{"id":5530,"date":"2023-05-18T10:16:00","date_gmt":"2023-05-18T08:16:00","guid":{"rendered":"https:\/\/datascience.unifi.it\/?post_type=tribe_events&#038;p=5530"},"modified":"2023-05-18T10:16:00","modified_gmt":"2023-05-18T08:16:00","slug":"disia-seminar-15","status":"publish","type":"tribe_events","link":"https:\/\/datascience.unifi.it\/index.php\/event\/disia-seminar-15\/","title":{"rendered":"DiSIA Seminar"},"content":{"rendered":"<p><span style=\"text-decoration: underline;\">Speaker:<\/span> Matteo Mio &#8211; CNRS, ENS-Lyon<\/p>\n<p><span style=\"text-decoration: underline;\">Title:<\/span><em> An introduction to Quantitative Algebras<\/em><\/p>\n<p><span style=\"text-decoration: underline;\">Abstract:<\/span><\/p>\n<p>Equational reasoning and equational manipulations are widespread in all areas of computer science. Consider, for example, the optimisation steps performed by a compiler which replaces blocks of code with &#8220;equivalent&#8221;, but more efficient, blocks. In recent years it has become apparent that sometimes &#8220;approximate&#8221; equational reasoning techniques are useful and\/or necessary. A block of code might be replaced by another block which is not truly equivalent but, say, equivalent 99% of the time (in a certain statistical sense). In this talk I will present the basic ideas of the mathematical framework of &#8220;Quantitative Algebras&#8221;, recently proposed by Mardare et. al. in [1], aiming at formally developing some of the intuitions mentioned above.<\/p>\n<p>[1] Radu Mardare, Prakash Panangaden, and Gordon Plotkin. 2016. Quantitative Algebraic Reasoning. In Proceedings of the 31st Annual ACM\/IEEE Symposium on Logic in Computer Science (LICS 2016). Association for Computing Machinery, New York, NY, USA, 700\u2013709. <a href=\"https:\/\/doi.org\/10.1145\/2933575.2934518\">https:\/\/doi.org\/10.1145\/2933575.2934518<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Speaker: Matteo Mio &#8211; CNRS, ENS-Lyon Title: An introduction to Quantitative Algebras Abstract: Equational reasoning and equational manipulations are widespread in all areas of computer science. Consider, for example, the &#8230;<\/p>\n","protected":false},"author":12,"featured_media":5065,"template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[35],"class_list":["post-5530","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","tribe_events_cat-seminar","cat_seminar"],"_links":{"self":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events\/5530","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":1,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events\/5530\/revisions"}],"predecessor-version":[{"id":5531,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events\/5530\/revisions\/5531"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/media\/5065"}],"wp:attachment":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/media?parent=5530"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tags?post=5530"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events_cat?post=5530"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}