Title:Geometric Scattering on Measure Spaces Abstract: Geometric Deep Learning is an emerging field of research that aims to extend the success of convolutional neural networks (CNNs) to data with non-Euclidean geometric structure. Despitebeing in its relative infancy, this field has already found great success in many applications such as recommender systems, computer graphics, and traffic […]

Title: Watch your step: Modeling on Time Scales Speaker: Raegan Higgins, Department of Mathematics & Statistics, Texas Tech University Abstract: Generally, differential and difference equations are used in the mathematical modeling of physical systems. Our modeling approach uses dynamic equations on time scales. A time scale T is an arbitrary, nonempty, closed subset of the […]

Given a unital AF (approximately finite-dimensional) algebra A equipped with a faithful tracial state, we equip each (norm-closed two-sided) ideal of A with a metrized quantum vector bundle structure, when canonically viewed as a module over A, in the sense of Latrémolière using previous work of Aguilar and Latrémolière. Moreover, we show that convergence of […]