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DTSTART;TZID=America/Los_Angeles:20210914T123000
DTEND;TZID=America/Los_Angeles:20210914T132000
DTSTAMP:20260503T075625
CREATED:20210822T191624Z
LAST-MODIFIED:20210829T182323Z
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SUMMARY:On Hermite's problem\, Jacobi-Perron type algorithms\, and Dirichlet groups (Oleg Karpenkov\, Liverpool)
DESCRIPTION:In this talk we introduce a new modification of the Jacobi-Perron algorithm in the three dimensional case. This algorithm is periodic for the case of totally-real conjugate cubic vectors. To the best of our knowledge this is the first Jacobi-Perron type algorithm for which the cubic periodicity is proven. This provides an answer in the totally-real case to the question of algebraic periodicity for cubic irrationalities posed in 1848 by Ch.Hermite. \nWe will briefly discuss a new approach which is based on geometry of numbers. In addition we point out one important application of Jacobi-Perron type algorithms to the computation of independent elements in the maximal groups of commuting matrices of algebraic irrationalities.
URL:https://colleges.claremont.edu/ccms/event/antc-seminar-pavel-guerzhoy-university-of-hawaii/
LOCATION:On Zoom
CATEGORIES:Algebra / Number Theory / Combinatorics Seminar
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DTSTART;TZID=America/Los_Angeles:20210915T163000
DTEND;TZID=America/Los_Angeles:20210915T174500
DTSTAMP:20260503T075625
CREATED:20210831T233907Z
LAST-MODIFIED:20210907T223233Z
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SUMMARY:Topic Models\, Methods\, and Medicine (Prof. Jamie Haddock)
DESCRIPTION:Title: Topic Models\, Methods\, and Medicine \nSpeaker: Prof. Jamie Haddock (Harvey Mudd College) \nAbstract: There is currently an unprecedented demand for efficient\, quantitative\, and interpretable methods to study large-scale (often multi-modal) data. One key area of interest is that of topic modeling\, which seeks to automatically learn latent trends or topics of complex data sets\, providing practitioners a view of what is “going on” inside their data. This talk will survey several new tools for topic modeling on matrix and tensor data which allow for use of various forms of supervision and which learn hierarchical structure amongst topics.  These tools are of interest across the many fields and industries producing\, capturing\, and analyzing big data\, but are of particular interest in applications where expert supervision is available and often essential (e.g.\, medicine).  We will describe two applications of these methods to medical data; an application to a large-scale patient survey database and an ongoing application to cardiovascular imaging data. \n  \nProf. Jamie Haddock is an Assistant Professor in the Mathematics Department at Harvey Mudd College
URL:https://colleges.claremont.edu/ccms/event/jamie-haddock-harvey-mudd-college/
LOCATION:Zoom meeting\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Andrew Bernoff":MAILTO:ajb@hmc.edu
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