Date

Tuesday, October 19, 2021 - 16:00 to 17:00

Dr. João Pedro Ramos, Eidgenössische Technische Hochschule Zürich

Title: STABILITY FOR GEOMETRIC AND FUNCTIONAL INEQUALITIES

Abstract

Date

Friday, November 5, 2021 - 11:00

Nikon New Confocal System "AX R"

Date

Tuesday, November 16, 2021 - 14:00

Automated light sheet imaging of cleared large samples using UltraMicroscope Blaze

Date

Monday, November 1, 2021 - 09:00

Language: English

Date

Wednesday, October 13, 2021 - 17:00

Seminar hosted by QG Unit.
Speaker: Karapet Mkrtchyan, Imperial College London
Title: Duality-symmetric formulation of electrodynamics and (chiral) p-form generalizations

Date

Thursday, October 14, 2021 - 15:00 to 16:00

Analysis on Metric Spaces Fall Seminar

Title: Quasiconformal and Sobolev mappings in metric measure spaces

Speaker: Panu Lahti, Chinese Academy of Sciences

Abstract: Starting from Gehring, the equivalence between the metric, geometric, and analytic def- initions of quasiconformality has been investigated by various authors. There are many results stating that if a mapping is metrically quasiconformal, perhaps only in a relaxed sense, then it is analytically quasiconformal, or at least a Sobolev mapping. In recent joint work with Xiaodan Zhou, we have shown an improved version of such a result, which seems to detect more Sobolev mappings than previous results in the literature. I will discuss these results as well as the general strategy of the proofs.

Date

Wednesday, November 10, 2021 - 16:00 to 17:00

OIST - Osaka University: A Recipe for Scientific Synergy-Series 1 by Dr. Svante Pääbo and Dr. Hisashi Arase

Date

Tuesday, October 12, 2021 - 09:00 to 10:00

Theory of Quantum Matter Unit and Quantum Machines Unit joint Seminar.

 

Date

Tuesday, October 12, 2021 - 15:00 to 16:00

Speaker: Paul Wedrich, University of Hamburg

Title: Knots and quivers, HOMFLYPT and DT

Date

Tuesday, October 12, 2021 - 10:00 to 11:00

Professor Alex Iosevich, University of Rochester

Title: Finite point configurations and the Vapnik-Chervonenkis dimension

Abstract:

The Vapnik-Chervonenkis (VC) dimension was invented in 1970 to study learning models. This notion has since become one of the cornerstones of modern data science. This beautiful idea has also found applications in other areas of mathematics. In this talk we are going to describe how the study of the VC-dimension in the context of families of indicator functions of spheres centered at points in sets of a given Hausdorff dimension (or in sets of a given size inside vector spaces over finite fields) gives rise to interesting, and in some sense extremal, point configurations. 

 

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