{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2025-12-14T01:33:56.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2025-12-14T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":55220,"title":"Matrix Quadrants","description":"Write a function that takes N as the input, and outputs a matrix whose upper-left (NxN) quadrant contains all ones, the lower-right (NxN) quadrant contains all N's, and zeros everywhere else. For example, if N = 3: \r\n\r\n","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: 363px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 181.5px; transform-origin: 407px 181.5px; 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=\"\"\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: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e as the input, and outputs a matrix whose upper-left (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eNxN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e) quadrant contains all ones, the lower-right (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eNxN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e) quadrant contains all \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e's, and zeros everywhere else. For example, if \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eN = 3\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e: \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 282px; 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 141px; text-align: left; transform-origin: 384px 141px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"321\" height=\"276\" style=\"vertical-align: baseline;width: 321px;height: 276px\" 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\" data-image-state=\"image-loaded\"\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: center; 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=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function M = foursquare(N)\r\n  M = N;\r\nend","test_suite":"%%\r\ny = [1 1 0 0; 1 1 0 0; 0 0 2 2; 0 0 2 2];\r\nassert(isequal(foursquare(2),y))\r\n%%\r\ny = [1 1 1 1 1 0 0 0 0 0; 1 1 1 1 1 0 0 0 0 0; 1 1 1 1 1 0 0 0 0 0; 1 1 1 1 1 0 0 0 0 0; 1 1 1 1 1 0 0 0 0 0; 0 0 0 0 0 5 5 5 5 5; 0 0 0 0 0 5 5 5 5 5; 0 0 0 0 0 5 5 5 5 5; 0 0 0 0 0 5 5 5 5 5; 0 0 0 0 0 5 5 5 5 5];\r\nassert(isequal(foursquare(5),y))\r\n%%\r\nfor k = 1:5\r\n    n = randi([3 20]);\r\n    y = foursquare(n);\r\n    assert( isequal(size(y),2*n*[1 1]) )\r\n    assert( isequal(y,y') )\r\n    assert( isequal(sum(y,1),[n*ones(1,n) n*n*ones(1,n)]) )\r\nend","published":true,"deleted":false,"likes_count":16,"comments_count":5,"created_by":140016,"edited_by":140016,"edited_at":"2022-10-03T14:08:22.000Z","deleted_by":null,"deleted_at":null,"solvers_count":874,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-07-28T14:24:20.000Z","updated_at":"2026-04-05T18:24:22.000Z","published_at":"2022-10-03T14:08:22.000Z","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\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\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as the input, and outputs a matrix whose upper-left (\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\u003eNxN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) quadrant contains all ones, the lower-right (\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\u003eNxN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) quadrant contains all \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\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e's, and zeros everywhere else. 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":55220,"title":"Matrix Quadrants","description":"Write a function that takes N as the input, and outputs a matrix whose upper-left (NxN) quadrant contains all ones, the lower-right (NxN) quadrant contains all N's, and zeros everywhere else. For example, if N = 3: \r\n\r\n","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: 363px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 181.5px; transform-origin: 407px 181.5px; 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=\"\"\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: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e as the input, and outputs a matrix whose upper-left (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eNxN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e) quadrant contains all ones, the lower-right (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eNxN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e) quadrant contains all \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e's, and zeros everywhere else. For example, if \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eN = 3\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e: \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 282px; 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 141px; text-align: left; transform-origin: 384px 141px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"321\" height=\"276\" style=\"vertical-align: baseline;width: 321px;height: 276px\" 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\" data-image-state=\"image-loaded\"\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: center; 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=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function M = foursquare(N)\r\n  M = N;\r\nend","test_suite":"%%\r\ny = [1 1 0 0; 1 1 0 0; 0 0 2 2; 0 0 2 2];\r\nassert(isequal(foursquare(2),y))\r\n%%\r\ny = [1 1 1 1 1 0 0 0 0 0; 1 1 1 1 1 0 0 0 0 0; 1 1 1 1 1 0 0 0 0 0; 1 1 1 1 1 0 0 0 0 0; 1 1 1 1 1 0 0 0 0 0; 0 0 0 0 0 5 5 5 5 5; 0 0 0 0 0 5 5 5 5 5; 0 0 0 0 0 5 5 5 5 5; 0 0 0 0 0 5 5 5 5 5; 0 0 0 0 0 5 5 5 5 5];\r\nassert(isequal(foursquare(5),y))\r\n%%\r\nfor k = 1:5\r\n    n = randi([3 20]);\r\n    y = foursquare(n);\r\n    assert( isequal(size(y),2*n*[1 1]) )\r\n    assert( isequal(y,y') )\r\n    assert( isequal(sum(y,1),[n*ones(1,n) n*n*ones(1,n)]) )\r\nend","published":true,"deleted":false,"likes_count":16,"comments_count":5,"created_by":140016,"edited_by":140016,"edited_at":"2022-10-03T14:08:22.000Z","deleted_by":null,"deleted_at":null,"solvers_count":874,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-07-28T14:24:20.000Z","updated_at":"2026-04-05T18:24:22.000Z","published_at":"2022-10-03T14:08:22.000Z","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\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\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as the input, and outputs a matrix whose upper-left (\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\u003eNxN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) quadrant contains all ones, the lower-right (\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\u003eNxN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) quadrant contains all \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\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e's, and zeros everywhere else. 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