Speaker: () - https://www2.cscamm.umd.edu/~jacob/StochasticsRIT.htm

]]>

Speaker: Shin Eui Song (UMD) -

]]>

Speaker: William Golding (University of Maryland) -

Abstract: http://www.terpconnect.umd.edu/~lvrmr/2018-2019-S/Classes/RIT.shtml

]]>

Speaker: Peter Ulrickson (The Catholic University of America) - http://math.cua.edu/faculty/profiles/ulrickson.cfm

Abstract: Atiyah, Bott, and Shapiro revealed a close connection between K-theory and Clifford algebras. I will describe an E-infinity ring spectrum representing topological K-theory which is made from Clifford modules, inspired by the Atiyah-Bott-Shapiro construction. I will further describe a notion of Clifford system, and sketch a way to produce a spectrum from an abelian category, generalizing the construction of topological K-theory. This ongoing project is joint with Dmitri Pavlov.

]]>

Speaker: Michael Rawson (UMD) -

]]>

Speaker: Benedetto Piccoli (Rutgers University–Camden) -

Abstract: Control of multi-agent systems has application to many different domains

(including traffic, biology and other) and can be addressed at many different scale. At microscopic scale sparsity is a desired property for applicability, while passing to mean-field limits pose mathematical challenges, as controls may become singular.

We first show some recent results at microscopic scale and in rigorously passing to the limit. Then we introduce a new concept of differential equation for measures which appear to be a promising framework to deal with this problems and, finally, show application to traffic.

]]>

Speaker: Sam Braunfeld (UMCP) -

Abstract: Two structures are called siblings if they are bi-embeddable. Given a countable structure M, we wish to count its siblings up to isomorphism. Extending work of Laflamme, Pouzet, Sauer, and Woodrow, we give a complete answer to this in the case M is omega-categorical, making use of the recent "Ryll-Nardzewski theorem" for mutually algebraic theories. Joint work with Chris Laskowski.

]]>

Speaker: Juan Pablo Borthagaray (Department of Mathematics - University of Maryland, College Park) - http://math.umd.edu/~jpb/

Abstract: In this talk, we consider problems involving the integral fractional Laplacian on bounded domains. The integral fractional Laplacian is a nonlocal operator that involves a hypersingular kernel; suitable quadrature is required to handle such a singularity. Nonlocality originates additional difficulties, such as the need to deal with integration on unbounded domains and full stiffness matrices. Furthermore, independently of the smoothness of the domain and the data, solutions to the problems under consideration possess a limited Sobolev regularity.

The first part of the talk is devoted to the analysis of the homogeneous Dirichlet problem: we discuss regularity of solutions and study the convergence of finite element schemes. Afterwards, we discuss two nonlinear problems: the fractional obstacle problem and the computation of nonlocal minimal surfaces.

]]>

Speaker: Paul Smith (U-Md) -

]]>

Speaker: Julius Ross (UIC (Chicago)) -

Abstract: TBA

]]>

Speaker: Xiaoyu Zhou (UMD) -

]]>

Speaker: Dr. Steven Damelin (Department of Mathematics, The University of Michigan) - http://www-personal.umich.edu/~damelin/

Abstract: Visual objects are often known up to some ambiguity, depending on the methods used to acquire them. The first-order approximation to any transformation is, by definition, affine, and the affine approximation to changes between images has been used often in computer vision. Thus it is beneficial to deal with objects known only up to an affine transformation. For example, feature points on a planar transform projectively between different views, and the projective transformation can in many cases be approximated by an affine transformation. More generally, given two visual objects in a containing Euclidean space R^k, one may study vision group actions between these two objects often with an underlying signature which are equivalent under some symmetry or minimal distortion action with respect to a suitable metric inherited by this action. For example, Euclidean groups, similarity, Equi-Affine, projections, camera rotations and video groups. The study of the space of ordered configurations of n distinct points in R^k up to similarity transformations was pioneered by Kendall who coined the name shape space. For different groups of transformations (rigid, similarity, linear, affine, projective for example) one obtains different shape spaces. Moreover, while these formulations allow often global optimal optimization, e.g. using convex objectives , many of the problems above require efficient approximation methods which work locally. This framework has applications to biological structural molecule reconstruction problems, to recognition tasks and to matching features across images with minimal distortion” This talk will discuss various work with collaborators around this circle of ideas.

]]>

Speaker: () -

Abstract: Schedule and Abstracts available at https://www.norbertwiener.umd.edu/FFT/2019/index.html

]]>

Speaker: Brian Swingle - University of Maryland | Department of Physics

Abstract: Positive Lyapunov exponents are one of the key characteristics of chaos in classical dynamical systems. Here we discuss the notion of Lyapunov exponents in quantum many-body systems focusing on a recent definition of a whole spectrum of quantum Lyapunov exponents (https://arxiv.org/abs/1809.01671). The talk will not assume prior knowledge of the subject, although some knowledge of quantum mechanics will be helpful.

]]>

Speaker: Davit Karagulyan (UMD) -

Abstract: TBA

]]>

Speaker: Jonathan Rosenberg

Abstract: We'll discuss charges and stability conditions on D-branes, from both topological and more physical points of view.

]]>

Speaker: Prof. Soheil Feizi (Dept. of Computer Sci., Univ. of Maryland) -

Abstract: Generative Adversarial Networks (GANs) have become a popular method to learn a probability model from data. In this talk, I will provide an understanding of some of the basic issues surrounding GANs including their formulation, generalization and stability on a simple benchmark where the data has a high-dimensional Gaussian distribution. Even in this simple benchmark, the GAN problem has not been well-understood as we observe that existing state-of-the-art GAN architectures may fail to learn a proper generative distribution owing to (1) stability issues (i.e., convergence to bad local solutions or not converging at all), (2) approximation issues (i.e., having improper global GAN optimizers caused by inappropriate GAN's loss functions), and (3) generalizability issues (i.e., requiring large number of samples for training). In this setup, we propose a GAN architecture which recovers the maximum-likelihood solution and demonstrates fast generalization. Moreover, we analyze global stability of different computational approaches for the proposed GAN and highlight their pros and cons. Finally, we outline an extension of our model-based approach to design GANs in more complex setups than the considered Gaussian benchmark.

]]>

Speaker: () -

Abstract: Schedule and Abstracts available at https://www.norbertwiener.umd.edu/FFT/2019/index.html

]]>

Speaker: Patrick Daniels (UMD) -

]]>

Speaker: Jianlong Liu (UMD) -

]]>