{"id":93,"date":"2020-11-17T13:42:09","date_gmt":"2020-11-17T13:42:09","guid":{"rendered":"https:\/\/blogit.utu.fi\/utudiscretemath\/?p=93"},"modified":"2020-11-17T13:42:10","modified_gmt":"2020-11-17T13:42:10","slug":"1-12-2020-tuomo-lehtila","status":"publish","type":"post","link":"https:\/\/blogit.utu.fi\/utudiscretemath\/2020\/11\/17\/1-12-2020-tuomo-lehtila\/","title":{"rendered":"1.12.2020 Tuomo Lehtil\u00e4"},"content":{"rendered":"\n<p><\/p>\n\n\n\n<p><strong>Time: 1.12.2020 13:00<\/strong><\/p>\n\n\n\n<p><strong>Speaker:<\/strong> Tuomo Lehtil\u00e4, University of Turku<\/p>\n\n\n\n<p><strong>Title:<\/strong> Levenshtein&#8217;s channel and list decoding<\/p>\n\n\n\n<p><strong>Abstract:<\/strong> The Levenshtein\u2019s channel model for substitution errors is relevant in information retrieval where information is received through many noisy channels. We consider a situation where the information is stored using an e-error-correcting code C in binary Hamming space. A codeword x is sent through N channels and in each of the channels there can occur at most t=e+k (k&gt; 0) errors. Now, the decoder tries to recover the information with the aid of multiple channel outputs. Recently, Yaakobi and Bruck expanded this framework by considering the problem where the decoder provides a list of codewords which might have been sent instead of a unique output. We have continued studying this problem and in this talk I present how the size of the list is connected to the number of the channels N and some other variables. In particular, the exact number of the channels required to have a constant size list is presented. Most of the results rely especially on the Sauer-Shelah lemma.<br><br>This talk is based on joint work with Ville Junnila and Tero Laihonen: &#8220;On Levenshtein\u2019s Channel and List Size in Information Retrieval&#8221;,&nbsp; <a rel=\"noreferrer noopener\" href=\"https:\/\/doi.org\/10.1109\/TIT.2020.3016269\" target=\"_blank\">https:\/\/doi.org\/10.1109\/TIT.2020.3016269<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Time: 1.12.2020 13:00 Speaker: Tuomo Lehtil\u00e4, University of Turku Title: Levenshtein&#8217;s channel and list decoding Abstract: The Levenshtein\u2019s channel model for substitution errors is relevant in information retrieval where information is received through many noisy channels. We consider a situation where the information is stored using an e-error-correcting code C in binary Hamming space. A [&hellip;]<\/p>\n","protected":false},"author":928,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-93","post","type-post","status-publish","format-standard","hentry","category-session"],"_links":{"self":[{"href":"https:\/\/blogit.utu.fi\/utudiscretemath\/wp-json\/wp\/v2\/posts\/93","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogit.utu.fi\/utudiscretemath\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogit.utu.fi\/utudiscretemath\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogit.utu.fi\/utudiscretemath\/wp-json\/wp\/v2\/users\/928"}],"replies":[{"embeddable":true,"href":"https:\/\/blogit.utu.fi\/utudiscretemath\/wp-json\/wp\/v2\/comments?post=93"}],"version-history":[{"count":3,"href":"https:\/\/blogit.utu.fi\/utudiscretemath\/wp-json\/wp\/v2\/posts\/93\/revisions"}],"predecessor-version":[{"id":102,"href":"https:\/\/blogit.utu.fi\/utudiscretemath\/wp-json\/wp\/v2\/posts\/93\/revisions\/102"}],"wp:attachment":[{"href":"https:\/\/blogit.utu.fi\/utudiscretemath\/wp-json\/wp\/v2\/media?parent=93"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogit.utu.fi\/utudiscretemath\/wp-json\/wp\/v2\/categories?post=93"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogit.utu.fi\/utudiscretemath\/wp-json\/wp\/v2\/tags?post=93"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}