Carleman estimate

Speaker: 

Lili Yan

Institution: 

UC Irvine

Time: 

Friday, February 2, 2018 - 1:00pm to 1:50pm

Location: 

RH340N

I am going to talk about Carleman estimate with Carleman weight first. To prove Carleman estimate, I need to introduce some definitions of semiclassical analysis first. Then I am going to talk about Carleman estimate with limiting Carleman weight and some applications. 

Dependence of the density of states on the probability distribution for discrete random Schrödinger operators

Speaker: 

Christoph Marx

Institution: 

Oberlin

Time: 

Thursday, February 15, 2018 - 2:00pm

Location: 

RH 340P

We prove the Hölder-continuity of the density of states measure (DOSm) and the integrated density of states (IDS) for discrete random Schrödinger operators with finite-range potentials with respect to the probability measure. In particular, our result implies that the DOSm and the IDS for smooth approximations of the Bernoulli distribution converge to the corresponding quantities for the Bernoulli-Anderson model. Other applications of the technique are given to the dependency of the DOSm and IDS on the disorder, and the continuity of the Lyapunov exponent in the weak-disorder regime for dimension one. The talk is based on joint work with Peter Hislop (Univ. of Kentucky) 

Localization and spectral statistics for Schrodinger operators with random point interactions

Speaker: 

Peter Hislop

Institution: 

U Kentucky

Time: 

Thursday, April 19, 2018 - 2:00pm

Location: 

RH 340P

We discuss localization and local eigenvalue statistics for Schr\"odinger operators with random point interactions on $R^d$, for $d=1,2,3$. The results rely on probabilistic estimates, such as the Wegner and Minami estimate, for the eigenvalues of the Schr\"odinger operator restricted to cubes. The special structure of the point interactions facilitates the proofs of these eigenvalue correlation estimates.
One of the main results is that the local eigenvalue statistics is given by a Poisson point process in the localization regime, one of the first examples of Poisson eigenvalue statistics for multi-dimensional random Schr\"odinger operators in the continuum.  This is joint work with M.\ Krishna and W.\ Kirsch.

Hodge metric of nilpotent Higgs bundles

Speaker: 

Qiongling Li

Institution: 

Caltech

Time: 

Monday, February 12, 2018 - 4:00pm to 5:00pm

Host: 

Location: 

RH 340P

On a complex manifold, a Higgs bundle is a pair containing a holomorphic vector bundle E and a holomorphic End(E)-valued 1-form. In this talk, we focus on nilpotent Higgs bundles, for example, the ones arising from variations of Hodge structures for a deformation family of Kaehler manifolds. We first give an optimal upper bound of the curvature of Hodge metric of the deformation space of Calabi-Yau manifolds. Secondly, we prove a rigidity theorem of the holonomy of polystable nilpotent Higgs bundles via the non-abelian Hodge theory when the base manifold is a Riemann surface. This is joint work with Song Dai.

Understanding Manifold-structured Data via Geometric Modeling and Learning

Speaker: 

Rongjie Lai

Institution: 

RPI

Time: 

Monday, April 16, 2018 - 4:00pm to 5:00pm

Host: 

Location: 

RH306

Analyzing and inferring the underlying global intrinsic structures of data from its local information are critical in many fields. In practice, coherent structures of data allow us to model data as low dimensional manifolds, represented as point clouds, in a possible high dimensional space. Different from image and signal processing which handle functions on flat domains with well-developed tools for processing and learning, manifold-structured data sets are far more challenging due to their complicated geometry. For example, the same geometric object can take very different coordinate representations due to the variety of embeddings, transformations or representations (imagine the same human body shape can have different poses as its nearly isometric embedding ambiguities). These ambiguities form an infinite dimensional isometric group and make higher-level tasks in manifold-structured data analysis and understanding even more challenging. To overcome these ambiguities, I will first discuss modeling based methods. This approach uses geometric PDEs to adapt the intrinsic manifolds structure of data and extracts various invariant descriptors to characterize and understand data through solutions of differential equations on manifolds. Inspired by recent developments of deep learning, I will also discuss our recent work of a new way of defining convolution on manifolds and demonstrate its potential to conduct geometric deep learning on manifolds. This geometric way of defining convolution provides a natural combination of modeling and learning on manifolds. It enables further applications of comparing, classifying and understanding manifold-structured data by combing with recent advances in deep learning.

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Choosing distinct points on cubic curves

Speaker: 

Weiyan Chen

Institution: 

University of Minnesota

Time: 

Tuesday, April 17, 2018 - 3:00pm

Host: 

Location: 

RH 340P

It is a classical topic dating back to Maclaurin (1698–1746) to study certain special points on smooth cubic plane curves, such as the 9 inflection points (Maclaurin and Hesse), the 27 sextatic points (Cayley), and the 72 points "of type 9" (Gattazzo). Motivated by these algebro-geometric constructions, we ask the following topological question: is it possible to choose n distinct points on a smooth cubic plane curve as the curve varies continuously in family, for any integer n other than 9, 27 and 72? We will present both constructions and obstructions to such continuous choices of points, state a classification theorem for them, and discuss conjectures and open questions.

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