{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-04-06T14:01:22.000Z","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":"2026-04-06T00:00:00.000Z","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":45543,"title":"Find the remainder with the factorial of PRIMES?","description":"* Take a number  greater than or equal to 2 and take its primes. e.g. 6 and its primes are 2 3 5\r\n* calculate the factorial of its prime (nearest less) and calculate the sum from that number down to 1. e.g. 5! = 120 and sum 5 to 1 is 15\r\n* As mod of the factorial of every number with 2 is zero, check the mod of the sum calculated with 10 e.g. mod of 15 with 10 is 5\r\n* Divide factorial with the remainder (if possible). e.g. 120/5 = 24\r\n\r\nDon't use builtin functions \r\n\r\n  primes\r\n\r\n  factorial\r\n\r\n  mod","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: 275.767px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 137.883px; transform-origin: 407px 137.883px; vertical-align: baseline; \"\u003e\u003cul style=\"block-size: 122.6px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 61.3px; transform-origin: 391px 61.3px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 290px 8px; transform-origin: 290px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTake a number greater than or equal to 2 and take its primes. e.g. 6 and its primes are 2 3 5\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.8667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 355px 8px; transform-origin: 355px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ecalculate the factorial of its prime (nearest less) and calculate the sum from that number down to 1. e.g. 5! = 120 and sum 5 to 1 is 15\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.8667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 359px 8px; transform-origin: 359px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAs mod of the factorial of every number with 2 is zero, check the mod of the sum calculated with 10 e.g. mod of 15 with 10 is 5\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 197px 8px; transform-origin: 197px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDivide factorial with the remainder (if possible). e.g. 120/5 = 24\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\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: 81px 8px; transform-origin: 81px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDon't use builtin functions\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.167px; 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; perspective-origin: 404px 51.0833px; transform-origin: 404px 51.0833px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 24px 8.5px; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eprimes\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\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: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003efactorial\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\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: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003emod\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = your_fcn_name(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nfiletext = fileread('your_fcn_name.m');\r\nassert(isempty(strfind(filetext, 'primes')))\r\nassert(isempty(strfind(filetext, 'factorial')))\r\nassert(isempty(strfind(filetext, 'mod')))\r\n\r\n%%\r\nx = 11;\r\ny_correct = 630;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 49;\r\ny_correct = 3.232790518889602e+58;\r\nassert(abs(your_fcn_name(x)/1e58-y_correct/1e58)\u003c1e-2)\r\n\r\n%%\r\nx = 5;\r\ny_correct = 1;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 17;\r\ny_correct = 6.2270208e+09;\r\nassert(abs(your_fcn_name(x)/1e9-y_correct/1e9)\u003c1e-2)","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":26467,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2021-05-20T10:42:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-05-20T09:50:35.000Z","updated_at":"2025-04-28T21:39:14.000Z","published_at":"2020-05-20T09:54:29.000Z","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=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTake a number greater than or equal to 2 and take its primes. e.g. 6 and its primes are 2 3 5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ecalculate the factorial of its prime (nearest less) and calculate the sum from that number down to 1. e.g. 5! = 120 and sum 5 to 1 is 15\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAs mod of the factorial of every number with 2 is zero, check the mod of the sum calculated with 10 e.g. mod of 15 with 10 is 5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDivide factorial with the remainder (if possible). e.g. 120/5 = 24\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\u003eDon't use builtin functions\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[primes\\n\\nfactorial\\n\\nmod]]\u003e\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":58573,"title":"Am I a city or a state","description":"Input will be an array of cities and states. Also, lists of cities and states will be passed.\r\nReplace every city name with \"city\" and every state with a \"state\" string.\r\nInput:\r\nA = [\"Natick\", \"Wayland\", \"Framingham\", \"Chicago\"];\r\nB = [\"MA\", \"IL\"];\r\narr = [\"Wayland\", \"Chicago\", \"IL\"];\r\n\r\nOuput:\r\narr = [\"city\", \"city\", \"state\"];","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; 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: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 260.919px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 332.5px 130.455px; transform-origin: 332.5px 130.459px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eInput will be an array of cities and states. Also, lists of cities and states will be passed.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eReplace every city name with \"city\" and every state with a \"state\" string.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA = [\"Natick\", \"Wayland\", \"Framingham\", \"Chicago\"];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eB = [\"MA\", \"IL\"];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003earr = [\"Wayland\", \"Chicago\", \"IL\"];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eOuput:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003earr = [\"city\", \"city\", \"state\"];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = sortCitiesAndStates(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nA = [\"Natick\", \"Wayland\", \"Framingham\", \"Chicago\"];\r\nB = [\"MA\", \"IL\"];\r\nx = [\"Wayland\", \"Chicago\", \"IL\"];\r\ny_correct = [\"city\", \"city\", \"state\"];\r\nassert(isequal(sortCitiesAndStates(x,A,B),y_correct))\r\n\r\n%%\r\nx = [\"Natick\", \"MA\", \"IL\"];\r\nA = [\"Natick\", \"Wayland\", \"Framingham\", \"Chicago\"];\r\nB = [\"MA\", \"IL\"];\r\ny_correct = [\"city\", \"state\", \"state\"];\r\nassert(isequal(sortCitiesAndStates(x,A,B),y_correct))\r\n\r\n%%\r\nx = [];\r\nA = [\"Natick\", \"Wayland\", \"Framingham\", \"Chicago\"];\r\nB = [\"MA\", \"IL\"];\r\ny_correct = [];\r\nassert(isequal(sortCitiesAndStates(x,A,B),y_correct))\r\n\r\n%%\r\nx = [\"Wayland\"];\r\nA = [\"Natick\", \"Wayland\", \"Framingham\", \"Chicago\"];\r\nB = [\"MA\", \"IL\"];\r\ny_correct = [\"city\"];\r\nassert(isequal(sortCitiesAndStates(x,A,B),y_correct))","published":true,"deleted":false,"likes_count":10,"comments_count":1,"created_by":3411694,"edited_by":3411694,"edited_at":"2023-07-18T14:25:28.000Z","deleted_by":null,"deleted_at":null,"solvers_count":11,"test_suite_updated_at":"2023-07-18T14:25:28.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2023-07-17T18:48:42.000Z","updated_at":"2026-03-05T11:09:23.000Z","published_at":"2023-07-17T18:56:55.000Z","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\u003eInput will be an array of cities and states. Also, lists of cities and states will be passed.\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\u003eReplace every city name with \\\"city\\\" and every state with a \\\"state\\\" string.\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:\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\u003eA = [\\\"Natick\\\", \\\"Wayland\\\", \\\"Framingham\\\", \\\"Chicago\\\"];\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\u003eB = [\\\"MA\\\", \\\"IL\\\"];\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\u003earr = [\\\"Wayland\\\", \\\"Chicago\\\", \\\"IL\\\"];\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\u003eOuput:\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\u003earr = [\\\"city\\\", \\\"city\\\", \\\"state\\\"];\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":43216,"title":"Encode Me From The Past","description":"Given this input\r\n\r\n x = 2, 5, 1, 2, 4, 1, 1, 3\r\n\r\noutput should be (Five 2's, Two 1's, One 4, Three 1's)\r\n\r\n [2 2 2 2 2 1 1 4 1 1 1]","description_html":"\u003cp\u003eGiven this input\u003c/p\u003e\u003cpre\u003e x = 2, 5, 1, 2, 4, 1, 1, 3\u003c/pre\u003e\u003cp\u003eoutput should be (Five 2's, Two 1's, One 4, Three 1's)\u003c/p\u003e\u003cpre\u003e [2 2 2 2 2 1 1 4 1 1 1]\u003c/pre\u003e","function_template":"function y = encodeThePast(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [2, 5, 1, 2, 4, 1, 1, 3];\r\ny_correct = [2 2 2 2 2 1 1 4 1 1 1];\r\nassert(isequal(encodeThePast(x),y_correct))\r\n%%\r\nx = [2 3 4 3];\r\ny_correct = [2 2 2 4 4 4];\r\nassert(isequal(encodeThePast(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":8,"comments_count":0,"created_by":13865,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":62,"test_suite_updated_at":"2016-10-29T16:40:55.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2016-10-08T10:22:58.000Z","updated_at":"2026-01-04T08:18:44.000Z","published_at":"2016-10-08T10:25:02.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven this input\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[ x = 2, 5, 1, 2, 4, 1, 1, 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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput should be (Five 2's, Two 1's, One 4, Three 1's)\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[ [2 2 2 2 2 1 1 4 1 1 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60926,"title":"Greater than before","description":"Given an array of integers, write a function that returns elements that are greater than the one before them.  \r\nFor instance, \r\ninput = [1, 3, 2, 4, 3, 5]  \r\noutput = [3, 4, 5]","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; 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: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 111px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 55.5px; transform-origin: 408px 55.5px; vertical-align: baseline; \"\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: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven an array of integers, write a function that returns elements that are greater than the one before them.  \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: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor instance, \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: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003einput = [1, 3, 2, 4, 3, 5]  \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: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eoutput = [3, 4, 5]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = gtb(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [1, 3, 2, 4, 3, 5]  ;\r\ny_correct = [3, 4, 5];\r\nassert(isequal(gtb(x),y_correct))\r\n\r\n%%\r\nx = zeros(1,7)  ;\r\ny_correct = [];\r\nassert(isempty(gtb(x)))\r\n\r\n%%\r\nx = [5, 5, 10, 2, 10, 20, 5, 2, 2, 20] ;\r\ny_correct = [10, 10, 20, 20];\r\nassert(isequal(gtb(x),y_correct))\r\n\r\n%%\r\nx = [2, 2, 5, 5, 5, 4, 4, 1]  ;\r\ny_correct = [5];\r\nassert(isequal(gtb(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":39,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-06-02T11:52:32.000Z","updated_at":"2025-11-29T16:44:04.000Z","published_at":"2025-06-02T11:52:32.000Z","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\u003eGiven an array of integers, write a function that returns elements that are greater than the one before them.  \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\u003eFor instance, \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 = [1, 3, 2, 4, 3, 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\u003eoutput = [3, 4, 5]\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":271,"title":"N-Cards Problem","description":"You have a deck of _N_ cards numbered in order from 1 to _N_. You discard the top card (card 1) and place the next card (card 2) at the bottom of the deck. Then you discard the top card of the remaining deck (card 3), and place the next card (card 4) at the bottom of the deck. We repeat the procedure: discarding the top card and placing the next card at the bottom of the deck. Eventually, you will have one card left. What is the number of that card?\r\n\r\n*Example*\r\n\r\n    nCardsProblem(5)\r\n\r\n    deck = [ 1 2 3 4 5 ]\r\n    deck = [ 2 3 4 5 ]\r\n    deck = [ 3 4 5 2 ]\r\n    deck = [ 4 5 2 ]\r\n    deck = [ 5 2 4 ]\r\n    deck = [ 2 4 ]\r\n    deck = [ 4 2 ]\r\n    deck = [ 2 ]","description_html":"\u003cp\u003eYou have a deck of \u003ci\u003eN\u003c/i\u003e cards numbered in order from 1 to \u003ci\u003eN\u003c/i\u003e. You discard the top card (card 1) and place the next card (card 2) at the bottom of the deck. Then you discard the top card of the remaining deck (card 3), and place the next card (card 4) at the bottom of the deck. We repeat the procedure: discarding the top card and placing the next card at the bottom of the deck. Eventually, you will have one card left. What is the number of that card?\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e\u003c/p\u003e\u003cpre\u003e    nCardsProblem(5)\u003c/pre\u003e\u003cpre\u003e    deck = [ 1 2 3 4 5 ]\r\n    deck = [ 2 3 4 5 ]\r\n    deck = [ 3 4 5 2 ]\r\n    deck = [ 4 5 2 ]\r\n    deck = [ 5 2 4 ]\r\n    deck = [ 2 4 ]\r\n    deck = [ 4 2 ]\r\n    deck = [ 2 ]\u003c/pre\u003e","function_template":"function lastcard = nCardsProblem(N)\r\n  lastcard = N;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = 1;\r\nassert(isequal(nCardsProblem(x),y_correct))\r\n\r\n%%\r\nx = 5;\r\ny_correct = 2;\r\nassert(isequal(nCardsProblem(x),y_correct))\r\n\r\n%%\r\nx = 50;\r\ny_correct = 36;\r\nassert(isequal(nCardsProblem(x),y_correct))\r\n\r\n%%\r\nx = 1000;\r\ny_correct = 976;\r\nassert(isequal(nCardsProblem(x),y_correct))\r\n\r\n%%\r\nx = 10000;\r\ny_correct = 3616;\r\nassert(isequal(nCardsProblem(x),y_correct))","published":true,"deleted":false,"likes_count":6,"comments_count":1,"created_by":134,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":156,"test_suite_updated_at":"2012-02-06T19:31:09.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-02-06T19:31:09.000Z","updated_at":"2025-12-12T09:01:04.000Z","published_at":"2012-02-06T19:31:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou have a deck of\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\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e cards numbered in order from 1 to\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\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. You discard the top card (card 1) and place the next card (card 2) at the bottom of the deck. Then you discard the top card of the remaining deck (card 3), and place the next card (card 4) at the bottom of the deck. We repeat the procedure: discarding the top card and placing the next card at the bottom of the deck. Eventually, you will have one card left. What is the number of that card?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\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[    nCardsProblem(5)\\n\\n    deck = [ 1 2 3 4 5 ]\\n    deck = [ 2 3 4 5 ]\\n    deck = [ 3 4 5 2 ]\\n    deck = [ 4 5 2 ]\\n    deck = [ 5 2 4 ]\\n    deck = [ 2 4 ]\\n    deck = [ 4 2 ]\\n    deck = [ 2 ]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1024,"title":"Doubling elements in a vector","description":"Given the vector A, return B in which all numbers in A are doubling. So for:\r\n\r\nA = [  1   5   8 ]\r\n\r\nthen\r\n\r\nB = [  1   1   5   5   8  8 ]\r\n","description_html":"\u003cp\u003eGiven the vector A, return B in which all numbers in A are doubling. So for:\u003c/p\u003e\u003cp\u003eA = [  1   5   8 ]\u003c/p\u003e\u003cp\u003ethen\u003c/p\u003e\u003cp\u003eB = [  1   1   5   5   8  8 ]\u003c/p\u003e","function_template":"function B = your_fcn_name(A)\r\n  \r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = [1 1];\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = [0 -1 1 0 0 0 1 2];\r\ny_correct = [0 0 -1 -1 1 1 0 0 0 0 0 0 1 1 2 2];\r\nassert(isequal(your_fcn_name(x),y_correct))","published":true,"deleted":false,"likes_count":69,"comments_count":13,"created_by":7968,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10291,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-11-03T15:44:34.000Z","updated_at":"2026-04-12T20:35:55.000Z","published_at":"2012-11-03T15:46:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the vector A, return B in which all numbers in A are doubling. So for:\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\u003eA = [ 1 5 8 ]\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\u003ethen\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\u003eB = [ 1 1 5 5 8 8 ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":55775,"title":"Taylor Series","description":"You can use a Taylor series to approximate common functions. The Taylor series for sin(x) is \r\n\r\nUsing only the first several terms in the series could get you a good approximation to the function. \r\nWrite a function that takes a point x at which to evaluate the sine function and a tolerance level that defines how close you want the Taylor series approximation to be to the actual value. Your function should output the approximate value of sin(x).","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; 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: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 157px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 78.5px; transform-origin: 407px 78.5px; vertical-align: baseline; \"\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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eYou can use a Taylor series to approximate common functions. The Taylor series for sin(x) is \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 46px; 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 23px; text-align: left; transform-origin: 384px 23px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"vertical-align:-17px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"294.5\" height=\"46\" style=\"width: 294.5px; height: 46px;\"\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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eUsing only the first several terms in the series could get you a good approximation to the function. \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: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eWrite a function that takes a point x at which to evaluate the sine function and a tolerance level that defines how close you want the Taylor series approximation to be to the actual value. Your function should output the approximate value of sin(x).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function yApp = myTaylor(x,tol)\r\n\r\n\r\nend","test_suite":"%%\r\nx = pi;\r\ntol = 0.01;\r\nyApp = myTaylor(x,tol); \r\nassert(abs(yApp-sin(x))\u003ctol)\r\n%%\r\nx = pi/2;\r\ntol = 0.1;\r\nyApp = myTaylor(x,tol); \r\nassert(abs(yApp-sin(x))\u003ctol)\r\n%%\r\nx = pi*1i;\r\ntol = 1e-3;\r\nyApp = myTaylor(x,tol); \r\nassert(abs(yApp-sin(x))\u003ctol)\r\n%%\r\nx = 2.345;\r\ntol = 1e-5;\r\nyApp = myTaylor(x,tol);\r\nassert( (abs(yApp-sin(x))\u003ctol) \u0026\u0026 (abs(yApp-sin(x))\u003etol/100) )\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":2,"created_by":140016,"edited_by":140016,"edited_at":"2022-10-17T14:02:37.000Z","deleted_by":null,"deleted_at":null,"solvers_count":207,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-09-16T18:00:45.000Z","updated_at":"2026-04-02T08:59:19.000Z","published_at":"2022-10-17T14:02:37.000Z","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\u003eYou can use a Taylor series to approximate common functions. The Taylor series for sin(x) 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:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\sin(x) = \\\\sum_{n=0}^{\\\\infty}\\\\frac{(-1)^nx^{2n+1}}{(2n+1)!} = x - \\\\frac{x^3}{3!} + \\\\frac{x^5}{5!} - \\\\frac{x^7}{7!} + \\\\dots\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\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\u003eUsing only the first several terms in the series could get you a good approximation to the function. \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 a point x at which to evaluate the sine function and a tolerance level that defines how close you want the Taylor series approximation to be to the actual value. Your function should output the approximate value of sin(x).\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":3092,"title":"Return fibonacci sequence  do not use loop and condition","description":"Calculate the nth Fibonacci number.\r\n\r\nGiven n, return f where f = fib(n) and f(1) = 1, f(2) = 1, f(3) = 2, ...\r\n\r\nExamples:\r\n\r\n Input  n = 5\r\n Output f is 5\r\n Input  n = 7\r\n Output f is 13\r\n\r\nbut, *loop and conditional statement is forbidden*","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: 203.733px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 101.867px; transform-origin: 407px 101.867px; vertical-align: baseline; \"\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: 113.5px 8px; transform-origin: 113.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCalculate the nth Fibonacci number.\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: 204px 8px; transform-origin: 204px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven n, return f where f = fib(n) and f(1) = 1, f(2) = 1, f(3) = 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: 32px 8px; transform-origin: 32px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eExamples:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; 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; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003e Input  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003en = 5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 56px 8.5px; transform-origin: 56px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003e Output \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 24px 8.5px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 24px 8.5px; \"\u003ef is 5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003e Input  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003en = 7\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 60px 8.5px; transform-origin: 60px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003e Output \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003ef is 13\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; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; 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: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ebut,\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: 2px 8px; transform-origin: 2px 8px; 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: 153px 8px; transform-origin: 153px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eloop and conditional statement is forbidden\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function f = fib(n)\r\n  f = n;\r\nend","test_suite":"%%% functions forbidden\r\n\r\n\r\n% Clean user's function from some known jailbreaking mechanisms\r\nfunctions={'!','feval','eval','str2func','str2num','regex','system','dos','unix','perl','assert','fopen','write','save','setenv','path','please','for','if','while','switch','round','roundn','fix','ceil','char','floor'};\r\nassessFunctionAbsence(functions, 'FileName', 'fib.m');\r\n%%\r\nn = 1;\r\nf = 1;\r\nassert(abs(fib(n) - f) \u003c 1e-4)\r\n\r\n%%\r\nn = 6;\r\nf = 8;\r\nassert(abs(fib(n) - f) \u003c 1e-4)\r\n\r\n%%\r\nn = 10;\r\nf = 55;\r\nassert(abs(fib(n) - f) \u003c 1e-4)\r\n\r\n%%\r\nn = 20;\r\nf = 6765;\r\nassert(abs(fib(n) - f) \u003c 1e-4)","published":true,"deleted":false,"likes_count":15,"comments_count":11,"created_by":3668,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":855,"test_suite_updated_at":"2021-02-15T12:43:32.000Z","rescore_all_solutions":false,"group_id":30,"created_at":"2015-03-18T15:03:18.000Z","updated_at":"2026-03-16T14:19:58.000Z","published_at":"2015-03-18T15:25:13.000Z","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\u003eCalculate the nth Fibonacci number.\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\u003eGiven n, return f where f = fib(n) and f(1) = 1, f(2) = 1, f(3) = 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\u003eExamples:\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[ Input  n = 5\\n Output f is 5\\n Input  n = 7\\n Output f is 13]]\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\u003ebut,\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\u003eloop and conditional statement is forbidden\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":3095,"title":" Return fibonacci sequence do not use loop and condition version 2","description":"Calculate the nth Fibonacci number,return sequence\r\n\r\nGiven n, return f where f = fib(n) and f(1) = 1, f(2) = 1, f(3) = 2, ...\r\n\r\nExamples:\r\n\r\n Input  n = 2 : 5\r\n Output f is [1 2 3 5]\r\n Input  n = 7 : 10\r\n Output f is [13    21    34    55]\r\n\r\nbut, loop and conditional statement is forbidden","description_html":"\u003cp\u003eCalculate the nth Fibonacci number,return sequence\u003c/p\u003e\u003cp\u003eGiven n, return f where f = fib(n) and f(1) = 1, f(2) = 1, f(3) = 2, ...\u003c/p\u003e\u003cp\u003eExamples:\u003c/p\u003e\u003cpre\u003e Input  n = 2 : 5\r\n Output f is [1 2 3 5]\r\n Input  n = 7 : 10\r\n Output f is [13    21    34    55]\u003c/pre\u003e\u003cp\u003ebut, loop and conditional statement is forbidden\u003c/p\u003e","function_template":"function y = fib(x)\r\n  y = x;\r\nend","test_suite":"%%% Clean workspace\r\n% !/bin/cp fib.m safe\r\n% !/bin/rm *.*\r\n% !/bin/mv safe fib.m\r\n\r\n% Clean user's function from some known jailbreaking mechanisms\r\nfunctions={'!','feval','eval','str2func','str2num','regex','system','dos','unix','perl','assert','fopen','write','save','setenv','path','please','for','if','while','switch','round','roundn','fix','ceil','char','floor','\\.','^','power'};\r\nfid = fopen('fib.m');\r\n  st = char(fread(fid)');\r\n  for n = 1:numel(functions)\r\n    st = regexprep(st, functions{n}, 'error(''No fancy functions!''); %','ignorecase');\r\n  end\r\n  st = regexprep(st, 'function', 'error(''No fancy functions!''); %','ignorecase',2);\r\nfclose(fid);\r\nfid = fopen('fib.m' , 'w');\r\n  fwrite(fid,st);\r\nfclose(fid);\r\n\r\n%%\r\nn = 1:5;\r\nf = [1     1     2     3     5];\r\nassert(isequal(fib(n),f))\r\n\r\n%%\r\nn = 7 : 10;\r\nf = [13    21    34    55];\r\nassert(isequal(fib(n),f))\r\n\r\n%%\r\n\r\nn = 20 : 22;\r\nf = [ 6765       10946       17711];\r\nassert(isequal(fib(n),f))","published":true,"deleted":false,"likes_count":0,"comments_count":21,"created_by":3668,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":64,"test_suite_updated_at":"2015-03-19T15:13:29.000Z","rescore_all_solutions":false,"group_id":30,"created_at":"2015-03-19T14:56:25.000Z","updated_at":"2026-03-16T14:37:24.000Z","published_at":"2015-03-19T14:56:25.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCalculate the nth Fibonacci number,return 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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven n, return f where f = fib(n) and f(1) = 1, f(2) = 1, f(3) = 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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples:\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[ Input  n = 2 : 5\\n Output f is [1 2 3 5]\\n Input  n = 7 : 10\\n Output f is [13    21    34    55]]]\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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ebut, loop and conditional statement is forbidden\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":56040,"title":"A Binary Search","description":"One way to locate a target value in a sorted array, is to use a binary search algorithm. Here, you test if the midpoint in the array is the target value. If it is, great! You're done. If not, then you continually narrow your search area depending on whether the target is less than or greater than the midpoint. The algorithm is as follows:\r\nGiven an array of sorted values (X), and a target value you wish to locate (target)\r\nCalculate the index of the midpoint of the array X by taking the average of the largest and smallest indices and rounding to the nearest integer.  \r\nIf the value located at the midpoint matches your target, set found to true.\r\nIf the target is less than the value located at the midpoint of X, narrow your search to the lower half of X by setting the largest index in the array to the midpoint - 1. \r\nIf the target is greater than the value located at the midpoint of X, narrow your search to the upper half of X by setting the smallest index in the array to the midpoint + 1. \r\nRepeat the steps above until found is true.\r\n\r\nWrite a function that takes X and target as inputs, performs a binary search and outputs the index of target value as well as the number of iterations it took to find the target.\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: 926.467px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 463.233px; transform-origin: 407px 463.233px; 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: 383.5px 8px; transform-origin: 383.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOne way to locate a target value in a sorted array, is to use a binary search algorithm. Here, you test if the midpoint in the array is the target value. If it is, great! You're done. If not, then you continually narrow your search area depending on whether the target is less than or greater than the midpoint. The algorithm is as follows:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.5px; 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 10.75px; text-align: left; transform-origin: 384px 10.75px; 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: 104px 8px; transform-origin: 104px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven an array of sorted values (\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: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\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: 126.5px 8px; transform-origin: 126.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e), and a target value you wish to locate (\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: 24px 8px; transform-origin: 24px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 24px 8.5px; transform-origin: 24px 8.5px; \"\u003etarget\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: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 164.967px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 82.4833px; transform-origin: 391px 82.4833px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 41.3667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.6833px; text-align: left; transform-origin: 363px 20.6833px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 149px 8px; transform-origin: 149px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCalculate the index of the midpoint of the array \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 195.5px 8px; transform-origin: 195.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e by taking the average of the largest and smallest indices and rounding to the nearest integer.  \u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 231px 8px; transform-origin: 231px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf the value located at the midpoint matches your target, set found to true.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 41.3667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.6833px; text-align: left; transform-origin: 363px 20.6833px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 188px 8px; transform-origin: 188px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf the target is less than the value located at the midpoint of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 127px 8px; transform-origin: 127px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, narrow your search to the lower half of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 34.5px 8px; transform-origin: 34.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e by setting the largest index in the array to the midpoint - 1. \u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 41.3667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.6833px; text-align: left; transform-origin: 363px 20.6833px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 198.5px 8px; transform-origin: 198.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf the target is greater than the value located at the midpoint of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 129px 8px; transform-origin: 129px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, narrow your search to the upper half of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 11.5px 8px; transform-origin: 11.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e by setting the smallest index in the array to the midpoint + 1. \u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 134px 8px; transform-origin: 134px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eRepeat the steps above until found is true.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cdiv style=\"block-size: 556.5px; 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 278.25px; text-align: left; transform-origin: 384px 278.25px; 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: 610px;height: 551px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"610\" height=\"551\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.5px; 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 21.25px; text-align: left; transform-origin: 384px 21.25px; 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: 84.5px 8px; transform-origin: 84.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that takes \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: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\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: 16px 8px; transform-origin: 16px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and \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: 24px 8px; transform-origin: 24px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 24px 8.5px; transform-origin: 24px 8.5px; \"\u003etarget\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: 255.5px 8px; transform-origin: 255.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e as inputs, performs a binary search and outputs the index of target value as well as the number of iterations it took to find the target.\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\u003c/div\u003e\u003c/div\u003e","function_template":"function [loc,iter] = binarySearch(X,target)\r\n    found = false;\r\nend","test_suite":"%%\r\nsortedData = sort(randi(1000,[1 200]),'ascend');\r\ntarget = sortedData(randi(length(sortedData),1));\r\n[mid,iter] = binarySearch(sortedData,target);\r\nassert(isequal(target,sortedData(mid)))\r\nassert(iter \u003e 0)\r\n%%\r\nsortedData = sort(randi(1000,[1 200]),'ascend');\r\ntarget = sortedData(randi(length(sortedData),1));\r\n[mid,iter] = binarySearch(sortedData,target);\r\nassert(isequal(target,sortedData(mid)))\r\nassert(iter \u003e 0)\r\n%%\r\nsortedData = sort(randi(1000,[1 200]),'ascend');\r\ntarget = sortedData(randi(length(sortedData),1));\r\n[mid,iter] = binarySearch(sortedData,target);\r\nassert(isequal(target,sortedData(mid)))\r\nassert(iter \u003e 0)\r\n%%\r\nsortedData = sort(randi(1000,[1 200]),'ascend');\r\ntarget = sortedData(randi(length(sortedData),1)); \r\n[mid,iter] = binarySearch(sortedData,target);\r\nassert(isequal(target,sortedData(mid)))\r\nassert(iter \u003e 0)\r\n%%\r\nsortedData = [3 12 35 76 221 225 301 367 399 512 783 800];\r\ntarget = 12;\r\n[mid,iter] = binarySearch(sortedData,target);\r\nassert(isequal(target,sortedData(mid)))\r\nassert(isequal(iter,4))\r\n%%\r\nsortedData = 0:160;\r\ntarget = 5;\r\n[mid,iter] = binarySearch(sortedData,target);\r\nassert(isequal(target,sortedData(mid)))\r\nassert(isequal(iter,6))","published":true,"deleted":false,"likes_count":5,"comments_count":8,"created_by":140016,"edited_by":223089,"edited_at":"2023-01-05T19:14:54.000Z","deleted_by":null,"deleted_at":null,"solvers_count":112,"test_suite_updated_at":"2023-01-05T19:14:54.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-09-23T17:47:03.000Z","updated_at":"2026-04-08T10:38:57.000Z","published_at":"2022-10-17T14:02:13.000Z","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\u003eOne way to locate a target value in a sorted array, is to use a binary search algorithm. Here, you test if the midpoint in the array is the target value. If it is, great! You're done. If not, then you continually narrow your search area depending on whether the target is less than or greater than the midpoint. The algorithm is as follows:\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\u003eGiven an array of sorted values (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e), and a target value you wish to locate (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etarget\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=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCalculate the index of the midpoint of the array \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e by taking the average of the largest and smallest indices and rounding to the nearest integer.  \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the value located at the midpoint matches your target, set found to true.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the target is less than the value located at the midpoint of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, narrow your search to the lower half of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e by setting the largest index in the array to the midpoint - 1. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the target is greater than the value located at the midpoint of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, narrow your search to the upper half of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e by setting the smallest index in the array to the midpoint + 1. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRepeat the steps above until found is true.\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=\\\"551\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"610\\\"/\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\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 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etarget\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as inputs, performs a binary search and outputs the index of target value as well as the number of iterations it took to find the target.\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\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":52557,"title":"Undocumented MATLAB tricks No. 2 - Tell the parfor index","description":"Your function is called in multiple for-loops, and the loop indices are given to your function. One of these indices are from a parfor loop. Can you find which one is the parfor loop index?\r\nThere are more than one ways, however you may need some undocumented MATLAB tricks to find the best solution of size 25.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; 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: none solid 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: 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: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eYour function is called in multiple for-loops, and the loop indices are given to your function. One of these indices are from a parfor loop. Can you find which one is the parfor loop index?\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: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThere are more than one ways, however you may need some undocumented MATLAB tricks to find the best solution of size 25.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function I=ParforIndex(a,b,c)","test_suite":"%%\r\n% Tests are randomly populated.\r\nfor T=1:10\r\n    rng('shuffle');\r\n    switch randi(3)\r\n        case 1\r\n            for a=1:10\r\n                for b=1:10\r\n                    parfor c=1:10\r\n                        assert(ParforIndex(a,b,c)==c);\r\n                    end\r\n                end\r\n            end\r\n        case 2\r\n            for a=1:10\r\n                parfor b=1:10\r\n                    for c=1:10\r\n                        assert(ParforIndex(a,b,c)==b);\r\n                    end\r\n                end\r\n            end\r\n        case 3\r\n            parfor a=1:10\r\n                for b=1:10\r\n                    for c=1:10\r\n                        assert(ParforIndex(a,b,c)==a);\r\n                    end\r\n                end\r\n            end\r\n    end\r\nend","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":362068,"edited_by":362068,"edited_at":"2022-05-08T12:23:21.000Z","deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":"2022-05-08T12:23:21.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2021-08-16T10:10:46.000Z","updated_at":"2022-05-08T12:23:21.000Z","published_at":"2021-08-16T10:10:46.000Z","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\u003eYour function is called in multiple for-loops, and the loop indices are given to your function. One of these indices are from a parfor loop. Can you find which one is the parfor loop index?\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\u003eThere are more than one ways, however you may need some undocumented MATLAB tricks to find the best solution of size 25.\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":45543,"title":"Find the remainder with the factorial of PRIMES?","description":"* Take a number  greater than or equal to 2 and take its primes. e.g. 6 and its primes are 2 3 5\r\n* calculate the factorial of its prime (nearest less) and calculate the sum from that number down to 1. e.g. 5! = 120 and sum 5 to 1 is 15\r\n* As mod of the factorial of every number with 2 is zero, check the mod of the sum calculated with 10 e.g. mod of 15 with 10 is 5\r\n* Divide factorial with the remainder (if possible). e.g. 120/5 = 24\r\n\r\nDon't use builtin functions \r\n\r\n  primes\r\n\r\n  factorial\r\n\r\n  mod","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: 275.767px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 137.883px; transform-origin: 407px 137.883px; vertical-align: baseline; \"\u003e\u003cul style=\"block-size: 122.6px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 61.3px; transform-origin: 391px 61.3px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 290px 8px; transform-origin: 290px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTake a number greater than or equal to 2 and take its primes. e.g. 6 and its primes are 2 3 5\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.8667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 355px 8px; transform-origin: 355px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ecalculate the factorial of its prime (nearest less) and calculate the sum from that number down to 1. e.g. 5! = 120 and sum 5 to 1 is 15\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.8667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 359px 8px; transform-origin: 359px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAs mod of the factorial of every number with 2 is zero, check the mod of the sum calculated with 10 e.g. mod of 15 with 10 is 5\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 197px 8px; transform-origin: 197px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDivide factorial with the remainder (if possible). e.g. 120/5 = 24\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\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: 81px 8px; transform-origin: 81px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDon't use builtin functions\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.167px; 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; perspective-origin: 404px 51.0833px; transform-origin: 404px 51.0833px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 24px 8.5px; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eprimes\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\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: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003efactorial\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\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: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003emod\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = your_fcn_name(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nfiletext = fileread('your_fcn_name.m');\r\nassert(isempty(strfind(filetext, 'primes')))\r\nassert(isempty(strfind(filetext, 'factorial')))\r\nassert(isempty(strfind(filetext, 'mod')))\r\n\r\n%%\r\nx = 11;\r\ny_correct = 630;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 49;\r\ny_correct = 3.232790518889602e+58;\r\nassert(abs(your_fcn_name(x)/1e58-y_correct/1e58)\u003c1e-2)\r\n\r\n%%\r\nx = 5;\r\ny_correct = 1;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 17;\r\ny_correct = 6.2270208e+09;\r\nassert(abs(your_fcn_name(x)/1e9-y_correct/1e9)\u003c1e-2)","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":26467,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2021-05-20T10:42:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-05-20T09:50:35.000Z","updated_at":"2025-04-28T21:39:14.000Z","published_at":"2020-05-20T09:54:29.000Z","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=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTake a number greater than or equal to 2 and take its primes. e.g. 6 and its primes are 2 3 5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ecalculate the factorial of its prime (nearest less) and calculate the sum from that number down to 1. e.g. 5! = 120 and sum 5 to 1 is 15\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAs mod of the factorial of every number with 2 is zero, check the mod of the sum calculated with 10 e.g. mod of 15 with 10 is 5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDivide factorial with the remainder (if possible). e.g. 120/5 = 24\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\u003eDon't use builtin functions\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[primes\\n\\nfactorial\\n\\nmod]]\u003e\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":58573,"title":"Am I a city or a state","description":"Input will be an array of cities and states. Also, lists of cities and states will be passed.\r\nReplace every city name with \"city\" and every state with a \"state\" string.\r\nInput:\r\nA = [\"Natick\", \"Wayland\", \"Framingham\", \"Chicago\"];\r\nB = [\"MA\", \"IL\"];\r\narr = [\"Wayland\", \"Chicago\", \"IL\"];\r\n\r\nOuput:\r\narr = [\"city\", \"city\", \"state\"];","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; 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: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 260.919px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 332.5px 130.455px; transform-origin: 332.5px 130.459px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eInput will be an array of cities and states. Also, lists of cities and states will be passed.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eReplace every city name with \"city\" and every state with a \"state\" string.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA = [\"Natick\", \"Wayland\", \"Framingham\", \"Chicago\"];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eB = [\"MA\", \"IL\"];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003earr = [\"Wayland\", \"Chicago\", \"IL\"];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eOuput:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9943px; 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: 309.498px 10.4924px; text-align: left; transform-origin: 309.498px 10.4972px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003earr = [\"city\", \"city\", \"state\"];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = sortCitiesAndStates(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nA = [\"Natick\", \"Wayland\", \"Framingham\", \"Chicago\"];\r\nB = [\"MA\", \"IL\"];\r\nx = [\"Wayland\", \"Chicago\", \"IL\"];\r\ny_correct = [\"city\", \"city\", \"state\"];\r\nassert(isequal(sortCitiesAndStates(x,A,B),y_correct))\r\n\r\n%%\r\nx = [\"Natick\", \"MA\", \"IL\"];\r\nA = [\"Natick\", \"Wayland\", \"Framingham\", \"Chicago\"];\r\nB = [\"MA\", \"IL\"];\r\ny_correct = [\"city\", \"state\", \"state\"];\r\nassert(isequal(sortCitiesAndStates(x,A,B),y_correct))\r\n\r\n%%\r\nx = [];\r\nA = [\"Natick\", \"Wayland\", \"Framingham\", \"Chicago\"];\r\nB = [\"MA\", \"IL\"];\r\ny_correct = [];\r\nassert(isequal(sortCitiesAndStates(x,A,B),y_correct))\r\n\r\n%%\r\nx = [\"Wayland\"];\r\nA = [\"Natick\", \"Wayland\", \"Framingham\", \"Chicago\"];\r\nB = [\"MA\", \"IL\"];\r\ny_correct = [\"city\"];\r\nassert(isequal(sortCitiesAndStates(x,A,B),y_correct))","published":true,"deleted":false,"likes_count":10,"comments_count":1,"created_by":3411694,"edited_by":3411694,"edited_at":"2023-07-18T14:25:28.000Z","deleted_by":null,"deleted_at":null,"solvers_count":11,"test_suite_updated_at":"2023-07-18T14:25:28.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2023-07-17T18:48:42.000Z","updated_at":"2026-03-05T11:09:23.000Z","published_at":"2023-07-17T18:56:55.000Z","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\u003eInput will be an array of cities and states. Also, lists of cities and states will be passed.\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\u003eReplace every city name with \\\"city\\\" and every state with a \\\"state\\\" string.\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:\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\u003eA = [\\\"Natick\\\", \\\"Wayland\\\", \\\"Framingham\\\", \\\"Chicago\\\"];\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\u003eB = [\\\"MA\\\", \\\"IL\\\"];\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\u003earr = [\\\"Wayland\\\", \\\"Chicago\\\", \\\"IL\\\"];\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\u003eOuput:\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\u003earr = [\\\"city\\\", \\\"city\\\", \\\"state\\\"];\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":43216,"title":"Encode Me From The Past","description":"Given this input\r\n\r\n x = 2, 5, 1, 2, 4, 1, 1, 3\r\n\r\noutput should be (Five 2's, Two 1's, One 4, Three 1's)\r\n\r\n [2 2 2 2 2 1 1 4 1 1 1]","description_html":"\u003cp\u003eGiven this input\u003c/p\u003e\u003cpre\u003e x = 2, 5, 1, 2, 4, 1, 1, 3\u003c/pre\u003e\u003cp\u003eoutput should be (Five 2's, Two 1's, One 4, Three 1's)\u003c/p\u003e\u003cpre\u003e [2 2 2 2 2 1 1 4 1 1 1]\u003c/pre\u003e","function_template":"function y = encodeThePast(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [2, 5, 1, 2, 4, 1, 1, 3];\r\ny_correct = [2 2 2 2 2 1 1 4 1 1 1];\r\nassert(isequal(encodeThePast(x),y_correct))\r\n%%\r\nx = [2 3 4 3];\r\ny_correct = [2 2 2 4 4 4];\r\nassert(isequal(encodeThePast(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":8,"comments_count":0,"created_by":13865,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":62,"test_suite_updated_at":"2016-10-29T16:40:55.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2016-10-08T10:22:58.000Z","updated_at":"2026-01-04T08:18:44.000Z","published_at":"2016-10-08T10:25:02.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven this input\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[ x = 2, 5, 1, 2, 4, 1, 1, 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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput should be (Five 2's, Two 1's, One 4, Three 1's)\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[ [2 2 2 2 2 1 1 4 1 1 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60926,"title":"Greater than before","description":"Given an array of integers, write a function that returns elements that are greater than the one before them.  \r\nFor instance, \r\ninput = [1, 3, 2, 4, 3, 5]  \r\noutput = [3, 4, 5]","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; 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: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 111px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 55.5px; transform-origin: 408px 55.5px; vertical-align: baseline; \"\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: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven an array of integers, write a function that returns elements that are greater than the one before them.  \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: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor instance, \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: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003einput = [1, 3, 2, 4, 3, 5]  \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: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; 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 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eoutput = [3, 4, 5]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = gtb(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [1, 3, 2, 4, 3, 5]  ;\r\ny_correct = [3, 4, 5];\r\nassert(isequal(gtb(x),y_correct))\r\n\r\n%%\r\nx = zeros(1,7)  ;\r\ny_correct = [];\r\nassert(isempty(gtb(x)))\r\n\r\n%%\r\nx = [5, 5, 10, 2, 10, 20, 5, 2, 2, 20] ;\r\ny_correct = [10, 10, 20, 20];\r\nassert(isequal(gtb(x),y_correct))\r\n\r\n%%\r\nx = [2, 2, 5, 5, 5, 4, 4, 1]  ;\r\ny_correct = [5];\r\nassert(isequal(gtb(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":39,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-06-02T11:52:32.000Z","updated_at":"2025-11-29T16:44:04.000Z","published_at":"2025-06-02T11:52:32.000Z","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\u003eGiven an array of integers, write a function that returns elements that are greater than the one before them.  \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\u003eFor instance, \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 = [1, 3, 2, 4, 3, 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\u003eoutput = [3, 4, 5]\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":271,"title":"N-Cards Problem","description":"You have a deck of _N_ cards numbered in order from 1 to _N_. You discard the top card (card 1) and place the next card (card 2) at the bottom of the deck. Then you discard the top card of the remaining deck (card 3), and place the next card (card 4) at the bottom of the deck. We repeat the procedure: discarding the top card and placing the next card at the bottom of the deck. Eventually, you will have one card left. What is the number of that card?\r\n\r\n*Example*\r\n\r\n    nCardsProblem(5)\r\n\r\n    deck = [ 1 2 3 4 5 ]\r\n    deck = [ 2 3 4 5 ]\r\n    deck = [ 3 4 5 2 ]\r\n    deck = [ 4 5 2 ]\r\n    deck = [ 5 2 4 ]\r\n    deck = [ 2 4 ]\r\n    deck = [ 4 2 ]\r\n    deck = [ 2 ]","description_html":"\u003cp\u003eYou have a deck of \u003ci\u003eN\u003c/i\u003e cards numbered in order from 1 to \u003ci\u003eN\u003c/i\u003e. You discard the top card (card 1) and place the next card (card 2) at the bottom of the deck. Then you discard the top card of the remaining deck (card 3), and place the next card (card 4) at the bottom of the deck. We repeat the procedure: discarding the top card and placing the next card at the bottom of the deck. Eventually, you will have one card left. What is the number of that card?\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e\u003c/p\u003e\u003cpre\u003e    nCardsProblem(5)\u003c/pre\u003e\u003cpre\u003e    deck = [ 1 2 3 4 5 ]\r\n    deck = [ 2 3 4 5 ]\r\n    deck = [ 3 4 5 2 ]\r\n    deck = [ 4 5 2 ]\r\n    deck = [ 5 2 4 ]\r\n    deck = [ 2 4 ]\r\n    deck = [ 4 2 ]\r\n    deck = [ 2 ]\u003c/pre\u003e","function_template":"function lastcard = nCardsProblem(N)\r\n  lastcard = N;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = 1;\r\nassert(isequal(nCardsProblem(x),y_correct))\r\n\r\n%%\r\nx = 5;\r\ny_correct = 2;\r\nassert(isequal(nCardsProblem(x),y_correct))\r\n\r\n%%\r\nx = 50;\r\ny_correct = 36;\r\nassert(isequal(nCardsProblem(x),y_correct))\r\n\r\n%%\r\nx = 1000;\r\ny_correct = 976;\r\nassert(isequal(nCardsProblem(x),y_correct))\r\n\r\n%%\r\nx = 10000;\r\ny_correct = 3616;\r\nassert(isequal(nCardsProblem(x),y_correct))","published":true,"deleted":false,"likes_count":6,"comments_count":1,"created_by":134,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":156,"test_suite_updated_at":"2012-02-06T19:31:09.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-02-06T19:31:09.000Z","updated_at":"2025-12-12T09:01:04.000Z","published_at":"2012-02-06T19:31:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou have a deck of\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\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e cards numbered in order from 1 to\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\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. You discard the top card (card 1) and place the next card (card 2) at the bottom of the deck. Then you discard the top card of the remaining deck (card 3), and place the next card (card 4) at the bottom of the deck. We repeat the procedure: discarding the top card and placing the next card at the bottom of the deck. Eventually, you will have one card left. What is the number of that card?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\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[    nCardsProblem(5)\\n\\n    deck = [ 1 2 3 4 5 ]\\n    deck = [ 2 3 4 5 ]\\n    deck = [ 3 4 5 2 ]\\n    deck = [ 4 5 2 ]\\n    deck = [ 5 2 4 ]\\n    deck = [ 2 4 ]\\n    deck = [ 4 2 ]\\n    deck = [ 2 ]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1024,"title":"Doubling elements in a vector","description":"Given the vector A, return B in which all numbers in A are doubling. So for:\r\n\r\nA = [  1   5   8 ]\r\n\r\nthen\r\n\r\nB = [  1   1   5   5   8  8 ]\r\n","description_html":"\u003cp\u003eGiven the vector A, return B in which all numbers in A are doubling. So for:\u003c/p\u003e\u003cp\u003eA = [  1   5   8 ]\u003c/p\u003e\u003cp\u003ethen\u003c/p\u003e\u003cp\u003eB = [  1   1   5   5   8  8 ]\u003c/p\u003e","function_template":"function B = your_fcn_name(A)\r\n  \r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = [1 1];\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = [0 -1 1 0 0 0 1 2];\r\ny_correct = [0 0 -1 -1 1 1 0 0 0 0 0 0 1 1 2 2];\r\nassert(isequal(your_fcn_name(x),y_correct))","published":true,"deleted":false,"likes_count":69,"comments_count":13,"created_by":7968,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10291,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-11-03T15:44:34.000Z","updated_at":"2026-04-12T20:35:55.000Z","published_at":"2012-11-03T15:46:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the vector A, return B in which all numbers in A are doubling. So for:\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\u003eA = [ 1 5 8 ]\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\u003ethen\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\u003eB = [ 1 1 5 5 8 8 ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":55775,"title":"Taylor Series","description":"You can use a Taylor series to approximate common functions. The Taylor series for sin(x) is \r\n\r\nUsing only the first several terms in the series could get you a good approximation to the function. \r\nWrite a function that takes a point x at which to evaluate the sine function and a tolerance level that defines how close you want the Taylor series approximation to be to the actual value. Your function should output the approximate value of sin(x).","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; 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: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 157px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 78.5px; transform-origin: 407px 78.5px; vertical-align: baseline; \"\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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eYou can use a Taylor series to approximate common functions. The Taylor series for sin(x) is \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 46px; 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 23px; text-align: left; transform-origin: 384px 23px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"vertical-align:-17px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"294.5\" height=\"46\" style=\"width: 294.5px; height: 46px;\"\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 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eUsing only the first several terms in the series could get you a good approximation to the function. \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: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eWrite a function that takes a point x at which to evaluate the sine function and a tolerance level that defines how close you want the Taylor series approximation to be to the actual value. Your function should output the approximate value of sin(x).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function yApp = myTaylor(x,tol)\r\n\r\n\r\nend","test_suite":"%%\r\nx = pi;\r\ntol = 0.01;\r\nyApp = myTaylor(x,tol); \r\nassert(abs(yApp-sin(x))\u003ctol)\r\n%%\r\nx = pi/2;\r\ntol = 0.1;\r\nyApp = myTaylor(x,tol); \r\nassert(abs(yApp-sin(x))\u003ctol)\r\n%%\r\nx = pi*1i;\r\ntol = 1e-3;\r\nyApp = myTaylor(x,tol); \r\nassert(abs(yApp-sin(x))\u003ctol)\r\n%%\r\nx = 2.345;\r\ntol = 1e-5;\r\nyApp = myTaylor(x,tol);\r\nassert( (abs(yApp-sin(x))\u003ctol) \u0026\u0026 (abs(yApp-sin(x))\u003etol/100) )\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":2,"created_by":140016,"edited_by":140016,"edited_at":"2022-10-17T14:02:37.000Z","deleted_by":null,"deleted_at":null,"solvers_count":207,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-09-16T18:00:45.000Z","updated_at":"2026-04-02T08:59:19.000Z","published_at":"2022-10-17T14:02:37.000Z","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\u003eYou can use a Taylor series to approximate common functions. The Taylor series for sin(x) 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:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\sin(x) = \\\\sum_{n=0}^{\\\\infty}\\\\frac{(-1)^nx^{2n+1}}{(2n+1)!} = x - \\\\frac{x^3}{3!} + \\\\frac{x^5}{5!} - \\\\frac{x^7}{7!} + \\\\dots\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\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\u003eUsing only the first several terms in the series could get you a good approximation to the function. \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 a point x at which to evaluate the sine function and a tolerance level that defines how close you want the Taylor series approximation to be to the actual value. Your function should output the approximate value of sin(x).\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":3092,"title":"Return fibonacci sequence  do not use loop and condition","description":"Calculate the nth Fibonacci number.\r\n\r\nGiven n, return f where f = fib(n) and f(1) = 1, f(2) = 1, f(3) = 2, ...\r\n\r\nExamples:\r\n\r\n Input  n = 5\r\n Output f is 5\r\n Input  n = 7\r\n Output f is 13\r\n\r\nbut, *loop and conditional statement is forbidden*","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: 203.733px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 101.867px; transform-origin: 407px 101.867px; vertical-align: baseline; \"\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: 113.5px 8px; transform-origin: 113.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCalculate the nth Fibonacci number.\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: 204px 8px; transform-origin: 204px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven n, return f where f = fib(n) and f(1) = 1, f(2) = 1, f(3) = 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: 32px 8px; transform-origin: 32px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eExamples:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; 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; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003e Input  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003en = 5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 56px 8.5px; transform-origin: 56px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003e Output \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 24px 8.5px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 24px 8.5px; \"\u003ef is 5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003e Input  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003en = 7\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; 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; 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; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 60px 8.5px; transform-origin: 60px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003e Output \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003ef is 13\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; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; 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: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ebut,\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: 2px 8px; transform-origin: 2px 8px; 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: 153px 8px; transform-origin: 153px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eloop and conditional statement is forbidden\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function f = fib(n)\r\n  f = n;\r\nend","test_suite":"%%% functions forbidden\r\n\r\n\r\n% Clean user's function from some known jailbreaking mechanisms\r\nfunctions={'!','feval','eval','str2func','str2num','regex','system','dos','unix','perl','assert','fopen','write','save','setenv','path','please','for','if','while','switch','round','roundn','fix','ceil','char','floor'};\r\nassessFunctionAbsence(functions, 'FileName', 'fib.m');\r\n%%\r\nn = 1;\r\nf = 1;\r\nassert(abs(fib(n) - f) \u003c 1e-4)\r\n\r\n%%\r\nn = 6;\r\nf = 8;\r\nassert(abs(fib(n) - f) \u003c 1e-4)\r\n\r\n%%\r\nn = 10;\r\nf = 55;\r\nassert(abs(fib(n) - f) \u003c 1e-4)\r\n\r\n%%\r\nn = 20;\r\nf = 6765;\r\nassert(abs(fib(n) - f) \u003c 1e-4)","published":true,"deleted":false,"likes_count":15,"comments_count":11,"created_by":3668,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":855,"test_suite_updated_at":"2021-02-15T12:43:32.000Z","rescore_all_solutions":false,"group_id":30,"created_at":"2015-03-18T15:03:18.000Z","updated_at":"2026-03-16T14:19:58.000Z","published_at":"2015-03-18T15:25:13.000Z","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\u003eCalculate the nth Fibonacci number.\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\u003eGiven n, return f where f = fib(n) and f(1) = 1, f(2) = 1, f(3) = 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\u003eExamples:\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[ Input  n = 5\\n Output f is 5\\n Input  n = 7\\n Output f is 13]]\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\u003ebut,\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\u003eloop and conditional statement is forbidden\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":3095,"title":" Return fibonacci sequence do not use loop and condition version 2","description":"Calculate the nth Fibonacci number,return sequence\r\n\r\nGiven n, return f where f = fib(n) and f(1) = 1, f(2) = 1, f(3) = 2, ...\r\n\r\nExamples:\r\n\r\n Input  n = 2 : 5\r\n Output f is [1 2 3 5]\r\n Input  n = 7 : 10\r\n Output f is [13    21    34    55]\r\n\r\nbut, loop and conditional statement is forbidden","description_html":"\u003cp\u003eCalculate the nth Fibonacci number,return sequence\u003c/p\u003e\u003cp\u003eGiven n, return f where f = fib(n) and f(1) = 1, f(2) = 1, f(3) = 2, ...\u003c/p\u003e\u003cp\u003eExamples:\u003c/p\u003e\u003cpre\u003e Input  n = 2 : 5\r\n Output f is [1 2 3 5]\r\n Input  n = 7 : 10\r\n Output f is [13    21    34    55]\u003c/pre\u003e\u003cp\u003ebut, loop and conditional statement is forbidden\u003c/p\u003e","function_template":"function y = fib(x)\r\n  y = x;\r\nend","test_suite":"%%% Clean workspace\r\n% !/bin/cp fib.m safe\r\n% !/bin/rm *.*\r\n% !/bin/mv safe fib.m\r\n\r\n% Clean user's function from some known jailbreaking mechanisms\r\nfunctions={'!','feval','eval','str2func','str2num','regex','system','dos','unix','perl','assert','fopen','write','save','setenv','path','please','for','if','while','switch','round','roundn','fix','ceil','char','floor','\\.','^','power'};\r\nfid = fopen('fib.m');\r\n  st = char(fread(fid)');\r\n  for n = 1:numel(functions)\r\n    st = regexprep(st, functions{n}, 'error(''No fancy functions!''); %','ignorecase');\r\n  end\r\n  st = regexprep(st, 'function', 'error(''No fancy functions!''); %','ignorecase',2);\r\nfclose(fid);\r\nfid = fopen('fib.m' , 'w');\r\n  fwrite(fid,st);\r\nfclose(fid);\r\n\r\n%%\r\nn = 1:5;\r\nf = [1     1     2     3     5];\r\nassert(isequal(fib(n),f))\r\n\r\n%%\r\nn = 7 : 10;\r\nf = [13    21    34    55];\r\nassert(isequal(fib(n),f))\r\n\r\n%%\r\n\r\nn = 20 : 22;\r\nf = [ 6765       10946       17711];\r\nassert(isequal(fib(n),f))","published":true,"deleted":false,"likes_count":0,"comments_count":21,"created_by":3668,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":64,"test_suite_updated_at":"2015-03-19T15:13:29.000Z","rescore_all_solutions":false,"group_id":30,"created_at":"2015-03-19T14:56:25.000Z","updated_at":"2026-03-16T14:37:24.000Z","published_at":"2015-03-19T14:56:25.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCalculate the nth Fibonacci number,return 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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven n, return f where f = fib(n) and f(1) = 1, f(2) = 1, f(3) = 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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples:\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[ Input  n = 2 : 5\\n Output f is [1 2 3 5]\\n Input  n = 7 : 10\\n Output f is [13    21    34    55]]]\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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ebut, loop and conditional statement is forbidden\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":56040,"title":"A Binary Search","description":"One way to locate a target value in a sorted array, is to use a binary search algorithm. Here, you test if the midpoint in the array is the target value. If it is, great! You're done. If not, then you continually narrow your search area depending on whether the target is less than or greater than the midpoint. The algorithm is as follows:\r\nGiven an array of sorted values (X), and a target value you wish to locate (target)\r\nCalculate the index of the midpoint of the array X by taking the average of the largest and smallest indices and rounding to the nearest integer.  \r\nIf the value located at the midpoint matches your target, set found to true.\r\nIf the target is less than the value located at the midpoint of X, narrow your search to the lower half of X by setting the largest index in the array to the midpoint - 1. \r\nIf the target is greater than the value located at the midpoint of X, narrow your search to the upper half of X by setting the smallest index in the array to the midpoint + 1. \r\nRepeat the steps above until found is true.\r\n\r\nWrite a function that takes X and target as inputs, performs a binary search and outputs the index of target value as well as the number of iterations it took to find the target.\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: 926.467px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 463.233px; transform-origin: 407px 463.233px; 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: 383.5px 8px; transform-origin: 383.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOne way to locate a target value in a sorted array, is to use a binary search algorithm. Here, you test if the midpoint in the array is the target value. If it is, great! You're done. If not, then you continually narrow your search area depending on whether the target is less than or greater than the midpoint. The algorithm is as follows:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.5px; 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 10.75px; text-align: left; transform-origin: 384px 10.75px; 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: 104px 8px; transform-origin: 104px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven an array of sorted values (\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: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\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: 126.5px 8px; transform-origin: 126.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e), and a target value you wish to locate (\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: 24px 8px; transform-origin: 24px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 24px 8.5px; transform-origin: 24px 8.5px; \"\u003etarget\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: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 164.967px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 82.4833px; transform-origin: 391px 82.4833px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 41.3667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.6833px; text-align: left; transform-origin: 363px 20.6833px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 149px 8px; transform-origin: 149px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCalculate the index of the midpoint of the array \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 195.5px 8px; transform-origin: 195.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e by taking the average of the largest and smallest indices and rounding to the nearest integer.  \u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 231px 8px; transform-origin: 231px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf the value located at the midpoint matches your target, set found to true.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 41.3667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.6833px; text-align: left; transform-origin: 363px 20.6833px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 188px 8px; transform-origin: 188px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf the target is less than the value located at the midpoint of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 127px 8px; transform-origin: 127px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, narrow your search to the lower half of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 34.5px 8px; transform-origin: 34.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e by setting the largest index in the array to the midpoint - 1. \u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 41.3667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.6833px; text-align: left; transform-origin: 363px 20.6833px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 198.5px 8px; transform-origin: 198.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf the target is greater than the value located at the midpoint of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 129px 8px; transform-origin: 129px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, narrow your search to the upper half of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 11.5px 8px; transform-origin: 11.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e by setting the smallest index in the array to the midpoint + 1. \u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 134px 8px; transform-origin: 134px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eRepeat the steps above until found is true.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cdiv style=\"block-size: 556.5px; 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 278.25px; text-align: left; transform-origin: 384px 278.25px; 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: 610px;height: 551px\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACYgAAAidCAMAAAAQ3bCqAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAADwUExURQAAAABRhgAAAABKhAAAAABLiQAAAABLh////wAAACoqKockILCwsAAAAEhISAAAAAAAAIklIoSEhAAAAHJycgBMhmVlZdiHJEmjP6Eg9SYmJisrKy8vL1FRUdaHJKAf9gAAAEdHRwAAAENDQ1RUVAAAAIckIIolIQAAAAAAAABMh9iJJEeiP6Ag9deIJaEg9gAAAIokIQAAAIkkIdeIJAAAAAAAAAAAAEiiPwBLiKAg9teIJAAAAAAAAKAg9gAAAABLhwoKChQUFCkpKSk0KDs7O0iiP1VrU3p6eoCifokkIaAg9rHfrbm5udeIJP///8xs3oMAAAA/dFJOUwATGR8mKTMzMzw9QERHR1FSUlNcXGFiYmZnbGxscXFzdnp9fX1+gIuXmJubnZ+nqri/wsTLzM3Ozs/P1dbX1/oe87sAAAAJcEhZcwAAMsAAADLAAShkWtsAAP5eSURBVHhe7N0L1xzHed17OrbOCSLKNqmQhnOxECcWEhqSYENHAaVlUXEIkYQD5/t/m9PPU7u7q7qemumed2aqp/H/rSWh773fnktt9gxefAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIB9eD94o+na6bUbXO1AAAAAh3G6IV2tP2080MvPNAEAAHBcpxvSxv7UtulAn715TxEDAADHd7ohbepPp2w50JthW4oYAAA4vtMN6fTaDTYc6KVtSxEDAADHd7ohnV67wYYDUcQAAAAGG/rT1VDEAAAABhQxAACATihiAAAAnVDEAAAAOqGIAQAAbGA95qVNfOaV5o1Pu2rBwJdo2n320n6Pl7bK1/rOPvHGt3gZlaVx7/Ik5YFsOg+Yb2sLJrQxAADwYKzCDNXmM/szSUWnWuBsNi9i3o2SYbH+mNcMh/GmlSybUnaKYb98rS/IpsuAcyDNJxQxAADwYKzCvPRfTz+xClQtSMq5okkNy9P/J6mILbfIFacYZGvzWZteBCzWTShiAADgwViFSR8Qzpa9p7wFNRWmsmUNK/z/tNKLWNriTfpw0qa00ixOMZjX5nM2vQw4rhyPPPz5Jj82AADAI/AeY//38uVn+sqWCtQwPy6YSk4+ox72xu5EzZ9AjmunDy3TAm0wf8qpHV763to4PI1P2//lgd5Pt79S4dMMAADAI7EeM3ijLjPd5Rp7UCo+Y9Ox6XjNWMXGtWMRmzpSuXmam5qX5seelq/zFYPGnhQxAADwsKzH1I1ouSBoSKmyZRWobEgqYsv1xc7zSca9NZOv9OX5psWWFDEAAPC4rMfkTUYFSnMDX5C3Ik378vmjxmW5SsfJ+pNOlc5UHDTJe1q+1qbLquULNJ2ORBEDAACPyHpM0aeWC7xf5a0oK0vviwaUVykVMc0kWXXzlWV9ynewqaKI5QHTecadKWIAAOBhWY+pi1jebGy+LmLez4p+lDpRUcTGmSTdMiunMlm/sqmiiBVNiyIGAACOwXpMUWR8gaadzddFzAtQWcSKLVvr/dDRytSv0kKbahexontRxAAAwMOyHlP1rrVFbFGArEoVRSxY78uKe1oj3yPtPk8FeShiAADgIKzHXFLEoi5VFTFNj6bSFBax7Mto81SQhyIGAAAOwnrMxUXMp2ZWilYVMftzuZIiBgAAPjbWY6reVS24YhGzb4HZn8uVFDEAAPCxsR5T9a5qAUUMAADg6qzHVL2rWtAoYosCdK6ITftELY4iBgAAPjbWYy4uYovfQGHLiiK2KEhT/5oaWc73aPz6Ck0nFDEAAHAM1mMuKWJZbZrMa+OCZIv80NHO+UKboogBAICjsx5zSRHLPkgcFYuCruWLfP08lbFl71OlsqlxtU1TxAAAwBFZj7mkiLUK0rilz5Trs08zfWVZn7zGaQeboogBAICjsx5T9a5qQVDEitrlbMGiiOW3xPKm5Z2s2Dntre3ztb5c0wlFDAAAHIP1mIuKWNowa1perhZF7H1WkbIbYipleRNLe2tzm9xSxPK+BwAA8Cisx1xWxFLVmspUalKLtXNh+iyt19yitY3zY6HK1/kKTSdFEQu+qwYAAPAYrMdcVsTGMmXt6bOxd01r5wVWmVTD3o/1aeptL32tNp6OnM/4Gk0ndREbjvPSjwQAAPBAvMZo2oULwiI2lqlCUcSWG+Rdqd55PnA+56s0nRRFLK03FDEAAPBgvMNo2oUL4iK2KFN+26soYi/9/ydlVVo2seyLXja7tohNp+CLYgAA4MF4h9G0Cxc0itgnL7M29ZlXq7KIjZ8cmnw/l++cPsEc+YJs+lQRmwodRQwAADwY7zCaduGCVhGzNuVF6KUVo6CIjV8A82+S1dTFll/w8j2y6ZNFbPyKWZkLAADgozUVMQAAANwXRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADzNz3/+c00BAADgrt6/f68pAAAA3BVFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAAAAnVDEAAAAOqGIAQAAdEIRAwAA6IQiBgAA0AlFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAAAAnVDEAAAAOqGIAQAAdEIRAwAA6IQiBgAA0AlFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAAAAnVDEAAAAOqGIAQAAdEIRAwAA6IQiBgAA0AlFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAIB9ePVcEz10PTk+YhQxAMAePH/7x35dqOvJ8VGjiAEAduDtH//Yrwt1PTk+bhQxAEB/r4Yq1K0LdT05PnIUMQBAfxQxfKQoYgCA/ihi+EhRxAAA/VHE8JGiiAEA+qOI4SNFEQMA9EcRw0eKIgYA6I8iho8URQwA0Jn1oElWiJ6/sl/wNXj79pUWOVs0LHhuBSpfo+3TL8m3ybe+eOI7lLu0Tw7cBUUMANCZWlAydaHUmiZZe0qz03qtea7WZoaDVEUsP97buXFpSUIRw71RxAAAnakFJepCzzU7m1uVzb2aN0gLsxo2eLssYsvjTas0n1DEcG8UMQBAZ2/fphY1/PlWBanuYYvuNPeudEOs7GF2LPs/X2XavS44OXA/FDEAQH/+ueF8P0q96dVzXzR+VWz8dDLNpe1fpU8Z9bmjbfF8/oxyKlbT8WxGh5tb1+LkwB1RxAAA/S26kFel7ItcqUiN1clnxo8kE1+w7F3TAs1Px0u1bfrWGUUM/VDEAAD9lV0oFae8GqXqpBmfLlan4qaZgZrXuCTdAtOMSes1QxHDnf08MxQxTbkfaxMAAO6o7EI+N92wcrakLGKacdWCVNxUxOpel6rZeAaKGO7rH4b2Ffu5tgAA4J7KLvT81du3i2bkzUmLbLLoaa3ipiIWrPb14w0zihju68eqXTWKGACgh7NdqCpi+ca+UtPiB1TTssnlwfNdKGK4s9YtMXoYAKCLpxUxmx9vb4l/HJkXsTQ58RPqLhlFDHfWuiVGEQMAdHG6C43/1lFexNKky0vXZF4WrqaIoaf4lhg9DADQR6MLPX/+avwlYqZRxPJSNbFlWRELqZ1RxHBv8S0xihgAoI+gC+mf6M5dVsTq44woYugluiVGDwMAdFJ1obA9bSlidiONIoa9im6JUcQAAJ0su1D2eeTg1fP6y/pp0oVFzJZRxLBb9S0xehgAoJdFF5p62NtX6d+HfHIRW67OUcRwf/UtMYoYAKCXsgv53DCflaNTReySvzWZo4ihg+UtMXoYAKCbsgvZzKI6nS1i+YJB3r6yyRBFDB0sb4lRxAAA3RRdKCpWvqhRxIqV4gfMitjicMP6t2/1qSdFDF2Ut8ToYQCAfooulJcoSd2sVcR8h/JbYH4HTcfIp0e2aDweRQw9lLfEKGIAgH7OFTHvUs0iVt9C80OMxyhaXJLW5zMUMdxbfkuMHgYA6Mi70HhPqy5iqYc1i1h1zytVr2lJ2l0zJq0vznfqr1UCt5DfEqOIAQA68mY0NSmbyXuVeli7iKViNe2hHjYt0Px000vzmlucHLiX+ZYYPQwA0JOq0av0a8NS8VI1yv6pI9218uk0ORr3t73nf51yKlc6xFs/wHOtX/YynRy4l/mWGEUMANBV6kID60JqRkNzejuWKv+zXcSmm2Yjn5/vci1XD7LSpSXFMuD2xlti9DAAQF/Tba9000ozo6FR6Q/ji9LkrKxab/0IcxGrjlh0rvLkwL2Mt8QoYgCAzsYilbrQ+PGhe2ulyRf4uriI5VVr2MHnil5VNLXFh5DlyYF7SbfE6GEAgO70XbDxLtZzfdVr/OKWr01FyZf7VCnt8ta/CVYXMfvyWHnIzOLkwH2kW2IUMQDAwWS9DdgvuyVGDwMAHI0XserGF7AzdkuMIgYAeGiv/vj21eLul38IqWlgv/6BHgYAeHDB7S9bwje+sH8/pogBAB7c8ndV6IYYXxHDA/ix/gQA4FFZ7cqbWPrbkZoBAADA7aRfPqHfDjb+m0h8VR8AAOAO0i2wAt8QAwAAuIuqiXE/DAAA4E7Kf0yS22EAAAB3NP6jSOnfOAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwO3/7m28AANiv3/ytRizgeP5RT3MAAPbqHzVmAUfzd3qOAwCwX9wTw0HxuSQAYP9+o1ELOBg9wwEA2DONWsDB6AkOAMCeadQCDkZPcAAA9kyjFnAweoIDALBnGrWAg9ETHACAPdOoBRyMnuDffPN/90BZvvnm/+yAouzt0mi+L2UhTUBZSBNQFtIElIU0NUWhiOGo9ATf26tfXagrReGNMaAspAkoC2kCykKagLKQpqYoFDEclZ7ge3v1qwt1pSi8MQaUhTSBdGEGmu9LWUgTUBbSBJRlH2kUhSKGo9ITfG+vfnWhrhSFN8aAspAmoCykCSgLaQLKQpqaolDEcFR6gu/t1a8u1JWi8MYYUBbSBJSFNAFlIU1AWUhTUxSKGI5KT/C9vfrVhbpSFN4YA8pCmoCykCagLKQJKAtpaopCEcNR6Qm+t1e/ulBXisIbY0BZSBNQFtIElIU0AWUhTU1RKGI4Kj3B9/bqVxfqSlF4YwwoC2kCykKagLKQJqAspKkpCkUMR6Un+N5e/epCXSkKb4wBZSFNQFlIE1AW0gSUhTQ1RaGI4aj0BN/bq19dqCtF4Y0xoCykCSgLaQLKQpqAspCmpigUMRyVnuB7e/WrC3WlKLwxBpSFNAFlIU1AWUgTUBbS1BSFIoaj0hN8b69+daGuFIU3xoCykCagLKQJKAtpAspCmpqiUMRwVHqC7+3Vry7UlaLwxhhQFtIElIU0AWUhTUBZSFNTFIoYjkpP8L29+tWFulIU3hgDykKagLKQJqAspAkoC2lqikIRw1HpCb63V7+6UFeKwhtjQFlIE1AW0gSUhTQBZSFNTVEoYjgqPcH39upXF+pKUXhjDCgLaQLKQpqAspAmoCykqaXrMtCoBRyMnuB7e/WrC3WlKLwxBpSFNAFlIU1AWUgTUBbS1BSFIoaj0hN8b69+daGuFIU3xoCykCagLKQJKAtpAspCmpqiUMRwVHqC7+3Vry7UlaLwxhhQFtIElIU0AWUhTUBZSFNTFIoYjkpP8L29+tWFulIU3hgDykKagLKQJqAspAkoC2lqikIRw1HpCb63V7+6UFeKwhtjQFlIE1AW0gSUhTQBZSFNTVEoYjgqPcH39upXF+pKUXhjDCgLaQLKQpqAspAmoCykqSkKRQxHpSf43l796kJdKQpvjAFlIU1AWUgTUBbSBJSFNDVFoYjhqPQE39urX12oK0XhjTGgLKQJKAtpAspCmoCykKamKBQxHJWe4Ht79asLdaUovDEGlIU0AWUhTUBZSBNQFtLUFIUihqPSE3xvr351oa4UhTfGgLKQJqAspAkoC2kCykKamqJQxHBUeoLv7dWvLtSVovDGGFAW0gSUhTQBZSFNQFlIU1MUihiOSk/wvb361YW6UhTeGAPKQpqAspAmoCykCSgLaWqKQhHDUekJvrdXv7pQV4rCG2NAWUgTUBbSBJSFNAFlIU1NUShiOCo9wff26lcX6kpReGMMKAtpAspCmoCykCagLKSpKQpFDEelJ/jeXv3qQl0pCm+MAWUhTUBZSBNQFtIElIU0NUWhiOGo9ATf26tfXagrReGNMaAspAkoC2kCykKagLKQpqYoFDEclZ7ge3v1qwt1pSi8MQaUhTQBZSFNQFlIE1AW0tQUhSKGo9ITfG+vfnWhrhSFN8aAspAmoCykCSgLaQLKQpqaolDEcFR6gu/t1a8u1JWi8MYYUBbSBJSFNAFlIU1AWUhTUxSKGI5KT/C9vfrVhbpSFN4YA8pCmoCykCagLKQJKAtpaopCEcNR6Qm+t1e/ulBXisIbY0BZSBNQFtIElIU0AWUhTU1RKGI4Kj3B9/bqVxfqSlF4YwwoC2kCykKagLKQJqAspKkpCkUMR6Un+N5e/epCXSkKb4wBZSFNQFlIE1AW0gSUhTQ1RaGI4aj0BN/bq19dqCtF4Y0xoCykCSgLaQLKQpqAspCmpigUMRyVnuB7e/WrC3WlKLwxBpSFNAFlIU1AWUgTUBbS1BSFIoaj0hN8b69+daGuFIU3xoCykCagLKQJKAtpAspCmpqiUMRwVHqC7+3Vry7UlaLwxhhQFtIElIU0AWUhTSBdmIHm+1KWfaRRFIoYjkpP8L29+tWFulIU3hgDykKagLKQJqAspAkoC2lqikIRw1HpCb63V7+6UFeKwhtjQFlIE1AW0gSUhTQBZSFNTVEeqYi9sLivNXNnfu5nmrkBO7wm/VwvNI3L2TV1esr3pSwUsZqykCagLKQJKAtpAspCmoCy7CONojx4EXtxw270ySfP5jp04yL2LP/JXjdOZRtpEivYQ+b0lO9LWShiNWUhTUBZSBNQFtIElIU0AWXZRxpFeegi9uz1LW9SDeebT3bjIlbcBLNTaTJnPYwitoFdL6enfF/KQhGrKQtpAspCmoCykCagLKQJKMs+0ijKIxex1p2j6yhuUt26iNmPkhexvG6ObBuK2AZ2vZye8n0pC0WspiykCSgLaQLKQpqAspAmoCz7SKMoD1zEbtyN7noyK1ma9FMFRcx7GEVsA79gRk/5vpSFIlZTFtIElIU0AWUhTUBZSBNQln2kURSKWMvdT6ZJP1X1XX3/XHKgWaygS7a3V7+6UFeKwhtjQFlIE1AW0gSUhTQBZSFNTVEoYi3lyW6r+q7+soiNPYwitoEu2d5e/epCXSkKb4wBZSFNQFlIE1AW0gSUhTQ1RaGItZQnuy37UaZz2Yk1OUofSxotwAq6ZE98vX3/zg7x7vsPmr+UJzHqQpt89923msr88J0d7tvvNLuFBzEKt96H7223d+++13zi12jyTkvX0m7b0niOQhlpoKzV8tP8WEbzp6WnR/DsaK6wXN+vzmQHcZo/rXXS+Epsf9S06aY09Uvng6+IH5dbX5vGC3lxgWympBUnaMOnpvEk797VKU9ctIAd3Wm+zYNMqmdB/ICk+PG1DPihjeZbTr+ox2um2dnJh7ZmGzvNnzS+4VXH/v6dnTU4Z3NFyI7uNGo9gOMWseImmJ1Yk4n/oKJFWEGXbOU7Y8xf4cnat8AGHeWCIvbdt9FuP/jBXFDSztCOmy/NB+02KN6ZsstkNl4q7bUtzen37MGcdVsx1E6r0mQ/d/m+21yhN/bVV8gPYTR/Suuk+aOmRW77o6Ytt6YpjzzHWY5V97k29fGrp4rmMmn5KdrwWtdm8aSd91hetJg2XpHm1LOg8YBku1w3zakXdfYsLk968qGNaOONj1TrAVm+uzRXxLQxRaylPNltWREbfxL7sfLz5jWMIraFLtmq11sse+0P1r2wWnSQjUXsh2+9hdW7ZT3sgiam/bZemuwtb5C9HZYrVr4fTrTXtjRnitiHPNK60UK0z5o0xY+dn6S1Iv2n+2D1FdL2T0lTPIvzJ3Gxw6pM2nJFmvZLJ1+TX7NbXpuTL+TgqaKZjC8+SRte7do0n1Cr3oe07Yo0zWdB8wEp9shTNmnbs2lOvKiLS5NdgpMPbUzbrnlQmz9qtqI8Z3NFg7aliLWUJ7stO5cm/cfKviI29bBn/vmklmIFv2xGT/ntipfhyhdWi46xrYiNLWygJVL0sO1NTLttvDTle17+xlReqNUjqWi3bWlOF7Ey6qrBYqR9VqRZPD1al2NakYVafYm0/RPSLB617Em8/VHTptvTzGdtPDJ3vTbFCzkKpJmMLz5JGz4pjZYk2fLyyT4/0dq06eY008VvPyCL+NdM035RL57F80lPPbQN2nTztcmOXazIz9lc0aJNH7WIefRR3saepebyevF1d19qf/qngNkOL17rC1ivs6VT+zF+Rl9Stj7tme/nbKGfPUV5vYxSO/VdfUUZllDENvILZ/SU3yy9+O2t4EP6r+Y17zktfiyjLrRKs4j5ou9+GKbSB5c2tYHvbhRuHb8G/pGkvjiRFg9szeUXx47lNL+KJZhHCNs7C6C37e8/DMveLQeSM9KuA803pWvgT4/ycrRWtMe2Nm3/hDQ2k76xkm5xzJdp+6Nm+zvNN7VfOj5nccqUN702p17I4VPFlqSpgQWdn+pNto/TfFM7Tbokdm3SVdLi8TEcdvAtzp5h4NsZzbfZqbLrMWo+ICmkxy8fwhNsO6f5Fjtg+VOPyaYLsrgEpx7aFt/FaL7JD+5veIsHJK0YZpfnbK5oss2cRq0HkHpNmvboo7hBvc4Lkq+wX8Uvaek876Y98sM0ilixZ1G1tGD6i46DRhUrTlPJtvFgFLGN7Ho5PeU3s1fV9DZjL6zphXgBT2LUhVbxr+N/U39HzG+IjeXLVm+8JWa7O4Vbxy7B+Nbi7ziabr6Zr2SHcppfpXjPLtPoiBc+Wr6v0XyT/dDj08MDjFegucKmh1XD/1ZnS7sMNN/UOmkz2SWPmh3Lab7Jzhq+dLI4PjldCJu51bVpphn4AZantGWarHZosX2c5pvaaWxvrbHl05G8e0QXrc02c5pvszNFz4K0e/3D25IxfvZonmSbOc23NF/U2RUoT9q+mG22v9N8k20zHjz/sfMZm5wP1FzRZFs5jVoPIHWSNO3RR2NByruPme4yad+5PaVmVNYwoyMVDSkqYssz5R3NZl8sjp0lyRSnqUzbqB9SxDbyq2j0lN/u+/mlV77iL5CiDNSFVhkKl/2tyGo3617z35bcetRLi9jwX3nzm669z0xzxcxmnsRofp132cNh58/egf098NI8tq/TfEs+ThRDSHOF5fr+QznanGPHcppvaZ60vDTFVtsfNdvdab6t9dLJf3ibnra64bVpp2k9VYZtpmXllW2zzZzm21ppijPZ9Hgl8sfQrtCKOLaV03xb61nQeEDKy7Hy8bJ9nOabWi/q4qlSnPSC9+hhq0TzTbbNdGnyHZbTzY2iC1uyrZxGrQfgvUWV5rU+URz+fD22nKodZf0n7esLjS+re9hYqF7kh/dFZRGrz5Q1MZvzT0BzYRNbVcSmTz79kJrGCn4VjZ7yT1O8FVwgRRmoC21R7WZFbP40cvtRbQ+ncBewvae3mdab+Tp2KKf5zXxwmM+/mN3Idnaabynf9bPBoblC6iUnDNsmmm9pnrR8aIojbX/UbHen+XXyl47tPJ2zPtJNrk0pT7PmqWJXab6ybXYkp/l18jTFD7+8aJpcXMEm28ppvu30s6B6QM49u0PDVonm1ygemurShCfNr9kJdmCn+aZim2ymOE+eprmibdgo0aj1APIiNii70dSOUnFRE5q2HjuPLXj2whdri7S9vlqWXQ6baZxMZ/Ivfz0bP96ckthMWvbi2bPhXD6ZB52M54xpo4kfSNNYwa+i0VP+aez9asVbTlOKMlAX2qLa7Ydvsxti/jmlpleyPZzCbedvk5rWxfHvUfiXRzayQznNb2ZvednO694BW+xYTvNNw3+LzwNY/vRorkg2xbMgTvNNrZN+yL8j99RHzXZ3ml8nTzNMzkPlva5NoTjpivPZCdZEsu2c5tfJ0xRh8ocqm4wuWsR2cZpv8+M1nwX1BTrz7A4NWyWaX8POPG1fxGg+aNdOU2yTzdh5pmuQP1DNFW22kdOo9QC8tzSLmNrRtCC1nPHbWeo82Ze10vbZgtSYyj6l6fJkyzNpXnPT+D+uT8edD1Wx/afVtnUWKkMR28iul9NT/inSlzU1cxFPYtSFtji9W/0VsrNsD6dwW6VvzM7veX51Riv+m7Sk/S6/vrZv9gbsh9I3iVe8Ly/Ybk7zq/ibbnSuYEVzHInY7k7zqzTTlM/i7Y+att2UpnjplD97fSVufm0WL2SfOflU8XWaPsm2c5pfo0xjpyrup4xrbHIa323m/CWyrZzm204/C04/IM0n2oJt5jS/hm0+HdpPpEvQOunq92jbzGm+yQ44XpH84OW+a1a02UZOo9YDOF3EUt/RjEn9SDOpiOVtyLcv6pFvMpWgcnV+snSm+cyLTucz+ZGrZAt27OK0mlygiG1k18vpKX8hf927zQ0jp2Ncv4h5D9v46/VtF6dwW8xv3OV/GM+2XiftdvEDlb9LD2z2nY9jZvODpv02pUnPEc3kohWnx7YF399ofo1mGn+Y5lNvf9S06do0KYgZD17+7PWVuOW1qdP4ojNPFbtIq55D6RgDzZ8RpPFDaDKt1pM6vyr+oJ0PZFs5zbedfhacfEBSSM2c4tsZza/gx168w6S/+2tTi5jRxWzTpufT+GG9oJd/CdKmNTnI5por2mwjp1HrAZwsYqkMZe1IzUUNJxWx/FaTL9B04tusKGI2WZ6pOLlPF+t9gaYDljM/bdEOZxSxjex6OT3lL6SDrHuNt+kgVy9i3sO2HtT3MQq3wTRsZe+SizfzrVdKe138QC0Gi/ldOclyrqG9tqRJZwzGq3DFybFtyQ9gNL9CM016lDQz2P6oacu1abRxdujyZ6+vxC2vjTbOf9AVTxVbrMnT/ABG82do4+Ky2wOSZhVMl8LnUjY9Zj59StpsoPm208+CUw9I84lW8Q2N5ldYnHjxSC0eKC2t0se07eZrMx3cZjQ5yOaaK9psI6dR6wGcLGJli0pskbb31cXP+uLF60Xl8To1LSpm8pMtYjhbNJ49n068QhXNrWCrNekHXxx7RBHbyK6X01P+QjrIhgEipINcu4ilHrbx14hdpYhl3xRJb1jpv1fTZFq8ku3hNL+V7ZqFmRLKqjfnmfbakCYNEsFp4hW3LBuDZpo0qjzpUbOtnObP0MbZkcufvb4St7w22ngZIFdfNdti3RMoHWGg+TO0cfHjehw725hrXOczwwOVbswMtLxN263Y8vSz4MQD0nyi1XxLo/kVbOvsuVo2sXyF0eJm1JK2XZNmvN5m/kltTpODbK65os02chq1HsCiAWXdaGAzy7KTVZeoPS2tLGJ+1EXly3e1yTLJmSJmazXpmy6OPaKIbWTXy+kpf6H5HWDdG3KDjnHdIqZfsL+1hz2liGXvTdnb4bv0Vp4mt16pdLSB5jfyB0jTZhrFhkTB0HKO72s0f1a6IMGP3FhxYmyr+SGM5s9qpdHTuBjCNj9q6RgDzZ9Rv3TKn72+Ere8NsEL+fxTxZauC2S7O82fEb6tpAdvNp553li0vE3brUlz8lnQfkBS1vPPGeObGs2f5z+xpl1xaZZnDS9mkzZdkaa87tNrx2Y0OcjmmivabCOnUesBnC9imh75FqnWXLGI2dTyTOmzyTTtk0XtKpMu2drpRNa2NLlEEdvIrpfTU/5yH/TrnFcPEQE/gFEX2qK1W+ph327uYU8qYoMP6b+dG3tvPq4fy2h+o+VYodE1vW16UJ9ay3c2mj8nXYrgqdFa0R7bAn4Mo/lzWidNQ0p+F3PBVmuyzQ9iNH/e4qVT/uz1lbjptalfyGnu1FMlWhazLZ3mzwveVtLDN5kerkUTO183tOGGNK7ao/mANJ/2Ed/WaP685XkXF6a+ABveo307o/mmxVWfHg6b1uQgm2uuaLONnEatB7AoU0W9KUuU+BZZEWvcakrGf7FIs80iFp4pb18+6VOjImnF1k7Jqn1nFLGN7Ho5PeWfJH0ooJlLeBKjLrRFYzf93n3NbeE7GoXbzt/14jc9u1LtAT9gh3Ka38j2zM+X3j7zN80bptGHRfUZmituWTaaJ02D1Knxe9Wj5kcxml8lf+lYjjlFfSVueG2keCGffaqUeU+yvZ3mVynSmPQADvy/deY0Uyfwv1twsyJWnnTQeEDaz+6Qb2w0f55tnB09/fgWRH0rOnN1MRv8AEbzTX48/4+X9F+e42W36TmAzWWT4Yo228hp1HoA3mdOFbFQ2sE3jovYsxfTvzc5uLCI+QEuK2K2a/5jVMcWithGdr2cnvJPZEeKXv4reRKjLrRFvFv6etjGvy+Z+J5G4S7QHg+qN/NzPInR/Db+Hq3pxBZMg4elWT20G9vbaf6kNEAEN5qaK25ZNpon9WHk9ElXPWp+GKP5lWyPdGxLOD9r6nPe7trMbI/xrDZ96qmy4dljh3KaX8n2yK/Bh3d+zg/V09q7wLvvV0ayvZ3mV7KDF49I/ICceHaHfHOj+bOWP73/7Jo+cQVsr/OhbCun+RYPMR4uT1RcJVsxRmuuaBs2SjRqPYBTRcxnQmkHX18Xseo34M/HL2ayk8VHekIRyze3LVv37ShiG9n1cnrKP9GmMaKWogzUhbaIdrv062Eu7TtQuEs0d6/ezM+xIznNb1M/Lnaoacnmh832dpo/xX7U8N22uWJws7LROqmPIecek1WPWjrQQPMrZT9xsbPNlOe82bXJ5OewvafzBSe39Zo8xzZ1ml+p9RPb4xEtzwvBCbaV0/xK1bMgjHfq2R3y7Y3mz1qetrga7UvQupgl291pvqU8TxahOI3NjJeiuaJt2CjRqPUAvM9csYgt/snv5J5FrB3aaKMJRWwjv4pGT/knWvcib0pRBupCWwS7Xfz1MOc7G4W7RHN3W3HHImY7lqezt8zpHdAetlulSQNS8KRorjCbnkh+IKP5E1onTT3s3O0L2+bsdfIjGc2vlP3E+WNjixdHutW1yeXnOPNUsSXnx9Jk2DTR/EqNn9gfzODx8AdzRSTbzGl+JdujOGsU7+SzO+Q7GM2fZdvmOcp9y7lM42Iu2O5O8y3l0bI5fxDGeDY9btVc0WZbOY1aD8B7y/WKWPlp5usXiw8di5nsZMGRBhSx/fGraPSUfxp/jZ1/XTV5EqMutEW9W/pY8pKvhznf2yjcBfzdOE0Olya7MNmKlWwHp/lN/HHRtNhb5vh2WLw1rmI7OM03+aGjgzdXJOtGC0mHGmi+qXnSNGzWY/eww+ZHzQ9lNL+OR9O5ssfGFy/y3ubaFPI0554qdlXWxrG9nebXKdJMfGnwWKaHaU019AMYzbcMZ8rOXj8L6gckZYvCtaVdBpo/x8+haWfz0xktU3gN4otZsa2c5lvK89gOYwSb1hq7ZOVG4Yom28Np1HoA3ltOFbHWh3qDev3Uw16/fpFWrCtivhVF7AH4VTR6ym82vJQ+6KV38q8JruL7G3WhLZa7/bD47WHfbv2E0nc3CrfK8DY33lYp/4aSv+Wk6Q/pC7Pn3wxztofT/Cb1SKHxxBYqZ1q6ku9hNN+SDj291X6YJpsrJErc5Mcymm9pnXT5rep30+Qlj5pt5TTfNByv8dKZzhQ+NDe5NifSzHHCp0q9pM13N5pvaqf53h81JcmeNsPDYzto6/nBPCFtOdB8ix3y1LOgekBSuPqJdpLvYzR/TnzW9O6TUo5nHWY2v0f7ZkbzTb6R50hf2ZuuvM9ZnOUla65oss2cRq0H4L2lUcTKElXzjYv6ZAuGXbJ+tKGIVWeyhbqU2aQUSTO+vEkbTShiG/lVNHrKb6X3w8yK11WTDrGtiOmLYLNUuNJflyz48tW007ZLo10yWpHeAHNasZJ2uuiB8ne+8f1xlN41J8vVp2mns2m0VUZPD81lxufN8kKtGMO05cVp6ifxUx41bXh20xMvnfKxmR+aG16bUy/kk08Vy7SmZ7h0gIHmW9ppyizZictrs+rprG3Ppmk/CxoPiOYy88Vs0pZn00j9ol48TNOBLnqP1pbn0ywvwXzsYk3+FGmuaNGmRylii95U8Y3zIpZKUNGO1hWxoGgNsoXZpJRJF/KtbcPqZtuIIraRXS+np/xW1Yt/9XtyRMfYVpm0S8YX9yli1Xv2+FZZXahVQ8VMe130QPk7saZnm98OM9rrbBptldH7tOYyWnHJqKENL05Tn3M80iWPmrY8m+bUSydfN//8t7w2J1/Ip54qtuh8DPEDGM23tNMUa/JHI8+48tmsrbenGc/bekA0k1lxhbTl2TQSvKgXOZtvPWsujzZdkaZ8y2s9bbQoaa5o0KaPNLR7nzlVxJY/ywv72DFt4RvnHcdrTVl6yuMXM/nJbGpZq/JdfX2alDLpwrBqOo2Foohdi10vp6f8ZuV70blvO5+ho9yqiG37bFI7bbs06W77JHv/LdesHrpG2u+iB8oeo+CE2UO3NY52O5tGW2X0Rq25jFa0xrZTtOHFaepzzoPY9kdN255/pE69dObBM1t8y2tzMk2+cnlKW6bJ8/wIRvNN7TTzA1Imma/Y6jchbX8+TetZ0HpANJNZ0X205dqraedePhZ5zuwqnHxoG7TxmjT54ctEU5zlj99cEdPGhyliXlPmlc4WaQvfOO842bpReYhiJj/ZIobLa51Nri9itq44pyZrFLGN7Ho5PeUvMP4K+Xfvvl++MWzlxzHqQqus/2jyDkVs8L3/kqPhenw//asoMq6pVqzgOxrNb/Ku8YaXHrl32+N4EqP5Fm2VURDNZcaE03u0rHir1pYXp6nH06L+bHzUfGOj+RNOvXTSqvKcN7w2g5Mv5OZTZShA658+fnyj+ROaaaILY9LXxvRdrlVse6f5U+JnQVo2O/vsPkFbrkpjwhe1rtryKmx/j/bNjeZP0sXZ8Jhse7BsW6dR6wEsGlBZb/xzxbLt+Ab6+Xz6dBFLRzhfxM6c6YIiNuWq9sxRxDay6+X0lO9LWbZ+iHgbirK3S6P5vpSFNAFlIU1AWUgTUBbS1BTlkYZ27zOLbjR3K+8p+U+TCpM2WGwcFLG0eVnE5sPlXSqdKdu3rHA+o+kk33nJDjausu3mn2+JIraRXS+np3xfykIRqykLaQLKQpqAspAmoCykCSjLPtIoygMXMa8/i9ms72hec1UR8wVZO9Lm8wG990zr8y51+kwbi1i+sW2XZyxRxDay6+X0lO9LWShiNWUhTUBZSBNQFtIElIU0AWXZRxpFeaSh3ftM1bxevMi/jz9s4FVm/K35Y/3xlXnJKdtU9k8dpQXjfa/Xw+FtruhS2vf1M1sw7jqu3F7Eiu5HEbsau15OT/m+lIUiVlMW0gSUhTQBZSFNQFlIE1CWfaRRlAcuYvNAq44zl6nJ1H6qIjZv/Xr8J7/TH1qd9jB+xrJLqYnl5p7ls5pOThQxW1UUMU0GPJ6msYJdL6enfF/KQhGrKQtpAspCmoCykCagLKQJKMs+0ijKIxexqSqNBavqR3P58W3Lu01TE5Nni8LkC43NlKuqM+W/F9YXaDpZ7JyzVVOsascCRWwju15OT/m+lIUiVlMW0gSUhTQBZSFNQFlIE1CWfaRRlEcuYlOVmgtWUa70kaXzfcsilpaNbJ3Xq2mjqWzZzLJLPat2nvgSTSfLnTMWeFxjJ8x/vAWK2EZ2vZye8n0pC0WspiykCSgLaQLKQpqAspAmoCz7SKMoD13ExjqUL3uhDxrHb46Jb7goYsNS7a9/bHLZofT1LztQ0KXGk487j3ypppNg51G+rW1WRZxRxDay6+X0lO9LWShiNWUhTUBZSBNQFtIElIU0AWXZRxpFYWjva3gAphppTetEEcNG/vQ2esr3pSwUsZqykCagLKQJKAtpAspCmoCy7CONolDEcFR6gu/t1a8u1JWi8MYYUBbSBJSFNAFlIU1AWUhTUxSKGI5KT/C9vfrVhbpSFN4YA8pCmoCykCagLKQJKAtpaopCEcNR6Qm+t1e/ulBXisIbY0BZSBNQFtIElIU0AWUhTU1RKGI4Kj3B9/bqVxfqSlF4YwwoC2kCykKagLKQJqAspKkpCkUMR6Un+N5e/epCXSkKb4wBZSFNQFlIE1AW0gSUhTQ1RaGI4aj0BN/bq19dqCtF4Y0xoCykCSgLaQLKQpqAspCmpigUMRyVnuB7e/WrC3WlKLwxBpSFNAFlIU1AWUgTUBbS1BSFIoaj0hN8b69+daGuFIU3xoCykCagLKQJKAtpAspCmpqiUMRwVHqC7+3Vry7UlaLwxhhQFtIElIU0AWUhTUBZSFNTFIoYjkpP8L29+tWFulIU3hgDykKagLKQJqAspAkoC2lqikIRw1HpCb63V7+6UFeKwhtjQFlIE0gXZqD5vpSFNAFlIU1AWfaRRlEoYjgqPcH39upXF+pKUXhjDCgLaQLKQpqAspAmoCykqSkKRQxHpSf43l796kJdKQpvjAFlIU1AWUgTUBbSBJSFNDVFoYjhqPQE39urX12oK0XhjTGgLKQJKAtpAspCmoCykKamKBQxHJWe4Ht79asLdaUovDEGlIU0AWUhTUBZSBNQFtLUFIUihqPSE3xvr351oa4UhTfGgLKQJqAspAkoC2kCykKamqJQxHBUeoLv7dWvLtSVovDGGFAW0gSUhTQBZSFNQFlIU1MUihiOSk/wvb361YW6UhTeGAPKQpqAspAmoCykCSgLaWqKQhHDUekJvrdXv7pQV4rCG2NAWUgTUBbSBJSFNAFlIU1NUShiOCo9wff26lcX6kpReGMMKAtpAspCmoCykCagLKSpKQpFDEelJ/jeXv3qQl0pCm+MAWUhTUBZSBNQFtIElIU0NUWhiOGo9ATf26tfXagrReGNMaAspAkoC2kCykKagLKQpqYoFDEclZ7ge3v1qwt1pSi8MQaUhTQBZSFNQFlIE1AW0tQUhSKGo9ITfG+vfnWhrhSFN8aAspAmoCykCSgLaQLKQppaui4DjVrAwegJvrdXv7pQV4rCG2NAWUgTUBbSBJSFNAFlIU1NUShiOCo9wff26lcX6kpReGMMKAtpAspCmoCykCagLKSpKQpFDEelJ/jeXv3qQl0pCm+MAWUhTUBZSBNQFtIElIU0NUWhiOGo9ATf26tfXagrReGNMaAspAkoC2kCykKagLKQpqYoFDEclZ7ge3v1qwt1pSi8MQaUhTQBZSFNQFlIE1AW0tQUhSKGo9ITfG+vfnWhrhSFN8aAspAmoCykCSgLaQLKQpqaolDEcFR6gu/t1a8u1JWi8MYYUBbSBJSFNAFlIU1AWUhTUxSKGI5KT/C9vfrVhbpSFN4YA8pCmoCykCagLKQJKAtpaopCEcNR6Qm+t1e/ulBXisIbY0BZSBNQFtIElIU0AWUhTU1RKGI4Kj3B9/bqVxfqSlF4YwwoC2kCykKagLKQJqAspKkpCkUMR6Un+N5e/epCXSkKb4wBZSFNQFlIE1AW0gSUhTQ1RaGI4aj0BN/bq19dqCtF4Y0xoCykCSgLaQLKQpqAspCmpigUMRyVnuB7e/WrC3WlKLwxBpSFNAFlIU1AWUgTUBbS1BSFIoaj0hN8b69+daGuFIU3xoCykCagLKQJKAtpAspCmpqiUMRwVHqC7+3Vry7UlaLwxhhQFtIElIU0AWUhTUBZSFNTFIoYjkpP8L29+tWFulIU3hgDykKagLKQJqAspAkoC2lqikIRw1HpCb63V7+6UFeKwhtjQFlIE1AW0gSUhTQBZSFNTVEoYjgqPcH39upXF+pKUXhjDCgLaQLKQpqAspAmoCykqSkKRQxHpSf43l796kJdKQpvjAFlIU1AWUgTUBbSBJSFNDVFoYjhqPQE39urX12oK0XhjTGgLKQJKAtpAspCmoCykKamKBQxHJWe4Ht79asLdaUovDEGlIU0AWUhTUBZSBNQFtLUFIUihqPSE3xvr351oa4UhTfGgLKQJqAspAkoC2kCykKamqJQxHBUeoLv7dWvLtSVovDGGFAW0gSUhTQBZSFNQFlIU1MUihiOSk9wAAD2TKMWcDB6ggMAsGcatYCD0RMcAIA906gFHIye4AAA7JlGLeBg9AQHAGDPNGoBB/MbPcMBANiv32jUAg7mb/UUBwBgv/5WoxZwNP+o5zgAAHv1jxqzgOP5Oz6dBADs2W+4HwYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADg0f3yd7/TVHdfff31HwZffaH5fpTk6x1ESTzOH77SXBdfeITZ1/u4Nl/pkdJsL1/5NSn1zrSjF9QXfn26P0qT4cpoqtQj5xdftc/XynkzP/vV73+lycKvfv/7fxn86meaB3Atv/zdvxrNdfZFKhuu78iRBdlJ3dAw33UcWxaxP/xBKzraz3Nmh0VsPxcnS3LnXhFJ1T169mZP8TtdsFT84mdKO+eN/OxX1rX+JShiP/MWllDFgOtRCTNa0lc2agw6jmHLwrGHJqYoFLFS8ZzpW3v2V8TKF1TPAlQ8c/o2MZUboyWZIuftX/YqYaZ6ppzMeQsqYaYuYlkNG4R3zABcQB3MaVFX5bDRcxBTgFn/JjZenH0Vsd7XZRGIIlZYBupYgJRAejYxRXBaNFs8n2799M4foOUzRYudFt3WXMOColX2MJoYcC2/VAdzWtZTelfy70roc4xew7yS2On1n6zdP02ZRoj+Raxv2ymMV8W+ATU8Zzo/Sv5U0fTAZ7teLAuwixeUXlF2+t6vp3R+0bKZL/UXvl+wW8f008nimXI65y2oYrllz0olzb86po8o+XQSuIq/UQdzWtaTv/WNb0ddh3x/E5wGLZvpN4TJeEOs78i+syKmHraXQP600fTAHzJNd1F0ibsUi5b0QKXXUD7dgZ4ziZZN/BHURSrejm5kelUPFqc6mfMm8pteyyLm68aFP4u2AHCZX/7rv/7ul5984l8U06KO8jfBQddh7Kv8C/r+lti5iKWLY/9HEZtZmr38zU0zPECLp02v5uPKADbX6wVV/JfN4oV+b8Pp7SZh+P5iy6aFd3gHshuVX30Rv6xO5bwJu9X1q5+FNctviP1eM6plmgZwFXsqYpoe7KH+JHtoHxbhD/2D7OFSzHyM6lp1TrnfCNpiAeYHqyhDd1ZeC5vpe2UG0cNTPr39gt3lyX7qZXX3p1GziGl64Jvw2SRwTTsqYtmo6m9Ouyhi93tDblLl6B5kV0XMw+zjKRKxcH1boiWYH6yeRcxOPV8Lfzb3fhZFBccvkaYHNneXR/Ahith8Q4wiBlzfTorY8DaYf8rUc9wopPFeM32MEez/d1DEdvKbOXcxnjftoL5bgvmJe/cRPWOnnivNDi5NfDkWy+52xXZfxIYm9vusePkNMooYcE17KWKFHdSfxEeNzrc2/M14eKPWH/2kR2XS/4bP8AzxX7e0n3//YHT3AbSW+rJmPE+vB8wfIk0vcvUSPT6LXJ5T0ze1/yJW8C34jhhwVXssYmnE7/xm7e+BiZb04RfDroX9uaci1vcB8gDzL2zvemFqFqnz81cvIquo+uWgvdpq0Sb0JNJcL1HBsUUUsXNFLPWw7JNKAE+3wyKm9+q+Y+s0xPf+iNSDWAT7c1dFrGfV8CzZY9S79pR8EO/dDZcPV7c8xdXQY6a5XqKCY4uWReweL/2HKmKph52+ZwZgq/0VMQ0gnUdWfx92fX9DwjyKTRPdWIIhg12PdHn6PUbzwzPaUxO7+/gZKptYx+vj50+vorE7+0w/FLGGM0VMPYwbYsB17a6I7aOH5f9McddbYnb+dC1sqn8RG2tpepjSdAdTERuuTe9fHF9TsM6yZ/CgY54UZHjuzi8qrenFc2h6ZIuyi0QRq9HDgNvYWxHbSQ9zX4wDh+Y78ABpOLCpvkVsuBzzyOQDVbc8YxFLeTq3wqW+l2Y0lR7p3sRyWtFLVHBsEUXMilariNHDgBvZWRHTALuLHmbSEN9tUPXT62J0DRLIot2f+vp4PbK+ugN3Hz4j6ZWU/1uTHZ88KYD7eg9XJ8pgi5ZFTNM39TBFLP2Tk/Qw4Pr2VcT0hr2bHjaO+Jq5u/yN2Cb3VMQsW+ciNp3+1GB2f0W0XizElCJdro5NVS9se5D8Dy3uJSo4tih71ChiC/rXKOlhwPXtqojp7Xo3tzbM3T6iCOj+YK77AD+5+yBR8IsxPyw2t5ci5o9a7zA+vM9PFn+wumby36Hx1Rf7uDrRc9eWZa+uuz29H6SIqYfxm1yBG9hRERu/kbWrHtb1ZktQxLqPYZO7DxIFvxaaHtjcXq5M3wsji77T81lc8CDdX+LRQ7RYZnN3+a+ehyhi9m+CG3oYcAv7KWLpLXpHt3wSiliDhen3YPml2ecdMcvS/Vm8uD67uUBeLbpfnajglNX1fi/7RyhivpiPJYFb2U0RU+nYWw+rRrR70jXJ7eb69CyoAz/9dDHKQbSvfWRZPm13EWo3v2kkLDjFQpu+TwV6gCLG1/SB29pLEUvv0PsYK/IQ/jZ51/fClt6Xp/xn2dN16TiiFue/+4B1wj6ylEU1her+4kpPmv49LH6MvLvqkt3xeu2/iOljyfCrYwCuYCdFzN9x8rfosg7dkSf5IgX5Qv9KX/cRzHQOkoapFOCLL3yu6+25NKinX/Pvj9Je7hXuJIueuf5E7nuBhkfKTq3nzB56WKPgpKs0PMV1406Lb2z3Raz6etjv6WTAlXgDK/2NVt3f+B5d6POGrZPnug+rupNgumWJHqKuQ2oaLGc7GN+NX6cdNPfsOSO9LtDimbOrJ81gzrO4ZLcOWj9C86v7ZM5b0LfAcvokUp9LlvjSPnANv1T5yv1O6+4vLGJ9RrM6SvceVpTDbmN8PTZ0HVKXgTqHmXgqTXe1HOe7XaDicdrBv9y6kL28i0t286A6T0EnPZPzBlSwCqlthUWMW2LANVDEWhYDmD6N60pRXL84+px21P+6ZM+avuN7zkL1r+5m+izQdIyUvaB6P0znCk4W9fZJdaICRQz4qNQfTf5uZx9Ndhvpx6+G/eHrr7/aQQ0rhzIt6uOr9A/UDO10F8VHD9TX+6lhg6/30wq/+MpfWMPTWAs6Sc+aPTxpyv+WMIsHS8+oe1ywxX/xmem0Z3NeW/DRpMoWRQwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAIf39v/7rv/69plf69bDLX/mU7TzNFHz5r9P0BacAAHT3i3/5l3/5haZX+u2wy1/7lO08zRR8+W81A3zsrlHE6v3TCooYADywaxSxev+0giIGJNcoYv/qMznbhCIGAA/tGkXsX3wmZ5tQxIDRVYpY9dlkWkwRA4AHdpUiVn02mRZTxIDkKkVseQAtp4gBwAO7ShFbHkDLKWLAxcoi5p9C+txsWGaLVcQAAB+Jsoj5p5A+NxuW2WKKGHCxsoj53a/ys8lxMUUMAD4uZRHzu1/lZ5PjYooYcLFFEfP/89mR3RD7K4oYAHx0FkXM/89nR3ZD7K8pYsBTLIqYVa7ys8lxKUUMAD4uiyJmlav8bHJcShEDkvl+lk0N/eqv/t6/3vXr6gv4tngoWFUR878imeYTW/pJVsRWnwIAsCPZ/SxVp1/81r/f9Yvy48Zh8bB0KFhVEfO/IplvbEs/WRaxv077D8fVAv3VynlH223r3xsAHsOyJdn/J7/Oy5W3MPf3VRGbD5EM6399qog1TgEA2JVlEfvr1JdMXorypcsilh3CDeuH4wwL5yLmJWv0W5Uvv5U2b1POAUeyaElz4RrMNSlf/OtlEVt+NjktjItY4xQAgH1ZFDEvR6O5Fs09bFi6LGK+j88n08Jp/3x3oybm9Wy8JWbb0MNwUGVLcn//9+ljyPk7XpodVvjEIC9ii88mbeEn7SLm6lMAAHamLGLut7/wL+AP8pKULy+LmO84bpoWpttdY6+ad/cSt1gxHyo7CHAoVUtSo/KelFak6axVDYoiNh/DDFsP254qYsEpAAB7oy5lrApNJSndGkvT3pe0XFWsKGLZMQbD1sO2WRHzQ43dK330Oc5Ma3xqPgZwLMuWNN3ZspqkJmXLp1tX/kFkWcTKzybnZY0iFp0CALA3yyI2FqZUjVLfqpeXRcwX+QIzL9M+tnbaPc2OlWs6idWzbBvgWBYtaSpJPpta0jzl/EZWUcSKzyZt2fBHu4hFpwAA7M6iiC0Kk/ct2yRbvvg80Xael6Rlwx9zEZvKltj+YxFLB5v+AA5q0ZJ8ys1NyppX2sT5/a+yiM0H8a1tt3YR8ymXbQMA2JtFEZsaUipH3p+K5lQUq3Hn7CC2tRWw7I7YL36b/c6KcmOddNnVgIMpW1JWi7xv+dQ0ITZfFrF52/GTyXYRC08BANidRRGL7lzZcl8i83bjzl6kfImvHRdlt9EyZRHzXU28MXAIZUtKU25qSVVdsg3LIrboZvZns4hFpwAA7M+iiKUpNxaxvGU526UsYotuZn82ithf/7VtkBUxP82AHoYjO9+SbHnxAaItWBSxeV99MkkRA4BHd76I2RZFTbIFiyI2H0WfTNZFbKhg+uX6g6yI+WmLW3HA4ZxvSbb8XBGbK1W+hCIGAA/sOkVsvmuWL5n3sg1zeRHzdcUJgKM535Js+bkiNn02aUt8AUUMAB7c1KWeVMR8X1tmS3xBXsS8pk3G447SbTLuiOHIzrekrFIltuGyiI1T4yeTFDEAeHTni1j1IWNUxMapYae0bbaXrXO//YX9m+HZGU3qYTQxHNr5llTVJdtwWcT8llj6Iy2giAHAg8tqkdWhNOXyIpYv912WRWzceVowF7F0P8wqWFIWsbR2UHQ94FhWtCSbSMUrsd8rVhUxm/wr//80TxEDgAd3voj58vx+la2oilhqZ/b/aX4uYrYsP26208BWpn9qfF4GHM2KlmQT2YqsmGW72OZ/P38ySREDgEeX1SJrQ2nKFUUsb0k2XxUx61K/sH10Z2suYvPmic1Ph7OTDJt5Wcs3Ag5lRUuyFVlfshtidRFLn03O8xQxAHhwK4qYtyRf5Gx5XcTS3vN8s4j53S+1tTRjK1XIgGNa05JsSqVKy4MiZtPW0TRLEQOAB7eiiPmKqSV5dwqKmE3bLpoti9i40bj7eLRp2hfPWwHHsqYl+eSv5+41qItYKmhTYaOIAcCDW1PEvCX9du5eg7qIpYY1Vay5iPkdNG2Vfq/+tJWvSpO+IrtxBhzJqpbkn0YOa/9eE1ER8/tm8yxFDAAe3JoilgrTMDf/avy6iPnu8+xcxFL3+u1vf/ELtbBBtmYsX1kpA45mXUuaCpixmaCI2cy8B0UMAB7cqiI2NrHEZoIilmqWZrIiVu6svyJpy31C2yzngENZ2ZJ8zv36r2zDoIj5JuMnkxQxAHh064pYqknut/7xYlDE6l41zuRNbNjRZqfFvoHzIjfeHwMOZXVL8o8lf/3rYQvbMChi6e9NjihiAPDgVhaxYUOb/+1vhyWNIlbunxcx7fwvv02/1dX2Glb5orx42TxNDAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMBxvR+80XTt9NoNXtqRPtMMAAAABqer1um1G2wsYi/pbAAA4PhOV63TazfYVMQ+e7N6UwAAgMdlBWlnRezN+k0BAAAemBWkfRWxjZ9iAgAAPCprPRQxAACA3bFOdJUitgFFDAAAYEARAwAA6IQiBgAA0AlFDAAAoBOKGAAAwAZeZNKU/Uau9y/nUlMtCKrWZ2mjNy9tJl87Hfflm/owo3HvtPvMd9YO04GU5012JJufRCcAAADYsbHneMlxb1KjqRYYn9W08b2TYbH+mNf4r72fLJvSZ2lxkp1EO2tBHXDeVvPJ8vAAAAA7l3pO0YneD5WmWpDYzFzEyo3eBEVsuUUua1YuW1sXsfJA77UuzQhFDAAAPJjUeez/Msvekz4aHNj0VJiWG3mzKopY2uJN+nDSprTSLHtYvrYqYstTvU8rxyMPf77Jjw0AAPAIvOdYnXnz8uVnn/nc+/f+baw3w/y4YLzbZNNj41E5emPr5k8giyJm0gJtMH8XTDu89L218dSlfD4vYr51HijcFAAA4KGo2EwVaWpUKje6qZVmiiKWthw70FjFxrXjcaeOVG6e5qY6pfkxRN6uxgON26r/jQeiiAEAgIelnjNVmfFDwMUCzdikGlFanlWgslstj6v1xc5zDxv31kxQxOYDpX2jzgYAAPBQUs/JKpHPZ58hpgWqOjapbX3HbLNFuaqOWxzI1xYri56Wt6v6QMWZ800BAAAeSuo5mjHVAm9IdRGzyfdFA8qrVH2YokD5yrI+5Tv4dFHE0nTija84D0UMAAA8oqrn+ILqBlRVxLwN5TfEFntWhxlvmZVTmazw5aesD0QRAwAAx1D1HF+QN6y86thk2rjabDCvba/3+hWtTEUsLcxP6dNFEavPQxEDAACPqOpE8YK4iC0KkFWpUwVpuueV3fya+R71wX26LG22hCIGAAAeXtVz4gWqOjaZKlDUpaoipunRdKSwiGWfOOanrPJQxAAAwEFUPSdeoKpjk1kR86mZbbmqiNmfy5UUMQAA8LGpek684HpFzA5tf1LEAADAx67qOfECihgAAMC1VT0nXhAXsUUBsi1PFbFpn6jFUcQAAMDHpuo58YK4iJX9yJedKkhT/5oaWS47cb5zlYciBgAADqLqOfGCqogF/ehsQbJFXsSinfOF+c7BtraEIgYAAB5e1XPiBVURyz5IHBWLgv7ki3z9PJWxZe/TefJTBgeyJcV5KGIAAOARVT0nXlAVsdSb0qT4hkVBKtdnn2b6yrI+eY3TDvkpqzwUMQAAcBBVz4kX1EXMF491yNmCRRHLj5M3Le9kxc5pb22fn7LKQxEDAAAHUfWceEFdxIrmZLxcLYrY+6wiZTfEVMryJpb2zsvXhiJWrgYAAHgMVZGJFwRFzJfPZSo1qWURez82sc/Ses0tWts4P542P2WVp0wRfFcNAADgMVQ9J14QFLGxTNm2n/lGZlnE3r+xXVXD3o+1bOptL32tNp6OnJ/Sp88UsWGDl34kAACAB1L1nHiBWo5NzrefVKYKRRFbbpB3pXrn+cD5Kas8ixQ24yhiAADgwcS9a10RW5Qpv+1VFLGX/v+Tsiotm1h2zvyUVZ5FiukU5TYAAAC7V/WceEFcxD55mbWpz7xalUVs/OTQ5Pu5fOf0CeYoP2WVZ5liPApFDAAAPJi4d60tYsPaN16EXtoGQREbvwDm3ySrqYstv+CVn3I60MyWZCn0FbOq6AEAAHycgv4EAACAe6CIAQAAdEIRAwAA6IQiBgAA0AlFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAp/n5z3+uKQAAANzV+/fvNQUAAIC7oogBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAAAAnVDEAAAAOqGIAQAAdEIRAwAA6IQiBgAA0AlFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAAAAnVDEAAAAOqGIAQAAdEIRAwAA6IQiBgAA0AlFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAAAAnVDEAAAAOqGIAQAAdEIRAwAA6IQiBgAA0AlFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAAAAnVDEAAAAOqGIAQAAdEIRAwAA6IQiBgAA0AlFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAAAAnVDEAAAAOqGIAQAAdEIRAwAA6IQiBgAA0AlFDAAA4I5+nhmKmKbcj7UJAAAAbuEfhvYV+7m2AAAAwE38WLWrRhEDAAC4rdYtMXoYAADAjbVuiVHEAAAAbi2+JUYPAwAAuLn4lhhFDAAA4PaiW2L0MAAAgDuIbolRxAAAQOHVc0300PXkN1bfEqOHAQCA3PO3f+zXhbqe/ObqW2IUMQAAkHn7xz/260JdT34Hy1ti9DAAAJB5NVShbl2o68nvYXlLjCIGAAAyFLGbKm+J0cMAAECOInZT5S0xihgAAMhRxG4rvyVGDwMAAAWK2G3lt8QoYgAAoEARu7H5lhg9DAAAlChiNzbfEqOIAQCAjPWgSVaInr+yX/A1ePv2lRY5WzQseG4FKl+j7dMvybfJt7544juUu7RPfjTjLTF6GAAAyKkFJVMXSq1pkrWnNDut15rnam1mOEhVxPLjvZ0bl5YkBy5i4y0xihgAAMipBSXqQs81O5tblc29mjdIC7MaNni7LGLL402rNJ8cuIjplhg9DAAAFN6+TS1q+POtClLdwxbdae5d6YZY2cPsWPZ/vsq0e11w8mNKt8QoYgAAYMk/N5zvR6k3vXrui8avio2fTqa5tP2r9CmjPne0LZ7Pn1FOxWo6ns3ocHPrWpz8qOyWGD0MAABUFl3Iq1L2Ra5UpMbq5DPjR5KJL1j2rmmB5qfjpdo2fevsIylidkuMIgYAACplF0rFKa9GqTppxqeL1am4aWag5jUuSbfANGPSes18LEXsk3+ghwEAgEDZhXxuumHlbElZxDTjqgWpuKmI1b0uVbPxDB9LEfsxRQwAAATKLvT81du3i2bkzUmLbLLoaa3ipiIWrPb14w2zj6WIffJj/QkAAJA524WqIpZv7Cs1LX5ANS2bXB483+WjKWIAAACBpxUxmx9vb4l/HJkXsTQ58RPqLhlFDAAAfMxOd6Hx3zrKi1iadHnpmszLwtUUMQAAgKTRhZ4/fzX+EjHTKGJ5qZrYsqyIhdTOKGIAAOBjFnQh/RPducuKWH2cEUUMAACg7kJhe9pSxOxGGkUMAADgrGUXyj6PHLx6Xn9ZP026sIjZMooYAADAWYsuNPWwt6/Svw/55CK2XJ2jiAEAgI9Z2YV8bpjPytGpInbJ35rMUcQAAMDHrOxCNrOoTmeLWL5gkLevbDJEEQMAAB+zogtFxcoXNYpYsVL8gFkRWxxuWP/2rT71pIgBAICPWtGF8hIlqZu1ipjvUH4LzO+g6Rj59MgWjcejiAEAgI/ZuSLmXapZxOpbaH6I8RhFi0vS+nyGIgYAAD5S3oXGe1p1EUs9rFnEqnteqXpNS9LumjFpfXG+U3+tEgAA4MC8GU1NymbyXqUe1i5iqVhNe6iHTQs0P9300rzmFicHAAD4uKgavUq/NiwVL1Wj7J860l0rn06To3F/23v+1ymncqVDvPUDPNf6ZS/TyQEAAD4yqQsNrAupGQ3N6e1YqvzPdhGbbpqNfH6+y7VcPchKl5YUywAAAD4W022vdNNKM6OhUekP44vS5KysWm/9CHMRq45YdK7y5AAAAB+ZsUilLjR+fOjeWmnyBb4uLmJ51Rp28LmiVxVNbfEhZHlyAACAj4y+CzbexXqur3qNX9zytako+XKfKqVd3vo3weoiZl8eKw+ZWZwcAAAAF8t6GwAAAO7Ji1h14wsAAADX9eqPb18t7n75h5CaBgAAwK0Et79sCd/4AgAAuLXl76rQDTG+IgYAAHBzVrvyJpb+dqRmAAAAcDvpl0/ot4ON/yYSX9UHAAC4g3QLrMA3xAAAAO6iamLcDwMAALiT8h+T5HYYAADAHY3/KFL6N44AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPK3v/kGwHX90//4f3biJ3/507/4iab7I00baU76s3+jIQs4nH/UwAHgmv6nxo/O/vKn5i801xtp2khzzp9p0AIO5u80agC4rv+u4aOrn/h4+tOf7uPeBmnaSHMe98RwTHwuCdzGP2n06Crd2NjLrQ3StJHmPG6J4Zg0ZgC4ov/1z4M/34H/MPiP/2mg+b5I00aa0/6dNTENW8CxaNwAcEX//L8H/34H/uvgv/y3geb7Ik0baU77c4oYDkvjBoArooiFSNNGmtMoYjgujRsArogiFiJNG2lOo4jhuDRuALgiiliING2kOY0ihuPSuPHNN/93D5Tlm2/+zx4oy96ujeb7UhbSBCyIF7F/uwPTgKr5vkjTRpo2ihiOzYcvo0GkL2WhiAWUhTQBZaGI1Rje20jTtq80FDEcmw9fRoNIX8pCEQsoC2kCykIRqzG8t5GmbV9pKGI4Nh++jAaRvpSFIhZQFtIElIUiVmN4byNN277SUMRwbD58GQ0ifSkLRSygLKQJKAtFrMbw3kaatn2loYjh2Hz4MhpE+lIWilhAWUgTUBaKWI3hvY00bftKQxHDsfnwZTSI9KUsFLGAspAmoCwUsRrDextp2vaVhiKGY/Phy2gQ6UtZKGIBZSFNQFkoYjWG9zbStO0rDUUMx+bDl9Eg0peyUMQCykKagLJQxGoM722kadtXGooYjs2HL6NBpC9loYgFlIU0AWWhiNUY3ttI07avNBQxHJsPX0aDSF/KQhELKAtpAspCEasxvLeRpm1faShiODYfvowGkb6UhSIWUBbSBJSFIlZjeG8jTdu+0lDEcGw+fBkNIn0pC0UsoCykCSgLRazG8N5GmrZ9paGI4dh8+DIaRPpSFopYQFlIE1AWiliN4b2NNG37eqQoYjg2H76MBpG+lIUiFlAW0gSUhSJW29eASpo20rRRxHBsPnwZDSJ9KQtFLKAspAkoC0WsxvDeRpq2faWhiOHYfPgyGkT6UhaKWEBZSBNQFopYjeG9jTRt+0pDEcOx+fBlNIj0pSwUsYCykCagLBSxGsN7G2na9pWGIoZj8+HLaBDpS1koYgFlIU1AWShiNYb3NtK07SsNRQzH5sOX0SDSl7JQxALKQpqAslDEagzvbaRp21caihiOzYcvo0GkL2WhiAWUhTQBZaGI1Rje20jTtq80FDEcmw9fRoNIX8pCEQsoC2kCykIRqzG8t5GmbV9pKGI4Nh++jAaRvpSFIhZQFtIElIUiVmN4byNN277SUMRwbD58GQ0ifSkLRSygLKQJKAtFrMbw3kaatn2loYjh2Hz4MhpE+lIWilhAWUgTUBaKWI3hvY00bftKQxHDsfnwZTSI9KUsFLGAspAmoCwUsRrDextp2vaVhiKGY/Phy2gQ6UtZKGIBZSFNQFkoYjWG9zbStO0rDUUMx+bDl9Eg0peyUMQCykKagLJQxGoM722kadtXGooYjs2HL6NBpC9loYgFlIU0AWWhiNUY3ttI07avNBQxHJsPX0aDSF/KQhELKAtpAspCEasxvLeRpm1faShiODYfvowGkb6UhSIWUBbSBJSFIlZjeG8jTdu+0lDEcGw+fBkNIn0pC0UsoCykCSgLRazG8N5GmrZ9paGI4dh8+DIaRPpSFopYQFlIE1AWiliN4b2NNG37SkMRw7H58GU0iPSlLBSxgLKQJqAsFLEaw3sbadr2lYYihmPz4ctoEOlLWShiAWUhTUBZKGI1hvc20rTtKw1FDMfmw5fRINKXslDEAspCmoCyUMRqDO9tpGnbVxqKGI7Nhy+jQaQvZaGIBZSFNAFloYjVGN7bSNO2rzQUMRybD19Gg0hfykIRCygLaQLKQhGrMby3kaZtX2koYjg2H76MBpG+lIUiFlAW0gSUhSJWY3hvI03bvtJQxHBsPnwZDSJ9KQtFLKAspAkoC0WsxvDeRpq2faWhiOHYfPgyGkT6UhaKWEBZSBNIF2ag+b4sCEUsRJo20rRRxHBsPnwZDSJ9KQtFLKAspAkoC0WsxvDeRpq2faWhiOHYfPgyGkT6UhaKWEBZSBNQFopYjeG9jTRt+0pDEcOx+fBlNIj0pSwUsYCykCagLBSxGsN7G2na9pWGIoZj8+HLaBDpS1koYgFlIU1AWShiNYb3NtK07SvNIxaxF/Y+8Fozd+bnfqaZG7DDa9LP9ULTuJRdUadBpC9loYgFlIU0AWWhiNUY3ttI07avNAcpYi9u2I0++eTZXIduXMSe5T/Z6+Wpnr3w03/z+jX9bDW/YkaDSF/KQhELKAtpAspCEasxvLeRpm1faQ5RxJ69vuVNquF888luXMTs8FPJslNp0gw/ZYYqtpKuF0UsoCyUjYCykCZgQShiIdK0kabtCEWsunN0VcVNqlsXMftR8iKW/ZR+5szrW3bPA9HloogFlIWyEVAW0gQsCEUsRJo20rQdoIjduBvd9WRWxDTpp5pPXNwOS2hia+hiUcQCykLZCCgLaQIWhCIWIk0badooYufc/WSa9FNNd8f8vMO8n/qFWpmvwWnpUg00iPSlLBSxgLKQJqAsFLEaw3sbadr2lYYidk55stuqvqs/FjFbMayafsrUy+4V66H5lTIaRPpSFopYQFlIE1AWiliN4b2NNG37SkMRO6c82W3ZjzKdy06syeWPPDaz2/3Ux+EXymgQucyH7+0Q7959r/lLeRKjKrTJD999950mM999a8f77gfNbuFJjNKt9/072+3d9x807z74sSbFuhW027Y02iWjFbOUtYx6nh/LaP4kPT3qZ0dzxeD7799p6jw7itP8Sc2ThlfigkfNtltdxD79/D8Pvvzyc81PPv/yy2HF559qtvD5519q6qxtA2ozjfvcEk2RfNNCvFfuWmk8SbDc9/jyy/Ci1dan+dSCzKrjhw+I4p+/KsnqNB6hoBVmvGZVxNMPbWX9tTHpmVEf+9P2A9VYEaOInVOe7LaW39Ufi1hQu/zTyXvlemR+6YwGkUtkY9W7rfWipKNcUMR++M52q4qYt7BkexXTjpuvjY/nST7aL4b0rUfVXtv20y4ZrRjNWbc9ctppTZr86aFFSXPF2Ik0c54fw2j+hOZJs0ctvxQXPGq22coilg3wi6HTRzZTjfBp0NPMWVsG1HYaMyVSD/GBvXB+WL1OmmWS0bzHup66Ps2iiJVXP35A8vhadNrqNDpqRisG86UpT3r6oQ2svzbFwVsPyOKczRUNFLFzypPdlrWr8SexH2s8rxexxS+ssEV8S+w8v05Gg8gFshFs8KQmpmNsLWLprtdgWcSyHnZBE9N+G69NOXJng/0FQ3pOe23bT7tktCLxe0Oy/v6T0U4r0hQ/dn6S1ooP78ZnlBacp+2fkqZ4FmdP4gseNdtsXRGbh02Tj0nZmnxE/dRvkxktOGvDgNpOM8hrV0p04yK26tqUF0fL3JoBfn2a4tADLT7xgBR7rGpiq9PooBmtWFy07BKcfGhD669N+0fNVxTnbK5ooYidU57stuxcmvQfa+xe0Y9oiyhi5/l1MhpEtluOVE9pYjrEtiL2g/YZLIpY0cO2NzHttvHalL00G+zLC7X53qH225ZGu2S0whVRb1TEFk+P1uWYV2ShtOQ8bf+ENItHbX5wLnjUbMNVRWw5vM9jUjFwzmNbtlhLzlo/oLbTDIJAty1i7TTlebOUWpKs6T6XppnvKTUfkEX8q6bRMTNasehb80lPPrSxS69NduxyRXbO5oqmRy9iNjnJq8oz3+qb5e+g96X2p38KmO3w4rV/2Gd7zEvTMcTPGFQi7Znv52yhnz1FWfHr8Jvf1fcTLA7vyzSNNrtMToPIdj6E+QClL9+kxRex/Z2q0CrNIuafVn5ry9IHl9+mxavZPk7pVkkDt30k+eGDX5pp6LY1T7g4diyn+VWK7e3hyQOk8uHfZPuwtRj6rkbzTb6Rf+8qfdw4naa1IqVyWnKetr88zfy1seWT+IJHzQ6wqoj5uOmjur7AkxZrpLIv0Hzqm0xDVXPcb1s/oDbTDNKJv7Svh3069hDbaK5etn7K2XSNNDZnJ07L5wuRNhv2SB8Vnm+FG8tGVKaaD4gv8m/TpTTnL822IqbJ9FOPydLTZj7pdAnaF7Np/bWZTrV8QNIKe0BsIjtnc0XTQYtY3qCK33zqK7JfUp+Wlr+0ft4jP0yjiBV7FlVLC9L3u5JGFStOU7ENXg9lL2078XWLcoaaXyejQWQ7G9DG8cxbiKYvYbs7VaF10i52+6ssYrZkLF/e1jbeErNdnNKtUgzcdm2mr4l1L2JFmrF8jA/dRrav03yLPyPyjjVegeYKj/WNLVn/k/ouRvMtzZM2k13yqNn+q4vYOEL7IKppX6HxyQetNKmB6z8Xi87ZNqCGacYBc1wptnAa6hc7tFwhjWdJa3z5FMFmoovWtj6NnSkqDK0HxJON8W31iraxOk1xNjv4eAlsxXii4qQ203hoW1anyR6P8sfOZnyyfK5EK9oOWcTy7mOmu0zad25PqRmVNczoSGeL2PJMeTOy2ReLY2dJMmeLWOTUOsz8OhkNItt9eDePWj6gzXObpSgDVaF1hsL13Q9+/6soYt69NJ1ujy0+uTzH9ndKt8732bhdjOIXDOk5T2I0v8pwzqJSZDv77MV5bGen+RZ7QszlL9uhuWLIZbemsiXn2cZO8y2tk5aXxqrgtJWtulUR+/TLadQqB0sbnjTp09nYZvfJ8tXnrB5Q22nScFlXiS+zZXkfOOEKafIzeQnQtIfUtF+h83E2pBmOF3Wp1gNSXo5qdWh1muGk5UOj6eKpUpy0/dA2rU5jj8F0aWwmf3Dy6eZG0YVdePQiZreKfNakRXU7yvpP2tcXGl9W97CxUPmdKGNH90VlEavPlDUxmxt/9eokbGKXFLF0as2gza+T0SDyRHakuxexJCxi86eRdytiORvSuxaxXH7XZ7CY3ciTGM232FnmJ0S2Q3OF1EtOsI2d5ltaJy0fmrsVscIwIo3jY3PYEttUk2etHlBLdopptLah+/Ro6X3g/Oh+jTQ2rcnqok3Xad34vj5Nq4iJxdBkUpadanXoomtTPDTFeZontRXnH6rVaarrPs4U57EZTbZXtD16ERuU3WhqR8UvoZ+2HjuPLXj2whdri7S9vlqWVRybaZxMZ/Ivfz0bP96ckthMWvbi2bPhXD6ZB52M54xpowVf1fioExm/UEaDyNOUdxa28yRGVWiLuml9982384eR9SeXZ3kSo3Tb2cBfDunf+18JXPyuqlUsiNP8ZrbvfDsoqyGXsL2d5ls+vMvOmT89miukXnKCbew039I86ff5Z7RPfdTsuJuLWH5roxjGi3seSb2k7aLhfXHS8+ezAXhNpCukKc5kV0oDf1EIgosWWJ/Gjvdl+ze7VWf79Ms5y8owl10bO/R0puI8xUzm2mlaRcwvWZocZA9Uc8UJxytiakfTgtRyxsqizpM1mLR9tiA1prJPabo82fJMmtfc1ADG9Sp8mgvY/tNq2/pEz0rnmk6NJr9QRoPIU6TvOWdj/WaexKgKbXH6lpd/TnnnIrb4ZVg+8E82VzHtd3mact902ydFvKAX2m5O86uU1yMTrGhtGrKNneZXaaXxx6koYrNVF8o23FjE0vecxwHJpjU5KOdMvaTtkuG9TKPRVd//ju+lFNufcIU0NlPcT9Ea22oa330XTbetT+PtZVJdA1uoyYBfOE2fcMm1KX9OO9F4CRonXVzMttVp/NLoivh09njM58nmmitOOF4RS31HMyZ1Fs2kIpa3Id++qEe+yVSCytX5yRaNbTzTuKfP5Eeuki3YsYvTajJQngkn+JUyGkQu5aOa2z6oz3SI6xex9DcrNbOW72OUboN5+J4/1iqH9M3XSbtd/EjZI7T4ZDLVMHOXWuhni3p6tMIWafI829hpfo1WmvQwaeaiR82221DEfMh04wBv05oclHOmXtK2dXiv0/iiVMNM1UIG+WB80hXS5CO3rx6rh01rMvWms4HWpymLWHVkW6bJmoc83zUuKmJ27Pnukqf031b/qZ90ETO4mG3r06Sfzw5pD80Up9m3mitOOFwRC24VeQFSaUlFLG8wvkDTiW9TNKL4ZDZZnqk4uU8X632BpgOWMz9t0Q5LVQdEi18po0HkQn67xT2lh80DqqrQFqeKWOphG2+IPaWIacei+iyG9K1XSntd/Egtdp06WLLxa1CXpEln1EwuXNHYNuYHMJpfoZUmPUpzQbvgUbPN1hcxH73cNDzajCYH5Zypl7RtHN6DNNkA7uaBf7JuNB1cIU1W+hTMp/NSotp0NtH6NIsitmwytkiTlRRSM6dsvDZucejykVo8UNHFbLv42owHL58T2VxzxQmHK2Jli0pskbb31UUZevHi9aLyeJ2aFhUz+ckWMZwtGs+eTyden9rtyVZr0g++OHaGHraeXyqjQeRCUxG79NchJDrItYtY6mFbf43YVYpYdsvFh/Tsd1VtPKzvYjS/kZ0yb1vpWLOb18LUfILTxCtsmSbP8yMYzZ/XSpN617I+b3vUbKsLitj86yFsTpODcs7US9o2Du9BGj9drh7H46WBK6RJcYb5dNdnoOW+sZWPca9rF7F0rykdPS0e1UsmKeSai7Px2hjLUtStvIktC3N4MZs2pCma2HRsO13Yt5orTjhcEbOZZUXx3pImo/a0tLKI+VEXlS/f1SbLJGeKmK3VpG+6OPYs9bAzPwUSv1ZGg8il0sBmntLEdIgrF7H0C/Y397BrfzQ5LJz+pWlfv+1K+dGM5jeyByhrhePhLFEKq+Ur+S5G8+fogrSaT73CFmryPD+E0fw5zTTpaVzcHtz+qNlWR/poUvP+C2Z9SssnvlTTp10hzfIOzHzmvIWYs+P7hjSfphqWJgdll7Elmiwpa7l1w9ZrM7CfOP8py0uzPGt0MZs2pCmu+1T/KGL2PhB2o4HNLD/+8y1SrbliEbOp5ZnSZ5Np2ieL2lUmXbK104msa2myQg/bwi+W0SDyBB/0lSPNXsL3N6pCWzSLWOphWz+XHPh+Rum2+aA7KHn7mdm6eE2LH8xofqPlnn4oVQ4vGDdNk5pPcLu0ucIWa/I8P4jR/BnNk6YncPtKrHvU7Bjbvqz/qb6CpdlsclDOmXpJ2wXD+yKNilgaYX2oXw6c6wZTc4U0y7qR3fkpGsGKunFBGlP/uHY2TRZS0lV3oC5Kszjv4sIEF6C+mC3r0yyu+vhwNPtWc8UJRytiZYkS3yIrYs1bTWb8F4s02yxi4Zny9uWTPjUqklZs7ZSs2neiX5NRnRkxv1pGg8jTePXYNqIXPIlRFdqiUcR+SD1s87/4PfAdjdJtlv6NI82UrAsUN17OskM5zW9jj0xxPo82LqnWnmV7O82f5s+L6AzNFX4CTZ7nRzGaP6110vRwnbrlte5Rs4NsK2IDG5DGEckm50HU5jQp9ZK2C4b3QZ4mjbT5+DpXn8TW36xsDIo0g6lwfLlIM3UC7xu3KmJ2+vIS2Ck1mfPc1dVq2Z5m+VikB8p+7NS3wjMvL2bD6jTpwbAD6lNbXfYynM3ppM0VJxyyiIXSDr5xXMSevZj+vcnBdPxiJjtZXMT8AJcVMds1/zGqYzv9gPFK1NL1GmgQeaLtI3ohRRmoCm0RFzF9PeySHvb0IpZ+oHBgv3cRs45RNGRvI5pOt4g0vY7t4DR/UvNGU3NFOoEmz/PDGM2f1Dpp8z7Z7IZFLB+fihZRD/t3KGLFaGnT0/l83NW0BItarpDGedH48nO/Uvkw/ql+1de6SBemqR+R+Gxehla0DNmeZvHT+w89Pm/aV6C6mKHVafyR0HSeqLxKtkLRmitOOFoR85lQ2sHX10Ws+g348/GLmexk8ZGeUMTyzW3LsC7Sw7ZKF2ygQeSpnnYoT2JUhbYIi9ilXw9zvq9RugtY3Ql7xr2LWLVjUcS2P2y2g9P8CangBH20ucLYGk2e58cxmj+hedLUz04/JrcsYtlobkPlNMAG4+a85Xnbh/ckO0dRxIKTF3FPu0KaXLNtLCpKw4VpyiZhohiebU3JGG1PY8fXpCmvRvsSLHaLrU5TnCePUJzGZsZL0VzRRhEbyk3Vwgb3LGLt0EYbOW0ZdjSE0hUbaBB5qqcdypMYVaEtoiJ28dfDnO9slO4CzSK2/dahJzGa36Q+nReSqY5Uq8+xvZ3m21Lz2fL1MGfrNHmeH8lovq150hM352brHjU70JOKmA9n+bC1GFBtkSbP2j68J9k5iji2IuiFK/vGFdJkPNgijLNysCLShWnqamyn0+TIs639epjbnGaZoyzE7XpswTTZtjpNebRszh6EMZ6FmbZqrmijiC0+zXz9YvGhYzGTnSw40uC2RUyNMTgEWtIlG2gQeSIfzjR9AU9iVIW2qItY+ljyoq+HubT7QOm282Ffg/u7fPjPV6xkezjNb2KPy+J0dqixVNjqGxWx5o2m5orEVmryPD+U0XxT66Spn4X3ybY/arbV9iLm5UHTNqmhKh+1RtmWZ20e3pM8TR4nyJPfBTnnGmkmvnR5bQapA62oQKvTfJk3Kj98WXFsiSbFs0Xh2jZfGztHnqMsZnb+8BrEF3NpdZriPHkE71hpjV+yYqNwRdshi9iJG0b1+qmHvX79Iq1YV8R8q/sWMXrYBdI1G2gQ2WwYnMah6uRfE1zF9zeqQltURcwWZB9L/rD5E0rf3yjdKsNQ/0HXo/hbpGkQT2vu/3vEgv08kD90+neO0uKVbA+n+Ybl99/fjZPNFSNbq8nz/FhG8w3Nk6bHY+pnH6bJix4122pNERtGoXF81/ecx2HVxydbl35X1vLGhi3T5FmrB9R2mjnO+O8cpcViGy8TtlwhzXBJ5iT5KP65VwBtvaoDrU3jP7/+cScd36cnyyX6FWdTthT5jNXXZrQ8qy9I1yl9SW4864mHtml1Gj9gOnz6sacrPz1XlpesuaLpaEWsLFE137ioT7Zg2CVrNxuKWHUmW6jqlE1KkTTjy5u0UTphmRTnpas20CCymXbPaMUldISNRSx9ADlT4dJcZuNnlNpr00+UBuucCo4P6YWNhVV7XXR9rYBUt4FSKxk1b03FtNe5NPXl0A7NFfWFWjS0iLa8OI1mMk951GyzNUXMR6GCVmioGs2dIg2vmfPj++oBVUfMaMWgiLPoOJ5pRc9wV0hTZplPXF6bNT1sWxErTK1Q85OUJxWMgi8/afW1EbsQ5Y9ZXpo5pWYzWnHC6jTVJdDy5ZrsKdJc0XS0IrboTRXfOC9iqQQV5WZdEQuK1iBbmE1KmXQh39o2XN5sG3uYZrFSumwDDSKblcP5YMW42aRDbCti6c5XLhUuzWTuUMSqkXsqOMsLtbH5rC0bIdutahBl1I0Pm/a6uPq0i5jmMlpxgja8OI1mMuPVuuRRs+3WFLHluJkPSfk6LRpoQUYr2lYPqKfSnBo5vXRo+qwrpMnX5D2kyLhmcH9Cmum8ms/44rsUMTvq4s5WmXNKeeqhbVmfZtHEGk+bImlzRcshi9iyHr2wjx3TFr5x3nH8476y9JTHL2byk9nUslblu/r6NCll0oVh1XQaC7UoYvSwC6XrNtAgspk+7hltvM2zoIPcqoht/GxSe227NuVgn38rvFiTr1hHO170SMW7ZQ/d5jja71yaoPqkMzVXzEeepOWnaMOL02gmMxWuCx4123LVR5PlAFmOSNO6vGxoUUYr2lYPqCfTZCvHD7lGVjpWDabmCmnmQXyRZN5jbZzVacpmlZ1XSzK+OChi57vP+jSJHVWTk/zE81U4+dA2bEiTH758TObHavHjN1c0HK6IebFa1BVbpC1847zjZOtG5SGKmfxkixgur3U2ub6I2brinJoU9bDGzmhLF26gQeQS379LI/q77/Vtmov5YYyq0DqrP5q8RxEb+o2+G/ZuuB5aJNOa5Yo1fE+j+S0+NO54pa89DUE1v54nMZpvuKCI3fejyXRwzWSyO1+bHzXbeOWX9T/333s1jF+f6ytIs/Rtnukf1nH5nQR33eH9RJoxTpnHfbn4gOyUa6SJLozTF9jWDe1mS5rxd9IPabTENB6QuxSx4dzBQXXVllfh1EMb2pRGF2fLY7LtwTpcEUuNpSgsvoGKjU+fLmLpCOeL2JkzXVDEplzVnqnhtfbFCX7hjAaRvpTloi/rX5+y7O3aaL4vZSFNwIKsLGK3t3V4vy3StJGm7ShFbO5WqbJoxqTCpA0WGwdFTPeeiiI2Hy7vUlU5Kiucz2g6yXdesoONq2y7+eczvmdrV5ziV85oEOlLWShiAWUhTUBZKGI1hvc20rTtK80BipjXn8VsVlo0rznfNy9iy4qjzecDet2a1vvmmjt9po1FLN/Ytssz6sjFIqzkl85oEOlLWShiAWUhTUBZKGI1hvc20rTtK81RithQU17k38cfNvDeMv7W/LH++Mq80ZRtKvunjtKC8b7X6+HwNld0Ke37+pktGHcdV24vYkX3K1pXOvQQYUnr0eaXzmgQ6UtZKGIBZSFNQFkoYjWG9zbStO3rkTpAEZuHWnWcuUxNpvZTFbF569fjP/md/tDqtIfxM5ZdSk0sN/csn9V0cqKI2aqiiGnSBaeR+GDI6EpRxALKQtkIKAtpAhaEIhYiTRtp2o5QxKaqNBasqrjMfcW3LW8kLXvbs0Vh8oXGZspV1Zny37bqCzSdLHbO2aop1nJH3y8UHwwZXSmKWEBZKBsBZSFNwIJQxEKkaSNN2xGK2FSl5oJVlCt9ZOl837KIlT3H1nm9mjaaypbN+LbZ8Z5VO098iaaT5c4ZCzyusRPmP15VFWcUsbN0pShiAWWhbASUhTQBC0IRC5GmjTRthyhiYx3Kl73QB43jN8fEN1wUsWGp9h+/dOVzadLo6192oKBLjSdffmPLl2o6CXYe5dvaZsWxKGJPoCtFEQsoC2UjoCykCVgQiliING2kaXvEInZEwzvbVCOtd1VdEReyQcNpEOlLWShiAWUhTUBZKGI1hvc20rTtKw1FDMfmw5fRINKXslDEAspCmoCyUMRqDO9tpGnbVxqKGI7Nhy+jQaQvZaGIBZSFNAFloYjVGN7bSNO2rzQUMRybD19Gg0hfykIRCygLaQLKQhGrMby3kaZtX2koYjg2H76MBpG+lIUiFlAW0gSUhSJWY3hvI03bvtJQxHBsPnwZDSJ9KQtFLKAspAkoC0WsxvDeRpq2faWhiOHYfPgyGkT6UhaKWEBZSBNQFopYjeG9jTRt+0pDEcOx+fBlNIj0pSwUsYCykCagLBSxGsN7G2na9pWGIoZj8+HLaBDpS1koYgFlIU1AWShiNYb3NtK07SsNRQzH5sOX0SDSl7JQxALKQpqAslDEagzvbaRp21caihiOzYcvo0GkL2WhiAWUhTQBZaGI1Rje20jTtq80FDEcmw9fRoNIX8pCEQsoC2kCykIRqzG8t5GmbV9pKGI4Nh++jAaRvpSFIhZQFtIElIUiVmN4byNN277SUMRwbD58GQ0ifSkLRSygLKQJKAtFrMbw3kaatn2loYjh2Hz4MhpE+lIWilhAWUgTUBaKWI3hvY00bftKQxHDsfnwZTSI9KUsFLGAspAmoCwUsRrDextp2vaVhiKGY/Phy2gQ6UtZKGIBZSFNQFkoYjWG9zbStO0rDUUMx+bDl9Eg0peyUMQCykKagLJQxGoM722kadtXGooYjs2HL6NBpC9loYgFlIU0AWWhiNUY3ttI07avNBQxHJsPX0aDSF/KQhELKAtpAspCEasxvLeRpm1faShiODYfvowGkb6UhSIWUBbSBJSFIlZjeG8jTdu+0lDEcGw+fBkNIn0pC0UsoCykCSgLRazG8N5GmrZ9paGI4dh8+DIaRPpSFopYQFlIE1AWiliN4b2NNG37SkMRw7H58GU0iPSlLBSxgLKQJqAsFLEaw3sbadr2lYYihmPz4ctoEOlLWShiAWUhTUBZKGI1hvc20rTtKw1FDMfmw5fRINKXslDEAspCmoCyUMRqDO9tpGnbVxqKGI7Nhy+jQaQvZaGIBZSFNAFloYjVGN7bSNO2rzQUMRybD19Gg0hfykIRCygLaQLKQhGrMby3kaZtX2koYjg2H76MBpG+lIUiFlAW0gSUhSJWY3hvI03bvtJQxHBsPnwZDSJ9KQtFLKAspAkoC0WsxvDeRpq2faWhiOHYfPgyGkT6UhaKWEBZSBNQFopYjeG9jTRt+0pDEcOx+fBlNIj0pSwUsYCykCagLBSxGsN7G2na9pWGIoZj8+HLaBDpS1koYgFlIU1AWShiNYb3NtK07SsNRQzH5sOX0SDSl7JQxALKQpqAslDEagzvbaRp21caihiOzYcvo0GkL2WhiAWUhTQBZaGI1Rje20jTtq80FDEcmw9fRoNIX8pCEQsoC2kCykIRqzG8t5GmbV9pKGI4Nh++jAaRvpSFIhZQFtIElIUiVmN4byNN277SUMRwbD58GQ0ifSkLRSygLKQJKAtFrMbw3kaatn2loYjh2Hz4MhpE+lIWilhAWUgTUBaKWI3hvY00bftKQxHDsfnwZTSI9KUsFLGAspAmoCwUsRrDextp2vaVhiKGY/Phy2gQ6UtZKGIBZSFNQFkoYjWG9zbStO0rDUUMx+bDl9Eg0peyUMQCykKagLJQxGoM722kadtXGooYjs2HL6NBpC9loYgFlIU0AWWhiNUY3ttI07avNBQxHJsPX0aDSF/KQhELKAtpAspCEasxvLeRpm1faShiODYfvowGkb6UhSIWUBbSBJSFIlZjeG8jTdu+0lDEcGw+fBkNIn0pC0UsoCykCSgLRazG8N5GmrZ9paGI4dh8+DIaRPpSFopYQFlIE1AWiliN4b2NNG37SkMRw7H58AXgqryI2fDR2zSgar4v0rSR5jSKGI5L4waAK6KIhUjTRprTKGI4Lo0bAK6IIhYiTRtpTqOI4bg0bgC4IopYiDRtpDmNIobj0rgB4Ir+1z8P/nwH/sPgP/6ngeb7Ik0baU77dxQxHNZvNHAAuK5/spGjt7/8afIXmu+LNG2kOe/PNGwBx/K3GjQAXNf/0OjR1U80oP5E832Rpo005/0bDVvAwfyjRg0A1/Q/NXh0lm5t7OXGBmnaSHMON8RwWH/Hp5PAtf3Tf9fg0d1P/vKnf7Gf+xqkaSPNSX/G/TAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAwCX+5pe/+6Ume/viq6//MPj6K833MybZQZTkC4vzhz9org+/JJl9XJuf/epfBr///c8038v/G9Cqbv7kT39kMX70p5rvKCX50z/RbG/DlYkvSo+cf/qjH2mq0sx5M40wO3oqAQfzN7/8V7OTIvaVBvjB11rUh1pPso+6oTCa62NZxPZwabyFJb/Xok5smFrSql7+VDEGzZH+PrIk/avYn6Q0UaXwsuHudcFSvwmfKSdy3kg7zH6eSsChqISZXRSxov384esvtLiDReHoWwqTsaNqto/9FbGfqYMlWtiJhqmCVvXxJwohPQvQ3G8GXZuYyo0JCk6e8w59Q73HaMnkdM5bOBFm8VT60V5uawIPb65h+yhiZQ8baPn9ZTfmkv5NbLo4mu9jWcS6X5ffq4GJlnaiUaqgVX0ow6Tf8JndTjEdx/E8SV1wir54+yam8zgtGp3OeQs6mdOiyaLSd35eAweiDub2UMTS2P6V3QhLTajbMO9n/8MXniR1j45355KpAmm+D0+h6T1QD/v9z37mH1F2/pKYDVDZmNl9wEpjuX/ZKU32+0hpOv2fpKrTr4n56aUqOLpiw1RqHjeOmZet5TNFS91ditipMEqzi6cScCy/UwkzOyhifs9n+jyy65DvSTSdSmHvWz/z3UIt6GNnRSz1sO5f0h/5WKVpDeV3GUIbii5xl2LR5O1LY3c+3UF+z6t6dHxpukjpgvnkzaRziJaNTua8hVNhfN30eaRH0zSAp/mboYn98m8++ZudFDGvGdOdJ28evb6B9MXX+amtffQuYnY1/tC/Bu2riKWv6f9Kc/0Nw1VWMHy46tV8jN+6mAL4XK9eaOeeLo3N9LswdkvuT/8kVYvl5SiuUbjFlQ0ntL+EGDWbUzlv40QYWzQ/ZHfMBHws9lTEdlZ/kh20j3RXzv9fS/rYVRFL39Pfy+2wpf6jldcKTQ9srtONqPJadK2Eo/DhKTuIzdznxk/UfeT+T6NWEctC2Cb97mkCR7SnIpZ9FWs/RcyC9W0f6YPJLyhiBf9gsvNvrGjzutHzhtiOilh5Lbxb3KfhtIUFp1x2v0fwIYpYdiUoYsC17aWIlZ8Hev3ZRRHz8tH31zQowl6KWPoLDP63KjpKN8Q0sz82dvVtG+Wdp/uP6LNFpbG5PRaxxbL7XbHdF7E/+VGRwbagiAHXtJciVtpB/XFDPTRdS0e6IaYPKLWsj3QxJl2vin9D7Ffp1+r//vf7+aZY0rP3jCzC2H88z33u79T85Isi1inKKHp8lrfAgk1uY/dFrORbdH5uAwezyyK2g/qT/U3FPdwQG67F7opY1wfIi1j6143Mbv7uZHK/z7XafMD0EVO/HLTXXYyyTcyxOqKItZwtYunx6/zcBg5mj0VMA77m+sh6WN+PSD2IRdhfEevZxNJXxLyEJbtqYj5YabqbNGTOtPj+8rN7tXiYInaX7vpQRUxPKs0BuIodFjGN931viKUv6bu+N8Q8gk30L2KeIP2T7N0/s1X9yuyoid1//AyVTazfTQyvOKnTqIdRxDKPVMT0lOKGGHBV+yti++hhah2u5y0xj+HXYidFbKyl/jD166hqX96/0geUO/oblMsxvY+x9Ei/PCmI/UZQvzCGIjZ7oCJGDwNuYndFbCc9zKVfGdGzb0wfTO6hiA0Rsk5qcfrl8e41tS//jHI/39j30UrT3Sx6WP8mlqOIzR6niNHDgNvYWRHTR1776GEm5dHM/fnp08XYQREreJ5uj5O+HqY5/2UWu7kldv/hM+JjZv4PBHashnkT8zAPUsTuEvNRipj+oVB6GHB1+ypi41fkd9PDOn8E59dDJ99bEfNsnYvY9L0wn9V0d8shvYtUvsYUPobe5f5ObGpiP0p/hZMiNnuQIjY+hPQw4Op2VcTUw/r+NcWlnon8cpR201Hzknh//r2w+R6Yz2q6Ox+vNN2Nj6jToJlGUc104QXM/jXD7kkGUcHxZXPLuF8Heowilp5BPcs8cFx7KmL6Rta+eli6Jabpe/PrsaBV3e2giM3fCttTEbv/6BnxYVPTgxOj/X15kN7DefQQLZbd77bmQxSxdIOVHgbcxI6K2N6+HiYUsVjf74j5t/X3eUfsfkP4KYth00Npuqd0Y6X31QkLjifT9D2b6yMUMV+8g2c1cEy7KWK7+5r+yELx0WStZ0Ed+LfCpu+I2cxeipgPWZrux0Lsr4ilGyvd76uEBadY6EHvU4H2X8T4mj5wW3spYuPXw/r3jK/KEN43+v3+ilnvL+sv/ll2vy4dP0UuviTmf2tyJ7++4v6DZ8jHzmnk9FD9i1ga0Pt/vhU/Rh4uXbJ7Xq/dF7F0MfwXwQG4hZ0Usepr+l/06mSeROf+YvxFYj7XWe8i5hfiK12Yr9L9y5612W+J+T/3rX9ycie/Wn8fn0xq9LRvxw/T6UZUr1DD4G6n/tNUw/bwPaO44KRLZr/wI12vOzWgvRexsYdp1hbQyYDr8AZW+p1WdeDD+kKf2y2qXoWefcPpY1vTLYvOn+v61yr8LlhmL79GzEctTfek2jPrVoB0funaw1Qqclme8pLdPGj1CM1d+XTOW2iH0Vyh64MIHIfKV+FvtO7+NLKXtO7OdPJM9x5WlEMtuzvdtMx07WHLJraXHnb/mxgti6G129hZloq+l0YhCtndnfyS3fx6pdtupfGkmi3c9C7UiTCaK6VVAJ5G3atAETPZ7SejT+N62kURm/7BJ+n/db6f6dfrm339+0b7+ORm/CzQdYyUxejdUBWjkF+ZOerteytFDPjoBR9NdvyqmAb3ktbd31dfq4x9/dUOathgDx9NmvG7YV9/vYe/vvDJJ7/6/fxNsb34k53cEDN/mrrYj/wfOuon/a27H6UvrHUVfORXhkpfp/vRXb4ElffkZDrt2ZxX1w6j2VJaBQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAcIn3gzearp1eu8HVDgQAAHAYpxvS1frTxgO9/EwTAAAAx3W6IW3sT22bDvTZm/cUMQAAcHynG9Km/nTKlgO9GbaliAEAgOM73ZBOr91gw4Fe2rYUMQAAcHynG9LptRtsOBBFDAAAYLChP10NRQwAAGBAEQMAAOiEIgYAANAJRQwAAKATihgAAMAG1mNe2sRnXmne+LSrFgx8iabdZy/t93hpq3yt7+wTb3yLl1FZ0ro3y5W+MJvOA+aBbMGENgYAAB6MVZih2nxmfyap6FQLnM3mRcy7UTIs1h/zmuEw3rSSZVPKVhUnKQ+klVmeeVvNJxQxAADwYKzCvCw7kVWgakFSzuXlyJan/09SEVtukSlXDbIiZbPjxja9CFism1DEAADAg7EKkz5enC17T3kLaqpTyyrlOxVFLG3xJn0AaVNaOah6WN6kbK4oYos848rxyMOfb7JjAwAAPATvMfZ/L19+pi98qUAN8+OCqeTkM6pSb6w/zZ9AjmunDy3TAm0wNbpxZ1vw2bj31MRsZjyQT9v/5YHeT5umwqcZAACAR2I9ZvBGXWa6UTX2oFR8xqZj0/GasUwti9jUkYrNdZpppeY1VxUxM26bjjOupYgBAIDHZT0m6zXjh5KLBeOdrGxV6k5ZBSobkorYcr1Wp22zlelowWmqgNpXMxQxAADwuKzH5E1GBUpzA1+QtyJN+/Lpo8ZB6lLjluk4WX/SqdKZskkpep1N5qcst/UFmqaIAQCAx2U9puhTywXekIIiZpPviwaU3/JSEdNMMlc3nypKWnnefHW+PPHzjGemiAEAgIdlPaYuYssbUHUR835W9KOyXgVdK930simvUuXOReHLJus8FDEAAHAQ1mOKIuMLNO1sPm9FadoL0KJLzWvb6/3Q00Rmrmn1Kd8XTavoXhQxAADwsKzHVL1rbRFbFCC7VVUUsWC9LSs+7ZzYwvdpD5sqipimE4oYAAA4BusxlxSx4vNBqYqYpkdjaYqLWHbEfL1NU8QAAMARWY+5uIj51MxK0Zoi5n8uPrakiAEAgI+O9Zi7FrGhgI1/lihiAADgI2M9hiIGAADQgfWYi4vYogBZKTpVxMZ9/DtiFDEAAPCxsx5zcRFblClbVhSxRUHyfYY/T/ytyXmSIgYAAI7OeswlRcwL0KKIzWvjgmSL/NDTRC5baFMUMQAAcHTWY6reVS2oi1hwV6tYFBQ1X+TrbeL9oj3Na+tTUsQAAMARWY+5pIi1CtK4pc+U6+dPM4stJf+sM19t0xQxAABwRNZjLipidZmyBdOSVMTyW2J+wywdOU0W9SltrxmbpIgBAICjsx5zURFLG2ZNy29pLYrY+6wi5be80rbZylTNmqfRdFIXsbzvAQAAPArrMZcVsVS1xjl1q8XauTB9ltaPcz7zfmpimtdcdcoTRcz3nEIAAAA8EOsxlxWx8RaY3Y76bOxdVRF7/8Yqk2rY+2XzevOZLXm5XGszW4rY+/cvX76c9gYAAHgMXmM07cIFYREbb4IViiK23CDrSipQuXmtza0sYmm9oYgBAIAH4x1G0y5cEBexRRPz215FEXvp/z8pqtKyib3J1vp8Nn2qiE2n4ItiAADgwXiH0bQLFzSK2PShovnMe1lZxPK6le9n5o8zTVGjbMHaIja1QYoYAAB4MN5hNO3CBa0iZlXMi9BLK0ZBERv7ln+TrKIu9ublYq0vzKZPFrHpKJoFAAD4yE1FDAAAAPdFEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHhIP//5zzUFAACAu3r//r2mAAAAcFcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAAAAnVDEAAAAOqGIAQAAdEIRAwAA6IQiBgAA0AlFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAAAAnVDEAAAAOqGIAQAAdEIRAwAA6IQiBgAA0AlFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAAAAnVDEAAAAOqGIAQAAdEIRAwAA6IQiBgAA0AlFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAAB0QhEDAADohCIGAADQCUUMAACgE4oYAABAJxQxAACATihiAAAAnVDEAAAAOqGIAQAAdEIRAwAA6IQiBgAA0AlFDAAAoBOKGAAAQCcUMQAAgE4oYgAAAJ1QxAAAADqhiAEAAHRCEQMAAOiEIgYAANAJRQwAAKATihgAAEAnFDEAAIBOKGIAAACdUMQAAAA6oYgBAADc0c8zQxHTlPuxNgEAAMAt/MPQvmI/1xYAAAC4iR+rdtUoYgAAALfVuiVGDwMAALix1i0xihgAAMCtxbfE6GEAAAA3F98So4gBAADcXnRLjB4GAAByz19pooeuJ7+x6JYYRQwAAGRe/fGtpjroevKbq2+J0cMAAMDs+R//2K8LdT35HdS3xChiAABgNlShfl2o68nvYXlLjB4GAAAyFLFbWt4So4gBAIAMReymylti9DAAAJCjiN1UeUuMIgYAAHIUsdvKb4nRwwAAQIEidlv5LTGKGAAAKFDEbmy+JUYPAwAAs1dWhEZ5IXr19q0WvnquRc4WvbLfhu+rtHDw/JVvnza2yUW7Squ1XponP5j5lhhFDAAAzOIu9FwlTN5m7cnmX827qYrlOwwbL4tYcby5vX0sRWy6JUYPAwAAmbALlTXMzE3M5l7Zr8NP0sJFb1sWseXxxqN9NEVsvCVGEQMAAJlXb/UR5PDneOOr7mFZE7OZeYN0d2u5gx9yLlb18XS06OTHlG6J0cMAAMCSVyFND9SN0re5/LtgxlcNNOtd6lVqT9rEStnz+UPI6Yjj8Wx6PNzcumzuyDfDknRLjCIGAACWyi6UPnacv8ilIjVWJ5+Ze5nxBdMBxo8txwXqYZobvy6muY+liPktMXoYAAColF3Ii1JRjWzB1Mx8JrujVe+gJqYlVa9L208LbOYjKGJ2S4wiBgAAKmUXsrnihlf66LEsYppx1YL08aOOWPe6VM0087EUsU/+gR4GAAACZRd69ertohl5cxoX2XRxg6uoaYlvox18Ok2OfI/8o86PoYj9mCIGAAAC57pQXcSqTyY1Ld600g7Z5MSPl99h+xiK2Cc/1p8AAACZc13odBHLV0q2Q3C/TMfIpj+KIgYAABA43YX0bx0VRUzTpmhpo3mZ75v3NmPLKGIAAADNLvT81fTvTQ5aRax5yysrYiG1M5ukiAEAgI9V0IX0T3RnLixiNhWjiAEAAFRdaPFPfiebipgdgCIGAABw1qIL+be+Jm9fBV/W17ThjhgAAMDlyi409bC3b1+lgvXkIuZTDfOmAAAAH5+yC9ncMJ/9TceTRaxYOZqX+aec2bEq86YAAAAfn6IL+R2usjqdL2KLe17ZDn44ihgAAECs6EJ+C6v8qNHLVKuIpQVl1cp2aHxyOX7qSREDAAAft6IL2cyiV3k3axaxoGplO6QbZs2eRhEDAAAft6IL2UzwyWS7iNWfTXrRGncoWlziq7kjBgAAsOhWPpMXsVS02kWsqlrlDmkuv2Pm25fn0zQAAMDHxpvR2L3S7ay5ialWnShiaYupiS13SL1rLmppfmpmxckBAAA+MqpKr/wL9OpRqkbZP3WUFkR3sLTLK9tn3mGqXpr31ep5VS/TyQEAAD4yKkeqR3OTGv/J7/SHbxp/lDjtIj4/da3xFllm7mGLkwMAAHxkVIVUsJa16nkqS7pH5ovS5Kzc5a1Xr6xYVUVNy52W1QcFAAD4CEz3rNLsdJPKTB9X6qNDX5gmM9ldr7fPi+1dtnrYoPwQcnFyAACAj4y+2jV+af5V6mLTr131uWqy8NwP8dZbVlXExtXZITOLkwMAAOBiXuPqwgUAAIBby79TBgAAgFt5VX/c6B81ahoAAAC3Etz+siX8NgoAAIBb82/mF7XLb4jxFTEAAICbs9pV/9YwzQAAAOB29GvH0j9h9FxzfFUfAADgDtItsALfEAMAALiLqolxPwwAAOBOin/CiNthAAAA9zT9E0aLf0kSAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD5yf/ubb/Ao/ul//D878ZO//Olf/ETT/ZGmjTRtpGnbV5rBn/0bDVnA4fyjhng8hv+pd6XO/vKn5i801xtp2kjTRpq2faVJ/kyDFnAwf6vxHY/iv+tNqauf+Lv0T3+6j/9iJk0badpI07avNCPuieGY/j8N73gU/6T3pK7Sfy7v5T+YSdNGmjbStO0rzYhbYjgmje54CP/rnwd/vgP/YfAf/9NA832Rpo00baRp21ca8++siWnYAo5FIzwewj//78G/34H/Ovgv/22g+b5I00aaNtK07SuN+XOKGA5LIzweAkUsRJo20rSRpm1faQxFDMelER4PgSIWIk0badpI07avNIYihuPSCI+HQBELkaaNNG2kadtXGkMRw3FphP/mm/+7B8ryzTf/Zw+UZUfXxovYv92B6W1a832Rpo00baRp21caihiOzYuG0XDfl7JQxAIWhCIWIk0badpI07avNBQxHJsXDaPhvi9loYgFLAhFLESaNtK0kaZtX2koYjg2LxpGw31fykIRC1gQiliING2kaSNN277SUMRwbF40jIb7vpSFIhawIBSxEGnaSNNGmrZ9paGI4di8aBgN930pC0UsYEEoYiHStJGmjTRt+0pDEcOxedEwGu77UhaKWMCCUMRCpGkjTRtp2vaVhiKGY/OiYTTc96UsFLGABaGIhUjTRpo20rTtKw1FDMfmRcNouO9LWShiAQtCEQuRpo00baRp21caihiOzYuG0XDfl7JQxAIWhCIWIk0badpI07avNBQxHJsXDaPhvi9loYgFLAhFLESaNtK0kaZtX2koYjg2LxpGw31fykIRC1gQiliING2kaSNN277SUMRwbF40jIb7vpSFIhawIBSxEGnaSNNGmrZ9paGI4di8aBgN930pC0UsYEEoYiHStJGmjTRt+0pDEcOxedEwGu77UhaKWMCCUMRCpGkjTRtp2vaVhiKGY/OiYTTc96UsFLGABaGIhUjTRpo20rTtKw1FDMfmRcNouO9LWShiAQtCEQuRpo1Hqo00bftKQxHDsXnRMBru+1IWiljAglDEQqRpI00badr2lYYihmPzomE03PelLBSxgAWhiIVI00aaNtK07SsNRQzH5kXDaLjvS1koYgELQhELkaaNNG2kadtXGooYjs2LhtFw35eyUMQCFoQiFiJNG2naSNO2rzQUMRybFw2j4b4vZaGIBSwIRSxEmjbStJGmbV9pKGI4Ni8aRsN9X8pCEQtYEIpYiDRtpGkjTdu+0lDEcGxeNIyG+76UhSIWsCAUsRBp2kjTRpq2faWhiOHYvGgYDfd9KQtFLGBBKGIh0rSRpo00bftKQxHDsXnRMBru+1IWiljAglDEQqRpI00badr2lYYihmPzomE03PelLBSxgAWhiIVI00aaNtK07SsNRQzH5kXDaLjvS1koYgELQhELkaaNNG2kadtXGooYjs2LhtFw35eyUMQCFoQiFiJNG2naSNO2rzQUMRybFw2j4b4vZaGIBSwIRSxEmjbStJGmbV9pKGI4Ni8aRsN9X8pCEQtYEIpYiDRtpGkjTdu+0lDEcGxeNIyG+76UhSIWsCAUsRBp2kjTRpq2faWhiOHYvGgYDfd9KQtFLGBBKGIh0rSRpo00bftKQxHDsXnRMBru+1IWiljAglDEQqRpI00badr2lYYihmPzomE03PelLBSxgAWhiIVI00aaNtK07SsNRQzH5kXDaLjvS1koYgELQhELkaaNNG2kadtXGooYjs2LhtFw35eyUMQCFoQiFiJNG2naSNO2rzQUMRybFw2j4b4vZaGIBSwIRSxEmjbStJGmbV9pKGI4Ni8aRsN9X8pCEQtYEIpYiDRtpGkjTdu+0lDEcGxeNIyG+76UhSIWsCAUsRBp2kjTRpq2faWhiOHYvGgYDfd9KQtFLGBBKGIh0rSRpo00bftKQxHDsXnRMBru+1IWiljAglDEQqRpI00badr2lYYihmPzomE03PelLBSxgAWhiIVI00aaNtK07SsNRQzH5kXDaLjvS1koYgELQhELkaaNR6qNNG37SkMRw7F50TAa7vtSFopYwIJQxEKkaSNNG2na9pXmIYvYC3vLfq2ZO/NzP9PMDdjhNenneqFpXMguqNNw35eyUMQCFoQiFiJNG2naSNO2rzRHKWIvbtiNPvnk2VyHblzEnuU/2evlqZ69sEXfvKadrWcXzGm470tZKGIBC0IRC5GmjTRtpGnbV5pjFLFnr295k2o433yyGxex4iaYnUqTxkra6PVNi+eR6IJRxALKQhGrMWi0kaaNNG2kaTtEEavuHF1VcZPq1kXMfpS8iC1+ykxeRNGmy0URCygLRazGoNFGmjbStJGm7QhF7Mbd6K4ns7alST/VfOJFD6OJraSrRRELKAtFrMag0UaaNtK0kaaNInbW3U+mST/VdHdMPeyZnTp9U4wmtopfKqPhvi9loYgFLAhFLESaNtK0kaZtX2koYmeVJ7ut6rv6YxFL3w8rVt3yZz4Qv1JGw31fykIRC1gQiliING2kaSNN277SUMTOKk92W/ajTOeyE2sy1YkshTexe6V6aH7ljIb7y3z43g7x7t33mr+UJzGqQpt89923msr88J0d7tvvNLuJJzFKt97372y3d99/0LzzZZN3Wrqa7bS5iP3nilZMPv3cln75uWZX2vY23TzH51/ais8/1Wzu088/X53plml82eRLLT1lW5p00i/ra/D5l7bmztemmcaVF8gvY+F8qCul8Yfwyy/rlM0VofVpPrUgs+Xx4wckfD61rU7jEQpaYfTsri/B6Ye2sv7aDJon/dTPGr7WbMXqa0MRO6s82W3lN8HyIpZuiGnGVQsQ8wtlNNxf4oMOMXhXVI/NdJQLith330a7/eAHc0FJO0d7br42WePKm2lZxIpVa9g+Vy9i8wCzpmLMtrxNZ4NYeRIfGZLFG3J6Y79J2diaJltsVmTakiY7+uLI05rlA3PDa9NOY+a16fp4jsL5UFdJ03zSbn02r0+TPWucFrvGA5LFX1c3VqfRUTNaMZjP2nx2r3vqrL827ZNml21xCaY91j1QFLHzypPdlhWx8SfJ7475jzg1NOO3xPhs8jy7Tk7D/QXKgvGkJqZjbCxiP3zrLazeLethlzQx7bjx2mS1dJDd+NpfEfs0e3teOVrIhrfpYgzL33fzk+dnT//pPlg3YgxumaZYvirT+jTl8F6MSdlp8+W3vDYn0gzy2pXW3baItdMUa/JnbXNFy6VpsldR8wFpPZ/aVqfRQTNa0Tzr6Yc2tP7atH/U/LTFJcj2WBWGInZeebLbsnNp0n+ssXvZ8vJHrKsZQn7pjIb77cri8bQmpkNsK2JjCxtoiRQ97IImpv02XptF35qbWLli86e4ttN1i1j59rxqsBitf5teDGHz+27x9j2fPtv+5mXjfJrlijWR1qdZnDQbk4o10/LbXpt2mkEQ6LZFrJ1GS5I8pRYlZfzY+jTl82b6CK79gCzir3lxrU6jY2a0YnHW+RKcfGhj669N66SLi5ZdgmKPNWEev4jZ5CSvKs98q+q30PtS+9M/Bcx2ePHabzLZHvPSdAzxM/qSsvVpz3w/Zwv97CnK6/O/EL/9Xf0XQ15NJ35Iith5dp2chvvtvF/4R5L6plhafBHb36kKrdIsYr7oux+GqfTBpU1t4fsbpVsl9VKrWR8++KWZiqnNPaGl2rEuKmKaTCNn/T75+afDW+SX60d2t/5tWucYptLNg/H92Idx//KIvmCSFt+4bKTjrk+TBo1xq3VWp0k/qp803ZuczuNrLE65/KbXpp1m4AvSF4w+HXuIXag5hq3P94hdIU16fIbZ9AjOCZormlan8ThBY2g+IO3nU9vqNHZATaYzFa8df3TKS3DyoW1YnaZ1Uj1p7CmzuARpj2G71WEOW8TyBlX8FnpfYb+KX9LSed5Ne+SHaRSxYs+iGWlB+n5X0mhOxWkq2qjkaxbNDwG/UEbD/XZ5v/AWoulL2O5OVWgV/zr+N/V3xPyG2Fi+bPXWW2K2v1O6VewSTF3Urs1066t7EbO3vfltUqVEMxtte5se32otwPh+nE8XG9n0sGr43+psN0zja9YMFbNNaabhqfiBszg2OT2ENn3LaxOnGUfSxWWwhdM2ftE0fcIV0tiJomvTXtG0Ok0ZJ2PnGdYM/ysfEFsybu+XZsVTaHUaO54my2tjK8azLhNM6euwoU1popPmP7dPTifNtrLJFQ/UQYtY3n3MdJdpLGJTe0rNqKxhRkc6W8SWZ8q7kc2+WBw7S5LZXsTqb+8j5hfKaLjf7sO7rF5coWw4VaFVhsJlfyuy2s261/y3JbcedWC7OKVb5/vsnmDVyu5dxIZ3wGkI8HdDTQ/8PXBbxZitfpsu3/bnBGWYfEwf9hj+K7oY5c+5YRrfZdtVWp1mONE0anmEaS6PY9NjgFtem3aadHnm2dGX2bLyyjY9PU3xUNn0eNbmirb1acrrMWs8IEWWtY/X6jTD0eMXtU1Pa/IV7Ye2aXWaEyedf2ybnk6ab2TTK15eD1/EXusTxeHP12PLqdpR1n/Svr7Q+LK6h42F6kV+eF9UFrH6TFkTszn97tVZ2MS2FzE/LJ9MruCX0Gi4fyI70r2LmFS7WRGbP4284Ki2i1O67eyz2q5FLGdjZfl+vOYtMLb6bbrsMXbONFUOB/VQtXLwSm6ZptxljdVpCvlQVQxb97o2hSLB4pkTWftsus61mX74VSva1qc53V6KU5tzz+7QRdemeGiG6anhlDOZdZdmfZrmSW16ekpkK4oAK6/NwxexQdmNpnaUvrK1/C30Y+exBc9e+GJtkbbXV8uy+mMzjZPpTP7lr2fjx5tTEptJy148ezacyyfzoJPxnDFtlNGpNIdT/EoZDfdP0+WjSal2++Hb7IaYf06p6bVsF6d021UfTX7vv2Hsot+3ZkGeVMTsvXB+z1v5Dtiw+m360/zbZz5ca3r4j/R54LbhpAyzKd4t0/iMf/vFv/Kzwuo0hfykNj3FyWMmt7k2heISlGNtyCKd2cQ9PU3xw5cPYWNF2/o0drwvm7/7qn5Azjy7Qxddm+HQ87FtRpPtB+3aaVon9Uum6eKsNnni2R06YBFTO5oWpJYz3j1S58luJqXtswWp5ZR9StPlyZZn0rzmpgowrk/HnQ9Vsf2n1bZ1eMtrqorTeXGCLtZVilj6sv4lJWPkSYyq0Band6u/Qnae7eKUbqv0a101k4rYZPtfabC9nlLEFmOlz+mbxCvelxcuGjT8LTh61/V34zJEPbadcMs0vtVoxZ2Ey9Kk7zlrZjGGlnODm1+bMo3Op69i1zXE+CpNn/L0NBamuJ8yrmmuaFufxp8Tk+U1SBeoJXh2hy66NnZsTafn6ngJyodwsnho21anaZ20vCjZXLbNoJxrOWARq+4WpX6kmdRi8jbk2xf1yDeZSlC5Oj/ZorGNZxr39Jn8yFWyBTt2cVpNZlJ8U/wVBDTpcj25iM0t4wmfvt2uiHkP2/rr9W0fp3QbpL87aebGVRSx7U3MdnpKEcvfMNPo8KW/jZt5+UqXDBrpbToYkdIopxk5PbYt3DKNbzZZc6E2ppnH+OngNqPJQTk3uOW1CdL4JUgDuImamO8VV7TSFdLYnCbT6vG8zRVN69PMQdziyCcfkLSrZk7ZeG2cPSrzpfFTpb+r6Q9XGXP+Ga76LG6dtLwo2ZxtlKZMOddyvCKWylDRUrwAqeGkJpPfavIFmk58mxVFzCbLMxUn9+livS/QdMBy5qct2mGS4g+K06JN1+upRczvhbmn9LCbFTHvYZsP6jsZpdtAOxZ9qyxim5uY7fOUIla+4y1GlvODVunSQSN6001RFgPZzYvY2jRpu8mKMWxjGh04P7TNaXJQzg1ueW3sZC77Qc9fgtWJrpQmzeo5PJ64uaJpfZrTL5dTP3747A5tvDbOjq1JUz5SiwdKS8MHsLY+TeOk5UXJ5myrNGXKuZbjFbGyRSW2SNunJpOmE/sVXWXl8To1LSpm8pMtYjhbNJ49n068EbYrlK3WpB98cWyTbqqZ87+VDEaX62pF7Gn/xpEOcu0ilnrY1l8jdpUiln1Q60XM//nJ9Jnlxs9wbZcnFDF7J8zegW02t+rNeXbBoJHer+vCl0aq5flPjW2VW6bxDf0DOX02lxafsDGNHdTNR7Y5TQ7KucEtr42dzGUn0JJJfdXipYErpPFnrj1A41N4XNdc0bQ+jT8t0m2fdPS0WGxR42Txszu08doYO3Fx7LwULU+qxSuui9mQJj5peVGyOdssTZlyruV4RcxmlmXH60uajNrT0soi5kdd1KF8V5ssk5wpYrZWk75pULXmIjacpn0kTHSxnlrE9vzRpH7B/uYe9pQiFn00OVyj6V8Bt8u18ZaYHewJRczeLbN34DScDIuG8XNlwchtHjTSgBSM1uldvBqpToxttZum+XL+XpStrZJWNqZRlsF0aJvR5KCcG9zy2gRpxhHcekhareUTX6rp066QRo/RbLoUzRUtG9J8mmpYmhwUT532A9J4doc2Xhtjh89PPF8vs3h6hxezaX2axknLi5LN2VZpypRzLQctYpoe+Rap1lyxiNnU8kzps8k07ZNFWSqTLtna6UTWuDSZ87/aefJvYKKUrtRAw/0TfEg3ep5yJN/fqApt0dot9bBvt/ewpxSxwYf0dxcaN762H9b2eEIRW7zh2RvjIL1t+nDhU2ttHTTSm/XyV4IO0khVj2K3LBvb04xsrSbbtqYZ8ug2y3ji8jTl3OCm16ZO4wHG0dsv3fLk6wNdIc34KE3mh7G5omF7Glf9uM2f/9zzqXBBGju6Jk1ZiYILEFzMltVpWictL0o2ZxulKVPOtRyuiJUlSnyLrIid/FRv/BeLNNssYuGZ8vblkz41KpJWbO2UrNq3lJpYdXZU/EIZDfdP89S/NulJjKrQFo3d9Hv3NbeJ72mUbrP0bxxppmRrtt07tENdXsTsjTD/D+H09jm+aebTq2wcNNLbf/0f4unrvdGpy7fxM26eZmQbnL1OG9Mk6dyaKVNkK5JbXpukSJPqxHi5lk8kY+vXPX2ukGbg88Y/Lc7O3FwRuyjNkGc4dnEJGg/I+edTYXua5WPh5/P/vkgfo9dP8eBiNqxO0zppGS67RLbRfE1sTpMnHLOIhdIOvnFcxOwfdJw/+puOX8xkJ4uLmB/gsiJmu+Y/RnXsXAp6slHC+HUyGu6fyJrY5r8POEtRBqpCW8S7pa+Hbf37konvapTuArZ32LfuXcTsPbIYK+wNcFpQrT1n26Dhb9DBCVIbDO5MFW/c5908zch2PbXebUszsZPr2MVpqmH/ltdmkqXx802DpV8lTUuwqOUKacyn+o1e1ZmbK0IXpqkekfgB8Qynn0+F7Wn8Z9X0wE84nq59BfKt2tamaZ7UpuerlD2lzzy7Q4crYj4TSjv4+rq/VL8Bfz5+MZOdLD7SE4pYvrltebpl+YlOdjUYu0xOw/1TPe1QnsSoCm0R7Xbx18Nc2nmgdBewYhreIbx3ERve78q3ZVswvYlvGtrNlkHD352j9357R269Dd+sbFyUZlSMIS1b0mSyn7j44W2mTHWza5PJz2HT85OnmHEb8lwhTc4ej/DMzRWFC9NU5SGMt+b5VNiexk6gSeNPbU2fuATrHq61adonLVbYjF45xfltZsU1oogNFv/kd3LPItYObbTRQtpHM2jyy2Q03D/V0w7lSYyq0BbBbpd/Pcz53kbpLtAsYnbYOxax+v3O3jKnJbb6bMHIbRg0UvMJbgykkaoxJKwbLeTmaUa2yR2KmIccz2PTZaxbXZtcfo4ijuVZjJzF6tOukCbjj1l05uaK0hPSFJcgirfq+VTYnGaZo4zRuGYnVhTWpmmf1K7AGM+Wj63szLM7RBFbfpr5+sXiQ8diJjtZcKTB3YpYOnxwMBT8MhkN90/U/lLUKp7EqAptUe+WPpa86Othznc3Sred/+XJVMSGyayRXXCdbI+Li5i9KZbvd/7WqLfDcqRdY/2gkQakxdA9SI2oedZ1o4XcLs2wPEvhe2u6bX2anCcYz2XTCpkPZ3Kja5Mr0uRxgjy+rabPuUaakS+NnkDNFUur03yZV/c6Tf2ApAi3ek2JPRT5aS3G/NC0zh9fzMraNO2T2hrN+DmnMDajXYJnd+iYRezEh3r1+qmHvX79Iq1YV8R8q45FLDsT2vwSGg33mw0FY/zdYSf/muAqvr9RFdpiudsPi98e9u3mTyh9f6N0qwwV64OuR/G3SG1a/8bkh/Ql/o3XyXa5uIjZe58mR94q7A05+s1I56x9m15+X/nLcTKdc3oP/nT5dmzrz48Wcrs0PlCkHJ+mva83hNnBPx0DpGRpxgIMbOwPz3mTa3MizRwn/F1qW+JcIc3n/uDoSZs/bZormtamSd0lxYleLdUVSNtMEapnd2j1tRnZOTSZ+Fk9SPqS3Pj0PvHQNq1O48cLTppePL5mcclOP7tDhytiZYmq+cZFfbIFwy5ZpdlQxKoz2UJVp2xSiqQZX96kjZYoYqv4JTQa7jfT7hmtuISOsK2I6Ytgs1S40l+XLPjy9bTXpp8oddGc+lYqZbm0fDXb5dIiZm94y7EgvWtOprfPVda+TWtUzKUVmsmM78dppJitGMNul2YZ5ppDWJ1mHpKK885X4K7XJhsgy/OWZ/Xn0dpnz9PTlE/bLEpzRdvaNIuXyuDMk1VzmexitqxNM7Jzlz/mMo1OevKhbVmdpnHSQXnZsqdIscuqR+pwRWzRmyq+cV7EUgkqCs26IhYUrUG2MJuUMulCvrVtmIW0v89Z3Xyz7SliZ/llMhruN6sKxrZvPpV0iG2VSbtkfHGfIjb/JlcZ/w5ptWLrdbJ9Li1i9oZXvflufzucrX2b3l59Lhk1bpemGoJXNI61aapj549B/uBo0W2vzck0zSF14KE0fdbT0xRr8iTNFW1r01RlY0zTekA0kzn/QK1PI8Fhy5xjypMPbcv6NPFJTX7iImn47D7lmEVsWY9e2MeOaQvfOO80fmOpLDnl8YuZ/GQ2tWxC+a6+Pk1KmXRhWDWdxkJlmcpAibfF5Q+Kil8mo+F+s/Qx2+QJn0sOdJBbFbGNn01qr23Xprwllv2LT+WF2n6dbK+nFDFNZrJhZMU4UVj7Nh1UnzRGaiajd/Bblo3tacZPM2XVZVqbZhFn8RcIpvNmI9str83pNNllWKzx/VY/fa6QZk6yOGtzRdPqNGWcOU3rAdFMZkX3WZ8mscNqcpIHmq9C+2K2bUgTn9TMTWxxzujZfcrxipgXq0VpsUXawjfOe1e2blQeopjJTxbVo7zW2eT6ImbrinNq0tn8onT5sShiZ/llMhruL/H9u9Qx3n0//RM+F/LDGFWhVdZ/NHmPIjY0Ln037N1wPbRIxgt10XWyHS8tYsMbYvjum74x8uX0b/istvZtuh6pzlafeTxNrjiEXZBm8Ln/bqphjFl5mdamGXyqr+x8+eXnVXlIX0aa/mEdd8NrMziVZoxT5nFfrh1NB9dIk5YHD0ZzRcuGNHOcIk1aNtOV0Fzmuo+Ui1/UerYursLJhza0KU18UtN6TKJn9wnHK2LpTlHRdvLO4tOni1g6wvkiduZMQXsqk5Zs3ZRruWfdLtPJ8x8EIb9ORsN9X8qy+UPE21CWHV2bi7+sf21bB43bIk0badpI07avNIcpYnMl8dKS15iisyw2DopY2rwsYvPh8i6VzpTtW1Y4n9F0ku+8ZAcbV9l2Re3yHYs9q58SMb9ORsN9X8pCEQtYEIpYiDRtpGkjTdu+0hyhiHn9WcxmrUXzmvN98yK27DjafD6gV55pvW+uudNn2ljE8o1tu/JmV+pd0zL9CtrGoZDxC2U03PelLBSxgAWhiIVI00aaNtK07SvNYYrY0FJe5N/HHzbw2jL+1vyxs/jKvOSUbSr7p47SgrEDvR4Ob3NFl9K+r5/ZgnHXceX2IlZ0v7KIjWdKP9P4E/oqnJQu1UDDfV/KQhELWBCKWIg0baRpI03bvtIcoYjNY606zlymJlP7qYrYvPXr8Z/8Tn9oddrD+BnLLqV+lJt7ls9qOil3LtiqoohpchSciR62hi4WRSygLBSxGoNGG2naSNNGmrZDFLGpKo0Fq2otc/nxbcu7Tcve9mxRmHyhsZlyVXWm/PfC+gJNJ4udc7ZqilXtOKj+Qcz4OFjQ1aKIBZSFIlZj0GgjTRtp2kjTdogiNlWpuWAVrUUfWTrftyxiadnI1nm9mjaaypbNLLvU+DFhUhzYl2g6We6cscDjGjthcLurPBM9bB1dL4pYQFkoYjUGjTbStJGmjUeq7RhFbKxD+bIX+qBx/OaY+IaLIjYs1f76xyaXHUpf/7ID+YZlCRpPPu488qWaToKdR/m2tlkV0Uw/Ey1sNb9gRsN9X8pCEQtYEIpYiDRtpGkjTdu+0jxkETuiYRCaaqS1rbCIYTsb3p2G+76UhSIWsCAUsRBp2kjTRpq2faWhiOHYvGgYDfd9KQtFLGBBKGIh0rSRpo00bftKQxHDsXnRMBru+1IWiljAglDEQqRpI00badr2lYYihmPzomE03PelLBSxgAWhiIVI00aaNtK07SsNRQzH5kXDaLjvS1koYgELQhELkaaNNG2kadtXGooYjs2LhtFw35eyUMQCFoQiFiJNG2naSNO2rzQUMRybFw2j4b4vZaGIBSwIRSxEmjbStJGmbV9pKGI4Ni8aRsN9X8pCEQtYEIpYiDRtpGkjTdu+0lDEcGxeNIyG+76UhSIWsCAUsRBp2kjTRpq2faWhiOHYvGgYDfd9KQtFLGBBKGIh0rSRpo00bftKQxHDsXnRMBru+1IWiljAglDEQqRpI00badr2lYYihmPzomE03PelLBSxgAWhiIVI00aaNtK07SsNRQzH5kXDaLjvS1koYgELQhELkaaNNG2kadtXGooYjs2LhtFw35eyUMQCFoQiFiJNG2naSNO2rzQUMRybFw2j4b4vZaGIBSwIRSxEmjbStJGmbV9pKGI4Ni8aRsN9X8pCEQtYEIpYiDRtpGkjTdu+0lDEcGxeNIyG+76UhSIWsCAUsRBp2kjTRpq2faWhiOHYvGgYDfd9KQtFLGBBKGIh0rSRpo00bftKQxHDsXnRMBru+1IWiljAglDEQqRpI00badr2lYYihmPzomE03PelLBSxgAWhiIVI00aaNtK07SsNRQzH5kXDaLjvS1koYgELQhELkaaNNG2kadtXGooYjs2LhtFw35eyUMQCFoQiFiJNG2naSNO2rzQUMRybFw2j4b4vZaGIBSwIRSxEmjbStJGmbV9pKGI4Ni8aRsN9X8pCEQtYEIpYiDRtpGkjTdu+0lDEcGxeNIyG+76UhSIWsCAUsRBp2kjTRpq2faWhiOHYvGgYDfd9/f/t3QuzI8d5JmhxaK5WY8q2pBVDMReT690wZxUMkTOMnaFCY2rGa9kyvfL+/3+z+DI/AFmFTBzgXJB1qp8nQmJdANTbierKtwvo05lFEeuIIIpYlzRj3qkxaca2lUYRY99K0Qg53c+VWRSxjgiiiHVJMybNmDRj20qjiLFvpWiEnO7nyiyKWEcEUcS6pBmTZkyasW2lUcTYt1I0Qk73c2UWRawjgihiXdKMSTMmzdi20ihi7FspGiGn+7kyiyLWEUEUsS5pxqQZk2ZsW2kUMfatFI2Q0/1cmUUR64ggiliXNGPSjEkztq00ihj7VopGyOl+rsyiiHVEEEWsS5oxacakGdtWGkWMfStFI+R0P1dmUcQ6Iogi1iXNmDRj0oxtK40ixr6VohFyup8rsyhiHRFEEeuSZkyaMWnGtpVGEWPfStEIOd3PlVkUsY4Iooh1STMmzZg0Y9tKo4ixb6VohJzu58osilhHBFHEuqQZk2ZMmrFtpVHE2LdSNEJO93NlFkWsI4IoYl3SjEkzJs3YttIoYuxbKRohp/u5Mosi1hFBFLEuacakGZNmbFtpFDH2rRSNkNP9XJlFEeuIIIpYlzRj0oxJM7atNIoY+1aKRsjpfq7Mooh1RBBFrEuaMWnGpBnbVhpFjH0rRSPkdD9XZlHEOiKIItYlzZg0Y9KMbSuNIsa+laIRcrqfK7MoYh0RRBHrkmZMmjFpxraVRhFj30rRCDndz5VZFLGOCKKIdUkzJs2YNGPbSqOIsW+laISc7ufKLIpYRwRRxLqkGZNmTJqxbaVRxNi3UjRCTvdzZRZFrCOCKGJd0oxJMybN2LbSKGLsWykaIaf7uTKLItYRQRSxLmnGpBmTZmxbaRQx9q0UjZDT/VyZRRHriCCKWJc0Y9KMSTO2rTSKGPtWikbI6X6uzKKIdUQQRaxLmjFpxqQZ21YaRYx9K0WDd6IUsbgozXa6TOf6XNKMSTMmzdi20gRFjP3KGZ53QRHrkmZMmjFpxraVJihi7FfO8LwLiliXNGPSjEkztq00QRFjv3KG511QxLqkGZNmTJqxbaUJihj7lTM878L//PuDv9iAf3fw7//DQa7PJc2Yd2pMmrFtpQn/VhFjt/7vnOJ5L/4urkez/dUvqr/M9bmkGZNmTJqxbaU5+rOctmBf/iand96L/yuvSVP9NC/TP831uaQZk2ZMmrFtpTn6Nzltwc78l5zfeR/+W16SJqt/YN7KH5elGZNmTJqxbaWp3BBjt/7mv+YUz/b93X/KS9J0P/2rX/zldv60LM2YNGPSjG0rzcGfuR8GAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAvHO/+d3vcmm6X/76t//j4Le/zvV5vv7++385+P7rz3PDbB/9uMi1KTLCyScf5Y65Pv6khPk4V2f5uIzJ0uxMP/poI4NzUJN8vI1zJuJ8kktLH5X38bEDdniXhscb5Xw7gyNu6FSCnfnN7/415Npsv44WVv02N81RSlj6fhtVLC6CB7k2xbqIzU1TfVRmh2ruHL/FItZkevR0vtIkmV/FaqXonb3NKf6gmLX49c+Ucc63Mj7idk4l2JUsYSG3zPXL7GDVb3+Zmx/v86xgR1toYsfrYK5OscEi1tSw2bWnmalO5iZavV8zC9DifZraxLJqhNzSWIzY28fMEhYuzpSrOd/EtSMuT6WN3AqHHcgOVuSmqZY97CC3P172r7P5Tex0Icz1KS6K2Ozr8SqQIraUIU7mvV2rsZl43mSCIjedrc6nt47Zjsr6TMnNRW56Y3mwIjedXPzOz+3Ay/wmO1iR26aq7evXcSOsfkQ569PJr0v5Kp9Ifp7LdcdEpz+s5voU5XI8t1ssHKeH+N7RR59s4sO3XD4oq1MHq87z5UtZdXHeAJ0On58kT2tibfe5/L1UtpbbPSXmW49XOVxanSnXc76Fq0esWzdxKsGu/HV2sCK3zVRuiJ0+jyzf2c/lRyvl63QTrDSx2bfEzn8izQ1TbKyI5aBsJVCZoXL5oEzluTxFHZ6sPIuVh2trzUMqztD5N9JBbjsp72Ab841PrXKMtDrU1Zxv4toRy77T55HTz2zYkd/867/+7jc/+lH5olhumima1/84fS+s1LJZf3fy6/YL+uULY7OLWFz6fjz9Aliux9spYpFmS19XObw/5zB17sqVKUqtOAUqa7Peuzj2aSxiZeotsfh7f93fSyVZLj+ibsTtwY8/6v+2upbzbVw5Ymw6v2UbuwzAHmypiDXNK26Jzf2bk6kUsa9zZZIyi35S/j+3TLGtK3CZMaZWnWseOIMOrE6XWJs0WsvzpuSafRb13p5ZMa/9tnr8aTQqYk3AeMhmf+PBu7SlItb8RcnNFLHyQeXcIlau1D+uf8UqN02xqSKWg5JrmxPh5k5W2yliJcnyhsrU8/igVzdmDdj7KGLN7zRFDF7bVorYL3+7+CgyetkWilj9URa5Mkm5Nn68niger84YM37iZUcOykaVMZqbbhlhZokuSc4TeaxNPY8PenVjta33kDex/SL20SeLfPEIRQxe01aK2FL5sv78n69//BuUuTbH8RbCNorY2dxrcYlwGJSYOD7ZzM9rP3r8BHopIhz7T33rJo1SOfiqiE1+w3rvT2xqzumH/XbbfhFbKo+Y1OlhpzZZxEoPaz+pnKD56fq5ZZJy4TtMXFsrYnObWAlw/rn6G5sYItLsmwZ1bGJg8geHzgq0bBrnWBOVELl8FJsUsSePWN+/yUUadmaLRaz2sHk/0TU0PWzu35ks1+mYIDZXxGZWjToqJUU1u/YslLdqejdshyfk5sdrj54n0TspYo/oG++riOVJlWvAq9hgEcseNveGWP1QMkz+xyZP173pRaxG2cbPdazHb22piT1+/uzKSTPNu4lR3qz6/hzLvCJ28q6KWJ5SbojBq9peEdtED/vR5xu5JXaeD8pS2TZLBDj+4K46o9blCU5F7DB1zv5x7Zcy2GTH0pPmjU8NEmfO6W1TxE7eUxHTw+BNbK6IbaOHFZ8f21iuT1Cu0nV6mF/EDo3nfAkucabNp8cZveaZ3ArX5g7N0aqHzW9iLUXs5B0VMT0M3sbGitgvN9TDQv3xFfN+jli59NUL3/withRxpt32yan9OH81w7QBj58+eyLEYYRiVLK21u0ztE1sCzW19w7FpnURy+U39W6K2PGvxuhh8Nq2VcTKP220oR42+weJtRfprRWxuCxPLmKnw1+bzB5vEW2WWr6Ok2aZQydmOjWxT+pf4VTETt5LETu+hXoYvLpNFbHsYZv4kfpHy38D/MHy2tdy36dYjUWsbaWIbaFp5NtzGqA6i+bKFKWAxc8Bnp7koHfuxramiD3s9H4nRSx72PQ/YMAebamI/XqDPWzuPzZZL35LuWu6x08SrdVQxNpWitjcgTmKEE2KbYQ6KEFmT+e90Vhti7WHxHwfRazeYNXD4E1sqIht7OthSREbiCzzrstlYtjmHbHIMn/CWqV42CdtT6g3Vmbf1u3VjeWNzGv16HW9iyJWNm/odjzsy2aK2Na+pn/ko8m+x81UXeXwp6KxnETn2kiWSLG9IlZizK+p3bqx2BjLjxmvd1DEfE0f3tZWitjx62HTe9jn3y/uf23hX/2uZk+lH59+hlgo08fMK/Pi+I+fsMY2kqXEOL0/9e3KlXnqhD7/dmH3PSq/wTJbecBj2vT2i1g9eRa//YHXtJEidvE1/V/O6mTlR4d9Xm+Bff51/UFi8358RWNyEavTVJ0wPpr8rxeGOjvUn85QZo/503u1kSx1fOoblm/XrIn08Pac36ZNDE6/4NTT6DBieQcoN7+xzRexYw/L1digk8HrKA1s6a9z1wS1hy3N+dJ+aV5L3+eueerEUMy6BNa5fGnq5bgZk2Ijc0MZp8G8+lDr8ZlXgPL4aWoPuxiU9rzJxnH01ifU6nDhNDZXc76J8RFzbWEDZRp24DdZvlq/y30TZPdayn2Pdf5HJo/m97BFCcptD3d5pZ5cfZaBJoc5Kalyea7VGzZt7lwWjqkdtfeniWZcFknf/ITK4yzkQZ/I+QauHDHXluou4EUUsZH6rbCT7zfwueQmitjpo6U0/65PMyzb+awkQm3kfsHiDZs4QE2MySfNUwWnaWJvP155oAVFDD4olx9N/m5jH01OKmLnr4b9y/fff72Jr4e1U9nUxvHxJzXJJ+W7WdN9lN8O29RXVj7ZUivMAZr8dtXvXH2S3zCcqfmNlFZv1nHAcvUtdT6aPB32yZyvbnzEXF2quwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHu+Hg29z+dL1vXd4tRcCANiN6w3p1frTnS/05c9yAQBgv643pDv709hdL/Szb39QxACA/bvekO7qT9fc80LfHh6riAEA+3e9IV3fe4c7XujLeKwiBgDs3/WGdH3vHe54IUUMAODgjv70ahQxAIADRQwAYBJFDABgEkUMAGASRQwA4A7RY76MhZ+VSvNtWS4uNhyULblc/OzL+Dle+ah2b3lyWfi2POLLXlnKfd+ud5aNzXIbsA0UG060MQDgnYkKc6g2P4v/VrXoXGwoYrUtYqUbVYfN+Z/znsPLlKZVrZtSs2txkOUL5c4mz/mxuV4pYgDAOxMV5stlJ4oKdLGhWq615Si21/+vahFbP6Kx3HXQFKlYPT44llcBF/tOFDEA4J2JClM/Xjxb957lLahTnVpXqfKkRRGrj/i2fgAZS7nz4KKHtU0q1hZFbJXnuPP4yof/ftu8NgDAu1B6TPzfl1/+LL/wlQXqsH7ccCo57UpWqW+jP50/gTzuPX1oWTfkA06N7vjk2PCz47NPTSxWji9UluP/2kA/nB5aC1+uAAC8J9FjDr7NLnO6UXXsQbX4HJtOLPf3HMvUuoidOtLi4XmY085cz7WLIhaOj62vc9yriAEA71f0mKbXHD+UXG043slqdtXu1FSgZUPKIrben7vrY5ud9dU6h7kImM/NFUUMAHi/ose0TSYLVK4dlA1tK8rlsv30UeNB7VLHR9bXafpTHqoeqVlMi14Xi+0hl48tG3JZEQMA3q/oMYs+td5QGlKniMXiD4sG1N7yyiKWK9W5upWlRUlbHrfd3W6vynGOR1bEAIB3K3rMZRFb34C6LGKlny360bJedbpWvekVS6VKLZ+8KHzN4mUeRQwA2InoMYsiUzbkchHrbSuqy6UArbrUee94f3np00LjXNMuD/nDomktupciBgC8W9FjLnrXrUVsVYDiVtWiiHX2x7bFp50nsfGH+oxYWhSxXK4UMQBgH6LHPKeILT4fTBdFLJePjqWpX8SaV2z3x7IiBgDsUfSYZxexsnQWpeiWIlb+u/rYUhEDAD440WMeWsQOBez43yVFDAD4wESPUcQAACaIHvPsIrYqQFGKrhWx43PKd8QUMQDgQxc95tlFbFWmYtuiiK0KUnnO4b9X/tbkeVERAwD2LnrMc4pYKUCrInbe2y9Isam89Gmh1WyMJUUMANi76DEXvetiw2UR69zVWmzqFLWyqeyPhR9W7em89/KQihgAsEfRY55TxEYF6fjIsrLcf/40c/HI1H7W2e6OZUUMANij6DHPKmKXZSo2nLbUItbeEis3zOor18VFfaqPz5VYVMQAgL2LHvOsIlYf2DStcktrVcR+aCpSe8urPrbZWavZ8DC5XF0WsbbvAQC8F9FjnlfEatU6rmW3Wu09F6af1f3HtbLyw6mJ5XquXRzyShErzzyFAAB4R6LHPK+IHW+Bxe2onx1710UR++HbqExZw35YN69vfxZbvlzvjZV7itgPP3z55ZenZwMAvA+lxuRy0d3QLWLHm2ALiyK2fkDTlbJAtc57Y+3GIlb3B0UMAHhnSofJ5aK7oV/EVk2s3PZaFLEvy/+fLKrSuol92+wt683ytSJ2OoQvigEA70zpMLlcdDcMitjpQ8Xws9LLlkWsrVvt88L548ywqFGx4dYidmqDihgA8M6UDpPLRXfDqIhFFStF6MsoRp0iduxb5ZtkF7KLffvlam/Z2CxfLWKnV8lVAIAP3KmIAQDwWIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMDL/O3f/m0uAQDwUD/88EMuAQDwUIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwCSKGADAA/1t41DEcqn483wIAABv4T8f2lff3+YjAAB4E3+eteuSIgYA8LZGt8T0MACANza6JaaIAQC8tf4tMT0MAODN9W+JKWIAAG+vd0tMDwMAeIDeLTFFDADgES5vielhAAAPcXlLTBEDAHiM9S0xPQwA4EHWt8QUMQCAR1neEtPDAAAeZnlLTBEDAHic9paYHgYA8EDtLTFFDADgkc63xPQwAICHOt8SU8QAAB7reEtMDwMAeLDjLTFFDADg0eotMT0MAODh6i0xRQwA4PHilpgeBgAwQdwSU8QAAGb4z3oYAMAcf66IAQBM8uf5XwAAAAAAAAAAAAAAAAAAAAAAAAAAgJ371Ve5MMNXv8oFAIAPz1f/+F0uPd6vvvtHRQwA+FD96h//cV4R++5wcEUMAPhQHarQtCL2VRxcEQMAPlSKGADAJIoYAMAkihgAwCSKGADAJIoYAMAkihgAwASlCB21beyr7+InfMXGxQ++L4+P/8beds9XZc935Sf0xw8mW7er8oTlU0oDPNHGAIAPTL+I/SpLWPruXJJqETvvbzdXhxe5KGKL1zv/a0q5oVLEAIAPTLeILWtYOLWkWsROD6ilqhSvs1+ti9j69Y67crVSxACAD8xX3+VHkIf/Hm98Xfawc00qRez8gLJt1cMO9Sz+71ysLl/veKDm2LO+pAYAMFPpQrl8kOWofpvrV8dbZmXX+Q5aPP5XX9Vn5ZZ4/OnhTRE7vl4sH/efdq46GwDAhyW60LmI1ftb5y9yZZHKspRFqtmdm1a967whe1iuHb8ulmuKGADwYYsudC5ipSidVw9iw7F61dbV7q7FrelS2cRyy0Wvq/uPGxQxAOCDFl3oXK1i7XTDqihlaVHELm6ItRvqKxzb1WWvq9UsVxQxAOCDFl3oXJW++uq7ZXGqzSk31SJWl6uLDfmYbFdluS4eteVLEQMAPmjRhZbVa+miiLUPLjsXN8SyfNV2dfH4g/YpihgA8EGLLvTsItZrUuXjyKaIrXpae5dMEQMAPmjRhcZFLP+to7aItcWqKV0nTbvq7VbEAABSdKFOEfvVV6d/b/JgVMTKzlw+WhexrrpfEQMAPmjRhVZFLP+J7sYzi1jZ3aWIAQBcFLHVP/ld3VPEypfKFDEAgCdFF2qKWKlRJ999dfllfXfEAABeR3ShcxE79bDvvvuqNq4XF7Gy1KeIAQAftOhC5yIWa4f1phxdLWK9vxbZtKve7pYiBgB80KILnYpYaUbLavR0EWs3HDTt66mipYgBAB+06EKnItYpVqUsjYpYu/MoNrVFbNXTDvuPn3oqYgDAhy260KlKxcqqGZVuNipi9Qm5XNVvmdXXaJeP2u6miAEAH7SmF/WKWO1SwyJ2eQstNpxeo21xqex2RwwAYHVPq6y0zaj2sHERu7jnVarXaUvd3T6j7s+Vy9cDAPiAlGZ0bFKlGTW9KnvYuIhlsTo9I3vYaUNdP98Tq+vHlygvv7xhBgDw4ciq9FX5An0Wr6xRzT91lBticXUH6/j8w+Kvao8Lp2aW61+VDbn/VL3ycIdjnx4PAPDhOJWnUo9O1eu74z/5Xf9THtotYsebZiflCadidbG76WGnmtY8HgDgA5JVKMvWqYmlX9X2VYtSr4itq1b9tyqbYrV+xfajyFMLXL8oAMCH4FSk6uqpG4XygeNxYVDEFlXrsLOs5Z5i0dTKZ5hnx6devigAwIcgvwt2vIv1Ve1ipx+7WtbK0qCIxSvES3xXvup1UcQOVSyPcHrJs/xemW/sAwC8XClWuQwAwAO5wQUA8Aj/+F39PPKs+UoZAABv5/L2V/nO17KbAQDw+sqX8NvaVf+KZK4AAPBm6t95PDex2sN8MgkA8PZK8cqfDvar+sNcfVUfAOARFj+tNfmGGADAI1w0MffDAAAepX4eeeR2GADAA9V/4Si+KqaGAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADANv3Nf/0978Tf/af/ZSt++le/+Muf5vJ80oxJMybN0LaG5uDP/k3OWLA//yXneN6F/5ZXpdn+6hfhL3NtNmnGpBmTZmhbQ1P9Wc5ZsDd/kxM878Q27on9tFymf/GLbfyRWZoxacakGdrW0By5J8ZO+VzynfnveU2aq/55eSt/YpZmTJoxaYa2NTRHbomxUzm98x78z78/+Ist+HcH//4/HOT6XNKMSTMmzdC2hib822hiOWvBzuQUz3vw9//Pwf+2Bf/Hwf/+fx7k+lzSjEkzJs3QtoYm/IUixn7lFM97oIj1STMmzZg0Q9samqCIsWM5xfMeKGJ90oxJMybN0LaGJihi7FhO8bwHilifNGPSjEkztK2hCYoYO5ZT/O9///9tQEb5/e//3y3ILNsYmjI2pYj9r1twuk7n+lzSjEkzJs3QtoZGEWPnStMIOd9PlVEUsY4Iooj1STMmzZg0Q9saGkWMnStNI+R8P1VGUcQ6Iogi1ifNmDRj0gxta2gUMXauNI2Q8/1UGUUR64ggilifNGPSjEkztK2hUcTYudI0Qs73U2UURawjgihifdKMSTMmzdC2hkYRY+dK0wg530+VURSxjgiiiPVJMybNmDRD2xoaRYydK00j5Hw/VUZRxDoiiCLWJ82YNGPSDG1raBQxdq40jZDz/VQZRRHriCCKWJ80Y9KMSTO0raFRxNi50jRCzvdTZRRFrCOCKGJ90oxJMybN0LaGRhFj50rTCDnfT5VRFLGOCKKI9UkzJs2YNEPbGhpFjJ0rTSPkfD9VRlHEOiKIItYnzZg0Y9IMbWtoFDF2rjSNkPP9VBlFEeuIIIpYnzRj0oxJM7StoVHE2LnSNELO91NlFEWsI4IoYn3SjEkzJs3QtoZGEWPnStMIOd9PlVEUsY4Iooj1STMmzZg0Q9saGkWMnStNI+R8P1VGUcQ6Iogi1ifNmDRj0gxta2gUMXauNI2Q8/1UGUUR64ggilifNGPSjEkztK2hUcTYudI0Qs73U2UURawjgihifdKMSTMmzdC2hkYRY+dK0wg530+VURSxjgiiiPVJMybNmDRD2xoaRYydK00j5Hw/VUZRxDoiiCLWJ82YNGPeqaFtDY0ixs6VphFyvp8qoyhiHRFEEeuTZkyaMWmGtjU0ihg7V5pGyPl+qoyiiHVEEEWsT5oxacakGdrW0Chi7FxpGiHn+6kyiiLWEUEUsT5pxqQZk2ZoW0OjiLFzpWmEnO+nyiiKWEcEUcT6pBmTZkyaoW0NjSLGzpWmEXK+nyqjKGIdEUQR65NmTJoxaYa2NTSKGDtXmkbI+X6qjKKIdUQQRaxPmjFpxqQZ2tbQKGLsXGkaIef7qTKKItYRQRSxPmnGpBmTZmhbQ6OIsXOlaYSc76fKKIpYRwRRxPqkGZNmTJqhbQ2NIsbOlaYRcr6fKqMoYh0RRBHrk2ZMmjFphrY1NIoYO1eaRsj5fqqMooh1RBBFrE+aMWnGpBna1tAoYuxcaRoh5/upMooi1hFBFLE+acakGZNmaFtDo4ixc6VphJzvp8ooilhHBFHE+qQZk2ZMmqFtDY0ixs6VphFyvp8qoyhiHRFEEeuTZkyaMWmGtjU0ihg7V5pGyPl+qoyiiHVEEEWsT5oxacakGdrW0Chi7FxpGiHn+6kyiiLWEUEUsT5pxqQZk2ZoW0OjiLFzpWmEnO+nyiiKWEcEUcT6pBmTZkyaoW0NjSLGzpWmEXK+nyqjKGIdEUQR65NmTJoxaYa2NTSKGDtXmkbI+X6qjKKIdUQQRaxPmjFpxqQZ2tbQKGLsXGkaIef7qTKKItYRQRSxPmnGpBmTZmhbQ6OIsXOlaYSc76fKKIpYRwRRxPqkGZNmTJqhbQ2NIsbOlaYRcr6fKqMoYh0RRBHrk2ZMmjFphrY1NIoYO1eaRsj5fqqMooh1RBBFrE+aMWnGpBna1tAoYuxcaRoh5/upMooi1hFBFLE+acakGZNmaFtDo4ixc6VphJzvp8ooilhHBFHE+qQZk2ZMmqFtDY0ixs6VphFyvp8qoyhiHRFEEeuTZkyaMWmGtjU0ihg7V5pGyPl+qoyiiHVEEEWsT5oxacakGdrW0Chi7FxpGiHn+6kyiiLWEUEUsT5pxrxTY9IMbWto3m8R+yKu29/kyoOVY/8kV95AvHwulmN9kcs8QwxmkfP9VBlFEeuIIIpYnzRj0oxJM7StodlVEfviDbvRj370k3MdeuMi9pP2V/bN6FCLRzEU71WR8/1UGUUR64ggilifNGPSjEkztK2h2VER+8k3b3mT6nC888HeuIgtboLFoXJxIXqYInaDGKci5/upMooi1hFBFLE+acakGZNmaFtDs58iNrxz9CqWt5/euIjFL6UtYt26FQ9SxG4Q41TkfD9VRlHEOiKIItYnzZg0Y9IMbWtodlPE3rgbPfRg0bFysRyqV7dKBEXsBmWgQs73U2UURawjgihifdKMSTMmzdC2hkYRu83DD5aL5VCd7+qXDyYVsVvUkTrI+X6qjKKIdUQQRaxPmjFpxqQZ2tbQKGK3WR7sbV18V79TxCLPgSL2tByqF7aNP/4hXuIPf/xTrj9TCRKyCt3nn/7pH3Kp8c//FK/3D/+Uq/coSUKmu92f/hhP+8Mf/pjrVRmjkz/k1lvFc+4uYj//j2s/zz0nP/+sbP40V29033V6eIwrB//05z+/yDryOmk+LaP12eqo5cEnn+XWa56T5rOLOJ+WHes01c9/fkuO6nXSFKsBipWl3HHFK6UpST777DLllUHruD1NCXJyMfr9k7XG749lx61prv+mPo5Zrp5dfWsv3D40Ic+Myzfk55/FUXtvYGy/+Y1SxG6zPNjbil/K6Vhx4FxslC+IHShiT8uhelERaxrGsnrcK1/kOUXsn/6h97x/Lq9WdEraE/KJdw/Nn/JpB39om+myiN07VPGU1y9i5eJc3DpdVPdcp8/HWB1kuCMv7Ldepl8lzae58WAxzzaPDzdkemaa5Suf46zfmDqh5srTXiVNOCfK8cm1Rt1+zSuPzaoRnZ9x29l8e5omS1jkGZyszVNeN82139TNWbw86NW3tuP2oTk4v/joDVltH5/dfYrYbZYHe1vr7+pfFrHyiw2K2NNyqF5QxJrecXDvjZ6FfI17i9g//0NpYZfPa3rYM5pYPu/eoVn2raaJba+ItbtXl8on3HGdXsxh7XV3tKM2jYObZozwCmmaGWw5Fosn3JTp9jSLgy6O2u5pU5YbDCE3PO010hx82o5DTZQrjbL5qtcem+EJddPpfHua4VkwPFkXz2hTDt2a5spv6sXQNENw9a3tun1orvxSmx2LY7ZxbhoaRew2y4O9rShix19J/LIujlu+IObHV9woxqnI+f5+q37xkiaWL3FnETu2sIPckhY97P4mlk+7c2iWvbRtYsuBuvfeYTznlYvY4gJ608x1cvt1enGQ9ro72NFcpG8oPdXL0yxnqnYwls+4JdKz05yPuoxzStk8Prc87RXSHPQC5UqjbL7qNdLklqrZvjzZzyfa2HPT9IrP6sxYxX/NNOPf1Kuz+HzQa29t3+1Ds3rx5rUXO5rtg7P7CkXsNsuDva04Vi6WX9bFV8RqmPr/PCXGqcj5/m61eESt+NOfStV4wffEykuFrEK3GRaxsumf/vmwVD+4jKU7lKeHTHebMgblI8n8pljdfBB7nj848VrPKmLnGWJ12avXyfKlkU//Y+f7NtfcfJ0us0b5Lkh+j6RuHu8Yz21jL08TK/WrLPUWx3k0YvWNxqb+UkuaesPpdJyyFnGWKes7VuSWp71CmuO+Q6LDts+O70tsqUsHEbSZa0deIU0dkhibOkq5+fgeHp5QHnHLCN2cZnQWDE/WGrLEX76FV9yaJl5w+as+JjsNyGoIrr21A7cPTXnxcvlYvSF1x2F1fcz6sMP6zUOzjyIWiydtG/tJedTvv1l1mbI1/ls+BWye8MU3+e2rb5qt9TVSOWLZsmx9+cz2eUVsLEevUb5ZR7n05Hf1y4GOdYynxDgVOd/fLYrYqWxE13jB18RKkJBV6Dbl6/i/v/yOWLkhdixfsfvOW2Lx9CLT3aatW6Wk5vL8IlYujLl8UK+CT1+Su26+TsdF93ipLQGOBxzuiOXDrsP/Tsmf8uI0w2R17b5BujlNHPV0oPYX3MQpi8ftdXqNR75REeumOYgjXrwdsS0XL54w8gpp4rC5ZzESZXA6g3bFzWnKkXpnQRyoviPLw8WWY/zm3bzq1jTD39TNCCwPOh7ModuHJg50fPH2l92uxOLpjWqilcUb3qhdF7G2QX3TFqSyI34Uf6pbz+vF6RntywyK2OKZi+KUG/LHTRSDKrY4zIV80EH9YFIRu1WMU5Hz/f3+2Nz0WbSy+9UkB1mFbnMoXPG3Ii+eF93r/Lcl737ZZxaxP/1h9WnkaW1CETtcAU/XxdUVuFwDzzvvdOt1uhwllxdTyHBHxDz8aXkx2zzlxWmWQ7N4VOy6YRZt3JomEpzfgMh2XGt/8bF83H54TNxhOGw553vKy9PUMbgchMNjTtuWIzv28jSLI8XycaDa9zAG7YY4t6cZnQWH7b2TdTkcF7v7bk4z+k3dnirLg47f2pHbh+bwcuehiZVcvFg+PqhNtog8tuMi1naf0JSWWsRO7ak2o2UNC/lKTxax9ZHajharX6xeu1+fbixi5WCROP6riD0txqnI+f5l4sO4hxexdPG8KGLnTyPvf9l4RpHpniGePbWINcrkcJ5M4gp+wyVw4Nbr9PKq31yChzvS5ZYrXpxmOc8eVs6z6GgKHrs1zVI7JUWA7txWXW654uVp1mdOz61n08vTLE6M9aDl4moEh25Pc/0sWGQKT53dXc8Zm8VbczE03YO2YzZ2e5hIkIuLlcVx2jTxmCtnd88+itg3+Yni4b/fHLvJRTtqWkt9btkYyrbLHnYsVF+0L182LYvY5ZGaJhZr5RPQVrc/3VjE4rXK82Nr94VYiHEqcr5/megaD/9oMl0875//obkhVj6nzOUbxTOKTHe/y48m/1h+4trqB4zdJF7qRUUsroXNNe+2K+DIzdfpw5/FzxNYTGfHy/FwRzWcR3penObT0xefDsrklsv5qPLtl/KVnxvcnGahTRMHq4sHl2Nz2HD7W/fiNDe9FxHpluF5eZpFmPatahZ7g9Zze5ryesOz4HKAnji7u54zNovf1IsYwzfttYemTdCuxHFOY9C8UU+d3T37KGIHy250akf1K1vZhE6PPnae2PCTL8rmfER9fH61rKk/sTI4WB6pfPnrJ8ePN09JYqVu++InPzkcqyy2QU+Ox+zLBzWHjoVzJEZinIqc71+i/ljXXHmOEiRkFbrL9eddfoXsSfGMItPdq35Z/9y5yugc3X3fMJ70oiIWl8LzJa9ep/Prtec541bPmTTq1bh31e3sGM4jPa+bpoxJLufa0dN3Eg6ek6a+EceVw+L6nWo1D33ai9Pk8fJL5/33pOzL5ateniYOtbjRctwTi6cTOVaePn1uT3P9LLh8i1rDE23lOWOzeOlyoByC0UFXb+3QfUNzHJH2xZfHOa8tB+v60B3ttYhlOzptqC3n+O2s7DzNl7Xq45sNtTEt+1QuLw+2PlKu59qpBhz3Z+HLtY54/ml3PLoJla9eXyuWFLGnxTgVOd8/U/27k+EFn0y+YRErPezOH68fTyky3T3OpWv5hbGze8cpnvOSItZepQ/iklkvyuHuJvacSaMk6M0BvR23XaDTq6Ypg3I+9GmMilua2J1papBwfPGnpqp4bC4+7cVpyqbPIkXRHYEYpFuG5hXStL/6ujtP3nacypv2dKDb01w/C66erDVkrlxz59iE8trL39T1j1Xl7VrF7A3m0O1hysuWgr7825GxnIsH57XlYF0dupO9FrHad3Il1H6UK7WItS2mPH5Ra8pDTiVoubs92KqxHY90fGZZaV/5ItlKvPbisLlYxYZ8sWaRK2Kcipzvnylf5GU97O2KWOlh975qeU7IdHco98KK5kthyyJ270jFU15SxFZXvOXMcsuleeG5k0bvotvdcdsFOr1mmjowuXJw/0DdmSZfuHnpp6aqeHAuPu3FaZoJvOq09tici9e9PE15Q+pqBsvBKWs1W75nZfma29NcPwuunazDE+3CnWMTVgdevVOrNyq3XqTvuiPMcmxOLx4ruXhwXnvq7O7ZaRGrZahpR1mAsuHUItbeaiobcrkqj7mhiMXi8kiLg5flxf6yIZc7Imd72EXXKr+IXI5FRexpMU5FzvfPlC/yom+IvV0Rqz3szh8j9ipFrP03jkoRK/8cZ/0M976hime8pIjFlbC5MsdqqzO7XnP/pFEnic4c0N9x2wU6vWKaOqs0o1E21LsMdbFuvuLONPGixemVn5qq4sG5+LQXpykBWpejFo/ojGXHy9PUOHG0Y67jvrJyeKPqjZmD3D52e5rrZ8HlW3QyPNEu3Tk2IV67/Z27aGLtjpCbh1EX7glzHO9w/pXGWi4enNeWg3Vl6Bo7LWLLFlXFpnx82b0oQ1988c2q1ZQ6ddq0WGkPtopRxKbj0dvlqpSpRXNbiN25WF68fe2S6fjUWF4dmI4YpyLn+2fa8keT+QP27+1hLyli3Y8mDxtP/yp67L9vpOK1XlDEyiU6l0OsHsRHCvXqndtvdPekUS/WnQlpsOO2C3R6tTQ5kS2msM/O36GLpz09p96Z5jx5Hl/6qakqHpuLT3txmhIgxDCUgbt4X2LrbW/Wy9Mc37yz45HPD065feyONFfPgvHJWrM+fc6EO8fmoPyKc7lYDM36qN3BHLkjzHLcT793YiUXD85rT53dPTstYrGyLjvN3aRee1q7sYiVV11VvvapsbhM8kQRi725WB7avHZ53dN6rDzxa+AgxqnI+f75/lS/nL6dvzWZag/7h7t72IuK2MGf6n2vwbPvft14wguK2PqCFxfG4xW5XEmfvhq27p006gzROcZox20X6PRaaeqUcuWH3MbuXBy7N83huPmT0DPRU1NVPDQXn/biNCXAQR2VMnRlqdHb1vfyNAeLutFM/ctGcEPduD9NES+ei9XlW5SGp33P/WnWx10NzOUAdAZz4PYwq1E/vR2xnIsH57Vl6OHQLey5iOXyUXlErTGvWMRiaX2k8tTcWBYXtWuZdC32ng7U3h07aKrkQaw88WvgIMapyPn+Req/cZQrz1CChKxCdxk8L3/ufq7dozwxZLr7rf7aZCtGqrlZ9rR4qRcUsbgQtgWjXLOPV+q4Gt7wp+TGfZNGflh0WXCGO268QKdXSlMnqWsDEc/shF26L02qoery8u24HInzI2/w4jQ50x5/2e1yuuP0eXmaUN/Ag/I54TnNqROUv1vwdKRnpemcBZdvUTE+u7vuT7N69frLjyDZt3pHvhjMvtvDlNcrf3ipn9oehz2WzwFirS4t35rB0K3ss4gtS1Qqj2iK2Oo21tLxXyzK1WER6x6pbV9lsSwdLZJeiL2Lm165eLB6YqxdHJkLMU5FzvcvFK90V79YKEFCVqG79J9Xvx5259+XrMozQ6Z7hmhi/U8gH1zEyjU6l4typc7ly71PumvSqBNE50bTcMetF+j0OmnqtHn1oPGQXtqFu9KcxcHra0fC81R1ecx4ZC4+7cVp6vJpWCLOaow6m0ZeIU349LNyzE8vTtzSBT77+Y2RnpkmXnzxjvRP1itnd9fdada/+vJrz+UrIxDPejLUzWFKiOPLtYkWoxQ7MlqzeHAxll07LmJd9Qnlwf0i9pMvTv/e5MHp9RcrzcH6Ray8wPOKWDy1/WWcX7scqgkdqxdH5kKMU5Hz/QtF83j+Z5M1yUFWobv0nvfcr4cV9bkHme45hk9/cBG7mCoWRey+qT3cM2nEtba99p4Mdxy8WREbHbTMIU9NCjdNG/ekaTS/4ghSlw5iZXnMxe6nvDxNOd7pvei8MbE/F5/yCmla8X70treF4Ipnprk4C7rxrp3dXXenWR92MRrjIRgN5sLNYZbHaSIsDhMrx6FYPCFWPtQiVla66hPK/ssidvET8M+vv1hpDtZ/pRcUsfbh8cjza8eetnit1+mLcSpyvn+hbRWxZ389rChPDpnuOYZPjx0PLGIX17ty/TxtOSzfMWMc3DFp1Ampc+Uf7gg3zRZHr5Cm9rCnbl/EY56cNu5I02p+xRHy+H7E5tWEernlipenWcQpO5ZDEFtuPXteIU2jvJmd96O8mTdEemaai6P24l09u7vuThMHaHPEei4eLNcag8FcujnM8tWatcUVJpaPj3ri7O5RxI5W/+R3dXr9xUpzsN4rPbOIjUOH9jWrWFfEnhbjVOR8/zLlL09upojVjyWf8/Wwojw7ZLpnaL4zdxiaZmDu/zJdPOHZRWz5x9Yithwvh4uZ9iY3X6drwenMlsMd1U2zxdHL09Rp83IMDk9oUpRH5fLYzWkWSrQ8VpmeasiyeZU3NuXi016epo3TyxOjcutb9QppTsrWzntZ36Zbzudb0xyOdPUsuDxZa7ZeuLF7x6YcI5eLxREjU3cM+oO5dnOY5XHitY8RYjn3xJCdHvTE2d2jiKXlp5nffLH60HGx0hys80oHb1HE8r89nRfjJAfp+W3jUCn+lLd2rv41wVuUp4esQndZP++fVz897B/u/YSyPD1kupsc+tbxZ4ct/xZpjE3+G5N/qn+p4b7CGs94dhG7nCnqNbDcAbr152O1br1Ol4vu+Rr86WlxuCN1Eo+9NM36W9WfnRbLDFJzfFof9XSoW9PEi3+aB6rvwWlaPR2pJq4bTzqbxl4hzTlOfgm8bj263DL2Cml+Xt61TNKcNoe3J56Qj75ler85TbzktbPg4mQdnWhX3Tw2qX/UelO3pjwe9bAyemtHbg9TXq/kqF/ZO4386QqzHrLTah2muvGqHRexi3p0drn/1MO++eaLuuO2Irb+4laliG1IDtKz61MtG63n3xB7ZhHLL4Kd1cJV/7rkQtl+s3zSfUOTT2nkjlpSW7njRvGM5xax7h886xX56JaZonHrdTpfvZHX41xrHC/Uy1w3JXtpmjohLJTtl2FumTZuTXN51NNcVWe0o9M7t9x8sH5PO14hzfq4y6PGEN189rw8zTJLc+DlW3XDyNyeZnwWDE7WXGucB3Po1jTp8jf1xemR26+9tSO3h1kPwfm1F3uad2qZ85Z3ap9FbFmiLpUHL+pTbDg8pak0dxSxiyPFxmxfzWJaJG2U7UPnPnFJEbsmB+nZRez8k1zT43+gaz6nUTbPKWIXfev4RbCLgbrrG2IvK2LlSpzLJ/dfDhu3Xqfz1Rt5nc61Ru54y1kjX7BRX/vymMfRupjbbhinW9NcvPZgrjr/+nNDI3dc8RpphlNqiE1Pv0Xp5WkWe9p3o824ijjy7DTH445O1lxp3DBCt6ZJnd/Uq5zHlFff2oE7wiyb2Oi0yU1FG+imU2efRWzVmy6UB7dFrJagRaG5rYh1itZBs7FZTMukK+2j44HnkLFnQBG7Jgfp2UVsdUus/Sd97pcv8mZF7L7PJvNJ9w1N/dTxpLk9uNxz933DeNJLitjlBa9+YlDc/nfs063X6Xz9Rl6oc62ROx5bxOpBO0XsNIktppmbZo1b06wOu3wPznNVszm3NHLHFa+Spt25HoPYlotPe4U05zdkmeQ8YjefzTenGZ0FlydO3ZcrjdftPiGOvX4v2pzNKFx9a/vuCdO+/DLRKc7ql989u6/YcRFb16Mv4mPH+ojy4LaIlc8Sl58wLl9/sdIeLJbWTah9atlfF9My6cph1+kwEeqc6Yu1eJnfl6XRixHKOIWc75/h+CPk//CHP77gY8lQXiZkFbrN7R9NPqCIHfzxDzkefzz9i0bpuOdixw3iec/+jthn/YmgftHms/z6yz1uvU6Xi+1CBsm1xjHhYs47eMUpLF+xUV/8cj5tJ4iflx9adZhjTv/IzXW3pjn4NL+yc3gLLt6DumtxzItbGzcEeqU0GeezyzE4hLptXMJrpOkMTJFn8x0n8x1pBmdB3Xb25Nl9xT1pQvc3dY7aehSuvrU994XJwbnjPRm9iX07LWKlWJ13FrEpH1Ee3PauZt/R8iUWK+3BVjGKttbF4u1FLPYtjpmLPbF7dWA6YpyKnO+nyij3fob4RjLLNobmZUXs1d07abwtacakGZNmaFtDs9MiVj5XXLad8oAsNmX5ehGrr7AoRd2DPXGk+sq5XC2TLsW+U66LZy7FbkXsaTFORc73U2UURawjgihifdKMSTMmzdC2hmZfRezcrcpNqbbG1MKUD1g9OCfrthzVhy+L2Pnl2i5Vj9Q8d1nhykouV+2T1+LFjrvicdeKVryMIva0GKci5/upMooi1hFBFLE+acakGZNmaFtDs5siVurParXpO7meaxdFrGxo2lE+/PyCpW6d9rdd6vqR7ixi7YPjcW3GtXisIva0GKci5/upMooi1hFBFLE+acakGZNmaFtDs68idmgux6+u12oVP5k1dtbbVqf6U3a2JWfZppp/6qhuON73+ubw8rG26FL53G9+EhuOTz3uvL+ILbqfIvZyMU5FzvdTZRRFrCOCKGJ90oxJMybN0LaGZjdF7DzfZsc5l6mTU/u5KGLnR39z/Ce/639yd31GKEdcdqlsYq1zzyqruVxdKWKxa1HEcrErXkYRe1qMU5Hz/VQZRRHriCCKWJ80Y9KMSTO0raHZTxE7VaVjwbroR+fyUx67vNu07m0/WRWmsjHEynLXxZHanwtbNuRytXpyK3adYl08cSX2K2JPi3Eqcr6fKqMoYh0RRBHrk2ZMmjFphrY1NPspYqcqdS5Yi3LV/rSt8txlEavbjmJfqVenB53KVqysu9RPLp58UrbkcrV+ciMCH/fEAa/2rHgZRexpMU5FzvdTZRRFrCOCKGJ90oxJMybN0LaGZkdF7FiH2m1f5AeNqx96Wh64KmKHrfn8/Mcm1x0qv/4VL9TpUseDH598VLbmctV58lH72HjYRcRWPFgRe1qMU5Hz/VQZRRHriCCKWJ80Y9KMSTO0rTfq/RaxPTrMRKdqFbXvahHjNjG/FznfT5VRFLGOCKKI9UkzJs2YNEPbGhpFjJ0rTSPkfD9VRlHEOiKIItYnzZg0Y9IMbWtoFDF2rjSNkPP9VBlFEeuIIIpYnzRj0oxJM7StoVHE2LnSNELO91NlFEWsI4IoYn3SjEkzJs3QtoZGEWPnStMIOd9PlVEUsY4Iooj1STMmzZg0Q9saGkWMnStNI+R8P1VGUcQ6Iogi1ifNmDRj0gxta2gUMXauNI2Q8/1UGUUR64ggilifNGPSjEkztK2hUcTYudI0Qs73U2UURawjgihifdKMSTMmzdC2hkYRY+dK0wg530+VURSxjgiiiPVJMybNmDRD2xoaRYydK00j5Hw/VUZRxDoiiCLWJ82YNGPSDG1raBQxdq40jZDz/VQZRRHriCCKWJ80Y9KMSTO0raFRxNi50jRCzvdTZRRFrCOCKGJ90oxJMybN0LaGRhFj50rTCDnfT5VRFLGOCKKI9UkzJs2YNEPbGhpFjJ0rTSPkfD9VRlHEOiKIItYnzZg0Y9IMbWtoFDF2rjSNkPP9VBlFEeuIIIpYnzRj0oxJM7StoVHE2LnSNELO91NlFEWsI4IoYn3SjEkzJs3QtoZGEWPnStMIOd9PlVEUsY4Iooj1STMmzZg0Q9saGkWMnStNI+R8P1VGUcQ6Iogi1ifNmDRj0gxta2gUMXauNI2Q8/1UGUUR64ggilifNGPSjEkztK2hUcTYudI0Qs73U2UURawjgihifdKMSTMmzdC2hkYRY+dK0wg530+VURSxjgiiiPVJMybNmDRD2xoaRYydK00j5Hw/VUZRxDoiiCLWJ82YNGPSDG1raBQxdq40jZDz/VQZRRHriCCKWJ80Y9KMSTO0raFRxNi50jRCzvdTZRRFrCOCKGJ90oxJMybN0LaGRhFj50rTCDnfT5VRFLGOCKKI9UkzJs2YNEPbGhpFjJ0rTSPkfD9VRlHEOiKIItYnzZg0Y9IMbWtoFDF2rjSNkPP9VBlFEeuIIIpYnzRj0ox5p4a2NTSKGDtXmkbI+X6qjKKIdUQQRaxPmjFpxqQZ2tbQKGLsXGkaIef7qTKKItYRQRSxPmnGpBmTZmhbQ6OIsXOlaYSc76fKKIpYRwRRxPqkGZNmTJqhbQ2NIsbOlaYRcr6fKqMoYh0RRBHrk2ZMmjFphrY1NIoYO1eaRsj5fqqMooh1RBBFrE+aMWnGpBna1tAoYuxcaRoh5/upMooi1hFBFLE+acakGZNmaFtDo4ixc6VphJzvp8ooilhHBFHE+qQZk2ZMmqFtDY0ixs6VphFyvp8qoyhiHRFEEeuTZkyaMWmGtjU0ihg7V5pGyPl+qoyiiHVEEEWsT5oxacakGdrW0Chi7FxpGiHn+6kyiiLWEUEUsT5pxqQZk2ZoW0OjiLFzpWmEnO+nyiiKWEcEUcT6pBmTZkyaoW0NjSLGzpWmEXK+nyqjKGIdEUQR65NmTJoxaYa2NTSKGDtXmkbI+X6qjKKIdUQQRaxPmjFpxqQZ2tbQKGLsXGkaIef7qTKKItYRQRSxPmnGpBmTZmhbQ6OIsXOlaYSc76fKKIpYRwRRxPqkGZNmTJqhbQ2NIsbOlaYRcr6fKqMoYh0RRBHrk2ZMmjFphrY1NIoYO1eaRsj5fqqMooh1RBBFrE+aMWnGpBna1tAoYuxcaRoh5/upMooi1hFBFLE+acakGZNmaFtDo4ixc6VphJzvp8ooilhHBFHE+qQZk2ZMmqFtDY0ixs6VphFyvp8qoyhiHRFEEeuTZkyaMWmGtjU0ihg7V5oG70MpYnFRmu50nc71uaQZk2ZMmqFtDU1QxNixnOJ5DxSxPmnGpBmTZmhbQxMUMXYsp3jeA0WsT5oxacakGdrW0ARFjB3LKZ73QBHrk2ZMmjFphrY1NEERY8dyiuc9+J9/f/AXW/DvDv79fzjI9bmkGZNmTJqhbQ1N+LeKGPv1X3OO553473E9mu6vflH9Za7PJc2YNGPSDG1raI7+LGct2Jm/yfmdd+I/5TVprp/mdfqnuT6XNGPSjEkztK2hOfo3OWvB3vyXnOB5F/5bXpJmq39i3sqfl6UZk2ZMmqFtDU3lhhj79Tc+nXw3/m4b98PCT//qF3+5nT8uSzMmzZg0Q9samoM/cz8MAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAZ/nN736XS9P9+re//R8Hv/5lrs/z9fff/8vB919/nhtm++TH4eNcm+KjEuHsk49yx1wfl6H5ZOrQHHxcxmRpdqbD2JTB+Xj+O/VRGZ/p79LJYWRyaWlGzo8+Hh9vlPPNjMJs51SCvfnN7/415NpkvywtrJpbxUoJS99voorlND91HlsXsR//OHdM9FEtqMXcKWKDRWw7g9MkeXCv6KnVvXf2Nqf4gwasFr/+mTLO+UbGYbZzKsG+ZAkLuWWupoYd/Dq3TvB5VrCjLTSxvAYqYkvN9DC79myviC0GZ2oBWpw5c5tYlpuQWxqLnG/fN7L3hIsz5WrOt3AtzOpUmntaw55kByty01TLHjaziWX/OpvfxI4XwqmXwIsiNvuPxqtAc+eHzRWxdaCJBSgTpJlNLCMUuelsdT699endvkHrMyU3F7npbV0Ls+phs3+nwX78JjtYkdtm+nVpX7+N+pUfUc76dPLrUr7KJ5Kf53LdMc9phph6BSwpNnQNPo5KfG3lo09mf+ZVZrJcPiirUwcrAtQvO+XnStN6c53k4/B1ad471daNy4JTtpZvPpYBe+uY5XBpdaZcz/kW8ljF+rTNd20LpxLsy19nByty20z1W/q58st25dFK+TrdBCtNbPYtsdOfSKfO7BsrYtnDthKozFa5fFDeslyeYtElHlIsRuobVefudnmCY3cvcttJeQdzkMqAvfGpdfpdfbA61NWcb+JKmOVglGhb+U0H791v/vVff/ebH/2ofFEsN01Uboj9NleyluXyw33dfkG/fGFschGrM8T0C+DGLsGRZit/czMc3qBzmDJW8+78HCwDxNqsXlhO3+PQtG1ngsPh485O+c2Um05i22lj9xGvK+4uffxR/7fVtZxv4kqY1Vv2uEzwwdhSEcvlg3JLbP4PsTgoRezrXJkkrnw/nl+DtlXEynwwtepcM3+2igDnN2tRhh5sORaxMn0e7709y9O7DNhDTvZrv60efhoNi1guH5SHbOfPP7AHGypi5xtiGypi5YPKuUUsK0f8/0NmhpFNFbESZrvzQYSb2xIjwfnNmlnE4tCrGyqzz6JewVn1jVh7yDv4LopYMxKKGLy6jRSxQxP7bVO8yg2yLRSx+qMscmWOWjnWM+sE9SI94ydedmxiPh8qYzQ3XSQ4z+EPn9EbcejzRL6BoekPx2rbw0Zs80Xs8Ja13wAob6AiBq9pK0VsodwQm/YdscYW/tZkuRgfro35n3lqIzyZf8PnMEWVH7f0yeZ+3PfDJ9BLtS/nSskz6w0rb1Eur3LN0nt/VrlKzlx+U9svYgv1KpArwKvYYhGrPaz5pHKG5qfr55Y5yoUvJoj475aK2NzZtATIv0x/MHVgLkWkyWWjvlvlVkb+cNBZbXXRJvIkyrVZFLGBa2GKPK1yDXgVGyxitYfN/Nn6B00Pm/t3Jsu1OObQ+O+mitjMy3HJUvtFtampoUzis7vh+u2almcxGvme5dostxaxR3TXa91nc0UsT6rZ5zbszPaKWPawyTfE6oeSYe4/NnmexeZfACPBIUO5yVIW57WfevzWlprYw+fPrmUTmzg+5fi10xy7c1mZRxEbeKKI5Sm1pd9rsAebK2Lb6GE/+nwjt8TO171Yml/Ejl/brZfkujzBqYgdxmZ7P+07g012LD3VxDw1yOHcPX+UnHtm6RWc2NQMkiJ2SQ+Dt7G1IraRHlZ8fmxjuT5BuRLX6SCW5haxwzx6npnKRDUtz7GI1TyTW+Ha3KE5OpWeNL2JtXLHLIrYwNUipofBG9lYEav/5OQ2elioP75i2s8RK5e+vPDF4vTZvdFEe7ycE47j0fTVDXj49NlTq2r7DwROPHlqgOKTLYxOL0NsWhexXH5T76aI5R9+9DB4ddsqYvWf+95OD5v9g8TaC3EsTpxLL0S2yUXsdPhr88fjLaLNEiFOKepwTWyq5UQOH9dguXkWRWzgSph8C6ef2LBDmypi2cM28SP1j5b/Bvhj5Z9BW9u5Dj58klgog3FuFrG2lSJW3rXZYcqMej5Zyps1NVP5GRoff7SN0emdu7Gt+d31sNP7nRSxEmVDt51hTzZUxH65xR429R+b7BSx6XPYycMniYUyFrl8EGtbGZm5A5NWfefabP9QJcj06bz3Fq22xdpD/tTzLorY8a9Z6GHwFrZTxLb0Nf2GIjYQYebdnytDs807YpFl+o3L1fhsZoDKhD59dHoFZ1ldH9dc30MRK5s38L7BTm2miG3ta/pHPprse9xM1VUOfxqM5SQ61zaybLOIbeUnjXQLzmJjLD+mAr2DIpbXIT0M3shWilh+LDn3x+mHz79f3P/awL/6neJSOHMqXf7Tv/XPyBNn1MXxHz5hXbGNLMuiWkNNL2L1pJnfw/rvUakbOWQPHK/tF7GSYgOnD+zWRorYxdfDfjurk5UfHfZ5vQX2+df1B4lN+/EVrcnXwjpN1QAffVT/jDzzj8h1Uq8/5r/MFFv58/pGstTZs/5z6HMH6PBOxaHznNlCDxsUnDpKh1M8b9zl5je2+SJWB6N52z7RyeCVlAa29Ne56/Hyc8mlOV/aL81r6fvcNU0tHcW0Kf44jbamTqk5P5xsYH4PZZw2MFE150yaNUCrM2dTJ83BOc9qyN466OU7dP7dfTXnWxiH6f3Gn/smwm78JstX63e57/G6RWzOLbHzPzJ5NL2H1Y/h0rQ5/nJumHw1XgbaytRQUuXyVOupddoALd6n9uPtx+uViuaPNoshe/OgeZyFPOgTOd9AHmShhukWMbfE4DUoYiP1W2En32/gc8m8+hXzLoH1A66T+dfiZoqYO7+3ItS025YLp88Cw8RITb2Z/TY9VXCaqG+fNA+0oIjBB+Xyo8nfbeyjyWlf3D9+Nexfvv/+6018PaydynLTHB/Xf6Dmx5/U7x7N9lF++Wk7Nezgk+20wo8+LrPoJ/nVvmnqWbOFk2b5Z4mwerPyjHrEgK1vWR6cDvtkztc2DqOIAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAzuhAAAAAzSURBVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADA1v3oR/8/RckqCqjkch4AAAAASUVORK5CYII=\" data-image-state=\"image-loaded\" width=\"610\" height=\"551\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.5px; 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 21.25px; text-align: left; transform-origin: 384px 21.25px; 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: 84.5px 8px; transform-origin: 84.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that takes \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: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003eX\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: 16px 8px; transform-origin: 16px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and \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: 24px 8px; transform-origin: 24px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 24px 8.5px; transform-origin: 24px 8.5px; \"\u003etarget\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: 255.5px 8px; transform-origin: 255.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e as inputs, performs a binary search and outputs the index of target value as well as the number of iterations it took to find the target.\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\u003c/div\u003e\u003c/div\u003e","function_template":"function [loc,iter] = binarySearch(X,target)\r\n    found = false;\r\nend","test_suite":"%%\r\nsortedData = sort(randi(1000,[1 200]),'ascend');\r\ntarget = sortedData(randi(length(sortedData),1));\r\n[mid,iter] = binarySearch(sortedData,target);\r\nassert(isequal(target,sortedData(mid)))\r\nassert(iter \u003e 0)\r\n%%\r\nsortedData = sort(randi(1000,[1 200]),'ascend');\r\ntarget = sortedData(randi(length(sortedData),1));\r\n[mid,iter] = binarySearch(sortedData,target);\r\nassert(isequal(target,sortedData(mid)))\r\nassert(iter \u003e 0)\r\n%%\r\nsortedData = sort(randi(1000,[1 200]),'ascend');\r\ntarget = sortedData(randi(length(sortedData),1));\r\n[mid,iter] = binarySearch(sortedData,target);\r\nassert(isequal(target,sortedData(mid)))\r\nassert(iter \u003e 0)\r\n%%\r\nsortedData = sort(randi(1000,[1 200]),'ascend');\r\ntarget = sortedData(randi(length(sortedData),1)); \r\n[mid,iter] = binarySearch(sortedData,target);\r\nassert(isequal(target,sortedData(mid)))\r\nassert(iter \u003e 0)\r\n%%\r\nsortedData = [3 12 35 76 221 225 301 367 399 512 783 800];\r\ntarget = 12;\r\n[mid,iter] = binarySearch(sortedData,target);\r\nassert(isequal(target,sortedData(mid)))\r\nassert(isequal(iter,4))\r\n%%\r\nsortedData = 0:160;\r\ntarget = 5;\r\n[mid,iter] = binarySearch(sortedData,target);\r\nassert(isequal(target,sortedData(mid)))\r\nassert(isequal(iter,6))","published":true,"deleted":false,"likes_count":5,"comments_count":8,"created_by":140016,"edited_by":223089,"edited_at":"2023-01-05T19:14:54.000Z","deleted_by":null,"deleted_at":null,"solvers_count":112,"test_suite_updated_at":"2023-01-05T19:14:54.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-09-23T17:47:03.000Z","updated_at":"2026-04-08T10:38:57.000Z","published_at":"2022-10-17T14:02:13.000Z","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\u003eOne way to locate a target value in a sorted array, is to use a binary search algorithm. Here, you test if the midpoint in the array is the target value. If it is, great! You're done. If not, then you continually narrow your search area depending on whether the target is less than or greater than the midpoint. The algorithm is as follows:\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\u003eGiven an array of sorted values (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e), and a target value you wish to locate (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etarget\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=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCalculate the index of the midpoint of the array \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e by taking the average of the largest and smallest indices and rounding to the nearest integer.  \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the value located at the midpoint matches your target, set found to true.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the target is less than the value located at the midpoint of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, narrow your search to the lower half of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e by setting the largest index in the array to the midpoint - 1. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the target is greater than the value located at the midpoint of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, narrow your search to the upper half of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e by setting the smallest index in the array to the midpoint + 1. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRepeat the steps above until found is true.\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=\\\"551\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"610\\\"/\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\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 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eX\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etarget\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as inputs, performs a binary search and outputs the index of target value as well as the number of iterations it took to find the target.\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\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":52557,"title":"Undocumented MATLAB tricks No. 2 - Tell the parfor index","description":"Your function is called in multiple for-loops, and the loop indices are given to your function. One of these indices are from a parfor loop. Can you find which one is the parfor loop index?\r\nThere are more than one ways, however you may need some undocumented MATLAB tricks to find the best solution of size 25.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; 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: none solid 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: 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: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eYour function is called in multiple for-loops, and the loop indices are given to your function. One of these indices are from a parfor loop. Can you find which one is the parfor loop index?\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: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThere are more than one ways, however you may need some undocumented MATLAB tricks to find the best solution of size 25.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function I=ParforIndex(a,b,c)","test_suite":"%%\r\n% Tests are randomly populated.\r\nfor T=1:10\r\n    rng('shuffle');\r\n    switch randi(3)\r\n        case 1\r\n            for a=1:10\r\n                for b=1:10\r\n                    parfor c=1:10\r\n                        assert(ParforIndex(a,b,c)==c);\r\n                    end\r\n                end\r\n            end\r\n        case 2\r\n            for a=1:10\r\n                parfor b=1:10\r\n                    for c=1:10\r\n                        assert(ParforIndex(a,b,c)==b);\r\n                    end\r\n                end\r\n            end\r\n        case 3\r\n            parfor a=1:10\r\n                for b=1:10\r\n                    for c=1:10\r\n                        assert(ParforIndex(a,b,c)==a);\r\n                    end\r\n                end\r\n            end\r\n    end\r\nend","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":362068,"edited_by":362068,"edited_at":"2022-05-08T12:23:21.000Z","deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":"2022-05-08T12:23:21.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2021-08-16T10:10:46.000Z","updated_at":"2022-05-08T12:23:21.000Z","published_at":"2021-08-16T10:10:46.000Z","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\u003eYour function is called in multiple for-loops, and the loop indices are given to your function. One of these indices are from a parfor loop. Can you find which one is the parfor loop index?\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\u003eThere are more than one ways, however you may need some undocumented MATLAB tricks to find the best solution of size 25.\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\"}]}"}],"term":"tag:\"loop\"","current_player_id":null,"fields":[{"name":"page","type":"integer","callback":null,"default":1,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":null,"static":null,"prepend":true},{"name":"per_page","type":"integer","callback":null,"default":50,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":null,"static":null,"prepend":true},{"name":"sort","type":"string","callback":null,"default":null,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":null,"static":null,"prepend":true},{"name":"body","type":"text","callback":null,"default":"*:*","directive":null,"facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":false},{"name":"group","type":"string","callback":null,"default":null,"directive":"group","facet":true,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"difficulty_rating_bin","type":"string","callback":null,"default":null,"directive":"difficulty_rating_bin","facet":true,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"id","type":"integer","callback":null,"default":null,"directive":"id","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"tag","type":"string","callback":null,"default":null,"directive":"tag","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"product","type":"string","callback":null,"default":null,"directive":"product","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"created_at","type":"timeframe","callback":{},"default":null,"directive":"created_at","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"profile_id","type":"integer","callback":null,"default":null,"directive":"author_id","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"created_by","type":"string","callback":null,"default":null,"directive":"author","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"player_id","type":"integer","callback":null,"default":null,"directive":"solver_id","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"player","type":"string","callback":null,"default":null,"directive":"solver","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"solvers_count","type":"integer","callback":null,"default":null,"directive":"solvers_count","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"comments_count","type":"integer","callback":null,"default":null,"directive":"comments_count","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"likes_count","type":"integer","callback":null,"default":null,"directive":"likes_count","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"leader_id","type":"integer","callback":null,"default":null,"directive":"leader_id","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"leading_solution","type":"integer","callback":null,"default":null,"directive":"leading_solution","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true}],"filters":[{"name":"asset_type","type":"string","callback":null,"default":null,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":null,"static":"\"cody:problem\"","prepend":true},{"name":"profile_id","type":"integer","callback":{},"default":null,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":"author_id","static":null,"prepend":true}],"query":{"params":{"per_page":50,"term":"tag:\"loop\"","current_player":null,"sort":"map(difficulty_value,0,0,999) asc"},"parser":"MathWorks::Search::Solr::QueryParser","directives":{"term":{"directives":{"tag":[["tag:\"loop\"","","\"","loop","\""]]}}},"facets":{"#\u003cMathWorks::Search::Field:0x00007ffbe49e3df0\u003e":null,"#\u003cMathWorks::Search::Field:0x00007ffbe49e38f0\u003e":null},"filters":{"#\u003cMathWorks::Search::Field:0x00007ffbe49e2e50\u003e":"\"cody:problem\""},"fields":{"#\u003cMathWorks::Search::Field:0x00007ffbe49e4390\u003e":1,"#\u003cMathWorks::Search::Field:0x00007ffbe49e42f0\u003e":50,"#\u003cMathWorks::Search::Field:0x00007ffbe49e3fd0\u003e":"map(difficulty_value,0,0,999) asc","#\u003cMathWorks::Search::Field:0x00007ffbe49e3e90\u003e":"tag:\"loop\""},"user_query":{"#\u003cMathWorks::Search::Field:0x00007ffbe49e3e90\u003e":"tag:\"loop\""},"queried_facets":{}},"query_backend":{"connection":{"configuration":{"index_url":"http://index-op-v2/solr/","query_url":"http://search-op-v2/solr/","direct_access_index_urls":["http://index-op-v2/solr/"],"direct_access_query_urls":["http://search-op-v2/solr/"],"timeout":10,"vhost":"search","exchange":"search.topic","heartbeat":30,"pre_index_mode":false,"host":"rabbitmq-eks","port":5672,"username":"cody-search","password":"78X075ddcV44","virtual_host":"search","indexer":"amqp","http_logging":"true","core":"cody"},"query_connection":{"uri":"http://search-op-v2/solr/cody/","proxy":null,"connection":{"parallel_manager":null,"headers":{"User-Agent":"Faraday v1.0.1"},"params":{},"options":{"params_encoder":"Faraday::FlatParamsEncoder","proxy":null,"bind":null,"timeout":null,"open_timeout":null,"read_timeout":null,"write_timeout":null,"boundary":null,"oauth":null,"context":null,"on_data":null},"ssl":{"verify":true,"ca_file":null,"ca_path":null,"verify_mode":null,"cert_store":null,"client_cert":null,"client_key":null,"certificate":null,"private_key":null,"verify_depth":null,"version":null,"min_version":null,"max_version":null},"default_parallel_manager":null,"builder":{"adapter":{"name":"Faraday::Adapter::NetHttp","args":[],"block":null},"handlers":[{"name":"Faraday::Response::RaiseError","args":[],"block":null}],"app":{"app":{"ssl_cert_store":{"verify_callback":null,"error":null,"error_string":null,"chain":null,"time":null},"app":{},"connection_options":{},"config_block":null}}},"url_prefix":"http://search-op-v2/solr/cody/","manual_proxy":false,"proxy":null},"update_format":"RSolr::JSON::Generator","update_path":"update","options":{"url":"http://search-op-v2/solr/cody"}}},"query":{"params":{"per_page":50,"term":"tag:\"loop\"","current_player":null,"sort":"map(difficulty_value,0,0,999) asc"},"parser":"MathWorks::Search::Solr::QueryParser","directives":{"term":{"directives":{"tag":[["tag:\"loop\"","","\"","loop","\""]]}}},"facets":{"#\u003cMathWorks::Search::Field:0x00007ffbe49e3df0\u003e":null,"#\u003cMathWorks::Search::Field:0x00007ffbe49e38f0\u003e":null},"filters":{"#\u003cMathWorks::Search::Field:0x00007ffbe49e2e50\u003e":"\"cody:problem\""},"fields":{"#\u003cMathWorks::Search::Field:0x00007ffbe49e4390\u003e":1,"#\u003cMathWorks::Search::Field:0x00007ffbe49e42f0\u003e":50,"#\u003cMathWorks::Search::Field:0x00007ffbe49e3fd0\u003e":"map(difficulty_value,0,0,999) asc","#\u003cMathWorks::Search::Field:0x00007ffbe49e3e90\u003e":"tag:\"loop\""},"user_query":{"#\u003cMathWorks::Search::Field:0x00007ffbe49e3e90\u003e":"tag:\"loop\""},"queried_facets":{}},"options":{"fields":["id","difficulty_rating"]},"join":" "},"results":[{"id":45543,"difficulty_rating":"easy"},{"id":58573,"difficulty_rating":"easy"},{"id":43216,"difficulty_rating":"easy-medium"},{"id":60926,"difficulty_rating":"easy-medium"},{"id":271,"difficulty_rating":"easy-medium"},{"id":1024,"difficulty_rating":"easy-medium"},{"id":55775,"difficulty_rating":"easy-medium"},{"id":3092,"difficulty_rating":"medium"},{"id":3095,"difficulty_rating":"medium"},{"id":56040,"difficulty_rating":"medium"},{"id":52557,"difficulty_rating":"hard"}]}}