Events for 03/27/2019 from all calendars

Number Theory Seminar

Time: 1:45PM - 2:45PM

Location: BLOC 220

Speaker: Wei-Lun Tsai, Texas A&M University

Title: Arithmetic statistics of canonical Hecke L-functions

Abstract: The canonical Hecke characters in the sense of Rohrlich form a set of algebraic Hecke characters with important arithmetic properties. For example, the central values of their corresponding L-functions are related to ranks of Gross's elliptic Q-curves. In this talk, we explain how non-trivial bounds for l-torsion in class groups of number fields can be used to prove that for an asymptotic density of 100 percent of CM fields E within certain general families, the number of canonical Hecke characters of E whose L-function has a nonvanishing central value is >> |disc(E)|^{delta} for some absolute constant delta > 0. This is joint work with B. D. Kim and Riad Masri.

URL: Event link

Noncommutative Geometry Seminar

Time: 2:00PM - 3:00PM

Location: BLOC 628

Speaker: Rudolf Zeidler, University of Münster

Title: Slant products on the analytic structure group via the stable Higson corona

Abstract: We show injectivitiy of certain exterior product maps on the K-theory of the Roe algebra and the analytic structure group by a partial pairing (or "slant product") with the K-theory of the stable Higson corona. We will explore applications in primary and secondary index theory, in particular to positive scalar curvature. This is ongoing joint work with Alexander Engel and Christopher Wulff.

Numerical Analysis Seminar

Time: 3:00PM - 4:00PM

Location: BLOC 628

Speaker: Serge Prudhomme, Polytechnique Montreal

Title: A goal-oriented formulation for reduced-order modeling

Abstract: The subject of the talk will be concerned with a mathematical formulation for constructing reduced-order models optimized with respect to quantities of interest. The main idea is to formulate a minimization problem that includes an equality, or inequality constraint on the error in the goal functional so that the resulting model is capable of delivering predictions of the quantity of interest within some prescribed tolerance. The formulation will be tested on the so-called Proper Generalized Decomposition (PGD) method. Such a paradigm represents a departure from classical goal-oriented approaches in which a reduced model is first derived by minimization of the energy, or of residual functionals, and then adapted via a greedy approach by controlling the error with respect to quantities of interest using dual-based error estimates. Numerical examples will be presented in order to demonstrate the efficiency of the proposed approach. In particular, we will consider the simulation of a delaminated composite material by the Proper Generalized Decomposition approach.

Linear Analysis Seminar

Time: 4:00PM - 5:00PM

Location: BLOC 220

Speaker: Martijn Caspers, TU Delft

Title: Non-commutative Lipschitz and commutator estimates

Abstract: In the 1940's Krein asked the question whether Lipschitz functions f: R -> C are also operator Lipschitz functions in the sense that the mapping B(H)_sa -> B(H): x -> f(x) is Lipschitz. The answer to this question is negative unless additional smoothness assumptions are imposed on f. On the other hand if the uniform norm on B(H) is replaced by the Schatten Lp-norm then every Lipschitz function is operator Lipschitz (Potapov-Sukochev 2010). In this talk we give a sharp proof of this result through so-called end-point estimates (weak L1-estimates and BMO-estimates). In order to achieve this we further develop the theory of Markov dilations and De Leeuw theorems. This is joint work with D. Potapov, F. Sukochev and D. Zanin.

AMUSE

Time: 6:00PM - 7:00PM

Location: BLOC 220

Speaker: Dr. Nima Kalantari, Department of Computer Science & Engineering, TAMU

Title: Deep Learning for Sampling and Reconstruction in Computer Graphics

Abstract: The field of computer graphics, specifically computational photography and rendering, has seen tremendous progress over the past decades and, as a result, is an essential part of the film, gaming, and camera industries. In a variety of applications within these sub-fields, the goal is to reconstruct a high dimensional function from a sparse set of input samples. In this talk, I introduce several exciting applications such as light field super-resolution, high dynamic range imaging, and Monte Carlo denoising. I will then discuss our recent effort to address these applications through deep learning.