvvnYOdbWO/i+HLvk0AGH8mQPzeYaRK0Uoo3k6dM38NiJo7lTXKRtbH1P46skL00xKuQxOrAI6RFHG5x95S+4/d6fwuhwyzj6HT1rH3lku6nVtRZLSw2cdpYJh65fxFiBwFIuGaplSLTgqVcqfPPFKta6vuqQmi6lK9CopObwvKYSJLmJazIHNqPfH5I51ZWUrKx18uewGyRSPKe1MD9f6hGQgyWPVNtI2SCbi+QtgzPLlaD7zmWdoPuyRwNJrWJpzyKbkUc2f9MTZ7XAtDaUfI977j7Mo988x/pGZ+w0kSHEghYjcAQmfV32IypB9pkiS4ue2P5rJUTz0uFCepVXYfH0F7n+2AeoTB3C6GhgWkivMOi+Xlyqs7bWehVB1S0tYky6KxOWcslSSS+l+OmUoohjUr9YMUplMge2gqCXRIyxzMz4SCVYXg23fXaDRPE8xljmZgJqabix6HmmO33eVF5kpiqbahaZtpBZRHq/QKZBOM2hf6Ztlj9dE1VRu4EsP81pNDa3k9nCqbJzZ/dIG0Mp8LjnrsM88uhZmq0IpcaHRIZog8mEiYDUcW5s9qPy1zZ9v/dngn4I6RGFK5w78ddI6eMm884mFhbLxmevpXQJUafPrLG62sx7I7z6hO979lZgrMAYgS78GCMwxi22rXo4TLAFtiBvV/rd49ABZzIqFgIcdNhocRzaWKan/F6/R+6/2bnnW7ymzGKR+TaKl2vT8wrb58+wwvlrhXUO8Z4f0fd7qt3YLHQ9PV8ePm3JSUPYHp9KUXMv3qtEGyplj7vecJDAV2MV4rs7RvxxxyjINmuQqsz5k39Hu3k+JZEdPPw2/o5WO+LUqVWarU5PH4ad3i2NSzbyqKCXRNLKvSXF4YOVfPffr43sKIqbDRyJzUz57Jsv5bvugZiuevwpWY5ZUfh2Hd75RijVSlIrFnl5NcfGfU5z+n7PtJSuduMqU5MTSqZqiO4QN23Eis9BCkGcGGanS7zutQfyS+ua3kYXEwK5EozIM5XKJ2wtsnz2a0hV3rHEwq38HVJKVldbnD691hO/PihVe8Idl48u2Xcjn6QUHFgos39fCSXFJiKBq5svNtV8M8FrUqfV/GzA/n2l/PgD83v0+IFEasHIjKGiILRTf2zq6RYij5O67P1gpkwLUiFfcJj3rAmbOVv6os767kXml4kSw+FDUxy9aY44ydby1dycwWNCIGMK67Y1CKFYPP0P6KQFV+kDKe6UsnNk+R3nFzdYXNrAOcs3q+M7CzshkCtEv3kkzz2Y8jl8qJJngW9X8fZSdkdFwikKaWMspZLi0IEKs6nZqjiOnXqoWfi4zcbS61zoMRl1I9RSEhH913v1yLSR/P9c0+kNAc7Hu+meiJzQksRw7OY55ufKaG1G3pQ1IZAxRabeKq/ExuoLrC8/i/LKZLvPy0Vx51o0WUWR5tTpNdbWWkgpB7eTLI6FwUWTXQvIzFj9fhGlJAv7Shw6WGF62kcpkZfVuJxghSJJZd/3PMm++RKHDlQol9Umv4u4gp3+1kgd8WkKcnbc/PceM1F6PaIYcjtYdK+yG+G1lW8yc+sLbK655CHPSnL7bfvHomHYKFTkGz+M0vMUEqObLJ35IvsOumrFlxvSu7XJSrBRD1larJNoPVB/x3YYpds8bsiEZ/+ztRZXzDBQJDMB7XZCu63pRBqtbR4t9GqPOJOFUgqCQFKretSqHkql1QVMv3lmJ59kN1HPBUDZ3Clu08FluR7F+zBsZOcV6S8uJcXmr7PkxpxoCiSitWHfXJmjN87x4isr+J4aWSKZEMiVYJQ0yrRK7/L5bxC2L+CXZrDm0vqDbxYuNq/bs7jYYHW9hYBCfseQHNt2Qh47hX7HbaY1ACgpmJ7ymar5JNrQ6Rji2BAnJu1hAcb2ZtZ5SuD5kpLvKuq6MirkxFE8Z/Z6oMgT+GwurBkxYdtDHoVx2k0Rwt1fEm05etMsixeatFqj2754QiB7AFL6dFpLrC49xpGjD5HoxkX9IdtFWYVhzOJSnVYaj559dqgTV2TBkBPsBPojrzKBn2kkAJ6S+DXXy9tk78GmhyCE0zwE6TGwGNM9zyADKxyKJizSHX3f+UZMyPZoIZkGko/Z5uasLGs+u49BoLjl6ByPP7WIZDSjEic+kDFHliRlreXC2a9hTZIvoO2cb9uZrNbW25w6vUq7vYvkgVtMxXpJE+wMuhpk8Xn2+jN0wfEt03lR/MkirbSxmAIBFX0uOzVfsmOnfuf0ddGE1WfOGnEU4t5yEsy8ODmNFDZ0SWI4dHCK+VnnUIcBhF9fJSYEMuZwi9WgvBLry0/RapzdNidkyxj01GR1/nyd8+c30tBPOTwTxDbodexOsNPoz3PI/pahP3mu+OM+3HUY9x9n58bYzeDueZ0H4Pa+HhcUA4iLLvee6Lb0XioluOnG2U1/HxVMCGSPQAhFFK6zuvQ4yus2m8qwvckq4dTpVdbWW5eYVT4cmAl5DBVFErmkn0LI6qCQR+ja3td7FVutPa0tCws1ZmdGUwuZEMgeQGbGAsvahcfROu4xY21OWhIIKVhdbXHq9CqdTrKrJqt+CAE6MSMxlgl2A7bwv+37297FVtnq1lp8JbjuyHQhsmx01sSEQPYAcjOWKrG+8uymnuk9WoeUJInm7Nl1Fpc2cjPWbpusihAC4tjkztkJrh0UfXrQrcadheaOwPQcOrSxHFioUql4Bd/gaBDqhED2EFyBxVXWl58p9EzvLUeyXm9z8tQq9UY4tMTAy4YQaV6CuSYFxrWM4nx0lbi7ta2uNRNW9toYS7nssbC/is4JZDQWxoRArgSj8ex6kJVFMDphffnpwkpzma1aG86d3+DcuQ20Nig5OiarfghAa0Onk2wKQZ1g72KrpMdsavb/vxex1VosTvuDB2qoEatSPSGQK8EIyjKRbs+U8qmvPk8cNxHSQ0pBvR5y8tQq632O8vx7IwYhXJ+EVjspCI7RG+cEO4ut6z5lBUH2/vPv1Tyge+3OmT47U2ZqKkCb0XGmTwhkT8EipEercZaovYRFcu78OmfOrpMku1OO5EogBBhtaTajPPFqFBbLBINFT+FF+k0618bz796DVNsS3b/7vmTfXBmjbbcS8C7jGiKQzJ7afZ39uCJsBoFJU5LsNp8ffQihMLrF2VOPcfpMMy2COPpaR4au6UJQb0TOD7Lbg5pg4Hi1DUI3ynDvo0se6f99t2V+vuI6Fu7O8DZhjxNIkQQclDQI4Xp1ayNJtKKT+LSjEmEcEGkPbSTGyPzz44LuQjMsLz5HJ3Q9lhl4eYmdQzZGKQX1Roc4NhM/yB7Hq83NodVfGyEIkW1Xe9UQYywz0yWCQO16nlRW02uP1sLqvbkyJQGtFa3ER2CZKod4SlMNOkxX2kyVQjqJz0a7QjMsE2uPVlQiij1KfoynNFJkGspoIlP1hfDR0Vms7SCE3FwraAwgpaDT0WzUOxxYqJIk43cNE1wiCqaqrc1X1x7Sylk90WfGWHxfMTUVsLzcAlVIzx8irE27KGq71wgk5e08dtyFALajEtYK5qoN7rv5Je6/5UXuOHKa2WqTwNMoqVHSYK0gMYo4UbQ6JY4vH+CRl2/l8ZM3c359ljgJmKpKZL9eOVKwIBRJtIRJNlD+PiAZu8WYRWKtrYUcXKgBo++7meDysVWFhOLra/eZi9SHnl27s6T4vmR6KmBpqYnnkZWUHBqyvLEothw5oPYSgWwmjzAKUNJw900v8/bbn+aum46zMLcKKgHtQWqmwnYTlnwV43sx1UqbhYULvPH2Z2g3azx79nq++NztPHL8btpxsIvXeXEIIbEmJO6cxSsdwOoYcZXdCoeJLCRZSsHKWptE600ZuhPsDfRGXvU6y6/159yrXKR5MRZq1SBN/h3+mFwZG4hj+Nkf9PYKgXTJQ0qDNpKwU+Leoy/zjx78B153w0nwI0h8bOJhYt+RTP9RbGpvTB+MSTwEUCl1uPe2Z7n31mdZ3XiEf/v/TfP0VxWBZ4a+A7g0SKxpkYRnYOZ+YLwEb9EP0mhE1BtRWgtofK5hgkvEpiJX3bDdcZqzg0B26cVbZKylWvWdI33I98dai6cE6w3L+79V8Z53ir1AIGmvY5zDux0FlIOIj7/rL3j/fV8HL8FGASas5Kanoglqq/ufzeXMrGWNxHTKGGOZP7DIe+95nv9N2pH0hhRjyJNoEWtjxjWCRQhBHGuWLrSYn62g6TV5TDDmsFkR867e0a26e+1EXr0aumXu3B0y1lIpu+6PSTI8CZSZrsLIcuSA4Bd+xAcz9gSSkYdFSUujU+bWg2f5xLd/lltuPIENy5hOCSnsRUmjiKLamCGPxkp8OrFzxDuz145e0FWj60hXjkBM5ExaYxbBlJdfUYILF1rcfNNcT82uCcYftqh5dE39zggwgmtrN5BrIdkf0t72pZJHFHdQQ7pJ2YYt0fBPfszn4PWCpGHHOYy3K0iUtDQ7ZR689Vl+/R/9EbdcfwrdrqTvdcNwhbj8gIXsO5mWQ+pfyd4bNXTD/xQ6uoA1bcYxWjvvhyAl9UaH5ZUWSnVv+IRIxh+iYJtxSyptEHWNFk3cDo5TRf5aSkEpUEPzgVhrURLWG5bv/06Ph96p0E33t/GTLEDR56GkoR5WeOvtT/FPPvQfKZc76E4pJ45sJ3O1E/LVTF2jhO4wnSM9iS6AUGR+kHFCNlxr4czZ+qRL4R5DsTlt0WBlJ+TRA2dT6M59KQWBr4aS22ytRSlBo225+zWST/2wB51sYy3GlUC65NHslLjv6Ev83Hf+GQA68XLfBQx2JzPKuyRXxTNGR8sIodK/7fKgLhPZeD1PsrLaZm09zMuxuI3rhFDGFambfMuGtGLi+9iEos9PCPB8mSfzDQqZ3yOOLTM1wa9/0qc0JdCJywOx1iK3bFc50nC+ByksUeJxeG6Vn3vvn+EFMVqroZHH6EOCidDxKghJf0b+uCCbk1pbTp/e6IlMmWB80e0CPi5FgnYXRW1cCNEtZzKgjVQxwitK4J/9pM9tr5UkbaeRZOeUsofZxFgQiRBu8hkr+Ng7/5LZuVV0HEzII0W3b7VAxytgNeMa0VLsDb14odWnhUyc6uOK3r1zVq5jEnm1HYqFFcFp5Vlw1mAiEoXze9QtP/bdHu95t0LXnd+jGAkpO5FrZ7opoWckiaSrfbQ6Jd75uie4//ZnMWEZKYZPHiN3e1Lk1UuFQserWBMxzgsz2w0lieHEyfWev08wpiiWLhHdch1ipKs87DLymAOL70nkgHqDuHwPWGtYvuOtip/7qA9tkLJLVnmu1qnTqyxdaLgmQymR9LpsRmehZtniiVbM1xp85MEvumzyIUdGab0rJWguGwKJTjawNhnQLmU4yMbueZLzS01WVtt4Ey1kvNGncHQDssZ3ng4Tg1rPmdO82ba8/hbJb/zXPghXh2ur+mTSGMvycoOTp1ZZXmn2EUmXPEbBTyLSfI52HPD2O57iwMISOvaRYkg5GemEj2LL6AcEWRASk9SxNmGcNRAozENjeenl1by1Z3F+TjBGsAKEdf+nG8NJ7sfFkO7+EQOpxpvXuYosc9OCf/nzPrU5gY5sIQer9wHJzCGTJJqlpTonT61yYblBkjgi2U5NGv6CTZ2pRlIrhbz7zsdBy13ZtUSJdYw81LNeCQTWdjBJM3Wkjy+y/ghKuYisU6c38H01QBvwBBOMGDLzXqq97aQIzirsZqHyv/lzPsdulyStXqd5/1KT2ZeLRHLhQoOTp1ZYXKwThkn+3lZaSfH/QcJaF9wXJR43Lyxy0/4l0F4eAjhoGWLTsgtOAzFsQcYjCIG1GpPUcRHb475LzyM/ePn4Ko1mlE/uiSlrfOCeUzEGaxKPdSkQxY1yn1P9apCbpQS0Qvjlj/u85S2KpO58Ia9WPkhmb7jPdIlEa8PKapOTp1Y5c3aN9fU2xmzWSoZFJlmF3Vgr3nDjcSh10GYXdtVC0AoNegw0ENcnPcHoRq6BjLOQzUgi6xXy4ksr+VycmLLGA0UbevYsr/XeH5eKHkNLxrtXe8ysZJCAjYblpz/i870f9NANF3GVm822eTa5BM4K8BXZxoVLWprNDufOb3Dy5CqLS3XCMM7fzyZANpjs/0EsaGMF1SDinqOvgJFpX2A7FLupE8aAhPW6Jk6Gc96rQ6qB6CZ7ZYdXdKifO9/gzNk6vtdrypqQyGiiX1P0PMlGPSLRZqJBXgJycWOziKirO15OHmmZko99j8cnfsTDti1ScEnEvmkLny9CukQipSOKONGsrDQ5dXqN06fXWFlp0ukkuW1aStlzsu0c8Fc6URKt2D+9wS0HzjvzlehW4h00XLihe73e0BjDwJJ4dg4CrMbodq6B7IVdXnGT89wLy9QbrnXvZBc7uugnD99XnD3X4MmnzgNjYA0eARRljbFXtx3sIY+65aMf9PjUx33opAmel6gVbluNt1hOIAuxc1qHO3CrHdFsdVxlyMCjUvEpl31KZR8v1Uys3d7EtZXgfbVBC2HRRrJ/qk7gJbtSMEcIILGs1TXKlZcaWYGVl3W31mkgtvj30RzzpaLflPX0sxe4/94jPfNnL1znXsBWXQZ9T3H6zAZPPr2I76s9ohsPB5nvVSfmijev/eTxA+9T/NJP+RCBMeQRV5eyfi6pnHu3fIQ7qPuBzOTVDmNa7QghBV5aarhc9ikFPqWS2tLUZQsCrXhh/X8rjsFYwWy1hVAaq4df36lLIImrBTO8U1828ocvRFqRd3yz0TMUJ3X2v++7qKznX1zmdXcsEMfOOOzezkJDxyLiYc9hK/LwPMnxk2s8+/wy2TPR2rr2rKO8oEYA+cYIl5fRTSy89GMUyWOtbvmB9yp+9WcCiN0xL7dlwmX1A+k3T/WTCbg+1s1mh0ajg5SuLanvewS+xPc9fF/lPxmpdI+RHRv69yRSWIxVzFZboBJs4g09a1UKgY0MiyuJKy0+8hPeCU6rO1ird3swl4X+HZAQoKQjbWNcYx1SNV5IycvH16lWA266YZYk0akNF6ywPfOpSygwIZXBoZ88hBAoKXjx5VVeeGkFT0osLhxeawOo/HsTzXFrZFYFCySJKbzBJU3losN8vW75wW3I43Lu/xU3lNqKTLK/F98zxhKGMe120acikELgeQrlSTxPOrOXkqiUdLrk4r7jKUucGGYrDVBmd6rmCIgiy7nl2IW3Dfv8VwIhMCYEmzptxgRSCIR0ZKG1JU6gEztSqJSgHLhaPUKCkmCs5MTxZaZrirm5Ku0OJFqgBHhe2rtA2tRv5s7RLZ0xPvdlHLAVeUgpePq5C5w4uY7npf446/IO2mHCVC3AjMeK2nVobWin6RWXmk6Q5XkgYKNp+dEPe/y3P+5dseaRYUc6EvYz1mZCAeglHG0tScc4p43tPVbX+tL9jqcsK/U2G/e1QF76jdspuJ0u1Fua1Y3E7YbHYL4LBNZ0gNGOdMn8bDLdYUWxJexAKYDZKcGxGwV3HJVcf0hw4yHB4QVBtQyBD6VAYCy0Q0ucrGFos7zhsbIhWVrzOLXks1ZXhB0nuALfImU3DnJCJDuHfvKQUmCN5fEnlzhzro7vdcPJhXClwdvtGCEFVruNwwTbQ6TJfu12fMnm+/w5WGg0LT/1EY9P/qgP4WbyuFztbyAtbbcaxFb2635i6fk8fVEHxkVxLddLLoR3mOSRmktQgvPLMZ3YjlFit8CaCGsNYgQH3U1iddpGveVCCG86InjnmxQP3iW542bJwryACs7SkeBcOrbwA8zOZZOiw1HZcVMrESSRYGnN48R5nydeKvPS2YBWKPE9i++5dkYmL6MxIZIrRXGNG2PxPEkUaZ54apGl5Sa+12umEmkUYztMnD2/7xgTdFHMBI9jQ9hJEJdQTNFai5KCWFuSxCUJ/tD3uFBda7kq8oABEchWeLXBbTVpHLf0Zs4oZVmpVyBRQ/d/2JRAzizFhB1D4I+eMN4aAmtjRs3glvkllHTaw0bDUi7Bu96s+NC7HHHU5tPnHwMadCMLMSyI+MJr5+YRgMz9I1I47fXIwZgjRyIefEOL88sej79Y4eFnK5xb8fCUJfBtGjU48ZFcLvq1DmtdcEOjGfH4k4tsbHQI0rIzvYmEIKSg2XS5IBNsj6I5sNmMSRJz0dmZFUYMO5bAF/zmp3y+/d0K03C+lKslDxgigbwaLm3wAiUsq/USJDIvoDgM5KYfT3DibEQ7NFRKktGf86kT3cTOByI8RoFIinbxZtsS+PDBdyj+i+/0uPcNEnwgBN2yedIUdMtJbzfpcyKxIGR3flgrsAnY2DkQDy0kHDq0wdvvbvLIcxW+9ESVU0s+pcCipMWYTEOeaCMXw1aRVkGgWFpq8uQzS3Q6Cb4v8xpL/c9MCkG9ERHHmiBQuaVhooX0IiulJKVgvR6SJAbfV9t+FgSeEjRalkMLgt/+eZ977nM9PYrr6Grv80gQyKXAJSsaFtdrxInC9zVZQbFBz7U8p0JbTpyLchE8FhNdOA3E9rXw2S1kKnWiLfUW3P9ayX/zUY/773NRCSa02I4jCyncDrWfMC52zzPTaDf8nLRqgfvdJAIbC8olw1vvb/Dm17X4u0drfP4bU7RCSblkJtrIJWA7Z/krx1d5/sWVdM3KvBT4VnDVXzX1RsSBheqrfvZaRX6fcQ70jY1OT0rEVgFNKg3Tves1kn/1Cz7X3yxJ0oZQO5knNT4EAvjKcHp5mudOz3PnbeexHS91hg5+wkkJhIaT5zp4nsizNccCNtn1KKw8DyDtNVCrCH7pxzx+6P0KWRVO28CRhlS9zv6rvc/9IeIZmVgjMKHAV5aH3lLnDcc6/OkXpnnqlTKVkklDxzMSmWgjGfqJI/N3aG148pklTp+uo5S4pLDQTCiurYccXKhtOv4EXQghiGPNRr2T14DrJ4/MJLxWt7z/WxW/9gmfyowgaVg81U3u3ql7Oy6GfAA8Zai3A770zHXgaWeasMOJhhJSEEeG42cjfDUeEVgOIiWP3bO3dR3lgvWG5babJP/rPw/46Pd7SCnQTec4V7J3VzUIASJEL6Go1NSlQ8mhhZif+NAK732wTqIFiRFIWTSVjs1DHxi2M1k1mhEPP3qWU6c3XLvVS9jlFs0yyyst4kRPSGMLZPdEKcHqWkgYJlsWtPWUIIwsUWz51A97/PY/DqhUBbqdkUeWZLtz93h8NBDrmNX3NF959jp+PAxS7WPQ500dt0qwciHh7IU4Z/JxgcXuWiJhf/LSB96h+Bc/7VOdFSQbTqXOyuMM0yTYNW+li1NaTOwydL/z7RscPRTxf/3VHM1QUvIt2kw0kV4HeLeS7slT6zz/4orzY3gqj6C8uKkxE4ySRiNio95hfq6C1hMzVobeSh2wdKGFsVnaZeE54LSOYzdKfvljHm/5FoVtdR3pg9LqxkYDyRLAyr7myRMLvHB6HhEkeT7IIAW6tYAnePFUh1ZoXBb6mMERyHDHXRQiG03Lf/Vhj9/6RZ9qTZC03K4IxEA1jouhSCRO2wAdSl77mpCf+tAK+2c07Ujmmkr66aGPczdRJPfstedJ4tjw5NOLPPn0Ut7J1FyhoEoSw4XlVp4Hkp1nAgcpBJ1OwupaO9fUwWkdcWxptCzf++2KP/jvA97yoEI33GZnJyKtXnVcO37EgSHbKToz1n/84u0uI92KvKnUQM6aZXt6gpdOd2i2TR4VND6waRTWEM+YkQIuwe9nf8jjF3/Kh0Rg4sGp1FeCzKyVOc2VtOhQcuRIxCe+Z5kbD0bXJIkUiaNokvI8ydKFFl9/5Aynzly6yWq7c4DTQpYutOhEya7Ph1FBkUClEiyvtGmn5isXhuvKsM/PCn7rUz7/3acCZmYESdOiVK+zfFD3dKxEoTNjCWqlmE9//RgnTu5HlRKMERRLVOzsObt1L14+1SHRNk+AGhtYy7B9IJnZaqPlMl9/4od9TNtedebrIJGRiLXCkUhHMjub8LEPrHJoPiaM+30iexebczssXhpR9dzzyzz62Dla7ZjAu7oaVtl3pBQ0GhHnF5s9Gv5YrbMBQQiB1pbTZzYQgFKuUkO9aXnv25zW8d73eG59xYNxlm+HsSKQrP+HpwwrjQq/95l7QRosohDzv/PnVRJoG1441SHwRRo6PGZCJLP1DeVULtZ8o+kKtv30R31s0703quSRoWjSUtKiI8nMTMKPvX+VmaohSUQhB2nvCbd+X5RJbeyeJ1lZa/Pwo2d46fhqXq/uSk1W/ed0x4DTZzdcktwIzo1hovgMlBIsXWiyUQ/xPMnahuXQPsFv/pzP//BLAQcPuhDdnUoOvByMFYG42H4XWjldjvjsw8f4/MPHUNVOwcm58yQipCBsG54/HuJ7wgU1jQ2coLO5BjJYoZc57RpNy4N3S37lJ32IU1IZgkq9E9iKRA4cjPmBh9bc3xGIPUYi/cSRvQ58RTtMeOrpJR559CwbG51NJUmuFkVn+sZGh6ULLef4pTfa61pBXnU3v27L6bMb1JuQJPCD7/P4w39Z4oPv9TAdi4kyf+JgIxi3wthEYRVhrUBIi5SG3/7jB3ndDcscOrCBjnyUNHlo79Xew6wsg/AEpxYjllYTpx7uzGUMF0NYhFlESBRbDuwT/NpP++AJdNiNBBll4ihCpIEZOYmEkttvDXnoTQ0+8+VpquXBmEyHja1MVQjwlKtjdfzkGidPbtDuxPie6nmOO/ksi3PjlRNrLOyv9uSRjNPcuVpkplSwBL7kxVcanD7b5qFv8fjYd3vcc7eEhE2Jge67w71HY6aBQK6FGEHJ05xdneKf/h/vIA59lKfRRu6YJiKECx3GFzzzckhzTCOwHAYv7bLJGyfwj3/U4/pbJEnYG0Y4TiiuRSmASPIdb67zuptDwkgW/CHjd20FYWmzAAAgAElEQVT9z6PoIBcIzpyt8/VHzvDcC8tEid5xraMfmRBUSrK+EXL85BpKyZ47O45z6PLRnVOubEnCXGWNf/NPA37nVwPuuVuiC74OGL7WUcQYEohD1uJ2uhLxteeP8Kv/5zsAHInonSGRYhXe50+EtMNxjMAaDjK/R71ped/bFN/xLg/dcDukUfZ5XAzF6CxjAA8+/LY65cDkwRtZKPKoC7hsjNk4i8/F9xTW2pw4Hn9qkUYrwvdUbnrMPjsoZMf2lOTEiXXWN0I8dS31uu+arKSEMBJ8+5sa/O6vwDve7mFii267pNtR8SWOqTjM0vgtiZbM1jp87pFj/MLvPUQY+qhKTKJl/hlrr5xIshImL5zs9EQ3jB8GO2YhBHFiWZgTfOL7PTBZyZDNJRfGDRmJSGnRkeDQ4Yi339WkE7u2AqYwH/qF827j1YhNKYnvKZLEcOLUOg9/4wxPPLWYC24l5dCFVEYWiTY89/wFtO72sdnb/pBs3rg6Vu2O5M6b23zwnS1AkTRcLbthVGu4HIwpgUAxokhrwXSlw18+ejMf/5338dKJ/XjTIRbQJrvEKyMSqQRRS/Pki22CYEwjsIbgOJcSWm34ofcrbjzmTFdX0iJztOFCk4kF33Zfg33TIdpISr7MS0tkWdjAlrv9QWO7c2avpRSpL0NSb3R47sVlvvbwaZ56Zon1jQ5KSZTqbfo0TBRNWcsrbV54aSU3G48aOe80sk1KlAgWZhM+8q4NMGA0OAvi6AWhjDGBQOYPAYE2ThN56sQCP/6v38uf/s3rkUqjKhHgiCRXEC1khLIVMqKxqfnq9GLMmaVxq4HVhwEVU7TWZclGkeXGw4Lve48HaTXdsb1XWyBzqgth0YmgMg+3HlziK18/x5mzdTodjZQuailLrIOtCWWniKX/OFsRhoA8DFcpSaejOXVmg28+fo6vP3KGF19eoR0m+fs9390lIZWd1/cVJ06uc+58Az/tJ1KMEts7sAUzqbu+j3zbOtOzCTrp5h65hNfRII4MYxmF1Ytu+G6iJbVyRDMM+Od/8K189uFb+NGHnuSB157Gq8YQedhEoY3bSWZCYStYINaCUiB5+uUWjZahUpZjKRRtGsY7CHevECAktDvw4Xcp5g8LdH38oq4uBUVTFqHg/W+X/MF/avLwN5tM1zympgJmpkvMzpaZngrwfYXvOTOQ63NRJBPyEvtXKwxdCfRsjFmWsqsCkGhLp6NZWw+5sNxkZTUk7LgOgEqJ3DmejWlUhFRxp/3McxeoVnxmpkvE2uQ+mb0xv7rkIYTze3zvO9Z5zbEQ0+mvfjB62AMEAlmuQ+ZY96RhumL44tM38JVnr+NNt53jvW98mTfffpYbDmw4MkkUJNJ913YdpdkmXUhDqRRDUOax5yPCyFCrSPQYEki6DR7Y4ePYcnhB8N3vVhBlgnYvLO6tIYQgiS2zhwQf+Y6A/+UPYxKtWVpusbjURClJECimpwJmZkpMT5WoVX2CkoeSripxcSdtXUWXtOhl77m20wYyQZ/9n5GUMZYwTNioR9TrHTbqHRrNmChydeOkEnhKbjpHdqxRQXZNWb+Qx548zxvvPUK55KNN1y8y3vOsSx5SQCOUvPu+Bm9/YwMT9bbtHtVL3CMEAkUSMWl9rKlyhLWCrz53HV9+9nrmpkJef+MFHrjjLA/efoZbDq2jlMWTBqks1giSRGIsXNiY4mvPHeGrz9/Ap//q00xXNdr4uN7iI/o0t8VgSplkSYP1Jnzn2ySHrpN5lMheRUaOzhcC735A8u/+RJBo8L10MwJEUS+hKCUIAkW14lMpe/iBIvAVvq8IAun+9xVSOcEhAEGvrTsjG2MsSWKIooSwowk7CWGY0En/b7cTYq0x2uYZ42ob0nDXNJoPLCMJT0mazZjHn1zkvnuOoJTIG0+NL4n0kUdb8sY72nzoWzcgFmkAStd0NarYQwQCxegswNkTBdTKrid4J1J86Znr+fsnb2Sm2mG22mG6GjFdjqiVYzqxR70d0Ah91psBa60qOunQ2jiPlApGoPDf5cItMsMgqvFmhSZ9D971gHIeteFVTNlVCAE2shy9UXL/6wWf/5phuipympZSIBEIldK3sbTbCc1WjC20d+0WxnNk4fsSz5PI1HyTva+NxWhLog1JYjDaYGz2d5MfLzuWkhJP9Sqe40Aa/XC5WBbfk6yuhTz6+DnuvvMQQeCix2RfVNLooxttlZmtmqHkza9t8YPvWQfA2G65nFG/pD1GIBkyO5R7WK7MCUhpmSrHefjvcr3C0noVkxZpdLuBLM5aM1M1RK0TtMwG3Qr84wiLtcmOC3aBK+p242HBA2+QufMcxkdAXQmyna824JXhW+9X/M1XDf1OJps5FSgI95RUii0IMq0CnGaxKcmP7qPrEoD7XUmBkmpLf17mcxk30uhHkURWVtp845tnuesNh6hVfeLY9Jjxss+PJrrkkTnGW6HgHfc0+Z5vWwfrNr3dWmujjz1KIBmKi82t7oxMhLB4yuaLuWftW7DWYKxP2DqN0R2kqsCOu6CHBGvBJuwkg1hrkUrQieCu10hqMwLTsSO/Y9o5uIVOBHffLpmuiU2NkLYTZMZaBMXPueP1v74cbNdLfHSF6eUhJxFfsr7R4RuPnuXO1x9k31yFONF9eSKjpgb3ah1KWjqxC+T54Fs3eOiBBuhezQNG1+9RxJiH8V4uRGGBup+skq/J2+O68iUWAVaThGdw/oNxTmLq70h49dfRdQLDnbdKCCC1zOwZofXqSMkygVuuF9x0WBAll7boB1UKZK9DCJH3X2+HCY88epaXjq/iec70l5mEuuVlRmG9ZpUKnNYhcCarhVnNT35ohYfeUscm6ftjRh6w5zWQK4cQEmNC4vAUQviMo//DQaTRPTHZrqxgWbkqaG2ZrgnuuUNCTBoaPY736PKRm7G0xZ8S3H2H5PEXDNUSJNtoAxNcPdx9tWkkm+XZ55c5dz7kllv2MT0VEPg2DX0tRFa6bw55pP1aB3Ri5zN88PUtPvz2DSpTGhPK3CIybuQBEwLZhK6WodDJGkm0hLNzjcJu5gphNVa3XcIGFiF2RvHUBvbNwNHrBCRXFr7bXw22+HrUhbAQwjnEPaeFAHkJ8gl2El2TlEwtCJ3IEkYwXZPccKDFe+5LqM3M8sUnarRCSaVk8hbFwyOSYiKnWw8qzSyPE8HRwxHvfaDOa28NwQhMxxXkdJ8ffYf5VpgQSB+yqCUhPZLwFFa3EXI8b5MTwhKswSTNvr9f3WwVAhINhxYE5RJ5lPCVkocxBul7oM1YkAek9xEggSMLgkqAK7g4wY6gOw8EUrp722g7Urjlesl7vkXy0LcoXntMQgVINrjvNSF/9fAUT75cJkoE5cBpJCaX7cWN4E7Nsd6IKSEc0WXEcXA+4a1vaPH2u5uoknE5HtDT3XIMpvuWGE/JOEB0nXCSODyNtR0EAcNuCbsTEIUZbUwTdjCUVwjX3Ob6gwIVuD7nl3PkInlobVDVEivnVpmqlQlKHnZMzEBCANrdh3JJEGvXOGuCq0cWwhzFlnYTahV4yz2S73qnxzvfJKntd5ovHdB1Z3a+7lDMD79/lRNnAv7+sSpPvFSm0ZEEnsVXzkdSJJMr004yjahLRpl/QxtBFAu0gUP7Yt5yZ5sHXteiMq0hclrHOJus+jEhkC0hsaZDHJ7Ghe+OZ7JSVrsJJEa3djQXROB2hPtnBfhgO6mF7JLG1SWPJNF4+6b58l9/k89/8Sl++Re/D9OJRyqGZjtk9ngM7JsV+B5EMeMd8b1LKK6vTNtohRat4bqDgnd9u+KD71DcebuEMhCCTtu4uhB9979J3DFuur7Df3l9h5NnAh55tsozJ0osrbkyRr7nfBJSusTjLPnzcszUQjjCMGnobawFxkC1ZLjthoi7j4Xcd3vbEUcscl9HpnWMq8mqHxMCKaAr2CTGtJ0DXY6vA72rgEh0vIY1cY+v4UqRfd1YqJb/f/bePNqy4zrv+1XVGe70pu4G0EA3gG4ADTQxgyQGkqAokpJIyZFjScmK4qXEThzJK8my7BU5kuxkeVlJZFm2pGiKpEWJEiWKpDiblkiKJCiSIDiAAIkZINAYGj3P0xvucE5V5Y864733Dd39ut+7951vrffufMaq+mrv/e1dgFp51xsgj8kGrzz9Gv/on/0Bv/Tzfx8aIWahi+eN0ChsIfBBncd1qFAmDSWdlj61Nuo1uGe35EcfVLz7fsWmrQIM2I7FzDrCkKXV+FyAOlUymZ4EAddeE3HttjO8d0GxZ3/Ai/tD9h4OOHlOsdCRyb7dwJ7Wx0stkrTHu0pA7pUjDEca2oDvWZp1w41bIm68pscbdnS5eksEvh1r4khREcgALEJ4xJ2jmHiW0Vc6WxAKHZ3G2gghQi52mCt2gDDMk2iWI9kB8mjVOPDyIf7bf/q7zC10ePv9u2GhO3pl4JNs/GYdzswO5BNWWARSulyIWFvmOm5gvuYKwTveJXnv2xT3vEFCQzgX1by7olKkyar5VS6Xe0mtmJRIXAwlDAy339Lm9lvaxB3J0VMerx4KOHTS58ys5MycYr6j0AVyMCa3bqQEJSw1zzLV1Ey3DJunYnZs7XH91ojJlnakoQU2FphOXrA1Pa5Rac7ng4pACkgD6AiPqLMfazpI1WD0hwOB1W1MPIsM62BXJ1AtBYS+LYhkFk/g6o95ePWQ00fP8I9//g95+bUj3HPHDq7bvgUiPTrEUYAQjkTsoHu8AmXpeFrqP1NSNQT33yF4z1sVP3hv0doAM+vWmkkl4nk7gmFtrThgu30lM38jsF33nqcs27ZGbNvecwN+BLEWLHQkc21JJ5J0uoJOzy3uVA8NYWCph5aJhib0LUpZRxgAscBqRxoimU/JQpxjBJvzilERSAGpKYzVRJ0D5ElAo1hAMYcQEmsjdHQKP7waS54TcjEwFjrd/PVy+SVZwDzw6Mwu8I//2R/w7Pf3EwQeb7h5O+FEA9Pusp4WzFkpjIFOUom4Ig+HsovKvRfFlrkFt0DSjm2Cd96r+KEHFG+4KYltnIe1sRTyr+XfL7qRbAw2SsrLCIvvW6YCzdSUhrQqd8mHBVhHbFgcIXXKQfBB0hhv8oCKQDIMxj/2JwmEo00eDgJreujeySyn5WJyQYohlG6PZQfMdCAxxqCSCn8/9y//hEcefZErt0xy7MQ57rz1Ogh9zHwnW4FuZJBImjvdJBdmrY9nDTEsrhHHlvlkonHVZsF73iZ59/2K++6Q1Gecksp2z9/aWCmKZJLWDJMiXY0lsVbSumTgiGJgI6WHbLuDaqrxJ40iKgLph1Do3onCYDvayHJB0MS9o+RW1YW7sIozrvmOBb245VGsUSSEgMDjF37pT/nsl77H5pkWnW7ERKvmCCSKR876cImDTjEUr7CUyTihVKwRt94I1sU1FrqgrVPqveUuyQ/eK3nbPYotVwkXWhyipLoQa+N8sdRAf/5ctbEIox8VgSTIEgiFT9Q+gDVthLz4gPNaI5OaCo+oewxrurjVUi4O1jo3xJETQI+ha4AUycMYi5ps8B9+/WN84KNfY9N0C60NWhs2Tbe45aZroBePVADd2iT+o+DYKUs3Gu+1UGAwCVVl5eahF1s6SUXm6QnBm26TvP0eyYNvlGzf5uql0QWTJAP2K6ny7Y75RRwjVASSoFjFM+oecJJXWUsa9lof3cXCIoRCd49iTQ8hg4sOpFvA8+DgUUvUs/i+yNa5gGJJGFzQfKbFh//8IX7zD/+G6almtqpcFGlu3HEV05MNNwoxOqVA0uJ+KDh4zLmw6jUxUFZ9lLEUYWhtWehBbKBZc9n4d++W3LNb8ebbBNdukxACPbCRRXfzfI00kJ5iVO55hTIqAilACIk1HaL2QZAeMA7kkUJh9Cxx7wR+/dqLzkq31gVGj592QdGZGZxypjAqZHLd6SaPPPQEv/z/fJh6zVl1bjVDSbcXcfNNVyOaNfRsO1sgaBRgrXWWnILDxw3dyEl5R3LZYwbJwg30IsmwdoTRjqAXQz10yZO7d0ruvkVwz27FLTsFjUkBPtBNSKNHJmdVJRfVxnb9jCoyoRGu7VcEQnHQUxi9QNw9jBCOQMYFxerCQeMGLN2LCqQDKCWYW7C89Lrh/isVJnIz1HQgirXL9Xj1uX38L7/0p1hj8UKJ1okwIckd2XXDVvBVIV4zOpAC6MLL+1x2s0s6W38uuKWOKZWeCimS1RPdSodx7DLrrYVGHSabgpuvF9yxS3LHLsltN0lmpkVShwrogelYTLtMGpWLavRRqltnLcaAF4iKQHI4N0/cPYLR84x+AmGOvKiiTsqzXHwgHdzgcHYOntljuP8BRXEFPG0MXhgwd3KWn/uF93HsxFkmmnVined5aGOYaNXYncQ/RqUKL+QdSipBe87yzB5D6DyDq3r8Fyt2SIPCIlMcOfl1eq8cWTgVWRy7bPpGDZo1wfarBDddJ7jxWsmN1wp2XSfZPJMQhsG5prRFn0uslYQ0PDm4OuAo3NMKgyi2P2udFeqFAtlQnNjXXT0CGdbQiw04e2/gCWUJHWnjLm39kibklBIIuwfHKIHQIQ2kiyRB0ph2khty4TW+3G8FvoJn9xjo5C4Kay0qcXL//L/+AE8+u5eZ6RZxXE4S1NrQbITctGPryCUQpvEP4cOrL1uOnbL4XlmcMLTtD5GDlr+wyJt9fSSr32T7+g1JCfOEGNKMam3c54HvEh4DDzxf0KjBFTOCq68QbLtScM2VguuvFtywXTIzKZA1XPA7wsltYye3hXz54mFWxijdywpl5B4Zdw+NcdWMvUAg64pzR3p88M8O88cfOXJhBFJuKClR5OavMfnMxph8xpPPfPoCaICQeeKQki5A62rUCKQQ2e+HHcPFIk8gNMSdw4xLAmEZTokVd4+go7N4/mac3+HC4ExZCAN49mXL8ROGK7ZITJTEN6bq/Or//WE+87ePs2lmkDyEEESx5tptm9m0qQVaJ21pNK63ta5TSR8ee1YztwBTLSdfdW6s/K+/3RtTHvzTvrBU8D2v05T/FftLqmjypMisiE1TgpkJmJ4UbJoUbJoWzEzm789MCWYmhKvjFQgX8Ba4ZhHjlllNXFLp/qC/BlVlZYwL+mvkmWTAVTWJDCWnDnT58GcO82cfP8qLr7ap1+TKCKSfMJyf2628FkfOTxprJ+usBVALBY06rtFOCZp1qIX5Z7XAzYSSUBraWObbMLfgHo+fthw46oKz7Y6lG7kZk++Dp1x5AbcE7eDxXSiyGEEh/jFuHUIIhdXzRAuv489sxeroomMOvic4dsryyPcMP/HjkmjBEG5p8YkPf4Xfe/8XnOJKl0vhW2uRUtCLYm6+4Wq8eojpRCMXVPU8Qe+M5fOPmExdFAYiG9DrNWjU3Hop9RDqYf7cWQICTzm3ke+5/mOTAGXmckqee8nn6fc95X6f96u8f022BK1Gsk1PuEinj/PKmuF/NraYXp7XkpLFYi6p/ucVRhP9pJFOeJQE1VIg4PU9C3zwU8f52GdP8Oq+DvWaZNOUR7KW2vJI6/JrbelFrmSDkjDRdGtB33qDYOsVgs1Tgqu3CK69WnDljCtv7flJ0pDCPYrksThuWVxDjt2jjWC+bTl4zLLvsGXP64bnXjHs2Wc5c85yruM6TOCnC83kPrrzbdP5BZQYfQ7dOwZjFkCH9NoIrI3pdfbS4P7k/YuQ8iYXXAj42uOGn/hhTTjT5PGvP8cv/l8fpha6SsaD6h6XLKhjww07roRagFnootSoxJ2StiFcX/gnP+Ex2YKplqBVdwHnWpgTiVJJVnba7tM/MeRvmV0Ofez/M/mjNWC7FttJJ2wkMZHkFAr77K9uW/ysIovxwQBp4KxiKUAGElmTMK959Ftn+ctPH+dzXznF0RMRjXpOHLF2bWSAQIYVPWt3LVEEE0246VrJHbsEt+9S3LFLcO1WgV9PzF8LaDIiSO1zV3emz2fb5ywWFM1zQasluGVKcMut8MMoaMOZc5YX9xoefdrwyBOG1w6aRHvvyKToJji/9p5U4O0exejOWHYWd0rWJUouvIbRFx8HSd1YjZrg28/E7D9cp+Ef4md/4Y/p9WJqNT9XXPXBGEsYeuy49go35Rkh95UbXC1Ww2RL8CPvVvnAnbV7BgZ7q91v+vtAMa6xxC4Xf6s/riLKXy+6vVzZjaXv9+jchworwbBlo1NXqpQF0ogMxw51+cLDZ/jY35zgO0/OMd/WTDRVRhzalCedXv+OhHCz+rhQ9Oy2GyXvul/y1rsVN24XeM3ELO4BMZiuxXRIDjIngyL6A+quMZdPqnTC2gXsbLJdKQXTU4L771Xcf5/if561PLvH8NCjhoe+rTl03BJ4buYnRe53Xq4v5AF0RdxLMrVVnXGzQBzyOEjcO4Zf2wa2l7lLzntrSXtR0jK7oHjfx2bZ8/SfsP/QSWYmGyXFVT+MMTTqIddfewXEo6TASjtgQiIGzBzlQVsMHe/Tj/LP+/sIYJOOcanLeFQYXyxGGFq7vDalBCIQjjS6hqOHujz8nXN84Wun+cbjsxw82kMKaDYccaRijCzoUGibXrpDIQRKiYw4Nk8L3nmfW9DlbXdL1IRwlkUEOi1FUJjZKDGoxFgOIiOTxb5vs9IW4GZvZsGdh+8J7rlbcc8bFf/DP/D44jc1f/M1zQuvGixOhihVErBc4piKiTG6dwxnQo1bAD2HEBKj5+nN7yGo78DSLXjcV45imRJtodXw+OgnPsj86ZeYnmwtSR7gLJBWM+S6bVsgCbCPxvVOZ/DJq6RDFhxEF731kQsGVVhTFPti/l6BMDyB8BLC0BY7r9m3t8PDj57jy988w7e/N8eR4z20sTTqiqkJVwPQZMSRYrBdemlA01o4N2eZmRT89I8qfvq9iq3XyKzoWTybr9glBS7p6BIH1vrVHU4eKjK3i2kDFrZsFvzD/8rjp9+r+Orjmo99QfP4c4YogmbDEePSRCKwpkvczeMfozGYnR/y8xd0F/bQtD9IGow6H+IvW4wGqVrMHv880dyj1GqtRd1WKdIM9a1XTjE14VK3R+dq5xMOh9UjjwoVlsIwonDvu8RPrIu3CSkQvkCGjjD0rGbfvg6PPTXHY0/P8vQLC+zZ2+bEKafCrNclE03l4txm+Ul3EZ6nBPNtl0X7k+9W/I8/4XHdDRIiJ+GzNpcKrrXWu7jfIpmY2GJ7bib4rnd4vOsBxWPPGD7+Rc3XHtPMt6FVF0gphrq2XAmTCB2dRCRroI8jsnwQGRAtvIaOTqO8Kdxa6StEyTzWSNWgM/s0s8f+OqkdlgYBliIQiLXm2m1b8MMAq89j/+sWFYlUuHgMcz9BLve2CVHIJMUBibMugmSyv2DoLmj2vtLle8/M8cTz83z3mTleeb3DmXMxUWwJA0kQCKYmc0vDJNs+37HdOzNrufNmyT//GY/73qxAg57LSywPxjLWRycpHocUYKW7+GbOXdh736S49x7FU88a/vwzMQ9/VxNrRyRWuItWnEUa00bHszBipTQuBEIodHyW7txLNDe9DXsemfdO5pm4+GRI3D3GmUMfTlhZLuv6S7Pio1izbesM1HzMbDxCNbD6LY6KOCosj8Wsh+zz5J/NcoNslucjpEAoAV7ypwR0Dbatac8b9h/u8cLLC3z/lTYvvtLmhZcXOHSsx9lZTRxbaqEk8IWzMmRqYQxaGhcytnv/6n/y+a9/ROG3BHrBuRKUAhgu51tvyE/aHatSzr2lF1y3vusOyW/dFvDodzV/9pmYbz/tNPuNmlvm0iQBdB2ddlKxMR8MsgZjDd2552jMPEB6zsuZreW1PVxplDOHPozunUSqBtYun02euSON5ZqtM+B7hTjUesQwl1XlwtpIWG7wH/6b5DH7hys/mDSVPH6cWBECRwwpUUjhyhy3Db2u4ew5zesHu7x+sMPrB7vsPdDltf3u+ZlzMXPzhnZH4/uS0Bd4nmCy5dxSGWFYsPGgpP5i4P3Dn/Lcwi7ztk8HPlpxgGK9FsiL+um2I8X771Pcf7fkoW8a/vTTMc+8bKgHEARgcWuGu9nzeFsguRsrpLfwMjo6tSI3Vq7mSNqGrHP28Efpzj2HVK0VkUdxW0HgsWXTRGoKruO2tpjFIfq+U2G9YbHgcv/gvjJaSFxLyb/0jhdTD4rNQqR5PwlBkL3GSb0jA5FFR5aoa1loa46eiDlyvMeR4z2OnYg4fLzH/oNd9h7scvRERLtjWGhrOl2DUgLfF/jKxXgbdUmrKQcrHgzJwVpNeHrOJslOeXB6/Xbm5VEMuEOBSOadWOCH3ql4x5sln3pI84HPxBw4apicEAi6YPWGcGFB4saKztCd+z7NTW9f0o1V9stqpGoyf+oR5k7+HVI1ccv+rnzfxlh8z2NmugVJwH39trsqaD4q6CcMi1MiZcFlVXAFJcvtZoP8Sm+nsYVETetq92uLjS06dgl2cWxZaBtm5zXn5mNm53T2d2ZWc+hIl5NnYk6diTl1NuLU6Zizs5pOz9DtWro9Q7dnkNJZEq5igUuvaDYUEy2Vu7sylxdofekJox+eyzK3WU7GuGCASBKC1HNOAvzf/KTHDz0g+eNPRnzmq4L5tl7XrrrVRLGRdWafoTHzFpZTY2XkIet051/m3JGP49aMP/9JR2qBbJ5pjWASYYX1hKGF/4wjDOm7Gk4oYE7TXtAcPhax71CXuXnNfNuwsKCZW9D0ouXtEGOh1zP0IksvskSRYW7eMDsfM7dgmJtPiGJB0+tZtLYZoWSPsc1KhUglUEntMlfzz5WeCQLFlFADNdVgcaJIcbn7kZcmFo1r/12USGZh82bJL/+vPn//nT6/+ns1vvYV4Upyj6cIK0NRjdVbeJm4exQvvBJr4oEGmM/q3HK/Ws9z9vBfuYRLGV5Qvoyxlnrgc+WWKVdEcV3P5qug+XpEfzxCmyToHCZZ1W3DsaM9Hntqliefn7SuuPgAACAASURBVOfZFxd48ZU2J8/GdLuGKLZo4wZ5rfPCgcshdVdlHiuZD/7Fx6IISSlnSYha7gJbqpCmMYmRs8TEbL1MuDbMeiD9ROIpgUmW2bz1jZKf/5kmX/+aHHvyKEIIhYln6cw+y0TtvVh6WCuTzwRll4AAITl76MNE7deRqnlecY8ijLE0GgEz002XA7Iu+kJyjtbmvbz4vHJhrQuU1qfADbaeJ/BaCiLD3lfbPPTIWR565AxPPD/HiVMx7bbGD5wSyUtiBp4n8njGeXiw0v0WX/THUoaNIf1WhBDLt6H1QhJLYcMQSAo3ICYyOZkMBnOaK7ZMUQ89ur0YKcc/DpJ2RCEUndmnaG1+h8uHsfnnOZJkwWOfo332sfMOmhchhMBow6bpFmHgFXrbGg7KaWJQ9pgcT/o8I5EU679jjxtKK+IV16doKLonI7705VN8/G9O8PXHznH8VIQUgnpNUq9Jmo1ycNltr08hdRljcGKgPY0uNhyBQH4Ds0ZjNFuvmGZyosGRY2ezpUnHGaWkwvY+egt7CZs3Y22HtMhiOVnwWWaPfxYp0+XoLnC/uJUIN0038X1V6NHpNPAyE0mWVVokkeRI+zNOK1x29BOHNhYvWZ/i9MEuH/uLI/zlfzrGMy8uYAw065LpSTespfJVrZdvVaMw21+P2JAEkiJzzxhLoxFyw46r2H/oJGHouYzPDQGJNW0Wzj5O2LoF6It7yIC4d4Kzhz8C1oD0LyjukUEkQfSwAdLD6mRRK5FOCUXy/Dz9CueDosWRvnZPxmViOPIoEYe16Dghjprk0CsL/MUnj/NXf32cl1/vEAaSiYZbu8IsUoqjuq2XBhuaQFLE2uBNNXjDrm188atPM9Ea32KKg3Ak0Z19hrh3HOVNY60uJAsazhz8EHH32IqTBZeCQKC14aotTQgUdiFKEhtFHl6wCYkgVtcgySyKRayLojU04MKqcDnQX8oj1hbPF8hJxfHXO/zxR47w5588xsHDvdL6FMalJ2XYGH137bHhCSTNykYIbr15G4Gv1nFOwqWBEB46OkXn3NO0tvxQkhMiXLLgkU8myYIXHjQv78wpZmamGuCrTNKYRSFF4Yv0kch5D+Y2JyNLOc6RbjezOpINl8ircmFdThTJQ2uXn+ZNecwe7fFn7z/E+z5ylL0HOq7M+IxXWOt9Y/XX9YQNTyBZML0bcfftO5hsNYhiPUK1mS4OxWB6++xjNGbeBgikarBw+pvMnfgSUl1c3KO0L8AaS70e5iuWWVm2ONy33UOmni2wR2opiP5BvsA02ddTBVlhG/0ymWI0VRRIpxqULgsGrI7Y4jUVxJaP/tVRfuN9B3lhzwKNhmLTtIfRbr2itSrqWiHH+MuNVgAhBESandddwbXbNxPF8YYZO0qlTdp76Zx7CuVP01t4jbOHP4EQipLg4CL3ZQEhBY16UHh/SMxDkBNEShKi+FykPy7vJJMcp5bLkG3076O4vaxOxQZpAGuI/soXsXbPvRmP55+d46d+9gV+9pdf5rX9HTbNeAS+W6/I3dbq/lwe2CVTGzY8gaQNUWuNP9ngjXfspNOJkFIOJCuNK7LztJb22UfR0RnOHv4I1swjhLeq8SBrLZ6SNBthZgm4MT8d5G1ujVAkg5RkCoQw8Nf3eWp1lCwUcsKwQ6yRCpcFA7JcA96EQmvL7/7uft773z/HQ4+cYWpCUQulIw5bEcdqwhney7X/wSTzlPittRWBQDIzNhY8xf1vvAnPk2wkv2opM739Oidf//+I2gey9T1W+zpIKanV8pR/keVepLGJ5PVAzMNSDJSWrIfMQin+RuQvMsIoEkyfGmuD3O+1Ru42FcTaIn2BmlB89e9O8yP/3XP8H//xdaLYMjWhElXVxumLq4nlJsDO0M6va5EY3J/BGoNJ/tL33c8EQooqBgJpHERCu8db772ZzTMTzLe7qA2QUJgiVV1ZExEtvIq4WLnuovtxbdZLk20yz1Ea30jf64tX9Mc5Fu0bfTGP1JChsK/UtUVFHJcT/bEOnVgd5471+JXf2c8HPn4May0zyTrcqUurIo+lYLF2eCmqYWWJir8rhQoL11lIiZQqIQmZ3APp/qTIXkslKwJJIQTYKGbb9i3cedt1fOWR55lo1ZZdnnVcUKqIK4KEPC7dvpSSDGWBYSqsLABeeF8M+3nfd/tluOnz0j4qXA70K6yUEnhTiq8+dJL//d/t5YWX20xPegjhiMO5Tqr7k2Kp5biLb5dIwpbjRSkZC6mQMiUE6Z5nj6qPtPPt2/I/oFJhAYUZkTZ4U03e8ZZb+dJXn0FssEacn2pan+pSnHsSKE0skKULKRYC6wOOWDHk8ApxkMImhj6vcFkwVGHVUtAx/Nqv7+W3/uQQ1sKmaS8rariR+twArMWKwV5RvCb9lkRePKFoHaiMEKRUSJU+dzMoIdLHbLeUZ2Q2e28pT1hFIAmEEJmc9wffeivTUw1ivQp5DyOFyzPaCsFF1hsbRh5LvV9hLVCyOpJqud60x7PfneWX/v1evvboWaYmPLf4Xrzx3FVDrYoCeSxmTbifOBeSI4eEJKRCqNz9RPL9YcHydGG4lWpIFluzpyKQBNnF6Ubs3nUNb9i1je8+/RqNeogxG8ONdblgLRhz8XklFdYnhloddQkC/uAPD/Dvfv8Ac22dWR16Q1gdFttnbw+3KnK1mUjEIRlJKIXKiEKVvpP8lNyKsIlWZHmGWOmCbsM+rwgkQV46QeNNt3jHW2/lm4/vodWsYe24N+7LCRcY18lCUpWIdrzQL8+11lkdx15v8y9/dS+f/tuTtJqKyaYaa6tjcEAeZlmUyUJIhVIKqTxHFollkbumBq0JmyoKl8BKCOJC70FFIAUIIdxtjjXvfPA2fv/9X0Draqa82rCWwnWtKGRc0J8U6PkCQslnPn2M//M/7uP1g11mplwJklRhNQ4YNjj3rz80SBYSpTxHFp6XkUXZoigTxVLWxHIkcamudUUgBWRlTTo97rrtenbdsJUXXjpIrRZUbqxVghBgrKHXi3KVVIWRxoA8Vzt5bvt0xL/+t6/yZx8/RuALpqec1ZF+d1Sx1LrjxaRc0liFFEjloTwvf1yELJazKJYiirW4phWBFJD7bA3+dJN3PXgbTzyzl0YjrNxYq4S0Gu9Cu5d1no2UtDluGCrPnVE88e2z/ItfeY3vPjPHzLSHteX6VaOE5QmjbF1I5aGKhKEUQsrzIov1RhSLoSKQPjg3lgVtedfbb+eP/vyhyo21mhDOfdXu9ECKqq7RiGIxea5ta37jN/fx2+8/SLtj2TTjAuWjNkkoHu8wwkiTUVNXlPJ8lOeIQ6Q5FiK/ThdCFqNwvTydrEldks4LUWogGwlFN9Yb79jJTTu38uIrh6iFfqVRv0hk1XgtLLS7eT7giA0uGx2DZded1fHcd2f5xV/by1e/fZapCUWzIUcqUD6MNMouKYGUMiELP7Mw5FDrwgzli2xsFeUcqFG4PsPgqZaEGNAWmyTyWJxmu8igG41UdKwJNrV459tu5cnnXqdRD7G2skQuBqmKRCnJqdPzEMUboi2NCxYtux4Zfu/3D/Af/ugAs3OazYnVoUcgUL44aTgrQ0iJVB6e72fEIZPyHstZF4uNmev9mpwP5ENfXuDVV7t02gYhQU0qvCmFrEmEciea1qXJydiWLt44Va0tzZaM5d0/cAfNeli5sVYJqYV35uw8RBstUXN0USyAqLXr796Mx4svzPEP/snz/Ktf30scWyZaoxEo7x/Yi8UDhRB4fkitOUFzcobm5DS15gR+UMuqdPcXFyxuqziGjIr1daHw/um/OUK9Jtg8rdi53eem63xu2Rmw+8aAG7b7NFsSr+WSgIis+zNudmGtRaTW2xhZKpkbq93jTXft5KadV/HSq4crN9YqQSnJqTNzEMVIKcZqAjJuGCi7bsmsjj/6wwP8+z88yOlzMZuSAojr3+oo19lKrQchJcoL8PwALwhRSqUuGPe9IYmvw2IX6/vcVx9e4EMUWQ4eidl7IOILX7f4nqDZkGyaktyyM+DmnQE3Xeeza0fAzu0eE02JaknwRE4qSUapTZaiHOb+GrWLq2NNODPBOx+8jaef31e5sVYB1lqUUhw+doZeLyYI/WUTodYD0va7UrIb9YnUUHdVKJB1xZPfOcev/PY+HvrGGZoNxVRLJQUQ1++5uvMZJA6pPPwwxA9qKM/LSGOYW6oijEF4IJDSrUsdBgIh00Qvy9GTmv1HFvj8w/MZqUy2BDu3+9y8I2DXjoCbd/jsuj5gZkqiGhJ8AT1HKla7ks0CZ6mMipWSH5NrRD/0A7fzxx/8cuXGWgVY60q5nzg5y+xcm82NwC1Jv9YHxvB4nyVptklZCAtZfNB9N/1t8pf8yJKLU4S1FJv5ep5QDRCHtijpssnPHe7yW7+xj/d95AgLbcPM5GgkBRavdXp+SnkEtTp+ECKUWjFprOfzXAtkMt60o9iCaiLwIPQTUjHOwjh1xnL0RIeHH2sjBLQakommZMd25/q6ZWfAnbcE7Lo+oDkhXQ0cbR2pJIG1pYL06+UGldxYd97ATTu3Zm6sEZgwr2tIKej2Ig4dPc3mqzdBL14zOW+/ZZFOnhDgKeHigB7O2lZJo42dzBttwdhEzon7XAm3zmfxe8mjMRajAeHaf5pFuR4mVIsSx4SrnPuJjx/l1//gAM+/3GZqQrlYxwhYHVkJkIQclB8Q1hp4QeByM6wdcE9VpLFyDOSBlDTPDJKK54HvC1oN97nWlrNzhu8+2+Gb32sjhaDVFGyZUdy2K+T2mwJuuznk9psDrphRzvWlEislTqwUDUKsTytFx5pw0wRvf2A3TyVqrMoSuThIKZlf6PLavmPcce/NmAWLdxkX7+onjTQorDyB8ASy5krN29mYUyciDhzpceBwl6MnIk6diTl1JmZuQdPpGBa6BiWg0VA0apJGXVILJVMTim1bQ7ZtDdixLWTTtI9Xk8gpCQboGYgsRlsXN5DOsuk/tkvd9vsnbRlxTDri+PKXTvE7f3qIr33nHL4nRkZhVbI6jEEqRVhv4oe1ocThsr8GA+IVlsaKEgn7L6a1EJsCqSici6uek8qRE5rXD83zn788R6MumWpJdl7rc/uugDfcGHD7zSE3bPdptCTepHSztMRKcTObspVyuQml5MYCHrx/N3/yoa9UVWRXAVIKOp2IV/Yec75Te+lzQfpjF1lAWAnUhAvaxedi9u3r8N2n53jqhXm+9+wcL73WodM1dLqGbtdk7hopQYp8PQVjLdY4g8RYixSCWiip1QT1ULFtq8+uHXVu2lHn1l11bt3V4LprQsIJzxFW10DXoOPEQpd5jkyK1bo+2TaT7WXXwhPO4pjXfP6zJ/njjxzha4+eQxvLZEu5fr/u8zryrPD0PINanbDRQqauqqQPCwqVdPryMiqsDBecib4cqQReElMRoDXMLRi+91yHbz3RRilBqyHZMiO57aaQO24JuOPmkNt2hWyeVm72g4WuhXi42+tyEEoxqfC+e27k6qumOX7iHJ6nLsn+NgJsEihQSvLavmPQ6SWL3FzC/RXahzGAABVKZCiJz8Z85+tn+NuvneHh75xl7/4uZ2djuj1LLZQEviOLWihp1JLsYsiSkVOIwpN0YErX855f0Dz/UsyTzy8QRZZGXdJsSLZdFXDPbS3u3N3gzXdOcMfuBuG050yRtsZEFmMoiVIu1NU7LPifJhGrmkQGks7JiM99/iR/8tGjfOu757AWWk2FEO5c1pOLeRhyl5VL5JNSOfltWCsTR3IN18ptOk5Y1VIm/e4vUzBzlRJ4fVbK4eOavQfn+czfzdGsS6YnJbfsCLj9lpA7bw64a3eNbVepbIZI16wJoZhIM33FNPfdcyMf/+tHmZlsEG+QpW5XG0IIjLWEgcfzLx4gnu/i+Qpr7KoOUGm2b/LCrb+dWhsWXn95gU9/4RSf/sJJXtizwHzbZITRaigmWumgmW0CXciDWuo4+/3nSrnZfT1pr8Y45eOevR2eeXEBa2GypdixPeTBeyf5gfumeMubJtiyNUAGEjrO3RVr6wQphfhJ8br2WyvF1xYXx7TWEZL0hJPnG3htzwKf/NxJPvW3J3luzwJSQKuhQOQkuL6tjkGXlReE1JsTSM8bJI51ToSjBLH73t+7rCHhUucCl0dCnqzYiyy9niUMBVMtyU3XB9xza8jdu0PufkONa65SyOYwQulXx6xOUN5VFzV4MxN88E+/wP/2b/6CqckGxlSR9IuBMYYw8PnCx/41O27YiunGiWrp4u8X5ERlDK6seENhzsV85dtn+ehfn+ChR85w7GREGLiYhVL9hLH6g8ygBNS1WYRzDfUiS7tjCAPB1isC3vLGCd711ineft8U110TwoRyPrKuix+m622kTbF4tGl/kBKETIL7fvLXMcyeivjm92b51OdP8qXkWtRCSb3mYlGjYHGk6J9ABrU69WYLKE8q10NMddxw2QmkH/2NVApHKqk8MIosna4lDARTE5Ibrwt4420hdyWEsu1KhWyVCcXN1FzngYtrPOmMRdYCXnnpID/6079GL4ovcknWCkIIFtpd3vebP8uP/+SDxGfn8NSFuwb7BwptwAsE1BWzR3t84nMn+OCnjvPUC/P0IkOrofA9gbFlK+Nyjy1la8W12dRCWehopBBsmva4/ZYGb3njJG++o8ldb2hy1RU+BBL8RDpvKTOJFC4/q6PRPcvJMzF7XmvzwsttvvW9czz+9BwHjvTo9QytZvlajApxwCB51JoThPVGJsutiOPSYs2r8fbfVFNQfSkp8GrO7WUsdLqWJ553cZScUHzuubWWWCgh117t402pLChvYputx3yhfmQhBPRidl53BbtuuJonnnmNej0c6leusDJIKeh2Ix5/8lV+/Cfe5lQwF+HbL8pP02Dw2cNd/uL9h/nAJ47y0qttfN+ppJp16bKmzeAAfrlRcvta0LFjMc8TzEx6WKDdNTzy2Dm+/I2zNOuSyQmP3TfWufaakE3THjNTHs0kriKFYL6t6XQsx09FHDjcZd/hLvsP9Zhf0MzOa4SAek1Rrw2/FqMy0PYTRL01SVCrD7is0ucVVh9rTiDD0N+p0uC8lIL6AKF0+dYTHcJAMDkh2X1DwFvvrvPA3TXuvCWkMaOQnjPbbZQkNiY6/JXMULIZrTaoyQZvvvsGvvX4SzSbNeK4ioNcKIyx1GsB3/jOi0Rn5/HPMw4yYHEkFRC8KY/OyYgP/OUR3vfhI7z0Wpt6TTIz5WXuHl3g/XV3//oktelEKovLaJhf0Hzre+d4+Dsur0SbpCKucr/XOp00CXzPkZHvOTHApinX5VN3Xdq33K7X2bVYAv3tpN6cqMhjDbAuCaQfKyWUbtfynac6PPJ4m2Zdsm2rx3131njgrhr331Vn29Ue3oR0pn3PutleIpnMtz/Y6IQQWAwIwT137CAMqppYFwtrLUHg8creo7zw4gHufNNNmIUuahnX4DDiEAJUkvD2yY8f5f99/yGefH6eek2yadplS/dbG6OA4rFqYxE2F6Q0PaeOKqq+iurc4nvpn9vOYJB/9FAOmIfNFkG9MUAeo3luo4WRIJB+LEUozbpLctQGDhyO2bP3HH/12Vk2TUnuuDnkbW+s85Z7atx6Y4g/lQxWHUcm1rgkqnzb+VRVCgG9iNt3X0urVSOK9CWVn24EeJ7i1Ok5Hnr4Ge68fzfWdhadNQ7GOBI5cNNp+7/y0Cl+6/2HePg75/CUyIijuAreKA8ow1y9uZ5YMCyFYbFBdJSvA+SxKmsMQb1Brd6qyGONMJIE0o+BmVryOvAFtdAlQC10LF97rM2Xv7XAZEuyc7vPA3fXees9Ne6/q8bkFg8U0M7jJkr2bT/SXLttM1dfOc2rrx8jDD0KHFPhPGCtUxGFgccXvvo0//znfgxPqYG4Upk4yJRHqqFAwne/c47f+dNDfO4rp4l1f8Lb6A+Wy2M4ecB4nnvuds6luv0lYcbxvNcrxoJAiujPRSn6kScaLsko1pYXX4t46vtdPvApwbarPN76xjrvvL/BA3fXmLnCy+ImOnKOcykTc7kRcvNNV/P9lw9Rq/lVIP0CkXb2Wi3g+RcP8viTr3D/g7dh5jooJQdyGrJs6bqrAv3ck3P87gcO8ZkvnWJhwTA5MToJbxUuDCUBjFQJeVxcgmWFi8PYEUg/hlknQghqITRqCmPhyAnNh/7zOT72uVmuvtLjgbtrvPO+Bm9/c52Zrckl6lh6XU3QrLP96k1Esc5mQlWjvXAoKTjX7fHhT3+D+x+8DejL5TAuzuHVXbb0S8/O8Qd/cZhPfP4kZ8/FTE54TE2qkUl4q3DhKAXNG60sSbAij7XD2BNIEQOSyWSSG3iC2qQjkxOnNB/73Byf/MIc27d6vOO+Bj/69gb331UnnJBQ99i2dVPmNaga7cVBG0urUePzDz3Jz7+wjxtv3o7p9BBC5GtQNBSHXl7g9//8CB/6zDFOno6ZbClm+hYxqu7F+CKzMoxxZdjDWhb3qLwAawe1ZduP/du1Poj1AJdZ7gLxjZog8AWzc4bHn+3wN1+Z54uPLHDwYMT26YDZ2RP87VeeqmpirRI8pTh9dh4lJe/+4TfSW+jhBRI54XH2eMTvve8g/+JX9vKlR87gKSdptYjK4tggKLqopOfRmJgeEEZUbWBtsOaZ6OsRRXNYyTznpN3RbJqps3XqVfa+9FGMlWuSfDaOcIUrJZ/90C+y+4GbmNs3y4c/c4I//NAR9rzaptlUhIFA6yrGsVFhraUxMZVZH5Xrau3hYS0WgRB5GeSNjnLcxD3WQkGjpog1vLKvm60y5+yWChcDay1KSc7NtvnV3/5r3vbWn+L9Hz3I919uU6tJNs14GD0KpcQrrDZKrqt6Az+oXFfrCZ5NUo6yx4rRh8IYMFikVATePJGNQXgIqkZ8sRBCoLVholXnq994ks98uUlQv52ZKYWxoiKODYqy68qn1mhRSXbXF6QUyU3oS9Sq2D2HuxbuelgMJjqev66u06rASXBddvqVU09QD04Tm1xlUyXcbDSUyaHWbCGErCS76wxy7uwZdBwjpMxWQMvKBFSdti8fQWHNPCZ6DYRPfyOvcDFILWCF0W2UeRhhI6xIVpHLSKRqkxsBWYVkYwjrDfwgxFoDom+tlwprChnHEfPnTtNdmANRWICe8bVGiueUPl/sL4cB4WN6r2L1KYSoFFirCQtpfQqsCJD2CJ59GHCl+kVGIlTWyJijP9u8Vm/lHpKEOarlZ9cHvJQkOvNzxFGUreJFYQAdxcqWNllztHjsA9/py3YeilQmiMTaCL3wWPpBlUS4inDWLwUSCVH2FawJicXbQFiE1VghXdwpLYhUYaxQintIj3prIrM6KtfV+oOX3RAhiKMuc+diwlqDoFZPfI5m3RNJf1n2/s9SlI9ZZCoq+qwNa0xigRisMRirwYYQPwXxAYQInexgHV2DcUCmaksGDCtCPPscgoiIH8AKH2EjQGCFSIiEikjGBIPre0wgVZVtvp7hlUlBYo2hMz9L1OtQa0zg+QGpOisbZAVrRiTDyKz4fv8xuVLs5CRhLcYYjNEYox1BaI0xJifLgnvL2TIKYY8SmG8gpZdYN9UM+FLARUJyWbkVNZTdA8wR8zasuAJBF2ENeS3zQh3zCiOJMjlYas1JvCAskUeF9QcP8gG3aI3oOGb+3Gn8sEZYa6A8FzR2A2x5I5eSTLKAmU1XOFhsnyKfuSZ/RsdorbFGZyRhjC6RSZbHMbTsNcmnEt8+hpRtLIEbvKrB6pKhTCI2cWcdQdrPEcs3obkFhEIQpX4vIDFGsgBrdX9GBSXhjjGEjRZhLV/fI+31lfWxNljMwyNEXy0skfgQiu6ZqNMm7nXxgpAgrOP5fupjGAg0X6yf0tokG6Vftmmzf/l202NN3Exax+g4dpZF8rgUSaREufRRGiw1PPM9lH0NK8LEP5/GRSpcKuQkQhZYF/TwzSMo8TIxd2HEdhAeECOsRhTbd2WVjAT6B6ew0aLWaDrFFVW+x/liKXf+xW538L1hxRQFpfWphXTa66jTJup28PzAEUkQZINwMYbQ/3jeB5oeGUNcUallYTQ6ihLSiDBaZ58ViUIAJPLkRffX3zgTtY9IArnS7kfZ7znZrq2yzy8nioF1dz8kCIW0RwjsMYzYiuZGjLgOK1ySWdEqyf5XcZJ1iYGYR3OiWlmwDxdCCMO+u/JrKLLBbbFfpPN5IZeoxpuZlORuLYA46hJHPaRSeJ6P8gM8P0BKmXfSZYLZw/ZTPIFc8m8wqSsqjjPCsEm8ojgwLJWpvFQjXJw8PIQ9h2+/jhDGxUFEUvZl+CWrcAmQKXczKS9Y4QMg7SEkB7FMou11GLETw5UgAlKrBCuy32YbrLCmyCanBYKotyZLFXY3AnksFs9d7Hsphl+TwYHfFjw3qWcp/SBVqeYT9v70BZu8nSZQF0IXyXc8Ty5fzl1QIBIEQrhl+ozW9OIYuh2klEjloTzPPSqFkCo70eUG9/SgjE0C2tlfjE4C3f0uCSHkAEWmF8PdkHysWFEjzMjDupmuNfj2YYQ9ixWhI5XUpVLhsiLrEJllabFWYEXgXtsFPJ7F2u9jxZVouwsjrseKBhBlAfeKSNYW5bhlsoS0H1BvTaA8f2zJYzkrYul0gnzMGeblcS58WxAApQpSSqIg97z83lLHQYEwhh21ALqcx3ogGZEUZg/pmbngdJeo1y2RhZASIWTf95NgeMpyxhGHNTkLDpKFGNrh+xtZyX11Pm2vQB4OHp59BGkP5OQxJo15lJHPrNxsKyV1KyQQIrAIexjJIYydRotb0eJmrKghbC9LRhRpG6vu6WXDsAoXQaNJrd4kLZY4IOYZQSxGFuebTmCtdQpRkyhETZpSYDIPzNCikjazM/L59QpFJeU48fKuLMEFLCjV7/IhI4whTKk1oNMvl6lMDDzJ9uVgUQAAHbZJREFUt79CV9SqNLJM5eWO0YoayjyJZ59LguZ22QtZ4fIib+N5rMsm7i1XYkYgmUXab6LsS2h5N1rcAFiEjatkxMuEfi9AOjlUfkCt0SqlCKzXHLPlMIwwhpJFIthJ3UopQWjjPC1GJykFGTmUJ9S5ECj7V0L/flb9PLEIKwZ2fXErEvbd6JJ1Mvjl8xqBh81CVr1hZf49gcBgRQ1pXsGz30n86Na5S6q4x7rF0HmIta6GlvCQ9hTSfBkpXiUWD2DFBML2sMjEiqlIZPXhhrvShNJapPII6g2CWi0R6pRdVunz9YxhcYtBwiinFJgkjUDrGKvLOWhlj0v2r7y9CyCF1bbixCLj96ouabuqB3yZGlIeNA+Q9mgSNBdZToG9RIxeYXVR4g9BZmFY4QECZV9F2hNE8u0YcS3CdpM4iqhIZBVQHrAKrqqEOPywRlCrI5XKXDUC4UQR65w8hh1f+T2RmsHOnR/HaB054U8clYLTZTfR0h6X/mPIxqUVHPPlupYbak30EkpBcw9h5/HMVxCii8XP4x7V4DJycB7JJE5CmtEeIuw8vvkikXwHRtyUkAhQ3ecLQpE0SvGN5FqqhDj8sJYTR1+gPK1Xtx6RDfiF11AY9JOgtI57xFGEjh1pOIVo6vrOr89S57mcxZBd54s9qVXGxiSQYjA1uSOe/TqS01gKQfN13LgrLI2hAXfhIdD45ivEEnRKIqmLsiKRZTGMNNL3sRYhJSoICcIanh8gpBxKHOs5UG4L7SALSBcEQMYYdBQRRT103Mvy0ErXZYVx3BTr9Voshw1IIEVtmk0yzR9H2b1YUaOUaT6a97RCAanCJXdVSoSweOarWOljxPWORIQsqPAqpOiPAPYrqTJrw/PwgxAvCFHKy6y6lDjcb9e3wqp0bH3WhjWGOOoSdbuONNLUgsSyGHZOlyWOu8bYeARii4NJiDR7UfYJSBRXlTdj/JCK7Ip5PkLE+OZhevLHsGImUWdV8ZBBaXy/3z9xOyW5X16SSKw8L7c2FskzKAbW1w2KCicG3VRGa6Jeh6jbQcdxWXnafzJ9bWfcyGIYNhaBDMQ9ZvHtNxOlFbniavzv+4ZDKqEsVRpgHs98m0i+J1dkbSAsN0PutzKklCjPR/k+nhcglcpIAyhZG4ttc72gRBTF3IkicXTb9DodjI6HkkYu4U3eXofneamxgQgkaTCFvA/PfhNBIdO8UlyNNVLr0sW30kWr9mPsHrR4A4KCK2ucBoNF1IT5wG5zgyGTlLqqE9Lz8JSH8n2U5+clixYhjfWeRd4vwx2wOOKYXrdD1G1jtHbXQcrC73O3aB7zuOynsW6wcQikOPukhrJPoeyr5bjHSjVyFUYWRRelm3cqFM+g7Y7EKh1dK2TRgbtAHv1Zy9nnQiKVQiqF8jyU8pyFIRRC5nGBYe6pftJYj+RhC4KZ7DU5ceg4Juos0Ot2sWYYcaQ5bu71+jvDtcHGIJAB19VJPPsELmvZZj7yddjuK1wCOPJIZJhJsqHidbTYjavws34bwkoLgw4rb5EHe0VGFGntOqm87POBzOohrqmRII00XpMFxt1DUVGl44hep03U67rzLBJHwXJbj+e3HrAxCKTk3xZ49jEE7ZJkt2oeGxkCyWtou6sg21obN9ZK8wHS7/b9OP1SNrOW0lkV2WPyXCQDpejbTtGt07/f/oS69TqoLlaHKldU2URR5YgjC4wXLY6kCvn6PMP1g/EnkGwWkaqu9iSS3TD7rJJdbTwISBa5tLiSJ0cR4gyWaQR61Y2QlcYF+r8zrEJq2f+eup9kgSw8hJIoqUoyU6dcL1sWi2ilstLfq1577hJiyRyLTFHVTRRVEWRLVcjB31TUsSKMP4Gk1oeQYLt4PE1qvrolU5fOEK0wxkhiXhaJoIu0R9FiCxBjrVxyTnG+geKliSE5GNufuEbBkpBIKREysSBKr90gmLpaBohiEasi3f6wUh2jMoDagtVVfJ1niyduqm7HuanSwHjmxqrmjxeD8SYQm6ZBGSwByj6LtMdLS9NWDWfjQog0uCoAg+AUzmm+vAGyPCFkn+RWQ7pTigQhnStJJpZEugSClMj0MVE+lQr1uZ0OHMNSCxMtppAaxQnUwHkMS/zrduh1O8RRb6ibKsUInv66wXgTSGJ9WKGyRYdA4awPhprvFTYYRCqhkAjOgo0Ksop+2IEHN/iIPnLI18ARyeCfEoOzGnKSGIgnJH53S+mf21+SyLecUGwpKe0okkWKJa2NhPK1jrPEP6M1rq/LEkusZ5nxqGGMCcQWOqCHtC8h7JlS7KNqQhUyCImw8wihQXgk8p0sxpARRTLDFQVSKC2cViCF1FqAfOAeZjm4t/KkveUmNou5nfq/My5IK/cW3gD6rI24R9TtEBfVVAVigdwKHKdrs9YYXwLJnclgIxR7AMlKXRQVNgby/AiBFF0aE02EauDGpuIA1OfuXMJSKGLYmhHLHtMSFkTxO+OMAWuDMmlgbV5mpNctlxkZ4qaCqs9fCowtgeRJgwGS/Uh7LJlZVqiwGAxKGYQXgI3pH3IWWzp0pTifLO1xJ4jF0J/wN9Ta6PWIum3iqFe2Nio31WXH2I6oxbwPafcBMRYvmaWs5ZFVWJ9w9oS1ESJLtl6aItI6SJkcdCV7qQa1AQy444Yk/OVFDbtovQJro7rOlwXjSSCp20BIhO0gOYw7VVspLiosAYETWSSvVmQppL+rcL7oj20MJvwZ4iiJbUTdZa2NSo57+TGWBJKV5cZDcAxhz4FwA0PVyCoMhwuxChlS6fMuHTKSSF+XEhbdu8bERN0uUa+zaGyjKmq4PjCWBCISuSNIpD2MoIelBrayQCqUkU8oLIgaiICKQFYXw9x8pYTJgrUR9zpEUa+c8LdEbKPqzmuLsSQQIJmexElyWDpDydK5KlQAii4oA7JFmidU4eKRD/ZlJdpQJdUSsY3K2li/GFsCcd7VCGHPkA4KrmzJWh9ZhfWEbJCzGqlmEMLH2h7VROPC4OIaOQUP5qsIrNFJ3kZ3RUqqLJfmsp1FhZVibAnE5XzMIVhInlezlgpDUHSHeJud1Nv21vCARg+DORs5ihVwdRwR9TrEvR7G6EpJNQYYUwJJSlPYs0DMsGqbFSrkDk0LIkB4W8FqoBq8lsPQTPhhhQx17PI2el1XATcljUpJNRYYUwIBl24euVpYa30oFdYl8ix0g5ANpHcl4BIIq0S0YbDJAL+49BZrMcYQ97oZaSzmoiqyRRXbGE2MMYGAsBowgCpONytUANJZrwQbIbyrEbKJtTFQWSBF2ES9mJfr6pPeCjBaJy6qLjpKXFTuWwMuqv7KxBVGF2NMIAI3m0yba2WHVCgjJwmL9K9L4h8R1UyjWLYlcfLZwqt+6W3UHYxr9LmNKxfVeGKMCQTAlXOuUGFRWAOygQx2ZvWvNqr1MbzC76CLKo6jzEVltF42rlHc5ga9tGOLMScQj2o2WWE4nNDC0kN61yK9LQmBbKxCfMNIY+iqflqX4hqLkUbRV7xRruFGxhgTiAV8shLuFSoUkFVhshYZ3grCT+If42+BrIw0wBjtalElcY0sGI4Audg1Gu9rV6GMsSYQKzywyboNVbuukCDLbLYxQm1GhbuS3I/xXWRsWdJIguHWaOJeXzA8WXpxMBhux/iKVVgJxpRAkqC5bWBRCKxjkKqtb3gUl6HFRqjaboSawJoOuJYyNs1kRaSBkzFnwfCo1xfXkKULUoprjM2VqnChGFMCATBYMQGEYNtONVIpQDY8sttvDcgmMrwjSR4cD9fVSt1TJdIYUFAtHwyvUAHGmEAEFouPZQLBPCAr8qiQ5X5Y20HVbkP6V+XWx4gGz/slt8X3+mMaea5GtAxpDBYwrFChH2NLIFgDIsTYzUgOAn6WEFVh48IVTjQgaqj6m9xzRm+QzJZ+LelDhmeF6ygiitKYhllCQZWjKmBYYSUYSwJxoT23JohhG/B8YpFUCekbGSXro34v0t82UtZHnoyXpoTnnw2SRo8o6p0XaVSocL7wYPx070IkQXM0VlyJsRNIZqlKmmxc5INvjJBNvMZ9pcTB3A20vtBPGsXjLJOGJu71XFwjrT+1XOFCqq5Q4cKQtiGvXOpjPJpTat0Lq7GigRHXI+1TrlRFYomMx5lWWCnyCZJBNd+B8K7GmjkgHZRFabBey1pNi5FGdg6F5D4dJaQRRVhbkNyuJCv88p1ShTFCsX16qbtnnBTdWc0dAAxa3ISy30dgsJWcd8PCNfwaYDDRfoSaQcgaTpYUY63OyYRkWnUZrPMiQSxGGulnaXn0OOoVKt2Cs6SWkNyOkYehwtoim2QJgRcGik4nXjyxdEQh0oCijbFiC0Zcj7IvgQgR1lR+4A2GogsrnvuiawdqBhXcgPCvQ/rXIGQLRyYR1pq8+qzbALA6A/Ewwuh/P22f1hiMjol6PeK4h4njMmn0J/dVpFHhMsBaaNR9vDD0aLfHrQJpMeHDIrDE4m6k3Y8gwgo5dlZXhaVRnDVB4AgiPkYcHQLhI9Q00t+BCm9B+NciZD2xSpK6T2mm9gUSyUAAnMWtDGsMcRyho8hZGlq7NUvS71WkUWGNYYyl0fDx6nWf06cX1vp4VhllPTu2B2ITRt2D0t8AUaskvRscbqD1QPiAxeoz6PgxdOdJhLcVFe5GhbsQ6goArO1lRFIkgsUG7KXcUvnv0vIhFmPijDD6g+AAsiKNCusEIplI1esB3hVbWuzbfxrfVxTa98hgmI/aWpt1PqkUnl/HD0KU9wNE545hu3sQslaa1VUYfwzc6zxQhhAqF1nEh4ij/eiFbyGDG1G1u5DBdSAU1vSSdiOTjO484L4SK6MktY0j4ihCxxFax2BM9p1hctvymhpVu62wdtDaMDNdx7vqigl0bNa1lLEfw2ZfJdKQCs8P8IIQz/edyW/dyoRe6z1E8TGsmUcIryKRjYyB+54O9kHirorQnafR3eeR/nWoxn2o4CacYispvpj2myUVU650iNGxI4woItbOyii15T4ro7/2TrWmRoX1ACEEUWy4dvsM3tVbJ4milEDW72C6HGkIKfH8ILE0fKRU6S+dOwCACOltxpv4e0RnP56d77jlwVS4MORVel27cFaAU2mZ6DXM2b3o4Ga85tuR/jVJjMTlkkDZLYW1GK3d4kuJlWFMDCZd2k/k+ygdQyFHo2qTFdYZ0mTcKNJs3zaNt3PH5uzD9dZgFyWNbKao8AI/sTQC5ydOXATWmtK2nJtBYk0HFd4MrfcQzX4WRFCRSAWgUL6j1A4MztIIAYvpfp9etBdVuwOv8RaE2oSgi7VOMaXjmDh2eRlGx64d5iWAhwTAAexAG6xaYoX1CNc+LUoKtl0ziXfjzs00mwFam2V/fDmwJGlY1wGVH+L7gSMNpQqkkbsS0t9nAUyyeSLWtFGNN2Ftm3juywgZViRSYRGkbTCxSpLYmV54FNN7BdX4QTQ3oKMOOu5gjC25nvpzM9y2kuwrkegAq2B4hRGC1pZWK2TXTVfg3bzrShqNgG43GlB6XC4sThqAtY40vCBzUa2UNIrbK+7HzRa7eM0HwfaI57+OkLVMXVB15Ar9yNtl4uYVDaw+Rzz7n9DcQCweANFEiAjEoGIq3cbAehpVU6swYtDaMDlR49bdW5Hbt02zc8cmepG+rMG5Yri+f5C3xnVS5fnUmhM0p2ZoTk4TNppIpbLv5B2zvO1hvuX0/RJBmC5e8514jbdkRfVGSUxQ4fIjazvWIIRCiABPvEzA55EcxxKCdW0zT0AUAxOkChVGEUIIej3NDTs3s3lTE+l5knvu3O6y0aW8xINnwTIovlsgBOV5hI0WzclpWlMzhI0mSnkDpFHexvCA5DAUScTi9P1e64fxGg9gTZuKRCqsBDaxgF1ttRrSnsE3n0faV7GiltRis1iRVoauUGH0IaWg24255+7tSCmQAPe++To8T14y943NOtCgpWGtRSmPsNGkOTlDc3KGWrOF8nz3vX7SKM7kVkga/RiMd0R4rR9G1e+tSKTCiuBCiYlayhqs8BHEBObLKPM0VvhOkJWQyEgmWVWo0AdjDLWax4NvuQFIyrk/+NYb2LK5yfx8D6VWJw4ybO3kgVyNIJfdulyNJOBt+hRU/b7jVSC5YrzDPcb4E+8FDLr9XVfKIpFjVsL7CsOQpHg4K8OarESOb78BRqPl3Qi6uQqraksVRhxRZLjyygnuv/d6AKS1lmu3TXP3HdtodyKkvPCZ9zBLoWhpSKkIag0aE1O0pmeotybx/EQBlXxnWH2g4uPqQZTdWdYCGn/ix1C1u7CmTdH5UM0fKwyDACcQFMJZGwisCPDtoyj7PFaE2WJmDlVLqjB6sNZJd9vtHve/+Xq2bG5ijEVq7Rr0j77nVidB5PwG60VJIwkkCinxa3UarSlaUzPUWxP4YQ0hZEIaZoA0+rd3qVEmEYM3+V8gw92ORITMy1VUqDAEIs0dTEjEvenhmW8i7X5HItYkrqw1PdQKFS4IWXKrFPy9H70VcAUVpUzquP/Ye97A1qsmiCK97MaWJg1XJ8gPatRbk7SmNtFoTeLXaogkSF9WUA2SxuVUqgghSus+WKsRCPzJH0f427G2m5FIhQpLoUgiltSd9XWEPYsVXhUPqTCyEAK63Zgd123ih991CwBKCaQQTtd7zdVT/Mi7dydxkMEBfHHSyGW3nh9Sb07SmpqhMTFFUKtnyq71RBoDKJyPI4sYIer4k/8lQk4kr6ugeoXlkZOISUjjHJ59lCyNtVDAsUKFUYALPwgWFnr80LtuYXqqniWey+LA/Y9+5j4ajYDUrVVErn/vJ42glKsR1BuL5GqsM9LogxggkS7SuxKv9SO4chZUAdAKK0Zqadj/v71zj5GrquP455x7Z3Z3ZunultY+oC/awm4hgbZQW6isbbXQUhCkAj7CQ2ggiBqDxMRofASiYhBDUHwkEKMS/lBjBBMfhKgJCaJVSYwP8AVSqIXusrPbnTsz956ff5y5d+bObtvdZR+zu+fzz27uZqbnNOee7/k9j2rBk3/jmRcQlQUEEWeFOGYPSqmk+vx912xKPdexuhgjbNm8kh296xgaKqEbrihMBEFRLfBrr6bddo6xVqP5RKORdBW8ti1PWs/Gaz0fMYENqrsX33ES4sC6UnHfNo0nf0LJMKI8G1Rv4vfA4YiJg+eDgwGXvrOHzRtXYIxJkq00pNt/3HrzhfgZL+WeqtVqtCe1Gi25djx/dNFI9fZpctFoJJ31pUAq+PntKG8xIhXnynKMCRsvr8ZDlI+mDy0vYDPnxbqy3DpyNDlKKYwI2RafAx/cBsTZ6Haf1PGGGFshOy5ez87edQwOlshkMmRzOfILOsl32AI/38/Yl+MEBX6KkS6r2UQ6vTdCeafg599G3JJxNs/NMY2IFRG7bnw8eR4kGNEry+FoRmLro1AI2Lt7AxdtXVPNvKrtj8lKrj9Z33HbxSzospZGLr8AP5OdoVqNmSPOzAIFpoRu7UH7pxNfJOROj46TERuxtk7EQ9OPllcRqtfozqH3xTH3UEoRRoaOjjY+cecuYGSz2ZQLS2tFFBl6t6/lvddsojBYwhaIzw/RSBHPS9kyMKUy6LaNNeGYq/N2TC7JPQIKiNC8nDxT7hDiaFJEBN/TDAwE3PiBLZzds5QwNHheuuVVypauD3J//I5tLF92CkEpTALqc140GlBJYZiNhXgt61H+IuwtdDgrxHFSaq+KdWNpjtgLqJJXz60hR3NhXVea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\" 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src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"240\" height=\"240\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function vn=optimize_GI(G,GI,irc,rgbGmed,rgbGI,v,scrmax)\r\n%Create vector vn that places GI regions such that\r\n%the score of created image versus G is good\r\n%vn=[2 1 3:100] would place GI block 2 in top left corner, \r\n%GI block 1 moves to block 2 location of the submission image\r\n% rgbGmed and rgbGI are doubles\r\n% irc contains GI regions r1 r2 c1 c2 of width w for the N regions\r\n% G,GI are uint8 [400,400,3] png arrays\r\n\r\n% rgbGmed and rgbGI are sufficient to create a passing vn\r\n\r\n N=length(v); %rgbGmed and rgbGI are [N,3]\r\n vn=v;\r\n %figure(42);image(GI);\r\n scr=calc_score(G,GI);\r\n \r\n %For seeing the data and flopping blocks [bi bj]\r\n %S=GI;\r\n %treg=S(irc(bi,1):irc(bi,2),irc(bi,3):irc(bi,4),:);\r\n \r\n %S(irc(bi,1):irc(bi,2),irc(bi,3):irc(bi,4),:)= ...\r\n %   S(irc(bj,1):irc(bj,2),irc(bj,3):irc(bj,4),:);\r\n  \r\n % S(irc(bj,1):irc(bj,2),irc(bj,3):irc(bj,4),:)=treg;\r\n %figure(2);image(S);\r\n \r\nend % optimize_GI\r\n \r\nfunction scr=calc_score(G,S) %Scoring function definition, not required\r\n  GmSsq=(double(G)-double(S)).^2;\r\n  dis=sqrt(sum(GmSsq,3));\r\n  scr=round(0.005*sum(dis(:)));\r\nend","test_suite":"%%\r\n%Google Drive Dowloads need to come from shared files\r\n% Tweak link: file/d/ to uc?export=download\u0026id=   while removing /view?usp=sharing\r\n% https://drive.google.com/file/d/1v3GsGgP3p905wzdvUqypL_-djYmxiyzK/view?usp=sharing\r\n% https://drive.google.com/uc?export=download\u0026id=1v3GsGgP3p905wzdvUqypL_-djYmxiyzK\r\n\r\n%28.png Starry Night w=40\r\nztic=tic;\r\nG=imread('https://drive.google.com/uc?export=download\u0026id=1WDYDJJZ3wJ8Fs0-8E2u6bYxlZbOapNJn');\r\nGI=imread('https://drive.google.com/uc?export=download\u0026id=1THVdMv7MENBxBBAaIbkl4qSvPnqnMnpv');\r\ntoc(ztic)\r\n w=1; %determine block width; assumes corner is not 2x2 identical\r\n while isequal(GI(1,1,:),GI(w+1,w+1,:)) \u0026\u0026 ...\r\n       isequal(GI(1,1,:),GI(w+1,1,:)) \u0026\u0026 ...\r\n       isequal(GI(1,1,:),GI(1,w+1,:))\r\n  w=w+1; %Grow along diagonal/Right/Down\r\n end\r\n nr=size(G,1);\r\n N=(nr/w)^2;\r\n v=1:N; %initial vector assignment of GI squares\r\n \r\n irc=zeros(N,4);% idx r1 r2 c1 c2\r\n rgbGmed=zeros(N,3);\r\n rgbGI=zeros(N,3);\r\n \r\n ptr=0;\r\n for c=1:w:nr\r\n  for r=1:w:nr\r\n   ptr=ptr+1;\r\n   irc(ptr,:)=[r r+w-1 c c+w-1]; % indices r1 r2 c1 c2 for each block\r\n  end %r\r\n end %c\r\n \r\n for i=1:N\r\n  % [1 1 3] tuple  %credit Christian Schröder\r\n  rgbTuple=median(G(irc(i,1):irc(i,2),irc(i,3):irc(i,4),:),[1 2]); \r\n  rgb=rgbTuple(:)';\r\n  rgbGmed(i,:)=rgb; % Median of G image block\r\n  rgbGI(i,:)=GI(irc(i,1),irc(i,3),:); %Blocks are singular r,g,b values\r\n end\r\n%Starry Night Best known score 41141, Greedy gets 49K, Hill using rgb blocks 45K\r\n scrmax=52000;\r\n vn=optimize_GI(G,GI,irc,rgbGmed,rgbGI,v,scrmax);\r\n toc(ztic)\r\n \r\n if isequal(sort(vn),v) || isequal(sort(vn),v')\r\n  valid=1;\r\n else\r\n  fprintf('vn fails to contain 1:N\\n')\r\n  valid=0;\r\n end\r\n \r\n S=GI;\r\n for i=1:N % Load S with GI blocks\r\n  j=vn(i);\r\n  S(irc(i,1):irc(i,2),irc(i,3):irc(i,4),:)= ...\r\n  GI(irc(j,1):irc(j,2),irc(j,3):irc(j,4),:); \r\n end\r\n \r\n GmSsq=(double(G)-double(S)).^2; % Scoring Function\r\n dis=sqrt(sum(GmSsq,3));\r\n scr=round(0.005*sum(dis(:)));\r\n fprintf('score:%i\\n',scr); %GI,G scores 99198 for Starry Night vn=1:100\r\n if scr\u003escrmax\r\n   fprintf('Score:%i exceeds Max Score:%i\\n',scr,scrmax);\r\n   valid=0;\r\n end\r\n toc(ztic)\r\nassert(valid)\r\n%%\r\n%33.png Scream w=25\r\nztic=tic;\r\nG=imread('https://drive.google.com/uc?export=download\u0026id=1IxjAN8VmMhM9d5CmTkgHHcVwYEYTWOAj');\r\nGI=imread('https://drive.google.com/uc?export=download\u0026id=1C0A0mBb3YNOGlVnE5-9J3Nyr4X3xG0yy');\r\ntoc(ztic)\r\nw=1; %determine block width; assumes corner is not 2x2 identical\r\n while isequal(GI(1,1,:),GI(w+1,w+1,:)) \u0026\u0026 ...\r\n       isequal(GI(1,1,:),GI(w+1,1,:)) \u0026\u0026 ...\r\n       isequal(GI(1,1,:),GI(1,w+1,:))\r\n  w=w+1; %Grow along diagonal/Right/Down\r\n end\r\n nr=size(G,1);\r\n N=(nr/w)^2;\r\n v=1:N; %initial vector assignment of GI squares\r\n \r\n irc=zeros(N,4);% idx r1 r2 c1 c2\r\n rgbGmed=zeros(N,3);\r\n rgbGI=zeros(N,3);\r\n \r\n ptr=0;\r\n for c=1:w:nr\r\n  for r=1:w:nr\r\n   ptr=ptr+1;\r\n   irc(ptr,:)=[r r+w-1 c c+w-1]; % indices r1 r2 c1 c2 for each block\r\n  end %r\r\n end %c\r\n \r\n for i=1:N\r\n  % [1 1 3] tuple  %credit Christian Schröder\r\n  rgbTuple=median(G(irc(i,1):irc(i,2),irc(i,3):irc(i,4),:),[1 2]); \r\n  rgb=rgbTuple(:)';\r\n  rgbGmed(i,:)=rgb; % Median of G image block\r\n  rgbGI(i,:)=GI(irc(i,1),irc(i,3),:); %Blocks are singular r,g,b values\r\n end\r\n%Scream Best known score 41328, Greedy gets 52K, Hill using rgb blocks 45K\r\n scrmax=55000;\r\n vn=optimize_GI(G,GI,irc,rgbGmed,rgbGI,v,scrmax);\r\n toc(ztic)\r\n \r\n if isequal(sort(vn),v) || isequal(sort(vn),v')\r\n  valid=1;\r\n else\r\n  fprintf('vn fails to contain 1:N\\n')\r\n  valid=0;\r\n end\r\n \r\n S=GI;\r\n for i=1:N % Load S with GI blocks\r\n  j=vn(i);\r\n  S(irc(i,1):irc(i,2),irc(i,3):irc(i,4),:)= ...\r\n  GI(irc(j,1):irc(j,2),irc(j,3):irc(j,4),:); \r\n end\r\n \r\n GmSsq=(double(G)-double(S)).^2; % Scoring Function\r\n dis=sqrt(sum(GmSsq,3));\r\n scr=round(0.005*sum(dis(:)));\r\n fprintf('score:%i\\n',scr); %GI,G scores 99198 for Starry Night vn=1:100\r\n if scr\u003escrmax\r\n   fprintf('Score:%i exceeds Max Score:%i\\n',scr,scrmax);\r\n   valid=0;\r\n end\r\n toc(ztic)\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-07-06T00:33:35.000Z","deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-07-05T22:59:16.000Z","updated_at":"2023-07-06T00:33:37.000Z","published_at":"2023-07-06T00:33:37.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2023.github.io/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP2023\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e Challenge is Jul 7-10. Start is 0500 PT 7/7/23, Registraion is open. Posted 7/5/23. Updates at \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://twitter.com/icfpcontest2023\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eTwitICFP2023\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. These contests are insanely complicated and competitive. The challenge evolves every 12 hours with new files and rule updates.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2022.github.io/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP2022 HomePage\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e has details of the RoboPaint contest. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://twitter.com/icfpcontest2022\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eTwit2022\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2022.github.io/writeups/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWriteUps\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.icfpconference.org/contest.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePriorEvents\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2022.github.io/Specifications/spec_v2_4.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://gist.github.com/RafaelBocquet/e464c2d199f2d7a725cafdc22b30df7f\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eImageSubmissions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://github.com/icfpc-unagi/icfpc2022/tree/main/problems\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePuzzles\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSawickiSolutions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e are useful links. .\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to move the blocks in GI to minimze the error between GI and G based upon RGB distance. The contest had these Initial files but the leaders did not use due to the high Cost for moving small regions. Assuming a low cost remap function this is to find the optimal image score using GI blocks. For Starry Night, 28.png, best score found is 41141 using Robust Hill Climbing where all swaps tried and only the best improvement is implemented. Greedy would start at index 1 and find min error then index 2 would look only from 2 thru N. Max error index swaps to minimize its index error. Many methods are possible. Hill Climbing using the block medians is fast, easily coded, and scores well. Greedy is easiest code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput: G-goal image,GI-initial image blocked, irc-row/col for each block, rgbGmed-median of Goal image block, rgbGI-block rgb values, v- remapping of GI blocks, scrmax-max allowed of Score(G,GI(remapped))\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput: v, [N,1] remapping GI([1:N]) to GI(v([1:N])\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"240\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"240\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"240\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"240\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" 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\",\"relationship\":null},{\"partUri\":\"/media/image2.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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PuaGZyNzURjdZRe3a5kM3Ze3sbGu1SBxPocBLiulRLCSYAwEc1dmCVz2YhZqyMQDLMwXwE0bktUppqI2KmEVH1TJRUGOdXyFjDsOdx+HbzB2RYGPZFKkl0WdLsWmtqXkS1ESGnA+NDcQ2ZlHOGUGRHP2dksg+ko/fAOMhJayb7iSr0R435aknmcEtQDXQ6f2U/fseMQDI00Sko5iUNL2KqZhy8XqhQKtEwuju6M8I4/OjmRG9lxLfEVaQ0U/WIyVqEJaHgSHqLNj0Fi5Ir5/R/t8TGrP8M0lpg57a5MO2nvN1o1UBI93Df3oFDCLTy0f6eeIOeu+ZIBzICOcExXxJfJ2nMNCKm6hHT9RA/0oY04sgg2WGWygjj6GNOm8eCQAiTNZ+gFiiqEz67p6DgSnryNr0Fo5kk59hXVYCsbxeION9Ce3tA+SDzLJkILFph9whhtCQwkVnsOw4xI5ATEGlneCeSbUob0pisxaQRahQ0M6c7SaPbOTIsIXRoFkkftfpSU/MU1YbPyDTkHUlvwaavaFHKWW3Hdpt0ZH2+HzRj0Qs0l6NdchCx2SpvviZJhvtARiAnEJovvhAmcq/LLLLuKyZrIRO1kLqvTPEGse/Q2gzHFjrNVOa7MBplvL0eKGY9QyblnEV/yZBJ2gHfLTS48/wZEsSE7S6DJRZ9BakgGSER7rJ4a5YHkoFYSKSnEh3EEUaaqbohjZmGcYTLrqSxsKzsDMcOupNJSzPRGqYbxt+1a1LQU7AZKNlU8u0mrlaljiz7vTtif4k7hJBuXDxxiU3JtEJIF+EOmO8LCKDICOQ4hbFStTpfd8kIrzYixmshU7UQLzS/S9E9csogEwjHM7qRiRBgJRMNpRmtBozNBhQcSX/Rpr9kk3daa120hbRqM+4yImlB5paDcAD/aN9KF2gQrtGSANqnnV2REchxhrZkJ+aaqZTWTNYi9s4EVL2ISNMsRd55nuzFP3HRnUwEdry57itmfZ+R6YDegsVQxaGS7/CVdBbNFPsXSMcv4id3BpDFU81aIVaRI7YGyAKgdYRw+pDlc+ItYp8RWJARyHGDzlpUncQRRIrx2ZC9VePbAEMatmg/R7fQxAwnNrqNhcS8qbRmbDZkvBZSyRki6StazdySzoS6E3likpRJtwdfZhalssuIJeALMX1iQTSDvexVyNyKZimW/SEjkGMc+yOOeqAYqwaMzxozleyibSQ4UV/sDAeOtFaShAUnvpKCKxkqG19J4nRvH6ewEAft8QTz3OZ57cEr8TZ9HHR0lO/KIAmiAHNvAGgVZ8vv59isFtaxiW7Vb5PvAFUvYs90wFQ9IlQaS3QQxH4ShDJkWDjagyuUNtWUc7ZgoGSzrOKQs09sIml7buVT/clLUbNPIqzCkjBjaa0QMk/50jvmFIPcF5ZYGECG/aGbSQBozgZnvYhnRxs8ubvO+GyI1hpbijn7Z+SRYfHQrJsCxGXnpSCMNLumAjbsqrN9wsMPVXOcttbHMERyvM9jRbOJIoR0ya17H+iAhfgZDieMb8qCcAZn1XUxeUQsNGAm00COEbRXwjWfoaVVzHoRI9MBk7UQpbuZqbIQ3AxHBt1K4kQaXEswWLZZXnFwu2okx3/4b7NttKJ63yuJpn6GsEqGWI7Kcwvjm7F7KV96OzJ/0oKq8CbINJBjAO2ztdZnIQQ1L2LTaIMnYo1DCNEkj/a5wfH7UmZYWugWwWVLQaRaGsmOrhrJXF/e8YRWccoQhCS37gOgQ+DokGaifehgCnfNuw15qJCk0vBCkGkgSxj7mp3V/IiRqYCJLhrHiRzpkmFpotPsmmgkQ2WH5T0OjjXXJHs8aiTpzH0hJPXHfxd/278i3IFYeB+ZZ9VaI6SNDqaw+l9M6XnfNAmOSZvDgkxrGYEsQezrJfJDxe7pgLFqQKRMOKVIHXc8vWwZjj90I5KcLRjucVhWcZBtQSEmkdF8Pz7Wh2lpIcZMpFXA7APXEk3cg3D64gz1w/ugSdiuVg1Ebpjy829BFk4ivX77Qu8hI5AlhH35OZTWjE4HjEwHeJHGEq0XKiOODMcauhFJyZWs7HXpL9nN7XD8aSMtEjECWzW2M/vTV6G87Qi797CSSJM8dABaU3reN7D7X2y0H2kfsCzJCGSJYF8vy/hsyK4pn5qv2sJxM+LIcKwjPYZVvBhZX9FiZa/brAJ83BGJNs9pQnqN4I6mHqL26PWo2iaE0ws6WtT3u3kuYaGjOiApXPAPuCve3EYecGBtmxHIUca+Xo6qF7Fr0meqHiHiMurJMcf0C5QhQwfSYzqKi3kOlh1W9rZHbB0vk6f2995kqCtvhPpjv0G491aEExc0RB3as2qNFoKkLKoOpxC5VRTO/xTO0MvNtbHQxlZ4wNfJCOQoYj7y8EPFzsmA8dmgzUF+rL80GTLsC53jO1Qa1zL+keEeN+UPOT60kfbkQqMFoBX1DR/E3/av5je7jAmWjeJj9u/bbm/HmDiiWdAhzqq3kz/zz5C55XG+h2y25cG0Y0YgRwH7egn2VgN2Tvp4oW6ripuRR4YTBd38I5WcxUkDLuV5zVrHppO9/TmMTwQgHLsdb/OnCMfvAOWBLCKkYxaj0grmraElMKShjZ8jqgNg9TwHd937cVf+Unzd1rUORbZkBHKEMUcNBxCCRqDYPuExWYvi9cUzrSPDiY1uZq3lPQ6rel2knJs7knw+1tD+HCr+bDSHcOwO/B1fIJy4F+3tMmRiFeM6VaaGlSBZrkEbconqzYWhrN6LcVb8Es6KNyGk03b+xWizjECOEPY12EemfXZNBqZmVWauypChhY41RcJIU8xJTup36S3MF611DGojace61pj10luLdil/jGjyXsKxHxKO32nWLdcRWgWANtoJFsLpxeq7FHvwSuyBlyDzJ6UuEZp9FpFwMwI5Augc4OnyI9snfKYbURZdlSHDPtAWraU1aBiqOKzqc3Gs41UbSYhEtJUW0VEDHU6ggyl0MAlECLsP4fQhnH6EVew4XwS0V9ZdrLbJCOQwYr5BrdHsmgzYPeVnTvIMGQ4A6XckVJq8LVjd7zJQcub8fiy/T91khyEC0fRdzH+sMqYsIWmWMDrIKKv9ISOQw4T5yKPuK7aOe7HWkc60PTZnTBkyHGl0aiNaw1DZYc2Ai3Uc+UYSdCeTzr+i7e+RevaMQBYZ7dnk7R04OhOwfcInynwdGTIcMjq1kaIjWTuYay6t28r4PvZJZKkiI5BFxHwznyBSbB33mZgNmxFWGXEsLXSf5bXtYP7QUddY0LmlDVlfH17M8Y0AK3tdVvY5CI4/bWSpISOQRcJ8jvLJWsjWcQ8/zLSOpYDOWWl6O7q1WJeQsv2vaHdkNo/BHJM+HkAphVYqXi9ImGDLVJ9nxLK46NRGevIWawdyFFyZkchhREYgi4BuqrJSmu0TPqMzATTLkOg4kzQbvEcK3QhDK1MeQkqJsCyklFiWjbRtLBuiEIJGgzDwicKAwPfxG3X8Rh2tVDN3x3ZcLNfFcXNYto20rOZ5HMfFydloDVGoiMIAFUUopVAqaiOkTLAtDjrzRiwpOKnfZVml3cGetfXiISOQQ8C+HOWbxxpUGwrbyrSOI4luw1krhUYb4W5ZRvA7kqAR4DVqNGarTIzsZGzndiZGdjIxsovJPbuYmRhndnKcRq0aaxRR6vxGO5HSEIFlO+RLZfKlCoVymZ6BIfqWr6Rv+UoGVqxm+Zp1lPsHcPNF3LxLFCoC30OFIUorpExFzCRXyMbLQSFNFErDYMlm7WCuu4NdiGyptUNARiAHiflMVmPVkG3jXpYUeAQxR8vQGqWU0QhsG8fNIyQ0ajWqE2PsfOZJdjy9gZ1PP8GebZuY2juCX6/hew2CRgMEWLZjNBRpNJTE15HuRR1fK7kHrY3ZKtEyVBhhOQ5uPo+bL9A7NMzwyaey6rSzWHfuRaw642wqfYPYroPf8AgD34RaykwzOVTMcbC7klOGchRdKzNpLSIyAjkIdI0117BtwmNkOsgc5UcAc4SA1iitsCwb23Fxcg5+w2d6bA/bnniczY8/zOafP8SuzU9Tr07j1WaR0sJyHCzLNr4OKQ1ZxNnPxCGi89cd6kBzNiuavg+tdUwqhlDCwEcpRb5YpDIwxMnnPodzXvhSznzeZQyuXI1l2/j1BmHog8aQSRzDnzxvhoWhzcGuNFIK1gy4DJUzk9ZiISOQA8B8Ibp+qNi012O6HmUmq8OMZrtjEjITR7Xj5nByObxajT1bN/H0Q/ex8YEfs3XDo1QnJ/AbNWzHxXZcpGUhpWWOj8mn8xqL0XdzzpM44xEoFRGFIYHXQAhBz9Awp1/0fM5/8cs445IX0T+8ErTGq9eM30XKTNgdJDpNWst7HNb0u3Mmedk7e+DICGSBmE/tnaqHbNnr4UeZyepwonOYKhUhpUWuWAI0o9u28OhdP+CxO3/AjqefoDY9AULi5vItwtCq3Zk+Tz8tRv8t9Nwi1niiMMBv1BFSMrBiNWde8iIuecW1nPHcF+Lm8zRmZ1FRlBHJQaJblNa6wRw5Z26UVtamC0dGIAvAfANs95TPjkkfoLmWczb4Fhedba+VwrJtcsUiXq3Gkz/7CT/7wbfYeP+PmdyzO/Z55JB2XGhPqbZzpftnjjCPI21NvJxRTMy/xJSV+D1oO6Z1rKksIEXreGhXcPZ1D4ZMNGFgyMTN5zn1Oc/n0tf8Ihe85CpKvb0ZkRwCOqO0HEuwbjBHb9HuMkE8BgsyHgVkBLIfzEcem8c8RmeC5podGXksLjrbXakI23HJFQpMj+3lsbtv46c3f51Njz1I4HvkCkUsxzH+i7hk9b7ME4mgV9rYx5UylV7DUBNFGiHAcSSOLXAcEUdb0fRvCREfF5NMFGmCQBEEmiA0x9u2wLLif1IQ+8ZJcdq89yilhVIRXr0GWrPmnAt46Zt+hYtf9moKlR4as9U5pq1s/C0MSVsliYdrB3IsqzgnnHO9a4h7mg1EqkDKPG2SEcg+0I08IqV5drTBZD3KyOMwoJvGIW2bfLHI9Ngo9333Rn78zS+ze9PTSMvCLRTNvmrfpJFoF0rFwj7U+IEJsy4VLYoFi+FlLqtX5BgacOjrsenvdejvtenrdXBdiWODbQlsR2JJ8IOYNEJNra4YnwyYnAqYnA4ZHQsYGfUZGfXYOxFQqynqjQgpBTlXYtuGUNIumPZ7j+85Zh2/XiOKItaefQEvu+5dXHzVq7Edh8ZsFRGHEp8IQm+x0OkXWdnrsLo/N+c3OP7ac847pkHK+Z9RKd3MZetsk4xA5kG3QeQFimdGG8z6KiOPw4D2REyz6lqh3MPs1CT3fvur/PjGL7Nr01PGYZ7Pt2kb6XM0Z/ECEBCGGs9XRJGmWLColC3Wri5w+roC69YUOPXkPCuHcxTzFm5OghuH7UbaVNNWumm20rFtStPSRMzF4gtKYT6HGuUp/EAzXQ15ZnONZ7fWeXZLnQ1PzTKy16fRULiuxHWMlpI2mXWSoIgZ0K/XUEpx9gtewit/9bc563mX4XsNoiBAWoe+wtyJhE6/yFDZZt1grm3CeLyRSOu5zGeZkmMTVZ+xGY+ZekjBtRiouAxWctiWmcQopZum2ma7ZAQyF90Gz0wjYtNoo+ksz+yki4du5io3X8CybB6961a++y9/x5b1D+Pk8ji5fNfIqQSJphGGmoan0Bp6KjZnnVbkeRf2cM6ZRU5eXaCnYmMXpBH4vkJFxoyllJmRJTWv2vo3pdI37735X+uzpmUikzI2fzkCLHOt6emQjU/P8vDjMzz42AzPbqkzW49wHUk+J5uaUmcbNYkEQaNWxbYdXvKmt3P1O3+XSt8A9epM5hs5QLSRSKTpKVicuizfXGPkeGrL9PMkWkfdC7l7wyjrt04xOuURhKqZw2Zbgv6SyxmrKrzkvOUM9eTmWmUyAmlHt0EzPhuyZayB0pmzfLHRrnVECCEpVsrsfOZpvvuvf8eDt94MaHKFEipO1EuIu91nYIRuwzOaRn+vzblnlnjBxb1ccmEPa1bmkAULIo0OFFFsyjLXZs5E4GAERzcbeoLEVyKFMYPJnCEvvxry5LM17n1wip8+OMXTm+tEkaaQl1iWQKl5NBIp0UpRm5lmzVnn8cbf/SPOe9HlNOo1VBgiLeu4EXyHG6ZNAYyJuuBITl2epxBHaB0PYb7d5NrjWyb55k+3s2eygW2Z8SaFSAWRaCKlCUJFueDw8otWcMUFw23nywgkRpJV3NnIe6YDto17barbsTqIlhK6aR25Qoko8Ln7xi/zgy/8I5OjuylWegBTnFDEKkDTPwAIabSNWl2RzwnOPLXE5S/q4yUv7GP1irwxR/mKIFAoNZcsjsQMs5uzUmlAg2UJbFeCLfBmQu5/ZJpbfjjG/Y9MM1uLKBb2TSRSWnj1GkIKrrzuXVzzzt/FzeVp1GexLLtt3wz7RtO5rjS2JTh1WZ5Kfm7m+rHWlp0iXgjBHY+N8O37diAEuLZsat3dIIXRzrww4oVnDvHWXzi5uSZ9RiDMnyC4a8pnx4SPMQEem4NnKaLthYwLG5Z6e9iy/jG+/vGP8sTP7sHNF3CcHCquPzVXcILvKxqeYmjA5UWX9PKKlw5wwTllnJI9L2ksBSHQSSiJRc6yBHZeQqR58pka3/juHn744wlmaxGlohVrXXOJJMlxqVenOe2iF/DWP/wz1p59PrPTU0i5uGtgH+9IR2hJAacuy9NbmBvmeyy1Y5oYpRTcs2GU/7hzC6W83Ywm3NfzNN85AdO1gKsuWsEbLl2Dyghk/jDd7RMeu6eCLDlwkdFmsooiLNvGzRe451tf4cZPfozZyXEK5Z6muaobcQSB8W+sXpHjVVcMcvWVg6xcbZzqYWzCWmqkMR86ySQhPDdvTFwbN1b5yk0j3HnvBEGoKRUttG5/6ZttY1nUqzOU+wZ423//31z8smuozcw0fScZiSwMnX1yylCe/tKxTSKGEAV7phr83U1PEEQKKybKhTxHywcHnh/xG1efwdkn9ZzYBDLfgNg6bmpaZZFWi4t0O0ZhSL5YolGb5cZPfYwf3/hlUxbdcVFRCLT3i5TGZzFbi1g+5PLGa5bz2pcP0b88B77C843XWS4yaXQzP3XDoV6v8/yJSSGXN7WwHnx4is9/ZRcP/XwG15G4bnezlmXZBL6H1oprf+sPefnbfwO/UTfFJWUW6rtQdPb7yYM5Bstzc0WWejsm1hWN8d9+9e6t/HjDKKWcRbQfzWPuuYwG4/kRp62s8NuvPvPEJZD5BsLmvQ1GZ8KsptUiI/1CKhVRqvSy4+mNfOljf8TTD91HqaevWduqzTwjBEJCdTaikJdc87JBrnvdClasKaAaEUHQ0jYSEw8cmoBsv9eUz6XLKTWgjV8/lXTV/je5n4UI785xmTyPW7SIPMX3fjjG576yk917fCplqyuJSCnRSlOvzXDVL/8Gb3r/H5vS8VGUkcgBoDNqae1g94TDpdyO6XudbYT8v/9aT82L9pn3sRAEkeI9rzr9xCSQbgNAA5tGG4zNhpnmschoexGVotjTw0O3f4//+Ms/YWZ8L4VyBRVFbfuB8QkEgcnhuPS5PVz/1lWce14FAo3nKUyh2oX3VaeghfmFQLLNLkiwpfF6h11eFQE4wjCJwrBNnD+SZLgrZY6TcuFmpLlBBibSzCnZjGyv8/ef384P75kgn5PYlmibTZp7N+G8tZkpXvS6t3Ddhz4KmJpbxmeSkchC0BqTxgy0ZiDHcM+xRSKJ7+PRzRN84bZNOLbc/0H7gBQw2wi56qIVJx6BdDrME4fZs6MeE7WMPBYTc2fTmmKlwp1f/yJf/euPIITAcfNEUdjV11GdjRjoc7j+rSt5w9XLQQq8eoQQCxfGcxzWGI1BSPNFdImuSx9zxz0T7BzxTELgTNjSdDBjybEEvT02ji3p7bEol2wG+x36em36Kja5nMQpWKA0gacOeEx1tmGkIJ+TIOGb3x3ln/5tO/WGopCXqbDk1vMIKalNTXLxVa/hHf/r/2E7LqHvZWG+B4A206vSrO53WdnrHhMkorXJtLek4HsP7uQ79++kUrDjMi4Hd78C8ELF2at7sBf1bpc4upGH1vDMngaTtaQUe7bs7GKgM9IKISiUKnz/C//IjZ/8GG6+gJTWHPKQ0kSLzFQVl17Sy/vftYY164oEs6F5Eay5GsR8106QJAdKCa4TZ5oHpgx86Os5L79SkCsz+GLMAAAgAElEQVRLbv/hGH/yl8804+JlXCVRKY2QNMePUhpLtmpn5XISx5GUixYrh3MsG3C49hVDnHt2Gd9TTdJaCDo1JtsS+LG/5/WvG+aMUwp87BOb2bStMcekpc3NUerr56HbbibwG1z/px/HzRcJA6+piSxV4bdUkJYXlhTsmDBrtazsc9vab6m2Y3JLtUZEy3J1CCZejBYy0wiwPvKRj3zkEO/vGMLcjt402mAiXscjm5EtLtLkkSsU+dY//T++/U9/Q75YaibCtTuABX5gHAq/+Sur+f3fPJneXptGLWpmdaf7qJvZKfnNfDfbXVdiFy2kht17PO64a5wvfn03o6MB559d6uo3sSxBtRrx4GMzFAsWA/0OlZJNT8Xm5DV5ykWLYkHSWzHb8nlDHpHS1Bsm23xiKmDHLo+fPjTNbF3xyssHUxFiBzbG0vsn9uugoVi5Ks8Vl/azdUeDpzbVyOfN0rhtJKI1uWKRHU+tZ9uT67noilfhODmi2CfSef4Mc9EW0CEEU3UzJit5u+s+SwrxhPjJHTNsGqmavA8OXgMBiLSmv+SeOCasbrOETaMN9lZjh7nW6Kw0yaKg2dYxQTj5At/4xP/h1n//NIVyD0kNq7RpybYE1VrEsgGHD/3OOl5waT9BNYw1h32T+7z+grwFAnZsq3PPA1M88Mg0T22qMT4ZMFtTVEoW//UvF9JbsQmj9hdfa43jSPaM+tQbxpSWvCiOLYgiY06SEjxPMVONaHiKvRM+09MhY5Mho2M+I3t9Rvf6vOV1w1zz8iH8xoFpIPO1b/K8UaTJuRKlNX/76a1847t7qJSMUFO63Xxn2TazkxNc9LJr+PU/+zsTtBCXhl+Sgm8JotOctXYwx/Il7FhPm7Bue2Q337x3e2zCOvjzSSlo+BEXrOs7MUxY7eRhtm0d89g7m4q2onuUTYYDQ9vMVwjcQpGvf/zPue1Ln6FY6SNZ1KkzRHdqJuTCc8r88QdOYfXaAo3pEEvumzzm+AcijZSQK1soT/HQw9Pc8qMx7n94ij17A6LYlFUqWJyy1uX1r1pGpWTIo9szBIFi2ZCDEG7TEW5+bx8r5ZLF0JBrJnSy3CqoGOhmXkouJwn9g9M+OpH2KSVam2UJPvjedQz2O3z2P3eSz8mmObDZPmFozFm3f5dS74f55f/xf/HrtSVvgllKaDdnGTkiBQzFIb5LrS2FEIj4PVk1UCDnyIMmj+b5MGbb1YPF459AOp2oINg27jGSreWx6GgPe9QUyhVu+oe/4rYv/TPFnr5m1nm7OQCmZ0Je9pIB/vv71lEsWjSq+18auLld62a56VzJhLredvtevn3rGOufrDJbiwDI5SQnrShw8QUVLrmgwgXnlOkbcAk940/odg0hBGGo2yYe+3p28zf+TquYom0ZMkrOuRjjLd3OUhpNRKmI699xEv19Djd8Ziu2Y2aeaRJRUUSpt48f3/hlBleexKvf/X5q09Mki5Vk78L+0T5+NVvGPKQQDKSSDWFptGV67J6+ssJgJcfYjNessHswUBryrsV5a3uPbwLpJA8hBDsmfXZnSYKLjpYAbYXq/uCLn+aWz36SQqWnK3kIATOzEb/02mE+8O61KK3xGmqf/qg55iodJ9spuPenk3zpxt08tqHazA/p67G5+IIeXnn5ABefX6Hc64DAlFqPI7o6hXr6msl97g/JPnOivrRumr4We2baSSJaa7yZkNdfO0zOFfzV328Bi7iWVrtjvVCucPM/f5yBlSdx6WvexOzUFJZtZ071BaKtL7Vm894GUuTpi1c3TPfN0WzLlllX49iSF541xI33bsO15UElElpSMOtFXHL6AKsHi8evD6TzsYQQ7J7y2T7hZ+VJFhltNvkwpNTby33fvZHPf+T3cfMFjFNXzSGP6mzEO35xJb95/UmEDdWMV983eZhjo0g3a0etf3yGL3xtFz97dAbPNyXcVw27XHHZAK946QCnn1oESxB5ypirdHPCvaTMDYeCtNBSCnIVm1tvHeX/fnKzqf7boYlIKYnCENtxee/ffpZTLngujdlqM7z3WG+PI4VO8/jpywvNAoydk9elcJ9eEHHDTU8wMtkg7yycRLQ2Sb2R1jiW5HevPYvhvvzxSSDdHFrjswHPjnoZeRwGJG0ZRSGFcg/PPHw///DBdxIFAZZtm0q6HYJ6thbxq7+0kt/41ZMIGtF+neWtYzVRBLmiRehFfO4ru/j6t/dQa0QoBQN9NldfOcSbXr2M4dUFCBR+vC5IJ2l0u86xjDkk0mPz7W+P8Ff/sIVC3pqzj7Qs/HqN4ZNP5X1/90VKvf2Egd/MVj+e2uZwImkrpTW2FJy1okA+VQo+PfE5qvcXT9Ce2TXDp295GqU0ri3jpQbmNxUDzQlIw4/4tatO5eLTBsyE5HgjkG7kUW1EPDlSn7M9w6GjNTgVjptjcnQ3n3z/OxjbtR03X0BF0RzNo1aPuP6tq/j1t68mqEfNOj37E+rJ707BYsOGKp/67DYe21hFCEHOFbz8FwZ482uWc+oZZfBMfSwh5gZHHM99300T+bcv7eDTX9xBpWS1OVC11iYya2qSi668mnf9n08R+n7b+Y7ntlpMpIV03pWcNVxommKXgszpJJEnd07z5Tu2MDHrU3RNpeduznUhTGxII1BYUvC6F6zmJectb72rxxOB6C6Jgo1A8eRInSDS2WJQi4zOiCvLsvj0h97D4/f8kGJPL1EYtjscpXGYX//WVbz7+jX4s2G86t/++8XYk01Ox7e+P8qnPrcdL15J8KzTS7z3107ioot7IDR+FNElb+REQbsN3tTR+ptPbea/vrOHnood56Kk829sZqcneNMH/iev/LX3GH+IZWfvygGiqYkrTW/B4ozhQtv2o9me6fcgIZGxaY9v3LuNjdumCSOFa0tEqkaW1hCEJvhj7bIir33BSZyxqmLCw2lGeB1PBNLeUZHSPLG7Ts1XzWVosxdicdA2IKOIYk8PN/3DX/Odf/5bSr39TfJI9rUswfRMyBuvWc4H33sygXdgUUnpfd7zofU89PMqQwMOr3vVMt71tlUUyjbebKvMSecxJxrSM87Eif7BP32Khx+foVyymiTSfGeUwrId3veJf2Pt2Rfg1WYzf8hBIGmvUGmWlR3WDXVZBnYJkQjAE9uneWTTBM/snqHWiJopDY4lWbusxAXrerno1IFURF/qWY4XAunG8k+N1JmqRxl5HAak/R7Fci+P3X0bn/nv78F23LbfE/KYqUa8+Pm9/J//cTpaE+dsLLxf0hrIfQ9O8YM7x3n55QO88AV9RA1FGGosC5IsbDixtI5uSE+kcjnJjl0eH/ifTzA5FeA4st2pblk0ZqucftHz+e2/+WybnwiytjwYhEqzus9lVV+qbhZJztkSIJGUJgHgh4q6F+KHCtuSFFyLvGs1j02TTtMkfTwQSLeIh61jrVyPjDwWF22zVsdhdnqSG377bezduRU3V4jXNhfNsL+6pzh5dZ4b/vwseio2QaDmJY/OvoT2l01rjROvJ45mTihu5/4nMtLtEUaafNninnsm+JO/fMbUBOvY17JsqlMTvOF3/hvXvPt3M1PWQSLdXpHSnDLUWktkKYzTTo0o9vEj53GiK21et273Pm82SbKjuUDrX3rbUkDnfQhhlqJNJwoebM2X+Z5xqTz70UDbGAAs2+HGT3yM3ZufJpcvtpFHosq7ruAPfvtkegccfH//5GE+g20LrFROSPK731B4tahrHkcm6FpImxBtS+BVIy578QDXvX6Y6myIlKmxLARKReRLZW7/8r+wbeN6coVSWwRdhoUh3V5SwJYxj5lG+3sBzLsG+ZG4v7Z7lCa7vFPWJ793Rq6m37EmgXQSBMTrGbRFbRhGTT/40SST1thvdcxkLWTnhN+RKHhw559PGHXOiE80iFjYFCsVHrjlm9z3vW9Q7Oltkkeyj5RQryve8/bVnH9hD43ZqI0Q0khvU0rj5iWjYwHTMyGO3R5WKkQyIzr6duWljraXXUJYi3jnW1fz3At6mK21+iMRIJbtUJ0c5+bP/C3Q2p7hwNDmXwI27W3ghx3h7Ee5XdNkkHzu9q9z3zQkdFFpSASAKV+cHJx8T1SdxIYqOFozlHa/hxcotox5TcI4EMHSef9Ka8JIEyqNikkyUmabqanU3mYnAtKmK8fNMb57J9/55xuwHQc6JiAm4iri6isHeNO1w/hVU9uqm/reaWpxyzb33j/Juz+4nj/886cYnwraiKfb4M4wP9oCSyKNnZe899dOopC32iKyTN9GFMo9PHrnD3jotu+QL1eaRTFPlHG+WGj6l4TADzWb9npzfLXHepvaaSGbOEkEMFH12TxSZfdkg4mqb0LTig7LenOsW15m5UABIZjjlYcj80J3doTSpoOCSB+w07zTZikFFBxJT94i55gV36SAMNIEkabqKWa9iCAuzZ0Q6okwE040TsfNcctnP8XI1mco9fQ31/Uw5CFoeIpTT87zvl9fi+6y0FH6fMlvUWynf/jBKf73DZuZroYEoW5Www3Dg9cmT3SkAxq8WsTZ51W47vXD/POXdrSF9pr+MZnqt37x05z7oiuwHMeU5c9wwEj7AqfrEdsmfNYO5IB2c/CxKjfstCNFSsF0LeDWh3fz0LPj1LyIIFRNG1hCMHnX4rSVZa5+7ipOGio2hcqRYtS0yp1g27jPTCNqS95ZyHmgFdYmBAyWbZZXHIqu3Oc5gkgxPhuxZzrAi9voeDanNIk6MnbyJx+8l598+6sUyj1zTFc6rlb7gXevpXfAwUuZrrq1TUIeuYLF5mdq/PnHnzWL30jB71x/EqtXF5r+jgyHhkQ7jGoRb3vDCu59cIqNT9co5FtRWUop3EKRLRsf5d6bv8ZVv/wuZqczh/rBImkv2xLsmQ4ouXKOU/1YbdfYhGXI48kd09xw00bu/PkIfqjI2ZJKwaaQsyjkLMrxZ60167dO8clvP8Fdj+9pc9YdbhJJO/3MH8GemYDRmaBZwXVh5JGcxjh6iznJmcMFThnKU8pZbR3b7Z9jSYZ7HM5ZWWBFjxOvend8qKXzQWuNiGso/eAL/0joe821JJLfzVK0IW9+9XIuuaS3jTy6ni8mb9cRTE8FfPSGTUxMhqgI3v3Lq3jtq5e3OcszHDzSJB9GmlzJ4j3vOCmedHWJdnPz3PGVzzG2ayeOm0PrTAs5WKSd6tvGfWreXKf6sQiZaBUPPzvBp295mulaQClvm6xtWsuBNh3o8UBL4oO/9uOtfOu+7W0kAoc3wiDd8FUvYvu411yqceHCuxUjP1C0OXO4QDlvtZFEcq30dZtHx/vYluCkgRzrhnJNreh4I5G07yNfKvPQD7/D+nt/RL7Ubh+XUuB5inVrCrzjzStRnjJrj8fobL9WP4KwBH/1D1t4alONSGle96oh3v6WVfizc8N0Mxw8Wn0F3mzExRf18IqXDjBbi5pRWUlfu7k8e7Zu4sc3fhk3n88isg4BbX4opdk05jULGR7LMkNKKdg2WuMrd2/BEgInLvPbxDwCNFF3Szmb2x8Z4d6Ne1Pmi8MTYdDp9wgizea9XtPpfyCmq6Qj+woWpy3PN/0mnc+ZfO/msE1rKYNlh3XL8mhotsGxOCDmg9Ym2axeneb2L/0LQkqgnWgFEISad7x5BZVBhyDYf/kYpcAp2Xz5v3Zz570TSCF44cU9vP9da4kai7uGRoYWzJgGIs11rx+mr8eOfUytca6UIlcocu/NX2N0+1acXL5tGeIMB4b0RKvuKzaPec3fOiNgjxVIgJt+uo2Gb/wHCTHsD2kB6diS7zywg4mqf0DZxQeCbg28dcyjHqgDqnGVdroXXcm6Zfm27enP850v+a3zmP6izeo+F9UUqgf/vEsF7dpHiZ/94FtsWf8IuUKxjdClhNl6xAsu7uGVVwwSzLbPaLudM4rNKA/9bIrPfWUnliVYtSLHH753HZZlTIuHazydyGjTGBuKk08r8uqrBqnVo7bcEK3jaLtd27nnpv/EzeXmaOgZDgxpp/r4bMjItD9ncnostaxcv22KZ0dmybumUueBvKiJIHYs43y/d+NegNjktXjNkJwp3dB7ZgImauEBZZp32nlP6nfbjj/QKLK5JAIrel168lasxR15LST9cieflYqIopAoDImiEBVFzQWekv32d07LtpmZGOdHX/0CVkfYriEDyLmS69+yChGXiO7Wjum2dhxBdSrghn/ZShAYc+D737WGZStyeF5Wv+xwIzEfak/zi68dZuVwDt9v1xqVUrj5Avd957/Yu30btusSF+M42rd/zMMSsGMyYLbTH3IMkbN86JmJ5nrPByPsDImAbUvWb5syYbBykQdXh+mq5it2TPgc6GUSORQpoy30FOw5QvRAhVU7iZhzrOhzm/d2pIRfZySHiqKm4M8Xy5R7e6kM9FLu7aVQ6cF2XbRWbXbtzv5vto1S5IpFHrztZrY/td6Uae/QPmr1iGteNsj5F1TwalEz52NfpiuZk3zuP3fx7JY6kdK85dphXvjCPrzq/I73DIuDpF+kFPi+YtnKPG+8erkpgS/T41Zjuy5ju7bz0A+/0/SFpM2XGQ4MbX4Ppdk65rW9T8eS+dt+etcMriPnzM4P+ERxeeA9kw1WDhQWbY7S6ffQWrNlrEGkDjzfI4EQMFS2U98Pbaab7nCtNT15i3LOYrpxZAo5pslPK4UG4+DWisnR3ex+9ilGt2+mVp2mUO6h0j/EmjPPZfnaUwlDn6DRQFrtCw6lIS2L2swM937rq9i20T6SPYQw64b399m85dphCJJcjRahpu8z8T3lihYPPzTNN78/ipSC888qcf1bVxLWVXPhp+T8GQ4fkgmA9iJedcUAN35vD+NTYfua9FpjOy733/JNLnv9dc1w3gwHj7QZseopth+j+SF23QsXZZ0MExqomKj6rBwoLIqW22rI1rbtEz7V1LrZB3rPSmtcS1DMtapMHo6OqhQspuoRcHiFYJo8VBRhuzksx2bDT+/k3pu/xjOP3M/s5AS+VycKQyzLxnZdipVeLrriaq582zsZPvk06jPTCCnnzH60UuQrPTxwyzfZsuFR8sUySTin1iYxrTob8Yarl7F6baFNe5jPhGVJQdCI+My/7yAINHlX8JtvX42ds4z2cpB9m+HAkO5r39cMDue46iUD/NvXd9FTsVGqtY+bz7PjqQ1suPdOLnnFtc3xkuHgkYxv2xKMTgdUchb9pfY11Zc65GJFuAiMaajuR4tzZ8l5Y9OQEIKpWsie6QPL9+iE1lDOW03fx2LeZ/p+yjkL6zC/X2lnv4oiCuUKM+N7+eKff4h//IN38cAt36Q6OY60rFjzGIzNVzkatSo/+trn+fhvXccDt9xEodKD1mrOWBBSEPo+99789diJ1E5aQagZGnB5w9XLUtrH/PeqFNhFi+/ctpfHn6iilObVVw1x4UU9GXkcBSTjRwgg1Fz9skH6e03Wf8eOKKV44JabiMKgLXhkzj+lUFHU9q/bfhnS7xJsm/Dm1Mta6u0kF+tGNcaemnMWR2rODdlVbB33m1rNgd5vWptxOiT7Ygmr9D25tmhbpnWxkT6viiIKlR7W/+RH3PA7b+Mn3/pPbDdHodKDZcWmOq1ARwitQGuktCj19lObmeRzH/4Ad339ixRKPW0lK7RWuPkimx9/mGcevo9csdQWxmmKJUa84qUDrFpbwPPmr7Sb3LPrCMZHPP7jphE0sHpFjl9580p0vPxshiOPxJnueYqT1xV5yQt6qTfaTYlaKXKFAk899FN2PvMkTj4/x2+YjB07lyNfKlPs6aFY6SFfKmPZTts+yXWXuoA83EjLOD/URsbFWGiQy9GEXcrbVOvhITu+tdbYlqS/bBYUOhTzVdJe7SG7frNkyMH5PVr7O4fJxp4mYlPahMMSk9ce/RRSrPTy8A+/x+c//HuEYUCpt78565MSgkDjB4ooAsuCfE4ipSAKQ2w3h7RsvvLXH6Z/xSrOu+xKGtUZY57Qxv9x//dupFGbpdTTh1ItDTOMNH09Dq99+VBL++jyvOmwXVGy+NbXdrFjl4eU8JZrhxkYzuHNhJn2cRQhhEBoDRpe+/Jl3Hb3BJ3lr6RlMzs1waN3fp+1Z5+HX6+3KhFoTaHSg9+oM7L5GSZGdjIzMY6UklJvHytOOYO+5StwXJdGrYYKwzkm0xO139OhvRM1E9o73OMeE1qIvW55mQefGaeQsxacA9INkdIs680x3GfyKg6RjtqYeaxqQnZNTa5DFDCiMz/j8IQkCg5vHkiTPMo9bLzvbr7wp/8fSpuQyyg0EwKlNNNVxfJBh9NOrrBs0GFsIuCxDVXqDUU+L1FRZJYu9TU3feovOeW8i3FyOaLYn7J3+1Z+/uPbcAuFJnk0fR9VxRUv6+fkdcVmuRHdpX9EbPZyXcHeXQ2+e/sYAGeeWuSaqwYJa/PnjGQ4/Gi9axA0Is45s8T5Z5X52WPTFAtWM0ozcaY/+qPvc+V178SyHVQUYVk2lmNz//du5O4bv8TOpzcSeB5hYHIcLNuhUOnh5HMu5OKrXsP5L76Scl8/9WrVaLQxCWX9b0J7d00G9BRsCo5sI5Gl2Db2haf08dCz48DB+hRip2ioOGdND7Ylm4UJDwZzTVeanZN+83xaH5xgbsVYQ6jS2xdP0KdnCpHSc2Zwi3UN40uIyOWLjGx5ln//6IcIAx8nl49faFMN17YEv/KmFbzxmuUsG3SQlgkb3LS1zl//01Z+vrFKqWARRRH5YoltTz7O/bd8kyuvu57qxARupcL6e+9gYmSX8ZGoVma4UkaTuebKQXNfxIsE6rn3CyZsV+QsfnDnCDt2e9i24I1XLydftrOw3SUEUxnA4qWX9nH/I9MIkTJRaY2Ty7Fr09NseuxBzr30crx6jSgM+M+//BN+8u2vIi0Lx80hLYucXWweV5+Z5tG7buWxu25l1RnncPkvvoMXvvrNAISBj5TWCU0iaZkXKs32CY8zlheAdvP7Umsa+ZxT+jl1RZmGH8UCYOEvcfqBKwWHy85ZZn4Qh6aOpu9h56RPI9x/SYz9oZWEaNb+ne96h4L0vfmhbmakLxbSNlEpLQLf58t/8ceM7d5ucjNi8qjVI1YN5/iLPz6d33r3WoaHXKJI43uKINCcenqJj37oNNatydNIVghUCst2eOD736QxO4vlOHj1Og//8HvNaJvWC27s5WefXuT8c8qEnmqSx1ztI440sQW1yYDv/8hoH6esyXP5Zf1Esa39RBUcSwVpvxae4tJLehkaMOVo0pDSwm/UWf+THyGkRFoWX/6LP+aub3yJYqWXXLEUm6ZACE1i0zSBHBXy5Qq7Nz3FFz/63/jch3+fxmwVxzUTnxO9/5Pnt6RgqhYxVg1Scusw2cMPERLgmktWYXeUjtgfWhFA0PAjXvacYQYrOVPy/SDtdmkhIoRgqh6yN7U07WIMMCGg6qlmIbPFQqdDsOpFRGrx7bpJrke+VOLWf/8nNt53N8VKbxyiK6jVFaedXORvPnImFz+3l8ZMiB8kmoP5V58O6R/O8etvXYWKWqTk5HLs3vQUuzc/TbFSZvsT69n8+MO4+ULTeZ70uR8oLjq3glM0a0nsq12UAisv+cmDU2zd0UBKeOXlgxS7RftkOOrwA8WKlXmef1EPdS92psfdpLQpsrjhp3cS+j73f/dG7vvuN+gZHEKpCK2MKTMIFLO1iIan4rVGAK2aRRrLff387Ac38Y9/8G5mpyZwcrmsWCPtUVk7J/14zaGlG5UlldKctrLC2166jijSbet/QPvsPP05IZrZRshVz1nBlReuaM5OD5480iYazY6Jg4+6mg9SCLxQUfNbWsjh6BizBrL5vBjnbrWLWath8+OP8MP/+CyFctn4MaTA9zUDfTYf/uCpLB/O0ZgxCWGdARK2LQhnI15wUS+nrC2Y7GMBUkoatVm2P7mefAnW33sH9ep0K5IrhlJQLFg87zk9EAuHzjDmbvjZI9NMTIX09TpccVk/eEnW84nrQF1KSPpQx27By1/Uj2MLtKLlJtQay3UY37WDn999G3fd+O/YjhubN82B9UbE8iGXy57Xx9mnlygVLWr1qBnZpbUmCiPKfYM8++gDfO7Dv0fg+1iWtWQF5ZFCM8FQCLwwloExlmKb2EKYyroXnzZAIWfzlbu2MD7jkXctZLx8bboWlQCUNlqHY0ne+KI1XHHBcPOEh+bwaalpu6cDZr2DTxjcF7SGsWpAJd/Kvk7+HqwfKP3cs17ETJyFDosfJiyAWz7/99RnpihWzIJOaLPGwwfetYa1pxRpTIfYtkzNaETbOZTSFMsWZ59e5OnNdfI5QEgCz2NiZBdeHTb89C4s2yHJDwGaqw2edWqRc88sEXqqjfi7QQgg0lx6SS8/fWiKN16znBUrcviN5NilZ9s9UZFE7kWe4sJzyqwadtk96uPYrdBFISSRCrntS59hcmQXtttaHKnRULz1dSv41V9aST4niSLN5HTIw4/PcOP3Rnl0Q5Vy0UIIiMKAUu8AG+69k5s//Te8+ff+J41aldgwcsIibcoaqwb0lyx6C/YcObMUIBOThFKas0/q4fdefza/cP5ybEtS9yJmGyF1P6LhR9Ti70przlvby/uuPZMrLhhuc34dzMN1HlvzIkam/WYi3uIJ4OZDMz4bNouYHco1us2Ydk8FJBXxF1P7SNbj2Hjf3fz87tvIlysoZXwY1VrEL75mOZdfPhiHxDKHPNKftQYcyWC/SxjFpUnimadXn2XHU0+x85knTAnvprlSNM0TL35BL265Zb6anzyMBhR4ipde2s8XP3k+b3/TCkJ/4ZpLhiOPKNJUeh0uOq8S5/ekzLTa1Fjb/tQG/EYdIYzPo+Epzj6jyHt+ZTWlollvXUrBskGHV75iGR//0zN599tWEYSqtfphFFKo9HDPt77C9ifX4+aLS0pAHi005YaAHRN+s72WWm6I3RIOht/rrrIAACAASURBVER6ig5vvmwtV1wwzMZtU+wYq9MIIpSGnC0Z6slx9poe1gyVgG5roh+8IE6wbcInUix6Hal2ExlsG/c4c0WhzUF/IDHprcguHUchCUZn0iHHi6t9CCmJAp/b/+NfiYIAJ5cHNJ6vWLM6x6/+0kp0rBHsi8ybzylNJFXzGpg8ALRm430/pj4zTamvvy35K4o0lbLNi57XB0HrWvu67+SafqBwHdn0eyy12VSGVl8qpcESXHx+hZtv29sMkDDdad7zpNuEMBF4nqd4/kW9yIJFYzpoRtaFIUS+Made/2snMTTg8Lef2do0rVqWzez0JA/ddjNrzz4f/wSOxkqQNmXNeord0wGr+ty235YC7PTNJH4NDQxWcrz43OXzHmhmIzQHwWKZf8aqQdP8czgGUbNjpGDGU2wd81g3lJ8jzBIy7Hb5tFDUKfKYqYds61gdcXEc//Fa5OUK63/yI5782T3kSmW0MjNDz9O8+dXL6Rl0m36PfV1ba920FgYdTmwhBVEU8cwj9zeTCdPjw/M0555V5OTVecKg3W81372nTW/NnIKMPJYsEjOW9hQXnlthoM9hJg61FkKDNoMnfkMAY+a2LMFgn9M1CtO2zNIPjamQ1752mA1Pz3LTLXuplC20Vtiuy+M/uYOX/+pvGbOpmltW50RDy5QFe6YDBko2+SWWGyLTN9rURuLPkTKhqInqqlLbIEU4B00eyd9WldZdU8Fht4c3Q0ulYG/VCP108mKL3VvqYmeUVfq+pRDMNCI2jR346oj7Q/O6plO4/3vfIPC8ptbY8BSnrM3zyssHier7L6OeQArAV+zZ65mZYrJdWgSNOjue3tBcB7sz+uq551dwSvs3XyVImwnTJqujPfgz7BthqFg+5HLemSU8PzFjkXKozz0mmSAkaDnmzWxaCCBQvOaqIQp54yPR2iQojm7fwsimZ4xTfgkIx6WApB1CZfLh0tuXApr2i05btBGM7QYpgRE8smO/g+/o9kGyZyY44BUGDxZJB1hSsHsq4MmRBnVfzXmebqQB7S/GyLTPUyN1gujQ81U6kZjIHDfPyNZn2XDfXeSKxTis1pQpec1VQ1QGnHhJ0oU/t1dXPL25jmsb04SKIoqVXqbG9zIzMYa0u0dfPfeC9uirDMcXkj6NFJCTXHR+pU1zTCOeYpliqpGmVo9A0JxIpc9pSARCX3PqyUXWrs4TxGPWLJc8w9OP3Ifj2m2BGycy0g71iVrIdL3db3u026hruEO3GeNizx471TA/VOyZDrCadtXDK5jSjW9bgul6xMbddbaNtypi7uuf0pqJ2ZCNu+psiwugHQ7iSzQ/J+fy6I9+wPTe0WZhuiDQDC9zueolA2hPN2eI+7p+Yse2HMGWHXW27/JwnBaB5IolVBRSr86YsMrUsUGoGF7mcMapRSK/FX2V4fiEFECgOePUYtMp3olYMY4JA0bHAwj3Hc4fKU2+ZHHhOWU8r1VEU0jJMw8/gN8wmemQjS9oz0TfNbW0tJCjGi+XboBdUwF+KmnmSCBtukt8LiPTAet31nl6pM7ItM9kLaTqRdT8iOl6yNhsyNYxjw076zwz2qDmq7hw4uEhDwDLsvBqNX7+49uxbBt0XAm3EXHZ83pZtjKH7y+sbkriu8KVPPjoDDNVk4BoflMUe3pMCXghm1XCEpu452nOPaNEuWIt2HyV4diFEKBCxbqTCvT1OkRxHc3mOBfpiaDJL9o14hHFlQnmni+egMYD68Jzy7iuWcwuSTDcuvFRpvaOmHGeAWhloVvSmMo7M9SPJpEcFQJJC1ohTN7EWHVxM84PBGnhb0mjXUzWI7aO+zwz2uCpkf+fvTOPs+Oq7vz33qp6e29qtdaW1JIsyTaShXebxQsQ7BC2ECAhMAkEJpOQhCGBZBhCliEJCSQhIQkEkgAmA4Rl2AyJQ1gNBi/I+yZ5kWTtvW9vq+3e+eNW1avXm3qVWnb//JHVev1eVb2qe++555zf+Z0aB07VeaKvzqH+Or3jPvWo4NLkgZZGiiO+LtvJcOrwkxx/Yn9Eq1UoZXqQX3NlezIhZxtOlBJ0LeSeh8YNW0xH3P4gYPXGHiojw0noLL4O8zdcuLMEi9DBcgXLG0kYK9S0lCzO68nj+RMMQ2rdig3IsZN16pGs/1RIEvSe4lm7SqxqsxNWnrRtxocGOfzwfWacp1oHPNMR2wghzGY7nETrPTvXddY8kLTVPDHiLWrdxHzQxBaKDIkdF1KmDET69fh6lyoXYLwFhZN1eOzuH1MdG0FaVpLM3rwxx4W7SsmO73T3LgnZ2YLBYZ/HD1bJZCRKY5R9W9sotrVTr5YRltX02TDUlIoWz9pVgpXw1TMGSoHIWZzXUyAINbGa9VSEfUsKRsdDhkZiCu/0xw0CTVdnhvPPixL0AqSQ+F6dgw/cnbxvZYwZpIkINd+E+9O/m5LRcAZwxg3IRO9jqBIwWlv8qu35YLrk+VQ7+7nUiywERrzO5fF770pEDUXEub9kTwuFVnvWelImdwPCkTx+qNoUvgoDn7bONTiZDJWR4aYQgsD0/li9ymHjuizhLJL1Kzj3kYztULNzW4F8TkYK02KKjvcRzdtTnOx1wWpssKY6pooS9HsuLJkEvSCi82Y58uiD1Ctl5IRNzDMdSUJdGMKRO6l74Zm/prMawtLa8JtF6vXlgtlSU5cKSf7Dthkd6Ofoow+Sidx6pSCblVx6UWsyi2cTvtJxttMSPHG4lmgTCSFQQcDqjZsZGxpMtpgJRVuantnnbc1TKFiE0zByVvD0Qvzs8TVbN+eaeoNMhXhj0zfgQeSBTDVGYko4vuaC84oU8laDzms79B05yNhQf8QCXOlamEZCOgpNvrbxOpwNL+SMGpCJeYLhKEEtl4H3sdwghJEucTJZjj32EOXR4SR8ZbyBDBfuLKK8hszEbBDLdR86UmvEqYXpL7K6ewsjfSeRslGs1Ngxanb0FCAb70LPDhISAFP32F5ZbBYPcU1YqDQdbQ6r2p2Gsu4EJBTdQNM/6IOMRQ0mP4847KsCxdbNedrbbMKQBp23Ms6xxx4xdUiLrJp9riPthQxVAup+nCc6O17IWQthLWfvYznAVLhrpCU48uiDeLWqMSCA5yl2bM3T1urMKKU+FaQU1OshTx2rJ/RdrRTZQpF8scTYYF+kvts4rlKQz1n0bM6fsfqPtEFIClmjJl1KaYLQXJfSjT9pavgKFg9KabJZSff6rKk1ojkHkrD1MBuUoREffD0lEwuaE/SlosX2ngK+r0ydmWXh1Wocf/xRszFa8UAmIR7nfqjpGz+7XsgZMyAr3sfcIaWFW6tz/MkDWLZR40SYibdrexGRa3gDs71/JtEZ0D/oJYnOMAgotLSjlKI6NjpFAaEml5Ns3pAzHH8W3+BPNBhgvqeKeqpYliCTk2RLFtlWm1ybTbbVJlu0yBbMn/jzK0ZkcaEUyJxF97pc1J+CuODIvCH+WRs5k8FhH+WrKT2VScfNW+zcWsAP4zxIJNT42CO4tXpSD7KCBpaTF3JGydbpyX1q1GvyPlYMyGRIy6JeKXPiyQPYmUwSvsnlJNt7jDcw2+6PSejHFpzs9RLmCwJUGFBsa0drTa083ty+FrOQd7TZrOpohDAW43lNXOxDpZNFyLYEZAzlJ6iGlCshx0+59PZ7jI0HjJUDajWF6ylqbshzLmvnuZe34/srGkqLiYThI2DD+qypl2Lq5288EMHwqI/v6xnbFCfHDU2hYj4OjQqNncly4sn9VMdGTbO0MFjCb3huYqIXsnlVNvpF/L8zM/7PiAGZuFAMVYKkAG+inMkKGrtwy7YZOnWcSpT/QBstsmLBYvuWfBImmO2CqTUgoX/Qw/OMVwGmSVWpfRXlkSG0Vsk1xMnOINRs3phL+jss9LuloZQxbJYlyOYkWIKgGnCyz+PAkxX2P1HliUNVjhyvM1YO8XxFEOiGoq80U+Xmbw7we2/dwstesga3GiJXxtWiIM5XEGg2rsummFhTvdeEsEbHAoJQY9vT74aTehBfs2NrgWLRolZXWNJsnCqjIwwcP8LW3RcTBCZMs/I8G2jyQsoBa1qclNBis4O4lDhjHkjaiPQ25T7OzBc9lxAn0C3bofepg3i1Gk4uh0ATBpq1qzN0tDlzqgYXQhj9VEvQP+Tj+opCXqK0eSaF1jbGhwZMyCBt8KXADzTr12SRWYlfDpHzCHxONhxmscnkJNgSrxxw732j3HXfGA8fKHP4aJ3xSojrKjKOwLYllgUZR5LLpKInmF1vpRryjW/3c8N1nYmqwAoWERGN23EEvq+nLRSUwjyLWj2ckbUVjy8VatpbbdZ1ZXjiUA07KxBC4taq9B09xHkXXw41nVDYV9BA4oWoZi9E64lZqqXDkhsQM5EbrPEV7+P0SDwQCwaOH8Fza2QLBdAhfmC8gWxW4kWJx9keUwAoGBsPGrm2KJ6dzRfpPXIQIRv6V+kK9DWrM4aaydx2go3nb6BU5G2UJGFd8eiBCnfdO8rtd48aanEtxHEkGUeQy0oKOVPoGLcqiYvY4kuP/xja8dNjLKVrjCbWI52p+qP0OUGjlaat1SabkXie0TRpntnx+yEMYbwcsrrz9BvEUJsE/eaNOR59vEouBwiJ77n0HT2MSCXSV9aKZqS9kMFyQFfJIZ+RZ/RenSEPJNrRAv3jwYr3MQsIKfG9kMETR5DS9IqWmHzEhrVZyErTPGoOGzMTilCMjgVNEu6gsTMZauNjCCmadBFiyZS1XU5TAn1WNScpno5S4DgCp2jhjvvc+t1hvvbNfg48WaFSNUYjm5G0tdqGGRYbDRFpMmkT7gpD8ycItVGLNYcnmxX8/MvXkslbeLXwnB9XyQ5dkYQkNCZkGasALIX+2nRQGnLR8xkZC7BtkRj15us2Ic9KNZzVBlhHBYXd63MmvwYorbEsm4Fjh/Fq3koifQbEzz9Qmv5y2gs5MxuNJTUgE5wPxmqmjewK8+r0kFLi1WsMnToeCShqFGYRXr8221RAOFsIATqEcjVaYKOEm7RsAs8l9D3EBIuktSabkaxZnQU1e7n49AJoDIdkdMDjO//Zzy3fHeCxJ6sIAdmsRWtLw2gkhB5tml15viIINfmspFCw6GizWbM6w9quDKs7MtiWCbHtuaDI5Ze04dfDM0IzXiim8jLMJUfCnBjmmVMQoAArekOgUb7C9zVCNJ7HUhsSrcFxJKvaHA4dqZnXmEzlFUIQJgZETDIwaSSJdKXZtCFLNht5m2gsx6Hv6GE8t5Y0mFrBZKS9kJFKwLpWh4x95ryQJfZAmrcoA+NB459njihwTkIIie/VGOnvTSQdtDbeQFenk9RjzGWgxJO7Vg+JNBRN9W8mi1er4rl1pJxoQMCxBavabIiKvaZk4ExYEMNQJ4ZjuM/jP28e5Ov/1c+R40Y+vlBofCetTDJcKY3nmRa92ayka5XD+ecV2d6TZ+vmPNu3FOhos3EciZ2V4IhoDAnwFF6tsTlZziGP9L2KSQTmvlqYcFGIFAHlsuILnxtheNSjkLfYsDZDz6Y827fkaV1lxoBbVwgafWji4y42lNJksjIqJoyuNzW9RervMNTUaqEpEpjBgqQT9Js35shnpREJlKbN7UjfSSqjI7R3rSNUISsLxvQQQuCGmqFKwLo20/r2THghS2pA0i52LIcu0yNtBdPAJA1r5XEjoCgtNKaQznEEnR0OzFNORClNra5MqIrIgNgOge8TeN6k4yllaMOFgpV0opx0tamBGiojO51tsRgZcLn5SwPc8t0Bjp50yWYErS1Wk6chBbi+wnUVhbzF9p48l+xpZe+FJS7cWaS9zYGsNJTlQEeSF+DVQlSVhicmWfRWwkuBdOgpjAr0sI2X4VfLgEDJVpyuXdxxyxE+9LHbKZXyaAS2bVEqZejqdLjq4hZuvL6T7ecV0Z7CS9Fml+L7xxTw1paZ5UwQZoy5njptfDr2QFSoWduVpVi0GB4NcKQJ4Xr1OoMnjtK5vpvAb3QNXUEz4uctBQxWAta0Ok0biqXEkhmQiRc/MB4QapLk+YoFmR5aGyrj+NAAge8nDBStDQupo91BTyMpMROifHmT+KLWGmlbuNUKYeA3TXoRLQbtrTaOLaekZE40HtmcReCFfO3rvXzh5l4OH6uRz1q0lozhUIqk/qRWDwlD6N6Q5dorO3j+Ve1s35In12J6nihP4fsK5UY1KTK10xXGbZ8qD7BcF5m08dAaskWLxw9U+Mot/Vx0YQs3vvIGVOlSrOIuyK1h78tO8tJ9cPKpI5THx6lVq4wOjzA+Kjl0pMot3xvixdd08EuvWU97Zwa3EmKc1SXKjUhBPmcRaR9OGcMyOQwiAzK7w8babl2rHAaGfBxbJCHc0YFeLMtaWTJmQPycpRDUPMVINWRVsbG0L+WGaskMSHpie4FiuJryPlZGwszQGiklldFhAt/DsmwzMZXpzZDNSGbaBE4L0WB4JU9Aa6S0qFXKhEEwqZGPUprWkj0tpz+tlZXNWzz8yDj/+Klj3P9ImYwjaGux0apB29Xa5GCkgAt3lXjJC1dzzZXttHZlINRoT+FXQ5Mgj+oKYsHH6Vzy5Wow0phYC5UpWHzt6738y7+doq+/ynd+NMq1v/ob5PNOElpcu2E9f/7Pn8St1/Fcl5NHj/LAvp9w5/e/y/133sH4aJkv/nvIT+4f552/tpm9z27FLYdIubgJ9oQCLohqh+JfMGUMy3gg5v3xd5npOrTWZB1JV2eGhw5Uoj2MxHfdKIQbbXTOged81pBiJA2U/SYDspRYkrNM9D4GywFeqJOGUSs4PaSUVMdGCYMA23aImUxtLTb2aXotnA5T5SNVGCbMKa2jlr6Y3WQ+Z2owppKNj3fTmbzFT/aN8Ad/+SS1uqIl8jjC0NQMCAy107IEV1/Wxi+8fC0XX94OUuAO+wz3ukYLSQocW5DPWyZ05Sl8T0XHOTtU1oViovFwspJ/vukon/5KH7YIKeYCfuEtbzLGQwUgZFILhBBkczmyuRwtbW3s3L2bV7/xTdx7x+18+sP/wD0/vo3jJzW/+6dP8K7f3MILruvELceeyOLsPpP5rCGXlVhyutSGRiBQCny/MchOd36lgIygq9MxPUdSGO3vRYVTe5orSCFOpkddC8v1kFLOWvJ7tuQ5kFBpBivBORGfXi7QRBLq9Ro6zlhq0xvdTOCZ2S2zQmrHaIxATH1NPZ+IDZXLSYSMJ/DkQ0kp8Oshn/jcCWp1RaloE0S9CixLUK0ZXaRrr+7gF1+5lgsvKPHkwSqf+tfjHHyqxrFTHmPjYXK8tlaLTeuznNeT54qLW9m+JY+Tl/i1EKV01AXy3KoNSAxtweLj//cY//qlPrK2T2t7O7/93j/j+TfcYN4orMZiGakiN+mEKYW0LC6+6mouvupqPvOPH+FTf/+3qFDz53/3FPms5OqrOyIjsnhx8DhklcvKpAvn5O9IMmb8QCdjbKbnlC5wXb0qk4qIaaRtMzrQi++5K4WEc4DW0F/2KeWa+8ovxVxZcj9npGrEvpZDw6hzAVrHFXPgu3VUSpMKTDdBIUHNR1IkqsSzLJHaQkYJ3SBInaX5enIZi2mlVTEhpmpVMTQSkI0KmSzLVCyXKyEX7ijyq/+tm8sua+Ouu0Z427sPsP/JGpWKb/Iu2getUCpESIkQFg896mDbDp/9apZd23O8+mfW8JzL2wHTc2IpE8aLifj6wlCTLVl8/9ZBPv2lXrK2z+p16/iLf/kkPTt2EIYhUspJO+3470RaJnItwiBAWhav//W3smHzZv7id99BECr+8h+P8KENOTZtzOFFrWUXeo8Suq3W5HNW0oRs6i9s9jt+oGEWaZAknBpq1qzOJD3SQWNZFiP9vfiui7SsM5IUfjpAChiphtR9lZI3OUdyIJPCV5Wg6XfLebIvB6Srv33PNYtqsogYLr4UEM50kGmgMYMrlnGHKIytFL7rTlqs4nNmMtL0d5im8DMINKVWm2uvaueTXzhJqWjh+4o1qzO86Rc28IuvXk9fr8tvv/sA9zxYRqsAFdRoa29l45bz6Nmxk1Wru2jtaKc8Ns5A7ykO7n+UE0eOMDI0xL5ygfseqfD8y1v59V/uZt36HG41TIohl/O4iqm62Yyk/6TLh286jiCkUCrx3g9/lJ4dO/B9H9u2ZwzTpJ8NkKgzB0HA9T/zUirlMh/8/XcxOCz48CeP8hfv3pHQvBcTti2SOh2YXAcSYy6aaUIACjrabDKOSLxMKSMiSeCTdRzjja/gtIgLC4erAeuXmNK76AZkInW34obJ5nW5TvLlhPSE9+q1xkwlCidlDV91unDS6SAlhlEVvyBMziPwvcQ6JEYMszA4jpiWkplQMX3Nr7xuI/m8xUMHymxYm+W1L1vLpt0t3PbNPv7iH55ivKJAVehc3cWLX/UmXvjSl7Ft1/nYjjPpuL7vc3D/o3z75pv59te+wlB/P9+/XfPI41V+/21bePYl7bjjwbI2Io3QE5CRfPEbvfT2+1i4vPl3/pCdu3cT+D6O48zam5po5C3LIgxDXvrzv8ADd93FN7/y/7jzXsGtdwxz7TWroqT6Yn2hqP6j+SVzXamf54IkN6Q07W2GrOH7OgrjStxqBa9aIVcoMb9t0zMLaUrvcCVg7RJTehfVgEy8yJFqSBBipLlXMHsIU1wX+M20Wq0hY4sZw0kzIQ4t5XOyiYmlwhCvXpvSA0E3aLfTHTPOdTmO4E1v2EjoKixbQE7yra+d4v0fPoLWCguXF/3sq3nLO97Jmg0bGsdQChXF94UUSCFxHIddey5i156LeNUvv5FPfPCv+e7Xv8rgkOI97z/Ee96+hauu7Iioq8tTPFEIgdLG+zh1rMZ3bhtBhzUuuvoqXvH6N5jnEfd5YW4brPRGLf7cW97xTn7yw1sZGBjjq//Zz/OvbE+Mx6IYWEFSh3M6gzGXISoE6FDT1uLg2A2tLSEEYRBQGRulfc16gmW4SVhumEjpHa+HtOXNMi+WgP26JJmpeEEZqgRNA3gFs4Mg7hQYTnjk06ugzgYak/BOJLlFYyFSM4QHxGnOGS9OSmm8amjYWlJwx21DfOAjR0CH2Ba8833v591//UHWbNhAGAQN5pcwvH/bcYzuUeqagiBgfXc3v//Bv+F//O/3YNuaaj3gTz/0FI8dKJPNmx7tYgl3WfOFSXoDWckP7xqhf9DDtuFVv/xGwBjuibmOuSD+zlJKwiBgzYYN3PCqV0NYZ/8TNR55rIKTs6LQ42IsHkY2RsUWZKZ3znGcxqHSbFYmBgohUGFAvVpeSaLPAxrjhTT+vfjdHZfsqYzVTRJHLmCCPNNhFvVGwlsD0hLpl+YGDViCYqGRkGzsTNPPp/lZqVBPe8J4B5ze4WYzkoE+l7/7+DHCUCGl5l0f+Cte8prXEoYhSiks204kWtK76KafpcS2bVQYosKQ1/7Km/mdP/1zhPao1kITFhszhWfLMYQFUe2LG3LPQ2UC32Pztu1cec215nfW4tEs42O88OUvp9RaYmzc4677xoyCsl6kDZww+a4ZDxXlR2xbJH3RZwMNWBaUUooHhnwQUK9EBmSZbRCWO6SA0VqIF6gFbVRmPMdiHWjiAE1bvhXMA/GaPSHgvJDnrzVGjqJkE05iyOhJP8fnD2M99RnQ5AFYcNMXTnGiL0QHNd7yjt/j+p95KUEQIKVEpuipM9E7zTVr0wteSsIw4MZX/Ry/+Gu/QeiVeeJwnc9/rRcZFVamKa/LBZYlGBsPeeqYiwpddl96GblCIWHXLRRpyi9Az46dbOzpQYU+B5+qo+sLz4Ek91SAHyimc0BispYQ4KSYfrNZtLQ2oe5iIdWwSghUGOJWK8tyc7DcIYTACzUj1UZ0IaaTLxYWzYCkLZzrK8bq4XxD9SsAEKaYME1vEYJEh2iut9bw7c0HW0pWIqZ4unkpiAq9ZjnopBS4dc2jj40yeOool19zHa9981ui/IvFVKEmpRRhFK4Kw3CS8mojMWiG65t/5x3sveIqhK7zje8McvSpWkQfXl6ertbGY+wbMG14pdBs3bkLoEHPjsN1ShEGQeMepGs/TjPjG2oACsdx2LbzfLTyOdHrUouovAtBMnaAwDeabFMPwKhfjDRswRizNepSioQGHvvEOlT4bn1BG6dnIpK8GjBcnbiZXzwLsiQeyHA1wA+XZ0jhXIDGMKyklKbIKoW5LOZNx0zx7Ve1OzhOzLcXDSMVZ0ZTj02ISNfo9Np45vo0ZJ2At/7a8/jV33s3/+v9H2icP2U8TKiKpP7Bsixs2zZGRkrCKD/QlNiPYv0Ab3r775DJWAwO+Xz3R0OQWcRQzSJBAyQ9whWZTIbWdlPLImg2HlJKLNtu3INo9z2Rvjvj+aL3dK5ZixSacsW0/13oNEx7INW6Mj1Kpn2zqYbOZc24ir/nbM4hBclGIPogSoURxZxJc2EF0yO+55YUVFzDhl2KMNaisLDSi4LWmpFqkFim5RqbXtaIwgDSticYC2GkHua5SMZ8+1XtDplULcgkTaMmr0dQq4fMRnzLyJ8YDaNLX/xzXPqaFwIkC+TEcRJ7JQO9vTy47yf0Hj/Ouu5NXHL11bR2dKDCMMmTQCOcpbXm4quv5oJnX8K+H9/JD+8c5edfsQ7HFjMrxZ4lGJaqws5kaO/sNC+m8kaWZXHs0CEe2LePyvgY3Vu3culznksmm03uwWy8EG3aPVJqbWF0tEznmtWL9h3izcfYeDBjDi4mauRy1qzGTPpzQookiZ68rhW+586fJ7wCQmU6wRaz6cp0sShe3aIYkPTOsuopqt5K8ny+SG6XACeTIz1rhADPNd7AXGNYyTMKNas7J/e2njg/TUW8GWSuq9CnaSbV8DACtNVCaO+MKpHDpKNi+jpi9tD3/v3f+fCf/SkDvSfxPR8n47CuexO/9Qd/xHNf9KJJxgcMucCyba5+wQu5+0c/4mSfz1NHa+zaUSR0bdtY5gAAIABJREFUTb5mOY07IUAKievVGBkcBJo3Vp/+yIf53D99jLHRUVQYYjsOz7rkEt75Z3/O1p07m4zIdN8rNq4A1//Myzlw3x1csP4IpaJNGC483yIEEBgDYs0QEtPaJMMLuTlk0CHZtKSPLRBopQnDYK7DfQUpSGmS6RtTMkCTSz/neewFH4Fm93qsFhIuzrU9MxH39dbgZLMIGUlZ60Y4SSs9r9sb51BWtTuG7ZJaVybO9TjuLaVgvBJGoZDpFy/zfgHaJ9PWg1XchBR6Ss8jNgp3//hH/J+3/QbDA/0UiiXaVq2iUCzRe/w4f/Sbb+WR++5FSplQjOOQVnwdFz77YlraWhgvexx4sgqZqSXnzxYEgNJ0tJmQoed5jI2MACTf6Yuf+Dh//yfvxfc8Si0ttHZ0UCgWue/OO/mDt/4aYyPDSMtCKXVaoyilRKuADZs38d4P/Aqve2UHSpvY5EINqhCgIgMyEyEqrjUqFqy5kz4EzQn/KGxl9OAWcvXPbEghcANF2Z29wOWsj73QA0xcHEZrzeGrFcwNsWy21pDJ5ZGWyYNoDC2v5irDoJrnAIh7nK9ZnTE9qCceJjL+8bOTEsbGgxlj6Q0ar0mjHjmh8bwwer2ZmguY/g7ATR/6EAC5fB6llKHrKkWhVMJzXT75t39rji+bE7LxsTZv20ahWKReDxgZC1JyK8tjtRHCUKDXrcnQ1mqjtODQYwcAyGSzDPUP8NmPfpTW9nakbTfdg47OTp589FG+dNNNwMzJ9GZKtkQBwfBjeG7EmBILn4tCmPDpWDk8bVLejg3IXMOJQkwqCtV6xQNZDCgFo7VmWanFWJ0XbEDSkzUOX8UvLZeJfC7B5BEEWkGuWMKy7GS7J6SgUg3Nwj/P48etSdevzRJM0RZXR5LcELGIpGCsHCbvnQpaa5QGOyv42rfKvO4Nn+Ef3/fH8S8nLQgAA729PPXE42SyOcIJRYxhEJDN5Tj02AHGRkaaxpEQItnW5gp5bNsxRq4cQqAXZbFcTIShpqVks31LDmFleejufYyPjgKw/8H7GRsZxrbtScyzMAzJ5nI8fM89QMPoTkRTFbvWhEqgAxc9/hDCyiJQi5KHlAJ8XzM04hs16Gk9EENdbinZcw+1am36wKSfNyLqyLmC+SC+b1Ka6JBSaZWJhd/VRfNAIApfLWB3vIIGlNbkii1Iy27yBsqVwAjVzfMWKw1kJRvWZhNDNNOhpIC6qxgZDaaV8SY+htY8fqiK62ke3PcTfN+btoI48P0ZF3ohBL7n4fueeWHK95oEtEBTr4dJz/blBNPrQnLJnhKZbJYjTz7Jvtt+CIDnutN/MFr0fd8/bc2IUQFQCCmxLIGljyC8PhBO8vuFIKYjD4/6jFdmritR2ni4paIpCJz1maMknB+oVLMzs2myHOcZm0CfaY7MZqMU30spBPUlCGMtyIBMDl81aj+W0y7wnEPEqCmUWhKtJBPCElSrCs9X81oojXcDKNi4Lksm7mwY5TumOqQQpjnQwJBnesgy07MVZDMC27GoVSqEQXMBU3wNAF3r1tG5Zo2RJU/lSeJksO+6bNzSQ2fXmsaFTDh34Pt4novSglLRgmm6Jp4tCCGiSnTFcy5rZ3WHQ6jgK//6KQDOu+BCMrlc03eP/7YsC8912bZrl5EqmUJqpiEhY3JKo0OD/PUf/Slf/vCfI4ROmF4LQaM4VHCq38Pzpq8riXNsbS3WtB0sp0M8LsNUqNTQ2SW2k3lG2o+myIA2EjIq5dHPVb5HKRirL24Ya0EGJG3Bar6i6oXJw1/xQuYPASgVUmhtx7Kd1IABL9CMjYczegPTIc5T4Cu2dOciTaxGwdFUh5NS4HmagWF/xhyD1oAtWL3KQQiL8bExBnt7k/OmEUYsqp/9b79EpVxGax3pYBk9LBWGeJ7Ha9/85uT9E78HwMmjR3HrdWzb5HRi6YzlMvbi6/R8xZr1WX7q2g6QeR7Ydydf/tdP0d3Tw3U//RKGBvqxoxoQKSVOJkO1UqGlrZ2X/cLrgMl5pKa6mug8H37fn/Hpv/8rPvLRH9I7oHAmssDngTiPhS3o7XOpuzNvXlSoaW8zoohzHZ9Kazw/GqNx6YmUpiPnM4yYk36+oTLtfPOOJGdLpDBagxNrq06HmI2l9OKFsRZE401f+GjUx3pFeXcREHkgxdZ2bMfBd2uARAgIAsXImM9mmQfmVmcjopmpAs2GdVnaW236B33sGUaBFOD6it4+44HoKc4ZHxel6enOUSw6jI2M8sj997Gxp8eo7KZoqDKKgbzi9W+g/+RJPvPRfzSGwHHwPY9Cqciv/+93c91Lfiah+6Ynio4arB946EFGh0cplVrpXp8F3xSjLZfao6RwC9Pr/edftpYf3DHCyVOKf/mrD3D+nov4X+//AEP9/dz27W9B5Hn4vs/qdWv57f/zJ2w7//wpa0Hiuo9QKWzb5ks33cS3v/plOrq6ufziEmu7Mvj+/HNlMdLn7B3wCAJzjwMNYsLiI4SpOVjVbmM7Au80xmbC3UJrM75TJzd1Mo5jNi4L/C7nCiYahvXtDl0lx6ytGgKl6Rv36R3zk3s0mzEvhVEJqbiKllxDh24hmLcBmRiSGKs3vI/lMoHPZWilcLI5Cq1tVMZGsBAQhZOaGUdzP3aoNIW8Rc+mHCd6XTKOlajzxpThmImlMb0FTva5yQI96Vq16VeuPcX5O4p0tGc4fnSMn/zwB/zUK1457c5ZCMFb3vm7XP78a/jRt7/F0OAAq9es5eoXvIC9V1w5rase51XuuvX7ZsHqcNi1vYiKQnvLaeyZCmvjxXWszvCbb+zmD/7yEK7r8ke/+eu8/xM38f5PfJL/+upXePiee6iWx9m4pYcXveIVdPdsnVQDEt+HMAhM5bqUfOtrX+WfPvA+hJWjvU3wW2/qxrIF4SLImEA0xjzFyT4v6b+S/C56g1E6MJ0XV7VnwJHo+uwNSMQBwA8aFHLzusSy434xy+e5LhUmPudtXTnaC6llWkBGCro7shQzFocG6pPm1EwIFYzVgsSALHStXpRCQjdQ1Dy10jhqEaG1xnIcWlZ10fvUQcC4oJ6vGRz2k3zEfO61UuCULHZsLfCDO0bMvBRmAdDRfyKyJlqDbUtO9roEbhw6m7jzNK/5vqarK8OeXXlOnspz163f48SRI2zYvLmpIDA94LVS7L3ySvZeeWXzNYZxe9tm+ZN4QT14YD/3/Pg2hJ3n0j0l2lY5eNVwXgZ1KRFfv2UJ3ErIc57TwZtfV+MfP3WC4YFB3v66n+cdf/YXvPiVP8uLX/mzTZ8NggAr5XnE8vfSspLc2E1/9yE++5F/QGFhWZJ3v20LG7pzTX1SFjofLUvg1hWHjtYi5ePmkKegsegLCatXOXMOJwogCDW1CRtRaUky2ZyRxX8GINE107C+LUN7wZ4032J0FG3KrkPvmI91mo1THAGUAsZd1TwHFzBG5p0DSZ+wXA8J1IrXsZjQSuFksrStXmuKzqIEeBhqBof8xvvmGMNMnpHS7NhWIJeN1U+nyGtAZEAER0+6VKoNBs6URgRACm68vpNczmF4YJDP/fPHku8zkc4rhEh0r8IgMCGZSExwovGIPxP/67Mf/SjjY2Vaig43Xt/ZiJkv4zEoJfjVkF/8ufW8+XXrCbVNuVzjvf/zN3jfO36bQ4891vT+uM1t/Cc2HEII7r39dv7n617LTX/71wTaIZezec/be7j00vZFa7IV32/LEgwOe/T2eTMmx9M1RgQmfDbraxDGgIyXm3sIWbZDobUNpZ45xYRaaywpWN3S2N9PDhkbdJacKTd1ExF/Qgioewo3WBxawoJDWADlqGApfn05T+JzBUornKxD2+o1ppAqSmZKSxgPZJ5MLK01QgKeZvuWPIW8hTsDswbAkjBeDjl+yuXC850piwpNqAb8WsgVz27lkt0l7rxXccsXv8BzX/Rirrz2WgLfT9rXpsdPepctp1DsjcdT/PnvfP1mvveNr4HM89zLW9n9rBa8WjippmW5IB1e0Frju4o3vn4ja1Zn+NinjzM8IvnPL32J27/3HS65+nlcdf317Ny9h65165Iiy3qtxvHDh3lw30+4/bvf5pH77sXzQrRVYkt3lt/99S08a3crbtm0+U2feyHXrUINtuDgUzWq9RDHNqt7Oqcd/6y1bhiQ00jfTIQURmWhUm3IIKE1lpOh0NoW1cksr+e6VNBAxhI4qTk5kZEVI2sLbAnBLLv9CmH6pZfdkFykmHzGQ1hNIQWtKad6Diy3yXsuwhSFAQLautY2aK4abCnoG3QJokV/Xh6I1gShpqszw5ZNOR7eXyGXM7pDcTA6PWBlVMB46EiNC/e0omtTGxxDKdUgBf/99Rt4cH8Fz1N84F2/y1//62fo2bGDwPeTXXT6HFMzu1JMlCDAdhwevf9+PvRHf4gWDqs6HN742g0Y7Zy50xrPJCYaEa8a8pIbu7hwZ5GPf/YEd9xjMTxc51s338z3/uMbtLW342QytHW047ketWqFerXG6MgoCBtp51nVkefF13Twy69dT6nVaTIei2FIE4/PFhw+VqdeV2RbDXNPIJpCWGA8kEJesrYrg442GbO5Bh3Ve4ynFA9isoZlWxRa2yMplwV9nacldJyznNV7Y8khEzVaXXKaXp8P5mVA4oQXQM1TuOninxUsGAm/P9CsWreBTC5vmEwCLFtwstfDdTX5nIjXzjkjDDXZFos955e498Fx8nnZ2ElOuBYpTXL08YM1s1hPsdOPF0bTD0SxY2eJ//GGDfzNPx9jZHCQ//3ff4U/+cjHOO/CC6eNu061+CfJYsfh/jvv5E/e/jaqlTIah99440Y29eRxy4sX719KNBtNjVsO6dmc50/etZ19943xnR8Occ9DFYZHfQaHaihV4fjxwehzgkzGpq1jFevWOFx1SSsveWEn3ZvyKE/hVpvDVot1H6QAXMWhI7Vk0xCTN5ryIFEIam1XhmLBYs76jVIwXg4IwogIgUBrRSZXIJsvoPUzxwMRgB9q3FCTl40N1lSkEjdQUfpgFseNN4TCRI1URPBYSB5knh5I4+fx+gp9d0kgBGEQ0Lm+GyeTIwx8kBIrku0YGfMpFnNG3n2O6rMN2i3sOb/UyINMcwytTb/q/U9W8KphJGUxnRGIkv3VkFe+dC2n+l0+85V++k+d4u2/+PP85nv+kBtf/Zqm6up4QiiljPcDSefCOFn8xU98gk/+7V/hewFe6PA/Xr+en3rhatxyGIn7LU/PYyLSk9+yDNUV4LJL27jskjaGBz0eP1Tl0JE6fQMefpRLaGmxWbPaYdvmPNt78uRbbPA1btUU7y628YiPZVvGM9j/RIVMRpheIOnFjMbGIQg0G9dlyWakKXad5XlM7syM6yCI2uFi2gAUW9uTMfBMgRCmzqNvzGdLZxZoHt/pn/vGfZQGaw7PXAqBFyiqrqK0QDrvgllY5RTXe7nvAM8VxPcwDAPau9aRK5YYHx7EsiyzOHuKE70eG7vz8zp+TLtVEe12VbvDyFgj/DGBzYvW4NiCYydcTvW7bN6Yx/Mm73om7rD9esivvXETji359Jf7GB+v8b7fexdbdu3lgj07UWEkj6JASGuS3lNlfJy7f3QbX/jEx3lo311IOw/C5rfetJHXvHIdXiSrMVsK43JBmv0ShyHdaogA2lttrrisnSuuFsbbi+1stKjiK0LfeC9CmN3kUnz/OBwpM5LHHxmnb8BP8h/NbySRwwlDTff6HGQl2lWIWVB0hBBEKyBDIz6er8g4FppIjaHNFNNOm7l/miF+jpYUDJR9ihnJ6hZnyveeGvUYKjfk9ecyBkIF426YGJAz5oGkdzl+qKm64Qp9d4mglSKTL9C5oZuR/l5sx0FIqFQVx07UufzK9mjHP7/7HkbdCc8/r8Ctd4xQKlqRQm8Uo4xjFZid6Fg54IFHy2zeWkS7akpNpKkSxm/+pW629+T56P89QT6rWSXuBnYiLDtpq1uv1Xhw308YHhhgbHSEQwcO8MBdd3LsqcMEAQiryIa1po7iOVd3JEnzc814xJiYEI29uiDUqCBEV2N6LNH74s+ZP2nDmT7eYsEYOMAWPLy/TLUWGkXhKUNTwmhgZSWbN+YSFd7Z5T900mo58biiHEgYBLR3rcXJZnFrtXPuGc8H6fEsgKcGXcbdkDUtDpnIgNd9Rf+4z3AlSIUVZzcHkmMLKNcVtC3seudsQNKDtuqF+FGZ/QoWH0opsoUiq7t7eOzu2xGiCBhFzWMn68lEnesC2ggZAVnJJRe18v3bRxq/o5Ek1ZjkPdLsSB/eX+GlN8SqtzBV+GyqhPF113Ry6UWtALQEX8F/6A5ky24oXYDV1sPfv/dDfPUz/0Y2m6Feq6O0QFpZnEyejRsyvOB57bzmpWvpWJ1JwjbnqvFIY8o8kjBJ5WYm2tTfdym+e8KIk6BqIQ88Wk7ou2ZPMZmDpZQmFxuQlCrybK5PCsBT9A342FGhYlzz0961DidjU68YNYNnApqMiIDBcsBwpeFphMqoX8/H80jnQaqeUdm2rfnnQebtgYCRb1cK5DPjuZ5xaKVwHElX9xbDkKJRl3HspEtYn9oLmC1ElCC9ZE8LbS02nm/YVXFBIZD8rBTkspIHHi0zNuJTKliEM/R7mJQwroYU82ag+CGI4ASqegjd959YuTx68AmEVSCbcyiWirS2GAn0S3aXeO4V7XSty6JdldQ4mHtxbhuPqXA643Amv69tS/r6XR47VCOTNOuaSLWIw63Gm12/NoOaAwMLjAftu4reATfK5ZjzWI5D+5r15t9zON7TAen5YwyFTuabECKuI573HIjzLFVf0WrFeZC5X+eCciDp3h9Px8l8NhEXj4UhdHVvIRPVAwgBji156lidai00jJdwfrsHIYw0yqYNOXadV2Df/WMU8hM6FaaO60QV6Q/tL/OcqzvwU4v5dN8h/rycMAEQGYTMgtaoUPHWN/Zw7XNXg4auToc1qzOUCnYUTw9NzF8ufrJ4vkh7WBOxlKGlMwWlQGQl9zw0zsioTyFvGe+DZtOhMaIIfqDZutnUFYVzpAYKAa6n6e33iJ0MrRSZbI6ONesJA5Uk68/V+zkfNG/CJvcwX+j9CBXU3JDWJA8ydyMyp/1relKESlN1V5pHLRXiwRH4Huu37iCbL6BUiNam5/ToWMCJUy4y2bHNDfHzUgpk3uLyva1Jh8Kpd4+mANH1FXfdO5pkTmPX93TniQ1i47garUOE0IQh5POSKy9v58rL29nWU6CQs/ADhRvVBkhJQjlsPs6ZRdrV19qEEoQwnfRiheR0055zjT3UmOOAr7jjntGIeRX9rhG7BCEQWkcbEWNA5IQNyOnOFVe6j4z5jJfDpFmVVgo7k6Vj3QYja3MWn/nZxEzfeb73I/2Mq/7CNGLmHQBJ+McLOv0KpoOIVnIVBLSvWUf72g2owFSkSymo1kMOHqkZ0To9v8EUx1jxFJfubaWlZE+/e9TG2OSzFnfdN8booIdjL/zp62gBUsqEudxaiOeqxFuJk8Xpaz7bSO8MswWze6tUQ1xPkclJMlmZGJHpvJTlDseR9PZ5PPBIOUXznuKNQiRNpLZtzifGZbbPKU7UHz1upOKljMa9UuSLJdo6jRLDvOIrK5gSjU1dlIbQ6Q3P3I41pxBWUwI9muTWPBlAK5gdVBiSLZTYsG0Xxx57GCeXQ6LxA82hI/XkffNxZ+PF2/cU27fkuWBHgbsfGI/CWFOLrTmO4Pgpl30PjPPC6zvxU3UYcw+hNRuGmUqJloPhSAo8lU6Svf/vq6f44Z0jnOxzKeSNQOVrX7aWHTuKeLVwziyZ5YA4fLXv/jEGh02+S6V53RMQhppi0WLX9gJ4xlOdPSsIsAVPHa8lTC+tIQx8Vm/qIZPPT2r3u4LFgcD0F/ICTc5pJkXMFvMKYYFxfeJ/nos7rHMFSimcrM2GbTtNNXpEmcw4kscPVQiqM+chZkJTGCtncc2VHUm4YqrJ3/AW4Nbbh2GGPulPR8QG1bJMIdYf//WT/M0/HeH+R8oMjQQcPV7nlu8N8rb3HODb3xsgk6ZFnwOYKnwVJ7B1/MME3QwhSAoIu1ZnjBQJc0igC8DXHDlWT4XDJYHvsXbLNrKFohETXcGiI05FVNyw6bW5YM4hrHgSVd0V+falRpJI9xUbd15ArlBK8iCOLTh8tM7wiN9IPM7TkEsJuIqrLm1j9SoH35/+OLHe0b77x3jyYJVMdu6d584G4nsTe1Tp3M3p8jjpY2gNVkZy0+dP8q0fDNHeZlPISWxLkHEkbS02rq/40MePcvhglWwqnLXcYeY2ZLKSQ0/VuPfBMfJ5mVSfN6c/RLKhcD3F+ecVyJ6GmTcVpBR49ZCnjtVxonBs3Mp27eZtSOvc8t7OFTTGvukmO/H12WLWBiR9YD/U1OepBruC2UNrjZBmN7Zh2y4KrW0mHoxJPI6XA548XEU4Mknmzheer1i3PsulF7VEsWhSfUGaf7Yswdh4wLd+MJTkYOLrXW5Ih+CU0qnF0CyW4QQhxtN9h4wj6T1V5xvfGqC1xSYMSRZNjemoV8hZDA75fONb/ZCJ6yeW371Jo2FUAUfwnduGGBkLEomi6dlmhlZ+wY6icSdmOQ4bBZRQjpSe7YgQosKQXKHI2i3bCX01qSZmBQvHxDxI+rW5YF5J9JqvCFd2BUuOJMQUhhTbO1jXcx6h7yOEREqo1RX7n6waHuU8F6mm5JkUXHtVB5YVUfpinQpIQqNxAWIuZ/Gd24boO1E3NQKL85UXFRNZgxlHkm2xEcIYjkxOki1ZyQJ/uvGsonj94aN1XL8hOz4xlxMqI2v+xOHaaanOyw2OLRgf8vn+7cNko9qPieMqHcgKQ01LyeJZO0sQ0fpnMw5j4yMcyeGjNSqpUGyswLBm81YC319ZZ5YQUhhB3GCeqqyzTqKnE+h1X68UEJ5BqDCk0NrKpvN388jtt5IrlJKd3yOPVVC15kZP89pJSAjrho21bXOew0frZLOyKfadLgzMOIKTvS63fHeAX35DN9pTEaV4eW0sYs8jm7foO+Xy1W/28djBKtVayPo1WZ5/ZTvXPW8Voa+biANTfofoVtRdZQo7ZyKQiElqMMsW8bxWCpyixY9/MMSRY/UkeW6MQsNsxCMipu/u3FZg3ZrMlH1iZjpnnEDf/2SVSjVOoAvCwGfNph5K7R2oMMDUQCzzm3gOQ2nDqrWtuetizSuEVV9AzGwFc0PaQ9hywV6cnCko1FEi/YlDVYaG/UTBdCEIAk2hzeGnruk0aqoCpi5gIvFCvvHtAfpP1MlmZNMCfLYRT4JQaTJ5i/sfGOM3f38/n/z8SfbdP87+x6t889Yh/vAvD/L+vztkhAPlzAZQCCDQnNeTJ+PIKb+nEUg0gpfd67Nk5pEXOFuQElQ95D+/P2CMBo2FfmL+w0jYmNDn3me14BTtpnDgbCCi3NvjBysJW00Iie+6bL7gInLFlpUE+hIjniPpNX0uxnpOIaxEliJYKSA8U4jzIL5bZ/MFeyi1xbsyk4sYHQ848GQVkWkkO+eKRpEf4CmuvbqDrs4MftBIMieGjIaRiL2QL36j11SML6N1MvY8Mo6kr9flz//+ML0DHp0dDvmcJJuVtJYsSkWLL/9HP5/98imsrMklTWcAzY5bsXF9jisubqVcCSe1MbAsox6Qz0le8NxVJidwJr7wPNHID4GTt7jznlHuf7jcqDyfdjyZz+Rzkot3t5ht7DTsvanOCaY5WrkS8NjBGpkkV6SwMxm6dz4rMmLLy6N9eqGRSE+3uJ3LBnDOOZBQEcV/5/rJFSwEYRDQ2tnFhvN24Xtukgepu4qH9peT9813959m1GzozvGcy9qopUJjjTeavwTGCykWjBfy+P4y2fzy8UKSnXNOcvN/9fPUsTotJRs/op/HCXStoa3V5su39NF3yiXjTD8l4rg9Gn7rVzaxY1uBoVGfUDVYXfW6YrwS8EuvWc+ll7UlqsHL2YzEXhO+5su39BvqcfR6w/2Y/Dk/UKxbk+X88wqEEStzts9da5COKSAcHPaSBHoYhuRLLWw+fze+5yHF1J7eChYOE5Y0j7c+z4r0WRmQ9AP0Qk2wUtdzxtCo1QjJFvJs3XMJYdDokZ7JSO5/tIxfbRYZnM95hIh4VhpuvL6TfM5q9mpSa2Ac3rAsQbkS8k+fOQ5pyYt5XsdiIlaTfeJwlUxGTKrJiO+hlIJqTXHwSA3hCKaLOJmFVuD5is5VGf7mj3fy8hevpqVo+rg7tmTr5jzvfec2fum1G/CrDcn5KVfgs4zE+9DG+9j3wBj3PBDpoUXeRyoDBjRyoVKC6yqevbuFUpuTUjCYnaE0bC/JI49XqFQVlhVR1gOfzg2bWbVuI2GwUoG+lGhEkYwBWTI13nT8s+41WiGu4MwgXtxVoDjv2VeQKxRRysSGHVvw1LEax0+69GyeutHTbJFUptdD9lzQwsW7W7jjntFEedcsKCktqEilt1Q0oY+bv9nHy1+2jvp4sCCJ6MWCEKZLXm2aHu6N9xkqbrkSTFUr14TEiHiKjjaHd/3WVkZHfY4er9PaYrN+bRYnb+HXwuQalhuxIA2tIzmiUPOVW/rwA002CyoxthOvWyeG17Ell+9tTQgDs+lLE28q4tqj+x4eT34X5z96LtxLvtRCrTyOkHLZ3runCwSxY6Bxkk3o7Gz3LD2QxpHqgZq1WNoKFgdmATJ5kO4dF7BqQzeB74EQ2LZgbNz0bFiMPEQcD8cWvOKGLiNux+TYtgZElC/QymghfeJzJzl2uEouJxODczahlCZTsFi/NovvTx9iMYuhYMvGfNLLYiqkBRJjT8RzFS0lm93PamFzdx4hBG51+RuPtPeRKVjsu2+MO+4eTdSYkzCkiWUhhGyE8DDqu2tWO1x0YQkV1w3NYfDZlmBwyOeRxxpaW1orbMdh20WXIlK9wFew9FBKU28qIJ5oAO+uAAAgAElEQVTds5xlDqRxMNdvZAXPdojimYJ4EoVhQLGtg+0XXYZfryNTPUPvfnDM8PBTdN75nCcOT/i1kKsubWPPBSVqURy/aTGMtigCUFrjOKYl6Yc+fhStGh32zlY+JA6/ADz/inZs2yyWE3fJtiWo1kJ27yqxbUse35u5QHaiEYlrSry6wvNMGOBcaXYVX6v2FP/2lZMEoW4ysiIOPUgLaTtorRBCJLm3Z+9uoWOVoe/OFvEGRWQljzxWZnikUawYBgGl9lVs3XMJXt1dKSA8Q4g3EvNhYs06iR5PiLofJh9azpPj6QYRJTKlJdlxyVVYjpnQcaOnBx8t0z+wOAq5QpgEuZWTvPLGrkb/h0l8XtDRYhmGmlLR4o67R/nITUexc40itLNhROLF0auGPPfqDl7102sYHvWTxS5OEo+VA1a12/zGm7qxHHEa5pFBHFJM/yyliAxK8++WI5qYVwWL798xzL5YRFPT/Ly0IpMrNWLYNDy2a67sAElyz073fSdW+t//SBk32vTETMNN5++mfc06wsBnORMPni5IS5p482BizYmFpbSJlS3TefG0Rkzn9ep1tl10Ka2dXSbJiCkoHBrxuf+RMiJrzZvOC+nEMgQ1xfOubOeCHUXq9UaVcROlNw6AQ5IP+fzNvXzl671kSs09tM+0EYmpyYGreOsbu3n7WzbTWjJKw15USX753lY++Mc72dZTwKurpBbk6Y6YAOFWQj731VNNqsEQjwOFtLNk8q2Efj0KY5kal80bcjx7d0vSFXNO4StbUBsPuOfBsahY1ageKBVy3t7LyeZzSY5vuRrhpwvSkiZeOPfcxJzk3INIS2gFZw+h79OxZj09uy/mvu/eQr6lFYEiCDR33z/Gi67rbGJCzXsCRqGZbMnilTd28b6/O5xiFDXvDTUidU5BPif58CePsm5Nhquv7qA+Hp7xpHo6hBT//QuvXs+N13dy8Kkao+MBWzfn2bwhh3TEM8Z4JAWWoSbbYvHvN/fyyGNVWkoN5eA456YCn/a15+G7VZQKsaSVUL2vvqyNYpuNW56C6j0N4tocKyPZ/0iZIyfcqCATlArIFVvY/uwrCPwwybms4MxAYDyQuUYMTvvo08fxAo3SMbFvBWcSCZ1XK5ysw85Lrk5+Z8JYFvc+NMbIkLfgqvQkXBZ5IS983ir2nF+iWktpHUU5hpi5lR54cVOgv/j7wzzxWIVc4ezJmqevy6uGtLbYXHJxG9df10nPpjyh0pOMx9N14UrTqx1HMDbo8cVvnCLjNHe1NPcrxMm30L52O9XRXqTlALEas8XzrmhPaNtzCV9pDUi4677RpM5ICEHguazfuoONOy7Ad+tnLXf2jIUwxIi53vJZ7B0aR/TDhqzBCs4OpJB4dY+dl11NqX1VEsZyHMGpfo97HxpHZudflZ5GvFN1Cha/+LPrkm1DImWRVupN7fRNBbhgdDzkz/72EKPDPpmzIHUSL2zxdSUJ75rpse5FKqTPBOMRI859yJzFl/+jL9E8a3o2QhJ4Ndb2XIxXG8d3yxGd1iTPd24rcP55RQJ39uKJMSxLUC+H/OS+sUSsUQiJ73lccOU1FFpaEsXpp/uzWE4QQKANlXcuOK0BST9EP9TTFlmt4AxBmD7pazb10LP72fj1WjK5gxDuuHu0qaBvvot1mpHlVUOee0U7V13aRqUadyCk4Yjq5s/EhqeYN4q0H/jIYaCxUJ/p3eVUCe+4h/nkwsL59wtZzkgb92xWcuJIja99s39SsagQAh0G5IoddG2+iP4jD2A5uYjKa+Tqn39lO3bkVc4WcW7NzkoOPFHhqWORirM27MJ8qYULrrqGMFgJX50taG3W+PS/T4dZhLCaPZCpXl/BmUG6Kt3JZbnwqmuT52D6lUvufnCcwUViYyXn1IAleP3ProtEE5ly4TX/aLwWRFLft94+zL989hh2Pub7L49K9YmGIQx1dH3m30pBEDa79eeyMUlCSI7gC1/vpX/Qw3FE8/MQEt+rsunCF1ArD1Ed60vCV0GgWdXu8JzL2hPp9vmEr35w5wi1eiN85XsuG7bvonvnhSvhq7MEQ+XVTUys2dSCzImF5YcrIorLAVJI/LrH+Vc8j5aOziY2Vv+gx777xxDRznIhaHgU4NZCdu9p4YXP66BcCSYxb2zHQUUtd9OvKwUtJZvPfPkU3/3eINkWm3ARwmsLRTqZH4ZGWSFbssi22mSyEts2vUJyrXayU053FjyXFriG1wfZnOSJxyr8162DFAuTvQ8V+hTb1rL+vCs58diPsOwMRJ5ora644uJWNm3O482h90eMuNfInXePGvaVAoQg9D2edfV15EullfDVWYRSzUys2TyDWUqZREnIqN/xCs4yoqRjV3cP2/Zexv23fpN8ybCxtIbb7hrhhus7E2O/UOaTEAKhNQSa//bq9dx57xjlVKMkpUJyuRYyuQLlkSFTo6JUU0jLsSUf+pcjbNuSp2dLHrdm9I/ORrFdOoymNWRLFrWxgHvuGWX/ExX6Bj3qrqaz3Wbj+hyX7W1hS08BAo3rNq57uRcKppEs9NJ4H+PlcDLzSloEXpldV72W2vgAo32HsDMFQCe1Hy96/qqUdAnMllBjak4k++4Y5tjJOvlI7VeHIYXWdnY/7wUEfrASvjoLSI9jP5z69ekwaxqv1iZLv2JBzi7ScXo747D32hfzwK3fAm3qdHJZyf0Pj3PsRJ3u9Tk8b2YdqNmeT0qB6yo2binw+let40P/coSWkmnpKqVFZWSY7p0XEoYBtfExLNtuirs7jmBkLOAvP/IUH/zjnaYy/HQNnJYAE6m9Tk7yve8P8pkvn+LgkTr1eghRrwulwLYlHe0Oz7+ijTf83HrWb8zhRsbzXDAiTcSGnMXDj4zz/R8PN3sfCW3Xo9SxkQ07n8PDt95kDiDMlK+7ih1b81y8u5Ugqf04PaGmQU4AQs0P7hyJPFBASNxKmfOveB7rt+1qCl8t53v6dEO6FsSfYy3IrENYSmPa2M7t2lawRBBS4tXqnH/58+jc2E3gu4DRxhoeDbh93yikWpIuNOQSJ9SDasgrb+zi2c9qoVoNsaIRJIRg6OQxtl90WRIuEaKhoxWGmlLB4r6Hx/nYp49h5c58PmRiAt92BP/8r8f4o786yBOHq0gR0rkqw6aNrWzqbmP92gLFgqRc9vjaNwd423sOcO99o2SLZ4+WPB807q3m8zf3UptQ/KfBhJICl617byCoVxg4+mBT8twPDJ0722LPPXmOUY0+ccLl3gfHyEfPHkArxZ7nv4hMPrtSPLgMMNfWtrM2IKGeO0d4BUuLwPdoX7ueC666Fq9eQ0pjMCxLcOsdwwTVxenHnY6Rh0pjZy1+9Q0bDTU3GhOW7TA22E+9Ms7F19+IVysjREzXij8LrS02X/6PPr73/UGyLQurmp8PtNaJfPlNnzvBTZ8/QT4naCll2HvRNm74qUu44acu4cUvvJifvuEyrrtmDz1buigWBIPDPn/4gYPs318mm08pFC/TiZF4HxoyeYt7Hxjnxz8ZafI+4qLB0Hdp6dxM9wXXcOzAbfhuFRH1rPYDzeqODNdc1ZHorc0pea6AjORHPxlhYDhI6pRC36dt9RouvOpa/LqLnG1F4gqWBALT72kuHuCsn1gQ6lnqM65gqZGevFop9l57A5lcHqVCdBTG2v9ElYf2l7FjqYhFqAnRWmNJoza756JWXnFDV5JQV0qRLRR58v67sHJrWL3pAnyvipBykpdhW5J/+ORRThyZogZhiZBeTLMFizvvGuFTXzxJsSDJ57O84LqLuOzi7bS2FJBSIqUkk7Hp3riaF1y3l717tpHJwHgl5IMfO0Kt0pCsT3+35YaYBEGo+fzXevF83eR9CGFiVFoF9Oy9Aa01p568K0qeqyR5ftWlbazfaEKisY7YbGFZgqAacuvtw0njKCElbq3KrsufR1f3FnzPYyU+fpYhTFuDuYzk2XsgavnHe59JaGhj1dh+0aVs2rUbr16L6hygXg/5zm1DRuyOxaWfCgnaVbz+VevY3J3DjeS8iRhYD912C1v3/jROtsj/Z+/NoyS5rvPO33ux5J61d1fvezfQDaABkCBIAgRIkCIlSqQo6cgcW5Z05GOdI8u25sxIZxZtHnkWWbJGHlmyfaSxSUriSKRIiTsJkiC4ACT2vYFe0PtS3VVde+Uay3vzx4uIjKzqpbKW7mopP54+TXRlRUZmRLz77v2++10dhgubDF3B2LjPH3/snDmeuDGlrDgABg3Fxz89glYax7F48J37WT/US63uEYYq+a6U0nhegO8H3H1wB/v2bMGxNYffrPLZr4xh5W58BrVYtL5vk3088+IMz740syD7AEHoN+kZ2sHmfe9i9OTzVGcutXWeZxzB+x7sN8dlcdkHtPd+HH6zwtGTNbKZVlnVchwOvvsDiIhQWexxu1gdmAyks0rTNQNI+kChWowquIsbjTAMyBYK3PPIB40EUsSzqi2efnGG8dEmrrNyPSFaG8mr52t6hzL87E9uwA/iEagK28lSnTxLZfoitz/wTwj8Boh2h9ow6g958tlpPvvlUZyCveoLcbyY2jnJUy/McPREDdvW7N2ziY0b+mk0PSxr4eMQCxA8L+CuO7fT21vEdeAzXxrl1PEqmezaGeM7HzFvpX3FZ740akpuLPRJ01qz7a73Y9kuI28+bX7ZuNGYzvNdeQ4eKBryfJHS3QW9H09Pt0YkC0HQbDK8bRd77rkfr96eqXZx86AwQWSxuE4G0jpQ0GFk6mJ1kXRVR1Pc7nzXe+kdXE/o+0BkbTLm8b2npxKHXliZHX68MPnVgA+8Z4C3HixTrYfRDBCF7eY48+o36N+0ny37343fMHzI/P6QXFbyiU9f5MQxM099NTmFpJSj4Knnp/E8RT6fYdfODQRBuKAxcr4NilKabMbh7rt2YNtGUfb//d2lpOqylha/dPbh5C1+8NwML1zJrl0IQr9B3/AeNux5O5MXjzF18RiWnUnI8zDUPPJAP06hM/I8hm0L5qZ8nkr1fshoONqBBx6h1N+y4+ni5kNr6OQyXzOApB+qTlObLlYf8SLge02Gtuzg9rc/RDPZzZmH92uPT9CcC1aETId5TWcapCP5+Z/egJOaJS4th0Z1krOvPcbtD/wTin0bCYJmEkTiBcy2BXPVgD/+2DlCXydDqJLPtsKwLUFl1ufVwxVsWzPQX6ZcyhFG0sUrlWjTzZSeH7B1yxCbNw3iOppv/2CKp5+bwV1QFrr50NrYtQf1kE994VKyFWzLPkw9im13/hBOJs+lE8/iN6st8tzXDA04vOv+XmgunjyPjx/PlHn50BznL0bOu0TWJaUyBx9+P2EQJhlqt3x186G17mijcJ0SVsrmQV/537u4eUgeOkNycN8PfwQ3m0MrlZDpx07WeOr56cRGBJZ//VqlLPDqIQfvLPHgfb0pnyyF7eQ4f+R7BF6DAw/9fGS82N6DEYaaQt7i+Vdm+dQXLmHnV49T0BqkLRi55DE5HSAFDA2WsS0r2Rhd633TP7vzju1kMg5BoPirz11CeZ3PxFgtpLMPOyf57lPTvPpGhVy2JaaINx6BX2dg8wHW77iH2vQol8+8soA8v/+eHoY3ZvH8xZPnrf4YQMETz05HsmdjnOg16my9/S42792f8HZr4bv7h45Ycr2CJawWgrDVRNjdKawdxGR6s15j18H72HHnvSYLETLRtHzl8XHwYqJ75dC6DwQ//aH15FJBSlg2zfos5w5/l/U738r2gz9spKGi/SSUgkLe4pN/e4mjb8ytWilLa8AWXBxr0vQUtmNRLOYW8AFX+5zxwuz7IUODZXbu2IBtaV47XOHxJydx1lAW0so+FH/31bFkvnh79qGR0mbbHe/DzhSYOP861emL7eS5K3jkwT7zuyw++4jhOJKxS02ef2U2mXuOMM4Fdz74XjL5XNL70cXagNYQdNBLuOglRXebCNc0tFJkcjnu/5GfiHaYpvkzl7V45fUKh45WcbLWikh6YV5ppxFyYH+Jd72tlYWgNZZlc+nEM1SnR9n1lg8xsOl2fK+eEKaJNNgS1Gohf/rJC6igfS73ii7GQlCpBvi+wrElhXwGpVvzTBZ5CMJQcfu+zRSLWbQ2zXmNuXZZ783A/Ozje09PcejoVbIPr87QtoMMbD5A0KwyevoltFYJed5sKvbsyHNwf6kj8jxGPPf82ZdnGJ/0E3PPMAgo9Q2y/x0P4zc8ZGRd0t2Urh2oDq7z4gPIkk6li9VGwklISbNW544H38uGHbvxm0b9JCXU6iGPfW9idSS9qQf/xz8w1NppopG2S2VqhLEzL+NmS+x7x3+HewVpbxhqCgWL516e5cvfvLwqqiwNIGG2EhIECikljmMvKgOJfx6fbxAoensK7N61EdvWHDtZ59vfn8JawTLhUhEHZL+m+NuvjLUpreLPgdZYdoatd7wPy3apTl9icuQIltMiz/1A8e4H+nCLnZHn8ftICXiK7z83nfzMuCfU2H33fazbuoPAa8IayNi6aCFeHxaLDjKQpZxOFzcSYeBRHhjk4Lt/GL/ZQAqZSHqffG6GyxGRCSufhfiNkDtvL3L3gaKx6k4d/tKJZ/Hqc/Su38nOez9EGDQXmChpBZmM5C8/e4mxC43INn4FS1mm5QHfN53opp7f2SHaPm8QsmfXBkrFHGjFV741ThAZRN4MXDH7OHKt7OMu+ob3oLXi8plXaNZnEdJY4wWBZqDf4aH7O7Ntb52LKV+dH2lw6GjVbCo0EDUQ3vng+7AdZ9HBu4sbCN2eLFzvGenAC6sbQdYqkodbSALf5573/AiFnj7CsCXpHb3s8cSzU4hMy4NqJS+pUiBcyQffOwhEN2G0052+9CZzk+dRYcCW/Q8zsHk/gddoK2UpbaYYXhxr8uefHQFHdLwbuhZMNzb099q4jkRpzVK9FUzWpCiV8uzYPoztwOvHqvxghcUKnSLOPrxqyGe/Mpr0sMzPPoRls2nfg0jLImjWGD39IlJaES9iyPP7DvawcdPSbNu1BlzJc6/MMj3Tsi4JAo/eoWH2vvWdeA1z/btYe1ixRsIubh0kJG+jwcbdt7Hn3re3SXotS/CtJ+f7Yy1/gYt3j1JC2FC8/d4edm/P04zGnQpp4TdrjJ56wRD70mLPfT+F7WQTy/f4OEpBsWDz6OMTvPDCDJlVIKYdR2JJEwAaDQ/Z4e43nYWEYcjOncMU8hmCQPHVb42DrxE3+Kman3088cwUbxytXkV51WBg0+30b7odrTUzYyeZHT9r1FeRbbttCd79jl5T8lyEQi19HhCVr5qKp1+YMYFbx+WrOrvvuZ++9RsIon6lLtYG0saa6UdNiGs/d4seadsdZbu2ka5127bNve/9UWIzw1jS++bJGkeOV7HdlfHHmo8g0OR6HB56e6+RfQpAKyzbZez0SzTrs6gwoHd4N1vveC+BV29rMEwWZqX52KdG8Ost23Tz82WcnACUply0kJYJIPWGh5BiyccNAkVfT4HNmwZxbHjpUIVDRyq4KyhWWCzi5s6wrvj8o5eN3QxXyD6kxdYD70NG5arR0y8R+k0QZixy01Ns3ZzlnjvLhI3OlHuxdYnjSM6ONHjjWLp8pZGWxf53PIxlW13rkjWGZCPH/BLWta/Pom6PuNzRvdRrH0bSW+e2tz3I+m278COiMibTn3p+GmyzGK8Umd7q3AZ8xUNv76On3CJfpeVQmxllauQotpsl8Opsv+v9lAe3EvjtDYZKafI5i1cPV/jqtybm9YYs/VwFgK/ZtjlLf6+NUoJ8LoNWej4ds6jPa/42pd2dO4bJZGyqtZDHnpg05oSsrFjhaoiDbtx1/tQLM7x2pEouFcTas4/9DG45gAp9vPocE+dfT3o/hADPUzxwXw+FXocg6Ozck7JoRvLcyzNMz6bKV75P3/oN7Ln3/qR81SXP1ybaL8syM5ArH7SLtYww8CkPDHLXQz+E32wmO0PXlTz9wgz11IO9UkgkvZ5ix7Ycdx8oJXMnEAKlQi6fe9VkRCrEzZXYfd9PxL/dthM15yr46y9cYnK0ieuKSG679FKWlAI/UGxYn+UPfms3//SjdzE02EcQLq8PIQhC1g31sG6oB9vSPP3CDFMrOJN+MdA6Khv5mi9+47IJilyp78Ni24H3IoRE2i5TF49FM89NNhKGmmLB4uG390HQGXneVr5qKDNWOV2+atTZc8/b6Vs33C1frXF0wg12IOPtRpC1jhaZLgiDgIMPv59csYQKjc2760hOX2jw+pEqVlRaWKkSQlJCU4AjeejtfRH5SlLGmhw5QqM6jbRdgmaNddvvYXjXfQReHeZlIRnXqHj+5oujiIxljrsMxMf2fM22nTnuur2E5+tkkVz6ccGyJNu2rsO2jf/Ysy/NIlNDk1YLLQECOFmLlw7N8eJrc+Su4HkV+A36N97OwJYDhH4DAVw++wpKBUn5qtFU7N9bZM/OPH5zCeQ54NiSkdHmgvKVZdnc9jZD3HfLV2sbq0OiRzLILtY2EjK92WTDzr1s2LkX3zNZiLF5Vzzz8kxS7FzpMoKUoJsh995ZYrC/VQaR0qZRmTD9BrZr3lcrdt7zQZxsCa2CBYR6Pm/xpccuc/LNyoq43ibNsL7CdfxUfb/z46XLWEGg2Lixn1IpS6gU33tmCjydHH81SzXJZ9Kaz311FM9vt1WJrW6EkGw58AhS2ghp0ahOMXnhcGScqJIy2AP39SKzVir4Lf6h1wrISF56bZapGdNYCaZ5sDy4jl1339ctX/09w6IDyDJL0F3cYKgwIFcssu+t7yTwvIhnML0WL742R2MuTMpYK/0w+75maMjl4P4SjWa6jKUYP/86WiuElAR+k/LgNrbe8Uhk+95OqNuWYHYujFxvl3euZsEXUeAU9JY0tr0ypVmlFMVCjvXr+rCk5o1jVUYuNXFsuaqPTFKazFm8emiOp16YNY676krZx20MbbkD36thOVkmzh+mXplMyPQg0PT12Nx/Tznp/TDvcf3zaAUrwFc898qs+V5Fq3y186576Vu/MXGL7mINo4NEoYMA0k0/bgWky1gqVOx724NkC4VkWqFjS85daPDmqSrWCquxWkowwJa8/d6eaDdMUsaavnQMrzaLkJbp6vYbbN3/CMXejaggDnQtWWohb/Hdp6Z4+dVZ3PmlmaWcI2an3VNS2JZCqVZg6vSY8XcdC0w2bezHdS0mpnxefG0Wssa+fHXMIdvP90vfGE+C9YLsA9h8+8PIWKqb8FHGukRKU7668/Yimzdm8X21qMAxH44jGB/3eO1INeVIAAjBbfc9iOPaqKgW2V1P1i5kBxFkUQEkVth0E5BbA2YBlnjNBlv2HmDd1p1JGcuyoFoPef7VOYjGi964MpZFozLFzNjJSPljMqVsoY9td/4QKvTbdj+xNNXzNZ/+4iUIO/Otmo94zVIasq6ir9xZn8OVEIsHglCxbqiXfN4lVJoXD80lO/nVKNck2UdGcuJkle8/P00+amJsKdYEYdCkZ/1OhrbeReDVsSyX+tx429yP+MF+4L5ecFOGmIv8TuJgL1zJ68eqTE77rbnngU+pb4A9b3kHXnfu+S2BTh6F61/NeDez1LPp4oYj6d0JAgo9Pdx23wP4zUZSxnJsM6Mh3VS40otcEGgGB1z27y0mI2+FkISBx+TIEeI7SghJ4NfZuOcd9KzbSbhA1muykKdfnOWpF6ZNFrLM5kKtBW5GIKlGnEHL52qpUMoMqBoa7IlceqtMpkwEV9ZZODXtzxY8+vgEswtmvphynVaKzbc9hO3m0CrAcjJMXDhMszaNsKK5H4FmsN/hrQfL6Gbnrs2xeScaXj40l3SvC2mGnW257Q76hzdF6qvuSrImkW4ebPvBMvtA4uN2U85bC0kQUZp9b30nmVw+KWO5juDk2Tqjl5vYtljRzLL1vkBGcs8dpbZhRtJ2mBw5Gg0uMrefViF2Js/2uz7Q2rCkOA8RfY6/+8oY2tfLmr2hlMbJS7795DR/+J9f4Nnn3mgLSEsvY2ksS7JuXS+2JZie9Tl+uoZwV1btFr+nBjKuZHSkyePfnySXnT91UhAGHqWBzazbca9RXkkLFfiMn301ebDj8tXdB0qsW+fi+51Jx+Lvy7YE9bmAl1+fM5MHNQgEKgzYc/f9ZHKZxLq9u5asQaQ6ztOXZ9md6Fc6aBdrH/GcEL/ZYPPeA5QH15nRocLYmszNBRw6UgV3der0QgBNxT13lCgXraiMZWZQ1GbHqE1fivoPNAhJ4NVZv+Ne+jfelsxRT8tU8zmLF16d49mXZnCWwYXEu/ZzIw0qNcXo2BQzM9XEN2opSFyFA8Vgf5ls1qFWCzlyvAqWWFG1W5J9RAH6Oz+YZPSyh5Oae9/iwHw27n0nmVwZpULT0Dk3zvTo8aiEGDUIC3jb3WWwZcclvbiUZjmS42dqXBht4tjm38IwIFsoseue+wj8EBFZt3exFnHl67IinehXP3wXax0qDCn09LFl3x1JGcvU7DWvHp5bNq9wNRh5q2bLxiw7t+VaPRdSEng1psdOIi23VTrSGmk5bLvr/SYzSZWU4iwkCDV/99UxmDczpKPzihru9uzIUS7ZeF7I+OQclrV8+5FQKcrlPPl8BkvCkeM1VD1c8UFeYDYBXiXgsScmcRy5wFlAhT750hDDO+8zViWQ9OI067NJ+SqIylf33rn08pXWgCN47XCValQWNQIJj6Et29m4c1/CwXXlu2scgjZ/uOs9Dt0M5O8pklJSGOLmXLbvv9vIZzG7w4wreeNYlXolWHEeJH7vMNTYecmdtxVTyh7zs5mxk6jQXyA3HdxyBwMbb79qFvL8q7O88MosTnZpWYgUoH3N7h15SgUbP1BMTMya72apQSkV6FzXpr+viB8EHD1Ro1YPsZaR3VwJsWni0y/M8OapGtlMS00XuzKHQZP1O95CrjyUfM8qDAz/FJW4Eui6m9wAACAASURBVPXVbUVTvupkFB0Lu88PHamYzxr1nfjNBrsP3ke+3IMKAqBbvlrrEKw0iR4fWKxsrbyL1YcQItrxh2w/cJBsvkgYBmiN6Zq+7HFupIFlL91Q8FrvDYCG/XuLZGJZp1ZIy2Fm7FTEg1it12tT4tp64BGEsKKFqLU4S2l6TD736BjMs+voBEGgGOhz2L0jRxjCxOQcnhe0vdeSPy+wd89mtm0Z4Kd+dIhC3u5oxvS1kMhzoyzqq4+PG/5m3jlrFeK4BTbseQdahYBASJtmfYbp0eNGzqtV9P1G5StHJt3+nS7ytiWYmPI4erxq5rho0FphOxm2H7g71VPSDR63AlYlgNiShHjr2prcGkgm6PkeG3bupWdofcSDCCxLUK2FHD4e8SArTPTGC75uKm7fk6cvZa4Yd0JXpkaiRja9IAvp37hvYRaiTBby7EuzvHF06a63SoHMWdyxt4jWgkqlzsxsDcta+rCt1rTCkIH+Hj7yoXv46Ic3oFW7KGA5SEt3j56o8vLrc+SyKZJeA0IQRt9heXCrGd6FjnpwTtCoTJnZH5iSYF+Pzb13lWEJ5au0fPfI8RrTs61sNgwDCj29bD9wt/Fjk93y1a0AIcDq4F69zi3TOlD6oOI6xEoXawNpOW++3Mu2/QfxvUZbienUmTqERoa5Gg94EGr6ex327MwbyawwASTwG8xePo205k2m02YU7pYDj7Qa4VL3npRQayi+8tj4kkb0Jq7BgWb/vgL5nEWj4TM2No2ULS5hKWiV20IaTRifIlK3LH/xbJfuSh5/cpJKNWyX7ppUBGk5bLrtXYjY0z3S2E6OHI7KWRIpjHX7vt0FhpdQvorPCQApeP1YJWlkNOUrj027b6N33QaCoCvfvVUgAKuDjcQ1X5qWcLXtTrr3wi0FpRRuxmbz3v2RU2vLnffN03Uzd2OF6/RpOa/IWezbVcAPdOre0cxOnE3q80ArC/HqDG25k97h3YR+g/QCrBTks5Innp3m3Nk6mQ6zp8T+3FPs25VneF2GINBcGp0iDMMkfV+KnDf+XFJAEAoqNdnqxF+hh8ZxBNOXmzzxzHTS7d0KeoLQb9I7vLstgxPSwm9UmRw5GvmQmbpXGGruPlBCpCTAnXyPYMj8oBbyxrFqQuYbRZrHjjvuJVvIocKufPdWQLwBsjsYy3zNAJJ+hmwpukT6LQpTxlIMb99NNt/qB7FtwfmRhukcXoWGwmTBCDV7d+ZNuSXiQSzLZfbymWiolNX+O1phORm23P6wKZeK9sXHtgWTUz7f+M6EGX2rF5+FxMcJQk2px+Gu24sEoWByao5qbXmd0mmDxTCEWl0mceN6evrFQCmQWYtnXprlfNt8+1i6a7iHDbvux3aykbuhRlo2lamL1CsTCecUhppC3uLu/UXwW9btncKK+I9TZ+s40bVQKsTN5tm6/y5UaDYs3eBxa8BkICsUQNoe2m4AuWUhpCD0fYa37yaTLyY7QksKqjXFiTN1cMSKT51MeBBfs2dnnlLBmseDTFKbvWwsviNezWQhktBvMLTtbspD2yMZqkhKOFoZU8jHvz/JzITf1gOxGMRcAhLuu7uM60pqNY/R0SnsVC/E0j5z6/9X6yIZ5an18lyEISbPFU88PRV9kPaAr8KAXHmIoW13JVMGdVTSmh49TuDVIg8y032+aUOGndtyhH7nUu4W/yE4crzGXCXEli3+o9jbx5a9+418t+u+e8tghTmQ1AulWLLqpYubh9iFNgwDin39DGzcQhgYxZGUUG+EnD3fgFVUYoURD7JtUw4/iGdwSEKvwdzEOaTltPV9gFESudkim/c9aJREosVfaCDjSM6NNHnq+ek2+/HF3J9xYFNNxd0HiqwfcvF8xfkL45Fl/OKPdbXPbM7TgWwrOC51F56Q547kzLk6L79RScpXyTEj6e7g5jvIFvtRYSybNfYx06MniJ5ghIBmU3H3/hLZok0Qdn5+yXcjBEeOV2l4Lf4j8Dw27r6NYt+AEW0s47N3ceOgMTL3TqrZi1dhWd009FaGUopMrsDwjt0EXjPazhqcG2kkzXmrgTDU2DmZEOlxuUSFAZXpkeR1bX5Uwti9r9t+L/nyelTgJ6/RWseCI775xCS6ubRmvSDQ9Pa73HtHiSAQjI5NMzOzfDWWKQ/CU89f5qknpyI/rKWPt03Ic1fyg+dnmJ65wkTJqCy4fse97b5G0sJvzDE7fjqS7+qE/7rjtmLMry+5fBXWQ46dqOFGGawQJtvddvtBsvlcYl/Sxa0BS4qO6LpFP3aW6DYT3opI5msohe1YbNp1G0ZEZ+wwclmLl1+fY3bKX/Ext5BamKRgx9YsduwAjEZYNtWpkWQWyIJzD32yxX7W73oLYehBZIUR78hzWYvXDlc4fKyKk1m8pDd9DAQ8+LZeshmLWt3j3PnLWNbCzu7FwgQPm0ujk3zhq2/wb/+f05w530gI5k6RJqv9asCTz0wb/7K284t8rwa30ju8x0h3I0WbZTnMXj5DszqTyHfDUFMqWNy+p5AMvupo8mB8ThKmZwNOnaub2Scx/5HLs2Xvfkzi2N143jLQJoCIlbZzh/jAXdyKaO3qScpXYDaqlgWT0wFj4x7SWvkyVqx6wlfs2JqLBh7pSG5qyN3AayTGivH5xn5OWoUM73wbjluImuJakBJq9ZDHnpwEi44W/biMFTYU995ZZtuWLL4PZ89dxvOCZXljSSGoVRs4tiAM4dLlpikRLuFYcaCzXcHxU3WOn66RccW8znOBUgHDO96Ck8knu36NRkiL6bGThEHLxsbzNdu3ZBkacJLy1VLOSTiSU2frzKXkxCoMyRVLbNx9G4Hf7f+4laCJK02L31B0ZGXiWN1u9FsJcUlIK4WTyTJ29ixPfv6vcNxse+ey1jQaKkldV8MXSwWazRuylEsWEYdv+gUac9TnLrc3FCbnEDvKbqFvw75kZ52W9GYzFj94boapeYaCi0UQarIli/e8sw+tjRrrwsgktt3KaDrOQiAVEJdnE5/u/XjmpZnEa6rtNSrAzZYZ3HoXYWD6PCDiP/wGcxNnEdIyAUWA55vZ53Y+1dzZIf+hNOAIjp+qUauFEf8hCAOfoS3bKQ8MRfxHNwNZ60ieOcDpcOO0qACitUYKgbtMhUoXNx7xjtx2Xb7+if/E5XOncDIZtGpNhvN8zcS0D3I1iXQz12Pb5ix+EPMghuCtTl00ASTFgbSsUBTSstmw+20mtZ5XpnIdwchok6dfnJk3y/v655U0FfqaRx7oZ7DfxfMUx0+MoFTn3lit18Y2K+Z9XMc09HW6jM4vXz33ymxSKkoyrajnonf9Lgq9w2YoV/wZpYXfrLYJFbSCbEZyYG8hkYwtZYGXAmgaBZ+Q8TWT+F6TLXsPkC201H5drG2kbYecDnpAoIMMBMBNZSDdOLK2kc4+csUSr3//2zzzlb8lV+xBK5XUOaU0ipzRy15SZlmNHaNSGisr2b7ZKLFkTKQHPrWZMZNZcIUFO1IXDWzab4wBVWuBTL/2Oz+YarPjWGwZS0Qd2Zu2ZnnX/T0EoeDS6BQjI5M4jt1RFmK+N/O9e15AGCpcR9JTtlmqRlprsB3B2QsNTp2t484vX0UvGti8v9UkaP4xKhGO4DXmkqwkCDXlosW+XXn0EviPGJYlaDYUp8/VcZ0WryUtmw0795rjLjP76uLGQgiSAWjmv69/7ToKIE402wBAdFORNYt0GUhaFvXKHF/5f/8DSqvWYpjaXWtgbLxprN1Z2oJyLaSJ9E3DGSwZBQvMzJLa7ChhNA/9ip8nDMkUehnaeufCiYUaclnJa0cqhsztoDM9yXYAAs2H3z9ET8nG80MOHz3fcRYSl93Q0Gh6KK2xbUFv2UaHLXnwYhHx4OBKXj1cYa5ypfKVwskU6N94W1S+ajWESmkzO34m4phM/0cQaLZsytLX6yTlq04QK+AsKZic9rl02UuEESoMyeTyRukXzf/o8h+3DqQwSUKMxVy76waQtmZCK9VM2N1ZrGmYRq+QXLHAd/7mE5x+/SWyuQJKhSaItJwxsS3B6LiP9jo31FsMEiI9al5rdaRjlFgzo4RBc8Gi39plm2BRHtqBlHa73BezG56dC3n6hRnjKqs7bYozWdjuPQUefkcvni+4NDrJmTNji85CWn5e4Achs3N1QNDXYyeGh50iqlCBp3j1cKXtveJgpUKfYv9mCj3r0cpwDuY8JCr0mB0/29ab4vmKPTvyOHlrSfxHol5zBKfP1qnXzUhghAkg+XIP67fuJPT9jo7bxc1F/Ey5druY5Xq4/kjb1EPjWN1u9LWOlnOtIpPNc+7oYb796Y+RyRfaNfmt+IFlCcbGPZqebhsms6KIAsiWDdmoCc7kIFJYNCqT+I1KYrMx//MQcSU963ZiZwpoHX+OlsGgbQt+8Pz0kua8CyGSLOSjH17PYJ9DEGhefu0U9YaHZbUcga93TCkEnuczM1MhDGHnthy51GK9WMTvY9uCmZmAN45FVukqzd+YXpr+jfuimeet62sa+hpUJs9H/IdKvqfd2/PJBrDTRb5F6gtOX2hQb0QEOoZAX7dlh+E/uuNrbzlIQUc+WNBhBuLaArkEVUoXNxYt6wvBox//EypTk1i208aLxLt6rU0AmZzyzdCnVchA4gU6VJreHpvBfiMfFYKEBK7PTWA8sdpluElJRoVk873kSoMROStAt/o5MpHM9eSZOrbT4gkWc26m3CNoNhVbdxb4xx9ZjxfA7GyVl185mSiyrhZEWv8Olm0xPjFLpWKkybftykOm87no8fGkIzl+qsbkFfp0dOQZ1je8tzX3Iw4MUtKsz9CoTieKMKU0+ZzF7u25xP9qKc+yjIQHFy42kw2lEJLA91i/fZfZrHQJ9FsKGpMgdBg/OiXR5ap1K3exfLQR54USh578Ni9/+1GyxZIhzoVEhT6F3mFsJ5cosaQwk+lm50z/w2ptD5TSuK5kw/oMYaCj6Wem1FKvjJtGN31l4lVrheVmKQ1ubU0yRCeZlGUJ5qoBz786u6QyVtwXEtRCfuqD67h7fxE/kBw/McKRYxfIZp2kdBQHkXTPSfxOQsCZs5fxvJDess1bDpahoToewZuWyh45UaV2hdG4KgqqpYEthKHfkjhH/Ed16mJUGjS/GIbQU7LYsD5jTA6X+CxLKfAbIecvNhLvsJgwH9q0HXMZuwT6rQStwbVlx7L1jgKIlMZUsZt/rD0sJM5nefTjf2webFq7eCdTZPdbP2IM7hKPJmOuNzUTmDtilS6wUiAzkuEh1zSwJUqsgEZl0jSdsXChbWUJNqW+DeazxP8uWmUsyxI8//IMqhF2pMZKS4eV0tiu5Fd/aRvloulZee75Y7x5fIRs1r3qMbVSuK7N6Og0585dJlRmlO+OrbnUON/OEI+KPXG6njQ2puW7KvAor9uBky0mmwHz3WiEZVGduUTotxoI/UCxdZNp5lwK/5E+r3pDMXKp2XJxjqxy1m3dTuirjheiLm4OWutGO4G+WCw6gMS9IJklWjJ0sfqIifNsocCTn/8rTh1KE+eSwK+zZf+7Gdxyx4Iyke8rpmZavSCrocTSAJZg/ZDb/kMpqM9NtHXJp9HidQKKfZuxnWyr3q9b5Z5MRnL0RG1J1iFtpayGYvvOPL/2S9sAs3N/+pkjvHHkHI5tYdtWdF6t78l1HRoNj2efP4bnhziO5KMfXg/WFWS3i4QlBbV6yPHTxmtKq/RxzPmWB7fNk++ScCPV6VHzOvMV4wemA93KLb5fJo04UEtLMHrZiwZame/BWLjnWLdlR5sarIu1jfR1cpZgZdRxxTubejC7O4y1gXTpys1kuXzuDN/9m0/gZnORdFcSBE1KA9vYduf7UCrAzZUx8yJEsrhMzwSdWXF2eo4AgWZ4KNMahoRGSovG3Pg1pbwg0GFAoW8DdibftmDGmZRtCWYrIS8fmoPM0r2nLEvQrIQ89HA/v/4r21Fa4/ma554/xneeOMTExCxCCDIZm2zWxbIko6NTPPb4K0zPVKjWFR/5wCB3313Gq4cdcw3pjGr0ssfEpG+I/LYXKWwnS2lgC1oFbZ5TcQd6dfoi0jINmjpS223ZlE06GpdmFglYgoujTZqeSpH5IcW+AYq9/Yb/WELA7OLmQUrIdqjAArAX86J0OpqxW0qs7g2ydhCXNizH5Zuf/FMmLl6gUO4lDAOzKGvFzns+iJMtEXg1MrkylYlzYJP0B8xWgmRM7GpcWyGAULN+nUvGlYQq5kHMjHQV+li2y/waWotzULjZIsXeDUyOHIHI/kToWJZs5KQvHZrjJz64rq2M1QmhDlGD5VzII+8ZxHUlf/inZ7k84XP6zBgXL04yMFCiv7+EZUmmJitcGp3CDxTVuuJdb+vll35uM2Gz5TzcmazYlNJwBKfO1qk1FNl5AVGrEDtToNi30Vi3p97DjJStUp+NeCU0YdSBvml9BoLW/I+OiX002IJLl5s0mopMRqK1IAwC+jdsws5kUEtJb7q4aYirS1lHtv3bYu6NRQWQ9M2fdWRCCHYDyM1Hq7wTkskXOPHKczz76OcTKaWQkqBZZ2j7QYZ3vY2gWUPaLk6mMG8XD7WaSiw3Vvr6xv0cWmmG+l1cV0a+Tq0FL/Dq2G4WHV55AdJKYWfylAa2MH7uELZrlFhaGJ5HKci6kjeOVZmc8unrsTs2C4wXeyEEUmqalZAHH+hn55Y8//WvL/Dks9PMzPnMVSc4fXbcfF9SoJQkk5H81AfX8a9+YYsZ9RqYklin32XyvEkTQJqeIp+VtD6Kud6F3mHcbLHtOhIZKDaqU4Rhy7Zfa20CyIZsEkA6RZJFhppLY17bdxYGAf3Dm3CzORrVVud7F7cGbGlUtjFWNAMhHhqAyUCkjHZIXawJmAfblLAe+8s/pVmrkouUV1oZ9dKue34s2UEKKbGc2FBRRGUkqNZDiMjt1YJSkMtJ+npsZitmroWOavbN2jS50gBGytt+E5uF3ZRMiv2bEJZN3FEfJx8QSZJnfA6/WeWBB/pQV+jevh7SQcSyoFkJ2bghw2//2k5ePTTHt38wxetHq0xM+eioE/7O2wr82A8Nccf+IqGvlxw8WucA+IrLk36ijGtlR0ZNV+zbiOVkCJr1VmOvNhLeRmUSFfhIyzYZZqgZ7HfoKVmEankEum4qxsZNB7ohWIxKrn94k5FQK41chdEAXawOlDYD2qwlxPxFlrBI0mfHEriWoB6VH7q4uYjrz/lSmRe/9RUOff9xcoViItv1mxW23fk+eof3EDSrUa1cYjkZoHUNpRBUq2FiZ7JaiOWCgwMOJ8/WU5/Bp1mLehZSAaH1e5HyKPQpDWw1mUokDoheAYjE2+vQ0QoPPNiX/G6ni2W67GRZAs8zu/y77ixz111lGpUAz29xFcWSBUIknEccPJYKKQWBpxi93EysQpKeGDRCSAo9wwhhXHZl9D0orUwGUpkkDDyk7SJQhIFmw/oMriuXZGGSnJcw5puXJ31j/w+gFI6boWdgnZkBIpYeOLu4cUj3L2VsSSzM6AQdxZz4DduJ9I7er4sVRizbrc7N8M2//FPSK28Y+uRKg2w/+MPGpVXE2YaF7WRaN4smyUCW0x+wGCilsVzBYL9LxLUmgaFZnUkWxCsvPlGwLA+RLfSlHIUhnjmuNTiO5MibVcKa6jj7aHu3eMGO1FlCgFcPadaMyqpYsCgVLfJZiddQSfBIB5+ldHqDuR6VmuLcSDMZIJW8RiksJ0uhbwNKBW19KQJAaeqVSZINgoBAmQxEukuzVUm+E2nMJyen/LaOf9vN0DO0DhWqVc1gu1g5pO/NNP/RyT27pEJlOoAI0Y0gNxNaKbKFAq985+ucfuMVMvl8ZAIoUYHHtjveS6Fn/QKFk+VkUztaswg3mqp9zvYqQGnAlQz2OS0SParpN+szyeJzpV4Q8+8K281SGthKGPhxBCLdWe/YgtPnG0xO+x3bmsxHrDJqLexGtaaUJgyjP9Ec9XSvxlJkuzG0BmkLLlxsGKns/J4WrbGcDIWeYXTY8r8yJywJQy/qq4mCMUZwN9TvGsflJT6yWpvPODXjU0+aI824ZMfN0Ds0bEQb3RkgtxQsCdnULJ1VaSRM3xBpJVZ3u3FzYWSnMHnxAmHgI6WFlBaB36Q0sJVNtz9k3FhTzqixBUa82weziHueWlVuK7mHNPT22Fgy3cEt8RsVdHjlXpDW51VIy6XUv5mFta5WyWm2EnDsVA3hiBUJiumAkPaiSv+Z/7qlvo/WQDTtr1YPzZxq4s8g0Dokk+9tkzO3NUMGPs3aVKS+M0HbcQSD/Q5ECcKSz9ESTE0HyUwXRNREmc1R7BtIJLxd3BpIFFj20kQPHTUSxsg60lhyd+tXNx1CSrxGwL63PUi+VKZRmyUMmqjQZ+c9P2pUOpEDL0TLrdbYdqbFN2Ce+WYUQFbr+U+XWHrLdtLsFxP7Xn3WSFKvsikxnloCrQJKg1uNECBlIBizN1JCo6F482QNlpmB3Ggk5xlqTp6tG74i3UcijOttoWd91EA473NFijyvUYkyTlPWcx1Jf59jOK4O+1Li89IRYT4zF0QzXUSSPRZ6erFsu1vTvsWgMf1Bnc4BibGkDCTnSEPsLfptulgtSClpNursuutefuY3/oC+9XsoDWzlzkf+OcO73krg1ZMegWQHC5HzbUtdJwQmA1nVixplrgp6exycVDlFCInXmEtq+ldCi0gPKPZtxJnXUChiI8aI2D55po5uLPSQWuuwLIFfDzl2oobrtOabxGo7pQLyPetNR75ulzwLBGHoEXj1pGSptcaxBT2l1mCrpYgKAJCC6VmfIJECG/Vfsbcf27lCQOtiTSJtYVLIyCWb5C5Sxtv+xlIK8o6k6atkd9Sted54JLX2aHb4wKaD3Psj/wOWLbHsLIHfANpr+NFvJgOGov80AcTXptyzSuebqPmUpq/HwXEEvq+jedoSrz6HCkMs2zS/Lfz9eQ2FfRuZHDkM0khV9Twe5Mz5Bo2GwnWNffxav0/Tqq+xcY+zI43EWRjSZSpp+ni4QjAQkqBZNZlZwicZG3czGXFpFSato3cTMDsX4sfNiMQZUR+W4xB4Tbpl7bWPFqcIeSe9w+rs2i15b5bPyAU3dhc3HkaFJWhUmxx96ViSZQReSyK7YOHU6QwkyUEIw9iob/XOVwjQcQnLlm0ZSODVUKF33ftJqzCx8QijLuz0rGWNWTDHJjwujnlY9uLt3W8mTEMoCFdw7GQtItBbvBFE/JWdIV9ehw5DU9LTrQ8vhMBvVBN7dzC9N9msUY2pZWQIpvwI1VqY3CLm/c3YZCvuzeniloElIeem5/Csoow3/QDmXXnLlQb+PmH+DIqzb16kVmkibZmUeq6lx5fSatuKxs1mN+L51wpcV5DNxuUZTG1fKfx0U9wV0PosgtLAFizLMduoqBqnRWsXP1cNeeyJCYhndmu9Zhe4tnMTgsNvVmmmZ7yLlADCdsn3rDNDm9KkvY7ECM0qKhIjCAFKa8pFOxqMtfRzFAIINdVqSEKfRdctWyxh2d0RtrcStNbYUpB3l76Qd9wHEiPXJdJvGlpqTo1lSaqzdc4cu4hlS2J32rQq5wpHQAiZ2LzHUArTB7La548h7koFK6X6MuR44FUT8veKv6t1JFX1KQ9uM5P44gmFAuOLpXVia/LYE5OMX2riums7+4hhWQK/GvDSoTlctzXXROgoW4zsXDL5ntbnTqCTAJLOQLQy3fKWXKEAUg8XZLS5QolrXLYu1iCUNs9Iegphp1n6kjMQxxLk0g2FQHS3Q1wv7QaXFUPbDlUrVBgSBiGWLTlzdIR6pYFlLWIHGFV74gwk/XqtdTSnY3UX27gmb0oq0WkJgVYhfrOWyE+veQwVkC32kysOJNPvRGTLEt+nrisYudTkse9NIjItC/O1uOmJs0k7NVnRXTBZ0XxHufIQ0nYXPF9xRhAGflspUmlNLmstOwMB0//SiDMjHZdQJW422ypHLu8tulhtpAj0OPtY6jOxpAwkLovkXWGsEwgROgTMqFRQrf/WamFQWYMP8FpGuqtZKYXluBT7eigP9FCdbXDuzYvYjtVRnf9mXoE4cyoV7ZRs2MhBY9XY1dDWUOjkkgmF7b/TGjLlupKvfXuc6vTCkbBrBfHmQGvAFnz3qUkq6dnu0f9iiW6uNLhgBkgLpk8kPSxMa8jnZHS8ZXAgEUfT9BTpIwlp4bjZpC2n+3SvcUTPihSQT/EfS+EIr6/Cihb+1sGjACAkOddFS4tAWOhIMigI0VjR/zeBROCbv7UiiVmpem8X10ccPDK5HJfPn+WV736DUl8vVmYbnqewrOsfA6DlthvPRW+3MJBxe/EqQmsQFhTyrQzEqPlU1PQYj6u9Mn8TfxfSsikPbDWvSWcyKX4om5GcPNPgse9N8OMfHiaYC6Kd+NpTZDmOYGbc54lnp419u4p7ZwRaxNdNky8NIi2bwNOpgNra/msVmu8uWua1howro+94eY+cUppmU6V4F42UEifKQLpd6LcGEjVtiv9YyjNxzQASN37FTqhgSLqoHEvRmqPPP4KtprFVBVvPIXWdUBQJRIlAFvGtQer2FgJRBiGRuonQPiDRQiC6geSaaNm1m2FRI8eP8mf/yy8xeuYkliUY3Hw79/7Ifw9kiN1qF3VcpVrkcwQhMBPmVuejtN4bQApyGYlu40AUYdBM7oVr9YPEBozloe0RD9LqyE4vqloLXEfwmS+N8e539lMqtsa5roUgEp9DGGqcgsV3vznO2QtNSkUr6aBPgohWWLZDrjRozDJp57riBVyFYdsmwJQsxYrUliIVdvuxhIjm2S//+F3cGGggZwsybVn5CmUg6ZKJacwKESIa49kcRc+8jJp7Hbt6hB3+DIIAqQNMCUsCCo2FFjYaG88apOLcTsW5jYp7B541hNQNhA7oBpLrI7keUvLV//pHjJ45ETmfhkxdepOx0y+x9ugwYwAAIABJREFUZf+78RsVs7W/LgTocIERiOnHuAHfvwaslAqLVlYUBs2E3L/2Am8aCgu9G8gU+qjNXsZ1XcJolkgr8GoyGcnp83W+8PUxfu5nNuOnspCbiXRJ2LYFtWmfzz86ljRYxnb76ddbtku+PJT0ebSyrVTAUEFSwop+EaPaXv61FURDK7vB4pZE67mAYtZqu3eWcnssCCDtwUNFJQ4L7Y2jRr+Mnvgu2puK7qQMCAuNjbqa2kcrMuEY2eA8g/Vv0bTXM579ISZy7yGQPViqhtAKLaQJIsvNsf8eoXWxQ7L5IsdeeIo3nv4uhZ4+At+YIwopI/sPFgSEa0HN46K0JnGcXW3EZG8uY7WtQwJB6DevUttPvS7VUOhkcpQHtzI3eQGnUMCSIY2mnxgbxg9LLmvxuUcv8/6HBxhel8Hz1LLmdawUhBAEUfbx5a+McexknXLJZEnmvEwWBUSft0yuNGgkvFcoFxkF2nx1Fq1+khU55xU7VBc3GPH9IgUUM8vjP2Aeid4ePMysBR3WCS98mvCN/wl18XPosI6wiwirmNrtthPnaUJdC4ESNqEsEsocbjjBpspfsGfqf2Ow/g20kGhhR0GkVVftwiA99OnJz/0VXqOeXOww9MiVBhnadpDQb7LoKXBxnXzeNtKyogCyil9/m4V0VrYZKiIEYdBMyjPXQ2ysWB7YilYmIOzZuZEwbJ9DrrXGcQTjEz5//jcX14Q/VjpDymYkoxfq/M0XR01WpkhW6dZ+ysic8z1DWE7WiFM6QNxrs1wIQTLgKvVhop6U5R+/i9WH1hrbEhSz7fzHUpAcIV6oWsHDQjcuEh75TdSFT6LDGsIuRYuUMq+Btj8I0fb3vC4DQCfBJBuOsHX2v7B95o+QuoESLkKH3SCSQrzTzuRynH79FV7/wbfJ5AuGvxCS0PfYtO9B8uUho0RaFCI/pcBLXXNz/TOOWLg4rDBahoqG4DakvfmZEKkM5DqLUcx1KBVQGtxGJldgamqOfXs2s23LOpOFpAh1paBYsHj0OxN8/+kp3Hw7x3AjkQ5sGsAWfOLTFxkd93AdgYq+o/nlKRWGV/XASiCuTGIrpVdkY2ACyMLPE0upu1i7SO6jSL7rtMn+l5GBpGtgSfCovEl49N+ga6cQTq/JRlKNS4tNedoCTPz/dYgSLqEs0dt8mp0zv4erJlEi2w0iEZK+D62Rls3TX/oM9cocMpJbqdAn37OOTfseJPRbs6+ve0yIdvpeFIjin0EmYyWln9VCWr2TychWR3NUgAsDL1ocr30eSWYWBhT7N5HJlak3GjQaHh/6wNvwvACR+izJPQ782ScvUJsNcBwRyYhvXBBJZ/mhgkzR5vHvTPC1b4+bvpiU9Xx8XpYlkVHjRaG8HmFZVz1fI3pZeC+ozhKWa2C+Uk+0ZSC6S46sWaT9r4pZa97PlnbMpBMwnXmo2dcIjv0O2p9CWHm0CtpOYKmIqyOGNDc9IqEsUfSOsmv6d8mEo1EQSZWz/oFCRAJ+J5vl4qk3eeV7XyeTz6eyjyYbdt/fYfaRHHzBQq0iyaslb1DcjqSlMuqObgtsiyjPxPeiUiFutkhpYAsZR/Diayd5+J13cMft26jVPaSU7eWirOTkmTr/5S/OI1NNVDciiLQFj9Ccy4UzNf7k4+ewrfaGrtZuUbNpwwBSgrQz5HvXJx5Y7UKo1n8Jabf9VAhBEGiWa7VsvicSJ+6osobWmsD3k3e82eKELq4FjSVXhv8AkHGPR5J51E6hTvx7UB5CZqJ/X0ECLvqjhZEVCh0SygLZ4BzbZ/8Dtp5t50T+Ad6MrcYyjeO6PPu1zzE7PoZlO+bnKiCT72HjnnckU/nS8tWrIt7Zpnb66Yc+mxHJgr6qiN40kxFt5K4QRJnR4ont2FywPLQT25KMXJpkcqrCL/7s+4m2K207+riU9flHL/Ppv72IW7RRauHCvdJobwY1nIzvKX7/P59hYsrHdduzISkl9YbHvl2buGv/DqZnKriZHIWe9ShlDCT1FY5vlFpO28+FMNMml+rEm4aUJvDHArF4HojfbCT82c2WRndxdSgNri0X9H8sFbKltrIMYX7yj9BhFSHdFQ8eaSQLlxBREClS8E+wee4TaGwgJe29Ulqs053tCnQY/VHzfnbrBiDbcZkavcgL3/wSbjZH7AMV+E2Gth2k2LcJFXjAInfPqUVGBc22smLcbGbq26v7nYnoLbKuZUpYyblL0weywOPpKseJeZAwoHf9LjLZPPVag2dePMpb33kn73vobuYqjSQLgTg4Qz5n8WefvMD3vjdBpmRH89lXh1yfHzziDvP/8z+e4sXX5ijk06qrdHal+MkfeyfNpk8YBmRyZeOBpVTb65Lvwrwblu2aYWFRE2EcQMIFDRydfg4QsiW/Nm8MWmm8Rj1VDr11n7m/r2jd/2b+R9rHcFkZSHqBVef+wnAeMr+qwSNGOohASCBL9DW+z2D9G4QyD8RZSOqXoqDQ6h1RaOGiZBlllVEya459i6q6kgutFG4uy0uPf43L509ju5kkWNp2hk17H4zsLeK692I5KeOnFPiNFm8SJXq5nIW4ERkImPKcPS9rEqDDMOE3rn8I8zqlfIr9G8kU+tA65OjxC+hqg3/+T9/Plo2DNJvtpSyzwzdqot/7k9McenWWbNmOhiRdaX7Kcj5me9nKtgWWJfh3f3Kax743mTQ2poOXJSVzlRrvfegg77zvNl49fAbXFhT6NpopjFct8UWciZs3dv3Re0spqFTD6H2W8VkALEExnzLBjDKOemUOFS5vMepi9ZC+LuU0/7HM6yU1ptdDTT+Huvx1hF0EVj94xEh2wVEwUMJluPpZssFZlMhckQ+JORQtLJQsYwcXKFa+TGnmM2SaR9DCQclbm5CXlkVtdpZnv/Z32HZsWS4I/Ab9G/fRO7zLkOd0qJqKypVmYp1ISE+lNMW8BTdw0mQiG45PDRbFfySvjxddpbAzBYoDW7EtzfFTlxifmGV4wwC//M8+aApZqeARZwK2LWh4it/+9yd441AriKRfu9RAEv9ufI5BqMnkJM2m4rd//wRfe3yCcsluI7djwrzeaLJ18zp+5Rc/zKWxKSYmZ5ECCr0bsezMtb8jrXDcHDLVUCoFVGohwXIDiAZSJpimYmXGETcqs4RBdx76WobWGscSlHN2+h+XdUxJ5FulLn2x7Y1uJGJORGiNFg6OmmSo9ihamJp/0mCYZB4q4kk8Bsd+i62n38eGC/+M4Yv/gi1nfpiN538G1zuBkvn2ILLGkc4+MvkCh599knNHX2+Vr7TJIDbuexAZz8FYApRSbaaFAlMbLRQssG9UBmLevv3KxJY5nZ2A1grLcikPbsOSkompOc5duEzQ9HnX2w/wj3/iIeYqdSzZ7vullCbjSKZnA379d0/w0gvTZHtstCbhI8zxOz2f9pKVUpAt2Zw/3+BX/+2bPP79qcSqpD0rkgRBiOs6/Oq/+Aj9GwY4evwC9UYTN5Ol0Lcx4q2uwndFhLbtZhFRxhWXnaq1MDWGtnOk36+Qt0iprxFSUq/MEQZXH0fcxc1DWr5bylo4K+jCIIUANfUMeu4NhJVrs8O+0TALvUKJHH3Np8j5p1DChaghsVW2EoDF+ou/wuD47yFVzdimRBlLsfI1Np37aRzvNFrmbj1VlxCoIOD5b3zB6OuFkR6EgUdpcCuDW+4g9ButANDBZxMisk2PZ2ZHCighoJBbGWXG4s+FVB0z/rszIWiLB/HpGdpBJlugWq1x5M3z2BmHSrXBz330vTz0zgPMzNWwUxJYI6XVZF1JpRryv/7ucT7z2Yu4WYmbkcbaHhaVjaSFD9HHIAg1rivJFC2+850JfuU3j/L60UrSad4WPKK/PT/gf/ylj3DvXbug3uTs+XEajSZOJkexf1PiPHzlcxFRAIlKWClHXs9X1BvKPPBLQLp/p5i3jBLLpHaG8K/MEgZ+N4CsQaSvSU+uvXy13OtlKNPxxzGNfjdP9dRG6AobR03R23wuCSBC61aZyyrSM/3nlGc/S2APo1Md8VoIQmsQ1zvJ4OX/g3g8krhJn6sTxIuJm81x/vgRjj33g1TjoFFfbdj9DtxsMeokX9K7mADSqNKyyjDzXYoFyyh1uDFZaPu9Gy28cURbJFo8SEChbyNuvge04uiJCwRNP3mfX/vln2Tvro1Uaw2sKwQRxxEoDX/8sXP8xv91nPMXGmTLZopfmJrUOF+tNT+wKGX+uI4kW7YZudTk3/3hSX7nD08yWwnbCPO24AHU6k1++Rd+lPc/ci/VWgPPCzh9bhRLQibfRzbfe0UCvf0LUTiZAtKyWxyIAN/XTE37YC1DWxKlqr3RPPv4GNKymB2/TOh3A8haRVy+KqX5jxV4xqVujqErR42vFYt3c10NxH0iJhA4FPzDWLpBu+OKRKgmheo3o8ChI5v4NHyULFGofhvHP4eOgtBaRrpx0HZsXnzsy1Rnp7AsU69UYUC2NMjwzrckjYOdkOcxYtWS36wmJLrWZsBTT9mmzV99lRGq9D2cyqY6eOs0D+JkCpQGtmJb8ObJEWYrdRzHwvN8ensK/OavfpShwR7qjeaCIKKUWciLBYvvPT3Nv/7No/z1p0eYqwRkyjZu1kokzkFgylJhqJPPIIQhxzMFi0zR4uJYk4//+Xn+9W8e4cuPTZDNSBxHLCDM487/Wr3JL/7sB/hHP/4glWod27ap1ZucPjeGJTSFvo3X7kCPEBsuutkycZ+PEALPV4xPepA4DXT2PCQqr1Az0GfjOFGJDI2UFpWZKRq1SpT5dLFWML985a7w2GGpZ19FB9MIcf3RIDcOGoVDPjhFJryEEg5JaBEWUs3hesfRwgQ9La5knSIQ2sPxTkRcytoOIDEsx2Fm/DKvfPfrOJksSkeNg0GTddvvIVceajNP7BhC4HtVVOi3LWSOI+gt20mvwKrvIwSpcbYk2t4rjdpdDOJZ4YYHEUxMVTh1dhTHto1KqO6xY+swv/M//wyDfWVq9caCcpbWmjDUlIoWlWrIf/r4Of7VbxzlY584x2uvz9L0FG5Wku2zyZTNn2yPjZszG5lKJeQHT03xB//xNP/y14/w3/76ArNzIeWSdUVexZLSTPhrePzyL3yQn/1H76FaM/Jq27a4ODrJ1EwVKQXF3o3Iqw6RSn8RCmk5ZAo9SeYqJXieZmLKNz5gemklSiFAKxjodyPLlejfpSDwPWYnLiOj77Qr5V0buGr5iqXdA/Nh6/oZYmO2tGrkZiHOQrSwsNUcRe8Qtfx2pG4mP9XCIZR9OPp0MpvkSsJPLSRK9pCU59Yo2sjzYpEXvvklRs+cJFcsoZXCTN/Ls3H3/YmN99KuUzQzu16J3AXixQwcR9JTcpbdrdwJVFsGEmMpC5vZPqjQN/0guQKzczUOHz3LW+7Zg65rLMuiWq2zf+8Wfve3fp7//f/+FCfPXKJczBMq1Xbvh6Hp1SiXTAnqv31qhE99YZThdS779xTYvDFLPmd6WCanfCamA0YuNrhwqcn4lI/nKXI5K1FZzS9ZCSGwLYt600MIwa/+y5/gQx+4n2q1njQ72o7NybOXqNXqZDM5Q6Cr8OoEegSNxrJsMvneVgaCGVU8PuVHr1n6s66UppCzKBVtKrWmmactJIHnMTMxZqx24uaTLtYErli+WiHYunYaZMQzrJGLbtRYEoEiG1xApAKA0AFK9tDMHiBXfwooIbRnSPbkdy2gQWBvxcvsQWiPtRxAYggp8RoNnv/GF1vXQghCv8HQ1oOUh3YQphoHO7leiYpLSLz6LCoMTJ0c87y7tqBcslIjZlcf83epGvMddHqpEh4kDCj0byJT6EfMVTj85nn8RqsHxLIsKtU6O7cN8/v/5hf4vT/+LM++eIxyMY8WrQwh5oaUEoYEz0jCUHPhYpOTZxumn8NqyXMtCbYtsS1huI+MTLiQ9HWKS1YCwcxcjW2bh/iVX/wQ9791XxI80t/GqbNjeJ5PqVg0BHrcgX6Na6+1NhlIvgelwmRDZknBxKQPvmI5dmdmsyEYGnA4f7EJTnzf1pm6NIJlt3idLm4uEsm6hp7MypevAKSun0MIe02ZoOmY3EVi///svXlwXdl93/k559ztrdgXEiS4s5tNspuUWt2SbLeWSLYWjyw7TixnMk5m4mTiiZPK2BnbmfIkcWInNTOVlO2JU4kdx47jpOJxOa5Ynli2pFZL6r1b3ezmToILSJAAARDE8vZ37z3zx7n3vvdAsJsEH4BHEt8qFEHg4S7nnnt+5/f7fX/fny5Gjaea0+x15nv+Or41iAyXohyHBEQSrpJhgVu9f4dQdkd/37mI1QAcL8WV0+9y8d3v4KQi3SsNINiy98NRYnR1qnhxzYeQimp5IWHzCAFBqOnqshoSFesBIajXtQljJbU6YBqXNbJhd3eoeIEOsJ0M+YFdKKk5f3GS+cUiqklnSilFqVyltyfHP/3ff4wvfvYjLBXL1P0AFQlVNhcyxjRcIYxxyGcVvV0WuawiF32fz1mkPJn0XI87HsbXFr+0SilqdZ9CqcKnP3aEf/GLP86zTz9GoVBpMlyYWpBSlbGL17EUePnBqAI9aLnfO40DgJfpRUqFjkgSliW4NlWlXg3vSzAzDDW2p9g65OL7YRTuNMSMm5MT6A1SON7E7WgJX6XbH74CkCKsNpo5dRqEQOkCggbjSAuJDMtU3UPcGP5lfNWHCqaRYQEZllDBTWRY5mbfz7DY9aPIsEDc9bAT0UiemwXm2At/SqW4hJIN1d1M9zD9255oUd291wmQvNACqqWFaHfaWPD6e2ycdTIgkU2kVg+TgrT4N1JZ3HVfkyYkjCZl0TWwC8uyuDVf4PzF6ziObSTSRUPdtlYzBvSnfuKL/Ozf/mG68xkWl4rJ7+903XHiPPYw/EC3sLTia0m+xxiEIAxZXCoy2NfNz/2dH+bnf/pH6OnKUihUUEouMzSSW/MFxidmUUKT79+B5aQTBtYdxzXecYY+6fxg0jckNiDXp6qGynvvw5sg1IAtGBl2k2hnrL81e+0KtXIlklHZRCdAa41rCbpTa5PjtkxmrLNilkkeBIkVLiG0nwgsxiEqGS5RyH+RmnuA/Pzv4lXeRVCnZu+mkPs8xexnEboI0Kgf6aB7XA5l2yzcnOHEi89ju6mm5HmNwR1HcdJd+NXSfd2DEEb+vFZeRAiJxlA8wxD6em2ULahVwrUfpmjhqdW16YmuYg9EIy17VbujZPH0a1EeJMv8/CKnz03w0Q8fNPUuTdRbKSVhGFKu1Pj+zzzD0Sd383t/+G2++s1jLC2V8VIOtqVAm/4cd0JzeKr5umPGVq1Wp1qr09/XxQ99/iP8+e//LgYHuikWK4AxFq2U4hDHtrhweZKlQgnLtsn1jiYepHwP42qMEOgwIN01iLIc02hMCpQ01eiTN6o8ti9j1HnvMWxtmFgaAs2WIRfPjZtfNRmQagXbcQjbpx+/iftAoGEgbUV1O+3fHXYS9WpFaFYOaWihkMEidWc3M0P/BBkWEToklDlMCGuRln7rHWw8dBjiZrK888KfMn31El46E3kmIbabZnjPB+8zeR5DEAR1KsX5pNBMYHbVfT022BJdXgcDYi6FWi3yQES8YTDxe4RcZZ1LY/edyg1QWCpw4uwVysVKslA3FwXGuY7CUpnBvi5+6ie+yOc+9TR/9Kev8cobZ5i9uYiQAtexo54cAjB6982z0TCPzfGDMMQPQmrVOsqSbNvSz8c+eojv/cQH2DE6SK1Sp1AoR+2DWwXtYk8UJTl/8TrlUoXu7jy5gVHCqEjvbnJfOgyxvSxupofirWsITMK/Ugm5OlnhsQM5dOXeQ1nm3ICv2b7Vw3UMi0wA0rJYmLnB4uw0A9t3EgbVqEC1c9+7RwFKQE+6scy3+3lYiffRQYtsoxIhpC67Iu8jWlBig4AxIiKsoIgr0WVkOOBBMB7Ni4fv+7z9jT+JqJdRaKleoX/bIXJ9o6tOnjdDCNM/vVK8ZRoUadACpIL+HsdcE2v70jdCaYJqLSRsyheYMJ4d7bbvDYlhCEMsN03P8D4mxt5mcbFEEIRYSjbJmze8BjBeQK0eUK377NuzlZ/5yT/P1euzvPLGGd56d4yzY9coFCsUSzXCMEQp1TJGWmt8P8CyFCnPYXigm8NP7ORDR/dx9PBuenvz1Gs+hSVjOGJjttKzlFJQq9QYuzSJlBov20s6N5D0QL+7MTaU5nR+kKXZK0jLeJrVWsjEtWpSVnWvcykhF/ia4UGHni4r6aIolaJaKnL94lm27NlPvVqJmmBtYr3RrPeWcSUZV972u3bB0tJBhLWklqJzYEjnvswTYqFoSqSLmJEVsbV0s/R7lDAVdLTxgDj5GGK5LjfGLzD21muN5Hm0LR/c9UGU5RjZ9lXkBpafL6hVqJUWojxDpNPkSAb6HYjE9taSzp0IOEoolkL8MD6nWZyU5SbXdq90rNjwhEGdbU98EiEt/uYP7sDzbKpV/7ap0OyNxCGnSqUGwPBAN3/xh57jBz/3YWZvLXHpyg2uT95kavoWN6bnqdUbHkEq5TLQ18XW4V727BhmdPsg+VwaKSWVSo3CkmFYNRuO+Pwxmo3Z0lKJC5dvoIQ2hZFOmsCvwPtQeBvHMgYk0zWUUH+1NuKRF8ZL6EpwX3mQIKLy7hpNMTFZxW1iYk1eOo9URMn7zmF2PkqIxzwEutPWMlJDmz0QkRpFF04jhEunFNsZwwCCgEDmDLNKl7nt5mOjt8IkFdz+8U5Co0I0xHYcjn/7ayzdukk6bwrAdODjZXsZ2H7ovpLnjXMZBlaldIsgaBgjrY1e01BsQNp2h+91LQalckDcVVdEv1OOF0mRsyo6b1xpn8r28thHfpSe4RvowFTdrzR0y72RmPJb9wOqiyWkFPT15Bge6EZYpsahVq2b3E0UWZVKYjs2CAjqPvV6QLlcRWvjUTSHq96LQRWGIbZtcenqNPOLBZSlyPZtRyoLv67vKjkthABtzpXt3ZYUH2qMvMrY5TKFYkNSZTWLfBiCyCp2bU/xjZduIYTpnCgti+sXzlIrV5GbFekbirj2oyezduErACnSOyC8h6ZEawxTRBi5ysKhokaiOhA6lkl1P1DKolwo8O43v4qyY9l2ie9X6d92kFSuP6k8vy9oItntuSSeDmYx8FzJQJ+NjjyQtd41CoAQyssS9qZg0o0S/Kubi8mCqH0WlwqMTYGKHJr3mt7NNNr4/3GOwPcDSuUqhcUihaUyvh8QhCFBEBKEIfW6T2GpRGGxRKViwlyxx7H8HO913TpahM9fvE6xWMb10uT7dzRUA+7i/dSR1x0GdfL9o1hOKqleVwpuLfhcvVZBrlJ1ObmHULN7RwrXlYQRQcF2HCbOnqS0uGAKCqEj1pRHEaE2fT9cq7WZWrshRfYgRLUTneRuCu3ji24K9hNIXQM6lGq8SjTXflw+eYyJsVM4rhclUkOUshnc9YGWz68+99GoASkvzRL4VUS0Iw8CTX+vTcpTrBdxRggg0BRLQYssOIgohCWS67634zYnpgW2Jbg271L1BVLGL9HdHaf5ePH3Sqk7Jp7j3Mbya7+X5yalxK/WOHN+AgE4qTyZ7i2mgPAe3TEdBniZHtL5AaPojNHqKpYCzl8qGcLEKiRNtNbGea1rHtuTJpsxngwRhXpxbpbrF89h2cbz6aQ15VFDs/cBa+WB5A8h3H50BxTbNS8kUtco2vuoqb6OLwS8VzTXfggpOfHS89TKpcjtN0ypTM8IPcP7TItXcX/eoZHiNsVe5aXZ5OcCU8ewZdA1PbnXyUAbJk/I4pKfLMixKJ/tZpId82rvOWZ1WRJmliwWSuq+qq+XG5I7fS3/7GqgMYakVlmke2hPi/Ly3Ry3EZILsRyPXP9oUjRqfq45f6kMfiPfda8QGEHJgT6H7Vtd6tGxlLKoFAuMn3oHy1HocFMTaz3RCItrUo68TftqLSCFlUPkDkNgVG83+oE3+n2ELDmHoyrzaGv8kO1mlGWxNHeTU698s0k4URD6dQZGn8Txcvch274MQhDUa5SXZhHSivq+mIVg67CLWFcPRFD3dWJATIpGI1RsQPSqBBWbzgCAFJpKXTJxy8FWcX/wzlzQGswzzV/90U/x3d//N9h55AuR8sC9GaUk3yItcn3bE/aU1uA4klPnCpSLftKb/V7e+fg6gkDjZBSP78lQq4WR0dZIJbl88hi1cm0zjLXOaGweoD9rJz1m1hISQA5+FmFlWI8+6HeCpsGqkrpKxRpl3vswUpeBh4sOGLOvnFSaC++8zszVS0nPcx0G2G6aoZ1HE+2j+93VmnNKwqBGaXHGeDq6IXExMuy2XNtaIzZcCwU/YQMZJpSF7UUeyH3mYrRhYRBquDZvE4Tm/1p35iYkNpph4OOk+xk9/DlSuV7CwF/GornLYwlBGNToGdqL5WYiUU6wLcHkjRpXr1Uj3arVhQqjb3hifybpDaLDENvxuHrmBEu3ZpNWBJtYDzTyHI4l6F2WPF+r91pqHSIyexC93wOBWaw3dscgENpnJvVZ6rI3qkLvzJd+NUjCV1H44MSLz+PX61GFsamkzg/sItu3raX2437OByZUVi0tmCr0aNUOQvA8ydZh975CGvcKIUwV+uJS0PBAIAphZVmt3lfrOeJGWZprt2zKdYESa5dMvF/EOXJbwfisYH4x0oBb9S7SGKN015DJowR14jxIoRTw7ukliKRr7vX4xtgD1ZCDj2XpylmJ/peyLBbnZrhy+ji266HDzTzI+iDyDDX0ZqyWtrVrOd+Trb0c/iJYOYi9kHV8yVq9jzIlezdz3vcgdYkkef6QTULLcpifnuLsmy/jeCkj/SBM7Hpg9EmsSMcI2uAVRLv70uI0gV8j1poKtcZzJSPDXmJA1hoxtXV+oU612qQMG4VcTMy/ffL7SsJCRXFjQSFoyLZ3ihFp1IWYMFPd10wu2MSikrGHcO/lhE2xAAAgAElEQVQFfyaRbjsperbsa2JymVfpreNLUA1bPMB7RT3QDPY77NmZTsJYUipqlTIX330zKVvqlLF+2KG1xpKCvqyd/GwtvQ8AKYQ0oStvC3Lo8+ggEh9cs1O2omE8IlK9hqn0DxHINEIHD5X3EcOErzzOvfkyc1PXsGzDgtNhgO3l6B89TODXo9qF+w1dNRhYpYXpRjdDAYGvGep36M5bBOHtxW3tRrKQKGH6ZtQbchpah9heBuV4bVpwTHWJEJqaL5gs5HG7UknDo04yIkIYAoNjSyw3y9Sih6V0VAqzWiKBiP5a0zO8DxXVg4QaXFdy6lyRyRtVbOve3/WEAh5oVErx5IEs9aQI1YSxzn3nVUqLi5t5kHVEqKE7pUjZa0vdbYZs/keNfAnZ93G0v2QSrWt88lbjIVFhicnsjzDvfRgVlsxC9xB5Hy2ieb7PiZeeb1SdC0Hg1+gZ3kumazgKOdz/BGhmYBXnp8z5Maes+5rREc/0u1iXRlJRuEoK5iIDIqKL0TrETXchpUW7Clp1lOdxbclXvvoK//bf/jcKxVKLLtZGIzZktlLcnFvk9778GjfnS0Z6RbPquR/XgwR+na7B3bjZ3kgOxeRBbt6q8+Y7ixALIq7iumM69tFDOdIplfQ/sRyHG+MXmbo8huW4HTPWDzukgP7c+nkfALKxG4uKjXb9HUTuALq+2DAibTYkmtuNhxUuMJv6XqbSP4gKTdX52tdFbwyUbTM3dZ0L77yJ46UaMt1a07/9MMqyk+fRjgkgpMSvVyjMX0cqKzIqhkmzfasHUQOktUdUHqhgdq5Ord4Im+kwwE11IZXd1o2LEIp6ZZ7vfOu/8C9/87/x//3ZG7ieQ9gBFNMWWRMp+Jf/7iv8i1/5bS69/ceJ2OV9byACHy/dTf9IUzuAaE/28psLUFtdGCsWVgxqIft3pxkZcqlHGwKpFOXCIuffeg3bsdA63PCxflgRj2sQarKeIuepdfM+ANM4zEwGE8pC2qg9P4PIPmaMiFD3XYfQjPgocc4DQIVLzHkfZyL3V5BEO2/R2UKIq4GRqwhwPI/zb73KwuwNlGV2DDoMcNJd9I080ZKnuB80PB6JXytRWpiOak00oTYV6KPbPIgUVdeLgYWvuTFTbWoRJiJF4m4zHm1IosfQWmPZKfI9w/R0ZXjz2BjlUrWlydRGw7IUN6Zvcfz0ZfI5z/RqaYPuWYMtBQOjTyVjG2pIeZJjJ5e4cLmE7dx7UWH8WT/QpHIWRw/lqFQbygJSKc6++TLVciWRNemEsX7Y0PzMBppzH+t0fknEuDdGRJl8iNOLevyfIPueQ/tL0YXe/yTQ5gDmfEIhtI/UNSYzX+Jy/icxDKzgoTQeDfVVSeAHnHn9RVMhHIVwgnqVnqG9pLsG2iNd0jgzQlkU56fwa2XizGYQaFIpye7RFNTXj4ElBQTVkKmZGlZEIzXiipJUto846t8uY6ZDHyeVo3vLYwgCLl2Z5uq1GWzbuudFs92IFVMd1+b46XEWl0rYlkPPlscNU+4+Qz9JGKteo3t4H+muoWRuKSVYKvi88MqtpCq92SO62+uPNcs++FQ+6WipwxDb9bh65jizE1ewbGddSTmPCpLCwVCTdRXdTV0H2/kOvRdihb7kghIjIl3Unp9GjvwoOqyhgxImKdkqDXw3SHbCEB0fVFggFB7j+b/FZPZHkLoeGY8479HW++wYWLbN/PQkY8fewEk1ha+EoH/0sOmH0abwVRxfl9Jiae4aft10ixOi0QNksN8hWCcNLIjkyushU9O1pK84WqMsBy/X16Iee79IEslhSO/WAzheisWlAm+9ewHLse55wWw34jCQDjQnz1yhVqsZYze0x1C4RXso9ToMcFI5+rcfbjouuI7khZdvsXSzhm3d+7OP6bxhNeTw41kGBxzqdROgVpbF0vwc5956BcdzTJHsJtqKhG0HDOXtRgHpOm6KEmsgbjMiUU5k5Euox/4xousIOiijg0o0qeVdu9nGe4mkrIMiAs1c6uOc7/mHzHnPocK4c2Cz3tXDZUGS4kHPY+zYGyzMTGHF4avAxP97tx5IXvB2oCWBfut65P2JpA5j7850lPxc+0U0XgiVFCws+szN11Eq8kAi7a9U3PeiTY++kUiu0TW4i1S2H3TIW+9epFapNTHA1t+IJOOhJEuFEifOXMFWmmzfdrxMT1u80CSMJYwRGd7zjKn0D4PEgFy+WuHF1+eRURJ8New039d09dhRGMvU9qBBScVbX/tjKqXyZhirzWjOfeQ8RU+mQXpaTyPSslIJ0RzOinsXhMjcE1j7/wFq788iso+Zz/hLxqDoYFmSrPFSmp8H6KBgDIdQiJ6PYB34RUojf5eiGMHSxrNpCVs9RKEraC4eFIRByNk3XiLwfWMo4gVuaHf7lHebYBLoZZZuXr0tgT464oEnCdZpc6g1YAmuXKtEvbkbFF7LzeBluk0e7h6lO973vNEOvGfrAWylOX1+gqvXZnE2MIwVL9SObXPh0iST07dQUtA9tBdlu20jUcQ1JoFfo2tgB70jjzf01TChrD/++ix+KS7qvLcQlrkPQAmOHsxFrVPNM3VSKcZPv8uV0+8assgmG6ttaB7Hoby94s/XA7dvdZNJET9skRgI2fMs1oF/inr8l1Cj/xOi6whCZRDSNkYlrBujEhr5BSEdhNWF7P0e1M7/BfXE/43a+zOQ3sNw1sdVdUJtDIaIqSEPMZRlsTA7zfm3Xm1lX6Hp33aoreyrBrtHUS0tUFq8gVSGIhtLuO/c7iU9QNZcwl0IQg3YsQGJmhoJgQ4CMl1DKKu9sfLlNMb+7QdxXI/5hQJvvH0ey7E3LIwVU4ylpXj39DjFYgnXy9Cz5bFEvqStz0SbQs2tez9ssp4RuTLlSU6eLfDym/PYaXnPXkgcxtLVkKcOZunKN6rSpVRUikVOvvICylbJOrLphdwflnsf3emN8T7gPXqiN0+ihjcSGC8is9t8Df8A+EvgL4K/hK4vQlgBlUXYObDyYOUQKp0cV2uNRuNYFn1Zm8mFOlZU+P6w2g+jSxTgZLKcePF5bt2YxPY80DoqKsy3lX0Vn1OHIVJZLN2cwK9VIgMSdZRLK/btSpsEulz7iRfH+/E1V69XEjshoj7mme5hlO3hV0ttnQgtieShPaTyA5TKE7z+1jl+8PMfbgljrdeLF79XUgpq1RrHT48jCEnlB8j1bU9kR9pxTcl7LIRpkbz9MLn+HSzdvIKy4iZygt//8g0++nS3MQarWIx8P2Sw3+HAvgwvv7lANq1MszTX5dTL3+TTf/lvtmyQNrF6JMrPwHBXq/ex3sb5PVcr0eSNxB9vDk0JrRFWDuGNILKPI3ueQfY9h+z+ACKzD+EOIVQ6MT6NyWMGYDBn46j1T/ysN5of6vm3X6Veqxrtqyh8lR/YSSrf3vBVbKiFUCzdvNLSA8Qo8Dr09zn4/vpReJUU1MsBF8fLODHzBxMuTXcNETeSWotrMWGsLnq2PI4lNWcvXOPylRu4jr0hYSwN2LbF5NQc5y5cx1LQNbQX20knnmm7rynuF7/j8Kcio2I2bumU5NjJAn/2wk3sjEUQ3v25E3XeEISn+ODhnMmpmVg4tuMydXmM8VPv4Hib2lj3ixbvI6XoSm2c9wF3KXN7e88DSSx30jAoUb4jyYnEXzEDy4gFxsfTWuNYkoGcncTgH0bXNkmWWhbFhXnG3n4dJ5Zuj8KDfVsPJFIT0L6FQwhJUK+wePMKQqpEwr1aC3lsdwZ3vRPoSnBr3ufKtQpWk4Krsj2yPVtXpTz7fmgJBWnNwI4juF6KxaUir37nHMpusLHWa/4Z71BjOzbvnLrMwmIBx3Hp3fp44n21cyFoVs/1a2WGd3+InuH9BLVKVOMFjiP4nT+Y5OZUFdeRhOG9yb1IAdRCjhzKk8u2hrFq5RLHv/21RMRzM4y1ejR7HxuZ+4hxz/ES0TTBG18y+lLRl2z6ipkgjc83T57BnI3nSEIdT9j23VwnIL5fy3G5fuEMsxPjSetaI3aXoXfk8UTsrh0ToREiUdQqBZZuTkT0YG3YMUqwb3caVinnfa+I8x/CFly4UqJYClAtGlhZMj1b0avovHc3aLCxqnQP7SHdvRVJyGvfOUupWL6t9exaolEPJAh9n3dPXiLwfdx0F91Dewj9WrSot/9FiEVSle2w66nvI65KD0PDyLo2WeXf/O4Ewmplp93NtQhhtNW2bXUZ7LeTJlOhDrG9FGdef5HF2Wkz9zexKjR7H91pa8O9D9jARhvJwqoEw3m7yXA8XBYk9tAsWzH29utUSoWoT4LpW53tHYnkts3i2bb6B62j/MdVauXFhEYZ5z8O7M9A3Ahoja22jgwXUnD+YjliYBEl0H2y3VuwnXSi1bRWL4IOAxwvS//2w9gWjF2a4vS5q3iuvf7JR0syO7fE8dOGvts1sBs31ZWMQbvRnAvxa2UGdjzF4M6j+LUSUhqPI5dRfOUbN/nyV6ZxchZB0Ervf69jgyloS3mKPTvS1GOZGm16pU9fvcT5t19PyCObYax7R7zBVgK2dG+89wEbZECWeyH9WZusq+7Zbe50NHsC1VKFsWNvGCotccOfOj3D+6O4d/sWzyT/IS0WZi4T1Bv5j7qv2Trksm3YI1gHCffGGEBQDjh2cimpQI8T6Ln+USynIV/fbjR7wWHgM7jzKG4qR6lc5vkX3zVzjvUJrcTV565rc+b8VWbnFrAsRc/IAWSUZF5LEbzGcTV7P/gFnFQu6bmutWHn/et/P8Hp40t4WYUf6LsyImA8GRxBf69tilNp9v58Trz49UQ8dDOMdW9IvA+t6c/ZpJ1WVekHJoTVTjRuvnMsavuhsWybuRvXmTh3Ett1o+5wIcpy6dm6P8p9tDd8ZSRTqizOXEJI2ch/VEMOPZ5NFgfz2bUdb5MwltyYqTF2qdSQvIjHYHhf0gNkzbyPpnqIXN8oXUN7cZTmte+c4/rUHI69Pt3z4uQ1Gt48NkalUsVN5egd3p+w8NZqYW28bxK/XiU/sINdRz6HX6sghAkjW0pQqoT80//nErPTVTxPRkoF729ENIAUpJoE/WI2oJtKc/bNl7k5OWGkTR6ySMNaIpYl0VrjWoLhLmejLynBhoewwEzKrpRFd7rRl+Jh2J3EL4/tulw6/h2KC7eSNp9hGOBleujq30kQtD/uLaWiWpxncWYcFeU/tDZS3k/sz5is5xrnP5LnGwKO5DvvLjK/6CcSJmEY4GV76BrYtSZj0IxGIj1EKYuhXU9j2RbTswu89PopHNdJCAVrdQ3xcS0lWVwq8c6py9hKk+vfSbprsO1FpO91HUJI6tUSowf/HH3bDlKvlpBSEYSalCcZn6jwC//8IqViYOT+38OINIgyQKAjUcXWeaUsm4WZKc68/iKO56LXpX3AQ4LE+4DhvJ10G9xo7wM6oNl4881v6bZRMq4JefC9kKT6PNRcPP4Wfr1uGGxCEvo1swtO5ZLwVTuQ5D8sh4XpS1TLi5E0uKk+z2YtDsb5D7k+hlpK0JWAb75yqxEyE9L0Pxnah5POt3UM3hNC4vtV+rcfJp0fRBDwjRePU16HZLohE2hc1+HM2ATXp+awlKBny2Mo22s7C+9O19D41zQae+J7/ge8bK/xgKQxFrmM4tjJAv/H/3WBYjHATSt8v8mrWIG5JgTommZ8ooJtR4WjNLwuDbz77a9Sr9ZaGFmbuDMSwkWoyTqGtdr8uzWPQb8POiSEZf5NO8rQeu+B/dGpaFBXLUqL81x89zs4XkzfNb/rHtrT1P+ivSEsgLnJs4RBLcl/GP2rFFsG3aT+Y63QeK5gu5JzF0scP1PAc6VZWLRGSsXAjqeMUdW3V46v2bUFAW6mm/7Rp7AVnLs4ybunx/FcJ2EDrsl5IzKBEII33j5PqVTBTWXpGznQVhbe+6E5lBX4VbI9Wzn43F8xHmAkJe8HmlxW8cY7i/zUPzrH2IUiXpeFFAI/0LexJYMAHE8yPlHm+OklPFcmBiYJY3kpLh1/mxvjF7Ad11THPwQbxbVE4vUBw93ObV7HRo/ehnsgyzGcd/As0UTrfTCNSMIysx2mLo1xc3Ii6sego7BWxvSq9mvJbrBt55YSv1ZmaXY8MVBCQL0ecvRQDpVWCU9/rUNYWgNK8N+ev0mxHKLi8FVQJ9M1TO/IgajR0do/60Yy3TCyhnc/jeOlqVSqfPOl40S/WpMEb2NDISkWy7xz8jKWDMn2jpLtHWmpPl8v6MiI16tFBnY8xcHv+THCwI8S+cYTyWYUZy+W+Lv/4Bz/+feuU/dDvLyF40qkFEhpxtRLK3Sg+Te/M0EhomkvvxVpWRRuzXLy5RewXZtwU9rkPdFK21X0bKBkyZ2w4QZkuRdiKcHWbieZfJ0wSKtBQt91LC4e/w6VwlLUH9owgdJdw6TzAwkDpl3nBJNAr1cKlBZnkhaxYQiZtOKpJ3JG/0qsIV02meSm//bEeJkXXp4j7cVaSyZ8NbDjKdzUOoavkmuLFAD6d5If2IOjQt44Nsbk9C3sNUqmJ0lQx2bs0iRXJmawFPRsfRxrHSjMK11P43uJXy2x7fHnePyjf4nArxojEoWz0p6kVgv5td+e4G///Fn+4A8nOX+hRKUaUquHBIHm3FiRv/ePz/PKW4ukUyaXIoRIZnbsfSnL5sTLz1MuFJN84CZWRjxnlBSMdLstP+8UdMQTXJ5Q78vazJV8FpKdTGdY23uFlJJatcb4qXdaYs9hUKN7eC+Wk060n9p2f1ojlc3S3AT1Wimp//ADTU+Xxa7RFGG9Uf+xlnTRMNTgSP7oz2a4eatOPmcRhiZ57qRybNn7bCLfvu7PV4coy2Fo99PMXTvJ9Ow8b7x9ji989sPUan6ikdW20+lYPFHyxtvnKRRL5LNZereukXjiXaBl8wbUq0V2HP4UUlmcfuk/ov0AZTkEoVFOzmUVY5fL/PKvXyWbUfT32KRTkrqvuX6jRrkSJO0BmkMvJBvERqOpq2dPsvvJD1ItmzqUTbQifi5BCNt6bFKO7JjEeTM66sk1D8q2bveBNR5J7YOyKC0ucPVsRN+N3HWpbLoGdsbC9+27PyEMXVdZFG5dJ6hXkvyHXw/Zsc0jm1E0elitTdV3UuvgSS5fKPInz9+MFhZM8rxeYXDHEfL9O0z4ah1DNw1ZDxP/Hxg9TCrXjw4Dvv3KKWrVetv7hLSErwpl3jh2HiU16e4t5Pt3EAY11jt8FaNlQRICv1pi9OAneOpTP4GTylGvFqMxE4mKcy5rjMT16SrnL5W5PGF6BLUaD0G9VmL3kc+R6x0xfW4wOcFKscDJl7+BslTEDtwMYzWjJXHuyRbBxE4bp44xIMtDWSnHDFzQzOR4QNCc/5i8dJ7F2WmjhKuNqKTtpskP7CQI6rSr6xxgdnoIdOhTXpolDo1JATVfs3NbChXx+tcCzV6kBlCC//Rfpgx1N+54F9V+bHv8OdPKVqxf8nw5wsDHy/bSO3IQW2lOnbvKxfGpSGCxfYZ9efhq/OoMloS+bQeTBk/x5zYCy41IvVJgaOdRnvnCzzG85xmCesX0EEGgaUi+O7bEdSWuIxPChJRGtr1eLbLj0KfY9+wP07v1gKEoCxmFdV1OvfICxYV5pNURQZCOQiNaAdt73NsT5x20oe4YAxKjeTEdyttk3XsXdtto6ChRbjmK8VPvUCkVWvIfma4teJmeNYn9C2mky8uLs+ZlNsu0iaMOu5Hq3dpNQrNzAjejePnVW3ztxTkj7R15H36tzNDuD9K9ZR9BvcJG7LxbDJaGwZ1HsR2PhaUiL712Oupd0V4PZHn4yvXS9G07GIXwNsaANqN5kRJS4ddKeNlenvrU/8yR7/1bdA3uJvCr1CuFKNGuozEyEiZaa8LAp1YpIJXN4x/9UpJP6d/xJJaTgjBAa6PQO3npfMRM3JQ2aUY85/xQM5CzyXqdUXF+J3SUAWm2sFprpBBs63ETqnOnDd57QUpJvepz7fxpYk/A5D/q5Ad3YjmNhlLtvS9JGNSpFG8Zrr2OxPJcyZYh11QjrYH+VbPb7TiC+dka/+q3J8wvE+cjwHYz7Hzy+5LiqI16pg2JjSrdw3vI9GxBCc3rb52jWGjUhNzvON05fLWVfP+oEU/coPDVcjTXdwipCIM6gV9naPfTPP35n+bI9/4tRg9+glSuHyEkYRgki7+UinT3EDuf/D6e+cLPsevIZ83f16t09e8g3z9qPG7MZ+uVCideer6RK9kMYzXeIa1J2ZIt3Z1TcX4ndJz/uDyUlfNMbcjUYh3rAciJNPIfikphqSFfErFQpLTI9W1PqLvtaiAVQwjDMKpXC9GxzYvp2ILebivpQNhOND8vAKEkv/ZbVxmfqJDLGsqwoYsusevI5+ga2Gli61Jt+PPUYYDj5ujbdpj5G5e4PDHDuQvXeerQLsrl6n0neI1RDfFch+OnLt8WvqpXChCpVncCWoxIFF71qyWElAzt+iBDOz9ArVqgtHCDamkRISVKOdhuhlSuD9vLoIMAv1qOKgtDLDfLwOiT3Jo8FxXWmna3p1/7NvPTk6Tz3YTB+jHxOhUNYwpbu52W9W6j35M7oaM8kDthS7dDypYPTG2I1hpl2cxev8L8zA2UFec/QpSTIt83SujX16b2QRi1VRNzbkxISwnyWQuM5FTbJmPz9QcBODmL3/+vU3zlhbkk2RoXrOX6trPryGfx/Rq02XDeKxo1IYJQB/RvP4zrZSgWS7zx9nmkivW67t8DMeErxRvHTPjK6bDw1XLEY5NsCGTDkPi1Mko5dA3sYmjnUQZHn6R36+PkekeQysavlgmCqNI8Hl+/zsCOp7DdbBS2NfnBm9evcv7t13Bb2js/mmgOXfWkLXozrTUfdNgcidGRBuS22hAp2N7rJLV2nfbCNSOmK1quzcS5U1SKhagWwyS3vWwvqVx/xPtvuy+QKNw2Fyaa5KbAtiOWVrvO1hSG8n2Nl7d447Vb/PrvXiOdkhHby3haWmv2P/sXcNNd6KjqeqN3VXFNSOjXyPePkukZQQnNd94Za0sYqzl8VVgq8tpb51BCGzHHgZ0dFb5aCSsZEqL5HfhV/FoZv1Yh8CsEQQ2tw8ggRsSQuOYmqJPp3kL38B6TjI8NSxDw7re+StCy2enMsVhLNIeuXCta6yJ0SsX5ndCRBgRo2p2bf7tSFkN5O1GQ7dSJpiMmFBqmLp0nqNcRMn5hfPK9203+Q68h86bp5W1BG8/VYjwCjZdVjJ0t8Eu/ejkxWHEYpF4tsePQpxnceSQKbcgNNx7xtUOcm0nTO3IAS8H4xAxjlybvm40VLwqe63Dq7FUuXZnGtgR92w5hOal1Lx5cLZpJB/H3QkiEjL6WN49b9jdxzc3gjqPJvkaHIY6X4vxbrzF77Sq28+gq9DaHrkZ7XRyrM2s+VkLHGpBEc69p7EZ6HLJe57OyhJRUSyUmL42hbBsdxr0RQrKRq7821248DKmsKEQUL/KGKVOrhYbme59nuc3zSCuuXa3wj/75RRYWfRzHJNMTmYzRw+x75gcTyZJOeiGSMFYY0DfyBK6XplAs8da7F5DW6tlYSVJYG2bcG8fOU6lUcL0M/dsObVjx4IYgUh7o2/YEbqYHHeU7lG2zeHOa069+C9t1CR/BMFYSugo0g3mb7mVyJZ2OjjUgcHsoSwjBjj63aXfbmUZEKkW5WODG+BiW7RgqrW70/tZhgFir3hdao2wvYWCBMSB+oFkqBOaJ30dsv3lH5PsaL6e4fLnEz/zieSYmq6Ti/hFS4tcrZLqHOfixvxolzMOO21U1h7Fy/aOk8kNIoXn7+EUqpcp9h7EsSzG/UODNY2NG+6p/B9m+uLCuM+fvWiAM6qTyA/Ru3d8IYwECwYmXvk69Wk1UEx6VMWlmLmZcybbuFUJXHfKe3AkdbUDgdiOSdhRbuxsFhp2EeNepLItbU9cpLS6a+o+4LsT2yPZuMzmKNTJ+5vyu6XIYyYNLIahUQ6Zmqqvug95g5pjrDkPw8hYnTyzxs794nutTVdKphvEIgzq2m+HwJ/8GqWxfJBq5ds2SVotGGMsIXHYP78eSmovjN7g6OYsdeSGrOW4YajzX5uSZK1y5NotlSQZGn8JaJ+n2TkBzvY0QksEdR017AR2FsVJpLp14m+sXziVsxYd9TKCZuWhs6Uob4wdhHDregMRoduuG8g49TYJtnbIoCSGMB2DZ3JycoB51ejM7zQA33W1ojmtS/xEhijen8gMR40UgpOlEeOFSuSVZeTfj1mw4gKSxkJu3+NY3b/IzvzjGzFydVGw8hEQHAUIoDn/yr9M9tId6rZxQdtfsvu8DzdfTs3U/juuxuFTk+KlxLLfRZvZeYMbMOIGvvnmWarWGl8rTN/IEgV9vO3274xHV3PRufdyQSII6YLz14sItTr7yApZjxvpRqgkJtGZLt0PGbS0Y7FTW1XI8ELN4JXdutM/FUZ0Vyko8EAVzk1epVSqmjkAYAcFUvj/Kf6xdrNdIRXhku7cQhn6yy7FtyVsnF/FLAc2lDXcat8ZLLJLddFxhrrXmN37zCr/wLy5Sq4V4TsPziKvrD3/ixxkcfZJ6tRjJW3Sm8YAGTTL0a3QN7MJJ5QmDgBOnxwlq/qo9J6UkhUKZd09dQvsl8oO7yPRsTRbPRw2mD0sX/dsPmRCe6WiGsh1OvPh1ykuFR0KhN16zglDTlVJs6XJum1+d95asjAfCgMDtoSzHkoz2uUl4oVOMiKnyhvmZaSK/PfJMQlLZPizbhTUyIGZxNjTLni37kcqJdnRGBO/UuSLHzxSwU4ogbGZ/NFN+W8dQa00QaBxX4mYUx95Z5H/9R+f4978/iW1LLMu8CKZy2bRkPfzJH2fL3meoRcaj9fo6F2EY4Ka7yPXtwFaa0+evMr9YxLJWlwcx7WEdPvXxp+ndup+dT32WuIAUOn882oXl0jEDO46gLAcib8NxPaeUZgoAACAASURBVK6NnebKmeNG2mQVHt+DgmbKrmOZnG6MByl0FeOBMSAxmkNZ3WmL4S4nofZ2CrSGoZ27CYMAHck9+HWfTM820wt6jSZIvJMO/Bo9w/tI5/uTRV0IU+j3W793Hb8S4joC3w9b/jYe29jbAHA8hZtVjF8p83/+8iX+3i+c48SZIvlc3NvdCOgF9QqW7fHUp3+C4T3PUKsUkjBNp78UDUNqhB5Np0jBzVsFLlyexLGte74Hs2kQSOFz+EOf5Jkf+Hl6hveatrGPWviKZdIxg7vJ9mxtkTaplooc//bXEvLHwxjGaplD2uQ93AeIsrsSHqiZvFIoa6THoTvdGfmQWBOoUizyzGd+kO/64l+iXqsR+D4f/cKX+Oxf/THCoBpRXNfuWnUQ4KS7GNr9dLJghaFpDHTsxBK/9CuXqNU0XpeNUo2uclIKLEsYo5GzUFJw6vQSv/yvLvOTP3+WP/6aSQSnmhR9hZTUKgUyXcN88HM/xdDOo5FMiXygXop4lxwGdbqH9kZV6WVOn5tAWOqeFrS4DEcIqPtwYiIW0qxv+BzdaOgwxPay9G8/bEJ5QhDqENvxOPXqNyncummUGx5i+KHJe3SlrBbj8SDigXtSK1F7d/a7nJmsUAtC5AYuWs3XJZXiL/39f8ZH/ru/gBCSnU88heUovJTL298+TblYxXashPvejutNdtIY2uT2Ax/n+vlXqZUWkMomCEMyacXXX5zj+o0qP/z5QT74ZJ50WmEpQb0eslQMmJqucup8kVfeXOD0+SLFkmkUZKRJ4sp2RRDUCf0yW/d9mMc+8iXcTHdL2OpBMR7QROcNfLK9W/EyvSwtFTh74Rq1Su2eNLGE0GgtsJRmZsni+ryDrSINmeQzD8a4tAuNuanRYcDAziOMn/ha1INdYLku01cuMXbsDY584jOUlyIG40OC5rxHT9qKuq4uy3s8gHPigTMgcLsRsZVkZ7/L+RvlDXUHm89rxOEC9h55BoBapYxfrNO3tYdnv/dJ3v72GeZnFnFci7hYrR3Xm5zfN7z7fR/6IY4//xtIZSXJ8GxGcfZCiV/8lUv09dj09tj05C3m5uvMzNWp1UIKxRClwPNUUydBosSn6ffgprvZ8+EfYfvBT6DDgKBW6fiE+Z0QPzutAywnTa5vlLnpcS5cmmJpqUQ2myIIwvd9TubWDXHBkpoL0w6VusSzwyjz8eAY1XajYaRr5Pu2k+/fwfyN8yjLQwqJX6ty4qXnOfLx72v6/IM1j1ZCc97DsyU7H/C8RzMeqBDWcjQbi5ynGOlxCDtAL6t5QlTLJarlEgBSGYn3bFeGD3/6SbbuHKBWjXMU7XFjm2mAfq3EyP6PsvfpH6BeNdcQ97lOeZJ0SrFUCLh4uczrxxa5dLVCuWKS7vmcIp1SCBqGQ2uNXy0ShgEjj303z/zAzzF6+FMEfs0wvqR8oF/6xHu0bLJ9oygpWVgsMj4xg21Z9/R8pNRU6oLz0y5Smp4sWhuSw6MO0ysnxeDOI4bSHrEUHS/Nmddf5ObUNSyn86XM7wYtG1pgV7+LpW6PojyoeGANyPJBj+tD+rIWfrhxUgDL9YCklEn4Q2uT+wjqAZZt8YGPHWTv4VH8umm0I9qYF4kZWX69wp6nf4C9T38Rv14xORGp0NEuWSmB6xpj4joSJWParuk+BwIdBtQrBYQQbNn7LE9//qc5/Im/RirXj18tRud7sI0HNO0UA59c3zYcL02xVObC5UnkXSTSm2s/lNBcn7eZXrKxVTwuD2acu11I3g0hCPw6/dsPY3u5hPpt2Q63blzn3Juv4HjeQ6HQG8+XUMO2Xvf2eo8HHA9kCCvG8lAWGDGyci2kVAs7qqd6I0RiDEUYNeI5+MxeMvk0J18fI/BDLMskvOO/uZ9zxf8G9Sp7n/4i+YEdjL3xhyzdvAqIKKwl0ULQ3DtE6xAdBkavSSq8TDdbH/tuRvZ/lK7BPSDAr1fMuWQjwdwJ43z/EOjAJ9uzFdtNEy4WuXRlGu3777ljbJmHWuC4FhdnPWq+IOXoJLT1qKMRxqqT6R6me2gPM1feMR0Lhfn98W99lWc+88UWmvmDOLfi6/YDzUDeYjDX0MC7bbOljZe63LAIQAtTG9CJY/BAGxC4fbFUUrBnwOPsjTL1QG9oUv1O19p8vfWqz87Ht5LNp3jn5bMUFks4zv3nRW4zIn6FoV0fpGd4H9OX32b26nGW5q5RrxQI/BqBX0UIibRsLCtFKttHrm873cN7DSW4a8jkOfxKfIaWyd4J43u/aDyfEMvNkMoPYc3NcPnKDUplk0i/066xebylhLMXZjg/tRsnko7ZNB4GDSMbopTD4M6jzFx5B2go9I698wbTVy8zsG0Hfq2hm/UgoTlp3p1W7Oj1kt81h7SSdzT5u7ixVrShQ0dlQ51J933gDUiM5sF1bcnufo/z05WOG/TluyqNplb16dvSzUc+c4Tjr5xj6soMtmPf93W3GhHwqyWU5bL9iY+zdf93EdTL1KtFyks3qRTnUJaHl+3By/RgOSksO2VyJvUqfrUUrYHiNkP4sEGHIZabJtszghg/zuT0PEuFEr09OXw/uO2+m3eVnufwH/7fb/Bb//FP2P3UpznwXT9K4JsmSg/jWK0aUZOxvm1P4GV6TIhUKpRlsXRrltOvfpORv/zj1KplpHiw2FjNxiPtSHYPeCa0uSxa0vA4YiamBJrvdXmYPkBr2VEbt4fCgKy0s896ip19LhdnKpiwa+cYEWh1VaUU1GsBbsrh6U8c4uyxS1w4fgUhBUrdX0irZbJJidZh0kZVKgcv65HKDzZCWKEJX2kd4tfKRHrkSUX9w2w4gIgtEyKEJNO9BUspSuUqV6/NMjjQTb3u3/HeLUsxe3OBL//p62gdUlmaeSji+GuFMPRJ5frp3XqA6+dewnIzEFHET7z0PN/9Q//9A0cJb2ZcOUqwe8BrCaU3L/wx609EBlLXZtEL76ArV6A2B9IGZwCR2YfIHUSoVHSOEIRs8WA2Cg+FAYFWI0Ikk92TsRjxHSZu1ZLk8EYPeDOar1lKQRiYvMgTT++hqzfLidfGqJZNvcj9hLSavR5ozluE6CCEoB7xT0XiZUR/yHIdqE4Zu7VCsgCEPumuYWw3RbFU4frUTT5kPYbWxuA3fx5M9X464/D6C8eYnVvEdW36R59E2S5hZLA3YZDMx6jd8cCOI0yOvRrlATSOl+LyyXe4PnaG0ccPU62U77s3/XpgOeNq94CHZ8s7Gg/zvUJXpwknfx9963W0vwjaB2FhSvIDkC7CGUQOfh9i8HMIGYlOio0v1u38p3IPaLCfzP+11gx3OaaTYbhy7HqjcVtIK8qLjOwe4qOfOUL/1t62UX2XM8QaXxIhVVN3ObHiZx8dCHQQkO4awLI9/Hqda1NzsYTVis9ASkG9WuebL58gDHy8TC8Do09FTbTkIzZ+74+Yah74VXq37CedH2hR6C0XFjn58jdQtmUMi+5saZPmRTzUsLPfJevdzrha/n14408ITv1vhDNfRYdVhEojrC6EyiBU1nwvXXRtluDKvyM4/XOEhbMmYqADom3fho3NQ2VAYOVQ1fZel560ofd2In0uXqCT6xZQq/pk8ime+XOH2X9kJ2EQEgZhCyV4E+1Hw6AH2G4GN9ONlILJ6VvUqrUW7yP+vNambe2Z8xMcPz2ObWl6Rw6SatIi23xeKyNW6O3b1qrQa9k2x1/8eqOnTgejeb0JQs22HofezAqMq/izmHkTXPlNgvF/HRmObBMTMgDCpi+NkDbCyqHL4wRn/yHhrddM6EsHG0oyeOgMCKxM793d75JzO0Mz605YnhcJAhM/P/DB3Tz9yUOkMi61av22z26i/dBam74quQGU1ExOzVGr+beFr8yXKRL96jePUSyVsR2P4d0fSjyWR8+De3+0jImGwZ1HUJFStdYa2/WYvHiey6feMQq9YWcq9DYbDz/QDOVthpvk2RMvnoiOS2g8rxt/Qjj1Rwgra8JYCfvqDmFiHZp8ifSAkODyr6FL49Hfhhu2HjyUBgSAZUZESsHeQY+MKzveiEBrSKtW9Rna3s9HPnOU7XuH8f0g8kZul2PfRJsQN+bKDSCFZubmEguLxds8QA24jsXE9Vleev00toJc3w56tuxLWrduPp+V0RzG6ooVev2GQm+tUmoo9NJ587zFeISagZzF9t6VZUpMLUdoFvzKFOG1/wRNSfH3NY5JlCJACAf8RYKrvx2FsBp5l/XGQ2tA4sFsXogtJdg7mCLldL4Raa0rENRrPm7K4ehzB/jAc0/gZdy2y6BswqD5Zc50D2PZDuVyhfGJ6ajFbVP/lFBjuzZf/9Y7TN9cxLIEW/d/l2lbGwYrHX4Ty6DDAMfLMrDjqaTdc6hDbNfj7BsvRgq99kZfZiuaXjc/0PRnLHb2m1qPFUsHmuZMOPOn4C8ihHV3xqMJIvJihMqgl06gF96JQlhh5OGsLx5aAwIr7+ZtJdg3aNgRnWxEYAWWVhji1wO27Rnio585yui+LQR+QLDpjbQV8c44DHxS+UFsJ0W5UmVqej6Rdo9h24qbNxf5sxfexpKadNcWBncexferCfOqE0MvnYAkjBWN9cDok1i2Z6quESjLZnHuJvVaFSE7awx1VNfjh4btuWvgduOxvI5DCIkOa+iFt0A6rLo2KD52WEUvvLHs5+uLh9qAwMpGxLEkewc9XMuo03a6EYFWd7lWNd7Ike95nA987CCZXKrhjchmOvMmVg+jAZbK9qEsh8APmLwx13h5gTAMcT2Xr77wNlevzWIrzdZ9H8HLdKOj5Pkm3hvxQhv4NfL9O+gbOUCtPE8Y1lmam+XAs8+R6+kn8DtnPON30Q813SnFnjsYj1bbEL2P5Ql0baZB010NhDB/K1300hl06Ce1JOuNh6YO5L2wUqGhZ0v2DqY4d6OMH3aW5MlyLK/jiL2RMISR3YP0DXcxdvwK42evE9RDbEfdV93Io47GfDEV6W6mG3XrJlevz1Kt1KJmYODYNjMz8/zRV17HUuDlBtiy76Ob1N17QMvc1pqDz/0YlpujMHeNj//Fv8Ynv/Q/JsWYnTCezVXmeU+xZ3ClKvPlxgNiY6GrNyCsIaTLqg1IfDwhoX4LdI2NWsofCQMCKxuRlCPZN2gkTzrdiMDK91Cr+tiOxaFn9zE82s/Zty8xOzmPsiRKyU1Dch/QOsSyPdLdW1DXxrh8dYZiqUIm5VGr+ziux3/+nW8xMXkTzw4Zeew50rl+6tVCUqy5Oe7vjeY8Xxj4IFM88/1/kz0Ht7Dz8Z1UK2WC4L2FLNf7Wv1Qk3MVewe9ljWjwbx6j4MERUO9bQuEKToMSqDSbTrmveGhD2E1Y6VwVto1uwgljPxAJ4ezoDXBDrE3YooP+4a7efbTT3Ho2X24nkOt6if5E9jMj9wrtNZIZZPrHcWyFHNzi1y8PIWUglw2xWvfOcN//ZPX8BxBumeE7U98DN+vEPdO2TQed0bzRgigXvORSrH30AjPfuoJtu0ZprS0SBgEG/5Oalo9j9h43Emi5D2hMtC2cJM2x5Le+390jfBIGRBY2YhkXcW+IQ9bdn5OJIa5j8YEFlLg18zOZs+h7Xz0c0fZc2g7UsoWthZsGpK7QWyow6BOz5b9OKkMpXKVb7x4HLsnx/jVaX71179sPqw1ez7wBZxUHh3cWStrEwbNczbwAwI/YHh0gI985ghPfGgvlm1Rq9YjZYSmOb4B4xqH1hLPwzNrxfKmUPB+xiOqB3EHQBgpkvuDoQVj92yoAXlkQljNWCkUlHEV+4ZTjN0oU/V1R/USuRNEpFuVTGDZCGt5KZdDz+5j255hxo5fYWp8hlBrLEu9T6x2E9Ca3M31bSfXu4Nq+TTffu00/b/xZb796kmu35jHosrIE59keM+H8GulTe/jPdAcrgr8kCAI6RnMs/fwDoa39ycKDEKQSOdv5Fgur/PoTploxcphq/e7xuj33naE04euTkeJ9FVdmMl/hDVEdj9CWpFBWv9xEvoR3o42P/x4QlTqIWPTFSr1zmpIdTdYfj9ag2WbtrTT1+YYO36Fm1PzCCGw7M38yPsh3n1aTorp8WO8/ZVfRUhFuRrgOhaEFfq2Hebo9/5klPMIW+bSJgwaHgSEgcb3A7L5FLsObmf73mEs28Kv317T1An5DjDGozdt3VGW/W6u03zWiEcGV34zqkLPYQoDV3OfAh1WUHv/PrL7abQOQKh1NyGPtAGBlY1IzTdGpNO6Gt4VtE4E1mia5JZtGSrq5RkunLrKwuzSZqL9fdB4NTTKcpk4/U3Ov/GHhssfavq2HeaJ537MdC4M6ohN76MFzYYjCDSBH+ClXbbvG2bXgW2kMq5p5xxqIwTdicYj0PRljfFo/t29XqeGqBJdosvX8E//LGj/nueM+axCB0VE/knUY7+AWPbOryceeQMCKxsRP9Ccn65QrAaJEYk/8yBg+T3FM8x2THz56vkpLp2eoLhYRlkKpeLOeZvGJMbyxcKyPYqLNyjMXcd2UnQP7QFMX4uHoSd8O9A8d5oNRyrjsm3PMKP7t5DtSichLNFBhgNu9zwGczY7+tyW363mWps3I0JIguu/TzjxHxBWHgjv6p2LjQfaRwPqsV9AZh+LPN+N2bxsGpAIKxqRUHNhusJiJcBqYjJt9CS/F6wU1pJSYNmK0lKFK+euc2VsknKhumlIVkDromFYWTKKOQd+DeisBXCj0DJXBIRBSOCHtxuOICT0w47yOGIsNx7DeTvRtrof47H872NhxXD81wlvfBlh5TB8pvC262h9BxU6rAACtfunkL0fJm5ItVHv6qYBacJKRiTUmkuzVeYKPpZ6MI0IrGxIlJIoS1JcLDN+7joTY1OUi1WUkkhL3l+d00OE5YuHSWICD2Ff+PuCgMA3bQcy+RTbdg+xbe9wYjgCv/M8jhjNzzjUMNLjsKXLyI1o3Z5rbjEiQqCB8PofEF7/PRPOUh4NYmz88sVrjg9BGeEOIXf9JDL/JHFnQjZwLDcNyDKsZEQAJuaqTC3WkVFM90E0InAHQxLlQoqLZa5dvMHExRsU5osgBJalzBzenCWbiLDc20CD7wegNbmeLNv3DTOya4hUxu14wwG0bBYFsKPPpS9r3/au3C9r8fa1BUCgC+cIrv8eeukEBBXz4biDpTby78LqQvR9DLnlhxB2t0mas/EdCTcNyAq4kxGZXqxz9VY1aQjzoBoRWDlHIiOPpFquMXVllqtjU9yaWUQHIcpWiYRH/PcP6r1vYnVYnt8IQ41fD1BK0j2QZ/u+YYZHB3A9OxL51B1tOKDJeIRGrXv3gEfOU3dcA9pxvubjxjkRAF0cI1x4C6o3ICgbI6IyiPRuRNdRhDsYHaPBuNro93DTgNwBtz9o8/2tks/l2Sqh7nzpk7vB8vBMc47ErwfMTt7i6tgUM9fmqNd8lCWRKt4dtR5jEw85okcc5zcc12ZwpJdte4fpG+5O5owpxo3+pEPfkeZrCkJN2pEtPcxhba9dm7hYZARChEkKvc/fxJpgnUPY2DQg74OVkmfFasDFmUpScNj8uQcVKxkSIQz9V2vNws0C1y5MMTk+Q7kYJfIs2eKVbOLhQbMYuRAQak1QN+1V07kUW3YMsG3PEPmeLAgMHVffvgZ24juxPFnelVLs7m+tLm/OfazldUCrN9IY+fi8rT9rTsSvN2V3JWwakLtAY3EFaNSKXJypslQJHujk+nLcNh2iuassE8IqLVWYvnaTyfFZbk0vRBpGJodickON4zzoY/GoYaXcRhgab8OyFb1DXWzdOcjgtj5SGZcwNBTdFmsToVOfffNGKdAwkLX+//bO5cdx5L7j3yoWSb1b3a3px8zs7MCbDYxNAhvrGLksAiNx4CD3IIec8h/lnD8g5yDwIcjNpwQIEPtgwI692Sxm59XTL70lPqoqh2KRRYpSv6clsT6AWmyJpKRiFb/8vYr4tFfLuYMe4+p+IVGjwLpaclZArsmyDK1X5wHOxjEoWd+DfBvSbpFThSxOwmOB4eUY71+d4eS7M4z6UwgulNAkgmpdXOtPuWgoYaCUoN6q4fCTHp6+PEC314bDKOKYQ3KZisY6xzhMisHyZ7seDjte7r1N+B3rhBWQG7AquP76MoAE0riIXm/TKYsFaVeFtkrCeYSLkwHevTrF2dvLxMUFOI4DQklOg/Q+t6FtNpUy0ZBCgnMOgKDW8LB/vIvD5/voHXVRM6wN0021KRdMxXiHxwhe9mrolATL9bLlelgBuSHLOtxozvHtWYAgFlsTFylS2lUkQCiBw9QU1dPxDKdvLvDhzQUuT4cIpiGklEm8hFoxWQcKlgYhBH7dxd7BDg6e76N3vItGuwZCyIK1kdvNBhy3XLyDq3jHy54Pjy0Gy++apltFrIDcAjMWYnbCiAt8exagP9vcyvXrslhcB4CoWVQdRiG4wGwS4Px9H2fvLnFxMsB8GijXiKMyudL7XFtBuXeK7aiFW8c0CFGWxt5hF0+e7mL/qItGqwbqKPekECIX29gUa8OkWBx41HHxfNcHCnVcm/a71gkrIHdgWSd8fRng/SDKxUWAzbhiuymrXFypmAiJYBaifzrExYcBLk76GA+m6X1Kllknev/b2G73zTLBkFJCCAkRc1DmoNbwsNvr4MmzPTx5uoda00+nVxdClLqo9PKmYLaFECpT8sW+j70my72/ib9t3bACckeWxUUuJjG+uwgQ8e1J9b2Ksq6UiQkBdRwQou4+NxnOcHHSx8WHYeLqMqwTSrPYCVBSBV+S9lMhyvrRgmBwDkIomOeg1a5j76iL/cMuuk/a8OvLRSO/z81r46LLqulTvOz5aHg23vEQWAG5B5Z1zCAWeHWuXFrOlmVpXUVZm0AVvCsxoQSUORBcIJiFGJyPcHEywOXpAOPBFFEYg0cchBLl7iIkfUi9o5LP3Ka2XfZ7tLBKoWoVpFAz21JK4HouGq0aur0OdnotdHsdNNt1uD5TLiyuCv1ylgaSNsXmnlBVWwGAyrKCBA46Lp51vaRW6eGLA6uIFZB7ZJlp/H4Q4m0/hEyqvM11q0CpmKDcOokjjmAeYngxTh+DizHCIAKPeHKiVAWMOoZylbDo77CO7b3qe+mXtWUhpYQUUsUnoGYLcD2GZqeOzm4L7d0mdp90UG/V4XoOCKUQcSIYQqdlp3sHNlw0NMUsK59RfLLnoduwLquHxgrIPbPMGhkHHK/OA0wCsVWFh7dhWTaXtk4IIYkrS2UBxWGM2XiOYX+CcX+KUX+C8XCKaB6lk/UJoQLDWli0qICoK2zoa+wb9vabHKMbH09i1BsnIgHthpISkqupKxzmwGEOmOeg0aqhtdNEq9tAZ7eJZqcBz2dgLgNADAsjb2UA2+m6KQbKd5sML/Y8uE5ZltX2/O51wQrIA7Cs4woh8foyxIdRFmDX61e5Y5dW4SaCQohKE6aJqIAQ8ChGHAtEQYTpaIbpeI7ZJMBsPMdsPMd0MkcccnDOIbhQczMBqbCAqPgKMc7gufZPXzaPybJhUjxueaEqWlzQlkTyghRqWU2t74A6FH7NRb1ZQ6NdR71VQ7OtlmsNHw6jYC5TMQwuEjdWtp9tFwxN0epwKMGzroeDjpu+r9NyZaKk29UC64EVkAdkWZbW5STGd5cBglhufbrvbSgVFGQZWiQRXy0sWhiUNaLcXMEsxHQ0w2wSIAoihPMYQRAinEUIgwhxGCuBSdw76YlYZlfv2iJIRUZ/uPFlct9QZoJHjO9lfk/mMrg+g+sxuJ4Lz3dRa/qoN33UmzXUWz5clyUV/6pQU31Hkfr2y6wL9dW2VzA0UspU+KWU4AJo1yhe7NfQ8KzV8bGxAvLALOvQUSzwuh/iYhyr+glrjaxkmaio99SzFhbdnoQSEErTeY60NSK4uoUo5wJRGCMOY8RRjDjiya1Ws2fJk6ym9Eo/yVrSrraCWGgLgTEHzDUfDMxjiYuNpokEellnT5lCpqwwmbOUNFU8QZZZHUcdF0c77kJ7VK1tHgsrIB8BuaTwEAD60xhvLkNMI5GzRsx1LMtZJSxZTD07CZtTeBBdeUx0BXLhOYsz52pcNOnh0e6RZH19vFOXld4+sRzM775KJNSu7cmwaMkLCezUHTzf9VH3aG6dqrfVx8YKyEdk2RUSFxLvBiE+DCMIicrUjTw0K8WldAPzydzGiIiQsnUX188lO12BPfEtp2h1eIzgeMfDk7YZ67Bt+FhYAfnIrLJGJgHH68sQw3lWN6K2sULysbmx+G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the minimum number of swaps to sort a vector","description":"Cody Problem 1401 asks us to sort a vector with the bubble sort algorithm and count the number of swaps needed. For example, to sort the vector [4 3 2 1] in increasing order, bubble sort requires six swaps. However, the vector can be sorted in only two swaps (4 \u0026 1 and 3 \u0026 2). \r\nWrite a function to determine the minimum number of swaps needed to sort a vector","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 93px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 46.5px; transform-origin: 407px 46.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/1401\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003eCody Problem 1401\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 308.433px 8px; transform-origin: 308.433px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e asks us to sort a vector with the bubble sort algorithm and count the number of swaps needed. For example, to sort the vector [4 3 2 1] in increasing order, bubble sort requires six swaps. However, the vector can be sorted in only two swaps (4 \u0026amp; 1 and 3 \u0026amp; 2). \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 260.083px 8px; transform-origin: 260.083px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to determine the minimum number of swaps needed to sort a vector\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = countSwaps(x)\r\n  y = length(find(x~=sort(x)));\r\nend","test_suite":"%%\r\nx = 1;\r\nassert(isequal(countSwaps(x),0))\r\n\r\n%%\r\nx = [2 1];\r\nassert(isequal(countSwaps(x),1))\r\n\r\n%%\r\nx = [3 1 2];\r\nassert(isequal(countSwaps(x),2))\r\n\r\n%%\r\nx = 1:4;\r\nassert(isequal(countSwaps(x),0))\r\n\r\n%%\r\nx = [4 2 3 1];\r\nassert(isequal(countSwaps(x),1))\r\n\r\n%%\r\nx = [1 4 2 3];\r\nassert(isequal(countSwaps(x),2))\r\n\r\n%%\r\nx = [5 2 1 4 3];\r\nassert(isequal(countSwaps(x),2))\r\n\r\n%%\r\nx = [5 2 4 1 3];\r\nassert(isequal(countSwaps(x),3))\r\n\r\n%%\r\nx = [5 1 4 2 3];\r\nassert(isequal(countSwaps(x),4))\r\n\r\n%%\r\nx = [2 4 3 5 1 6];\r\nassert(isequal(countSwaps(x),3))\r\n\r\n%%\r\nx = [2 5 3 4 1 6];\r\nassert(isequal(countSwaps(x),2))\r\n\r\n%%\r\nx = [10 8 7 6 2 3 1 5 4 9];\r\nassert(isequal(countSwaps(x),8))\r\n\r\n%%\r\nx = [8 1 10 6 3 2 4 5 7 9];\r\nassert(isequal(countSwaps(x),9))\r\n\r\n%%\r\nx = [12 14 13 18 9 2 7 5 16 10 8 4 11 6 3 1 17 15];\r\nassert(isequal(countSwaps(x),13))\r\n\r\n%%\r\nx = [87 79 11 21 140 69 109 126 24 162 10 142 91 7 174 90 157 124 42 147 84 4 96 34 143 95 2 116 101 56 88 134 160 177 85 65 180 35 81 139 179 128 20 137 141 93 151 156 135 28 98 163 45 33 13 37 61 59 19 43 17 166 82 14 123 54 60 120 127 149 80 168 146 30 171 169 119 108 136 75 117 78 18 15 164 175 104 89 118 155 165 145 133 125 58 112 150 5 132 130 38 105 57 97 121 110 26 29 3 115 36 6 70 178 12 74 50 122 39 153 158 83 167 27 66 40 172 138 176 86 152 8 92 129 51 114 159 76 16 113 44 48 99 131 63 62 71 144 72 68 25 67 53 49 170 173 1 100 103 52 9 64 47 94 55 46 111 161 77 31 32 107 73 154 106 41 22 148 23 102];\r\nassert(isequal(countSwaps(x),174))\r\n\r\n%%\r\nx = [169 154 170 136 92 138 108 172 144 94 33 21 23 22 8 146 53 93 145 153 95 31 25 104 90 27 58 71 13 64 65 5 74 137 30 117 125 143 85 51 68 89 157 48 176 11 115 79 91 178 52 39 98 120 107 131 82 18 158 174 142 173 45 50 38 7 105 165 84 97 101 10 81 67 111 109 46 160 139 155 47 114 40 126 110 66 179 20 41 69 86 166 49 4 128 167 124 87 57 56 17 24 132 43 3 149 103 123 100 135 60 121 34 80 2 63 156 118 147 159 28 72 55 16 168 96 127 73 175 164 141 140 76 36 133 19 152 6 113 129 163 9 1 44 29 134 150 177 130 99 12 35 37 112 119 14 32 70 161 180 106 15 42 148 102 88 171 162 116 75 122 54 61 62 78 83 59 151 26 77];\r\nassert(isequal(countSwaps(x),173))\r\n\r\n%%\r\nx = 1:10000;\r\nr = randperm(10000);\r\nn = randi(1000);\r\ni1 = r(1:2:2*n-1);\r\ni2 = r(2:2:2*n);\r\na = x(i1);\r\nx(i1) = x(i2);\r\nx(i2) = a;\r\nassert(isequal(countSwaps(x),n))\r\n\r\n%%\r\nfiletext = fileread('countSwaps.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext, 'regexp'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2023-04-15T15:26:41.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-04-15T15:26:35.000Z","updated_at":"2023-04-15T15:26:41.000Z","published_at":"2023-04-15T15:26:41.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/1401\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCody Problem 1401\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e asks us to sort a vector with the bubble sort algorithm and count the number of swaps needed. For example, to sort the vector [4 3 2 1] in increasing order, bubble sort requires six swaps. However, the vector can be sorted in only two swaps (4 \u0026amp; 1 and 3 \u0026amp; 2). \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to determine the minimum number of swaps needed to sort a vector\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1907,"title":"Capture the flag(s)","description":"Flags are distributed randomly on a large board. Starting from the corner position your goal is to capture as many flags as possible in at most N moves.\r\nDescription:\r\nThe board is described by a matrix B with 1's at the flag positions, and 0's otherwise.\r\nE.g.\r\n B = [0 0 1 1; \r\n      0 0 1 1;\r\n      0 0 1 0];\r\n\r\n N = 6;\r\nYou are starting at the top-left corner (row=1, col=1) and are allowed N steps (steps are up/down/left/right movements, no diagonal movements allowed).\r\nReturn a trajectory attempting to maximize the number of flags captured. The output of your function should be a Nx2 matrix of the form [row, col] (not including the initial [1,1] position) visiting as many flags as possible.\r\nE.g.\r\n path = [1 2;\r\n         1 3;\r\n         1 4;\r\n         2 4;\r\n         2 3;\r\n         3 3];\r\nThis solution captures all 5 flags on the board.\r\nScoring:\r\nYour function will receive a score equal to the number of non-visited flags across all 50 of the testsuite problems. You need to leave at most 10,000 flags univisited (among 50,000 total flags) to pass this problem.\r\nNote:\r\nThe boards and number of movements allowed will be large. Optimizing over all possible trajectories is very likely to time out.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 676px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 401px 338px; transform-origin: 401px 338px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFlags are distributed randomly on a large board. Starting from the corner position your goal is to capture as many flags as possible in at most N moves.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eDescription\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe board is described by a matrix\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eB\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e with 1's at the flag positions, and 0's otherwise.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE.g.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 90px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 397px 45px; transform-origin: 397px 45px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e B = [0 0 1 1; \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e      0 0 1 1;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e      0 0 1 0];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e N = 6;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou are starting at the top-left corner (row=1, col=1) and are allowed\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps (steps are up/down/left/right movements, no diagonal movements allowed).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn a trajectory attempting to maximize the number of flags captured. The output of your function should be a\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eNx2\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matrix of the form\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e[row, col]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (not including the initial [1,1] position) visiting as many flags as possible.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE.g.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 108px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 397px 54px; transform-origin: 397px 54px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e path = [1 2;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         1 3;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         1 4;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         2 4;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         2 3;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         3 3];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis solution captures all 5 flags on the board.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eScoring\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour function will receive a score equal to the number of non-visited flags across all 50 of the testsuite problems. You need to leave at most 10,000 flags univisited (among 50,000 total flags) to pass this problem.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNote\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe boards and number of movements allowed will be large. Optimizing over all possible trajectories is very likely to time out.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function path = capture_the_flag(B,N)\r\npath=[1 2];\r\n","test_suite":"%%\r\n% test cases\r\n\r\nrandn('seed',0);\r\nrand('seed',0);\r\nN=randi([1000 4000],50,1);\r\nS=randi([1,50],50,1);\r\nBoards=arrayfun(@(s)convn(randn(100),ones(s)/s^2,'same'),S,'uni',0); \r\n\r\nFLAGSLEFT=0;\r\nDOPLOT=false;\r\ntic;\r\nfor board=1:50\r\n B=Boards{board};\r\n sB=sort(B(:));\r\n B=double(B\u003esB(round(numel(sB)*.9)));\r\n n=N(board);\r\n path=capture_the_flag(B,n);\r\n assert(size(path,1)\u003c=n,'too many steps');\r\n assert(all(sum(abs(diff([1,1;path])),2)\u003c=1),'no jumping allowed');\r\n if DOPLOT\r\n    imagesc(B);\r\n    hold on;\r\n    plot(path(:,2),path(:,1),'y-');\r\n    hold off;\r\n    axis equal;\r\n    axis off;\r\n    set(gcf,'color',0*[1 1 1]);\r\n    colormap(.5*gray);\r\n    drawnow;\r\n end\r\n B(1)=0;\r\n B((path-1)*[1;size(B,1)]+1)=0;\r\n fprintf('test %d; left %d flags\\n',board,nnz(B));\r\n FLAGSLEFT=FLAGSLEFT+nnz(B);\r\nend\r\ntoc;","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":43,"edited_by":1,"edited_at":"2026-02-11T16:03:17.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2026-02-11T16:03:17.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-10-01T05:38:22.000Z","updated_at":"2026-03-16T11:31:01.000Z","published_at":"2013-10-01T06:27:29.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFlags are distributed randomly on a large board. Starting from the corner position your goal is to capture as many flags as possible in at most N moves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDescription\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe board is described by a matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eB\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e with 1's at the flag positions, and 0's otherwise.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eE.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ B = [0 0 1 1; \\n      0 0 1 1;\\n      0 0 1 0];\\n\\n N = 6;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are starting at the top-left corner (row=1, col=1) and are allowed\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e steps (steps are up/down/left/right movements, no diagonal movements allowed).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn a trajectory attempting to maximize the number of flags captured. The output of your function should be a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNx2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e matrix of the form\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[row, col]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (not including the initial [1,1] position) visiting as many flags as possible.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eE.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ path = [1 2;\\n         1 3;\\n         1 4;\\n         2 4;\\n         2 3;\\n         3 3];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis solution captures all 5 flags on the board.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour function will receive a score equal to the number of non-visited flags across all 50 of the testsuite problems. You need to leave at most 10,000 flags univisited (among 50,000 total flags) to pass this problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe boards and number of movements allowed will be large. Optimizing over all possible trajectories is very likely to time 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