Simon Fraser University

In this talk we summarize the classification of regular polyhedra and polytopes and extend the concept to that of hypertope: a thin, residually connected incidence geometry. We present the characterization of the automorphism groups of regular...

The abstract grammatical system of a language both shapes and is shaped by the sounds of the particular words that speakers know. I present GLaPL, a computational model which concurrently learns gradient grammar and gradient word representations...

The phrase "non-problems" in the title refers to a collection of adjectives that start with the prefix "non". These include "non-convex", "non-differentiable", "non-traditional", "non-trivial", and "non-stochastic gradient" (as a counter to a topical...

The Fq conference is a biannual conference that covers various topics in finite fields including but not limited to: Theory: structure of finite fields, primitive elements, normal bases, polynomials, number-theoretic aspects of finite fields...

The CanaDAM conference will bring together researchers from the various disciplines with which discrete and algorithmic mathematics interact. Particular areas of interest are the following: graphs and digraphs, hypergraphs, matroids, ordered sets...

A wide overview of mathematical techniques of interest in various big data problems. Organizers: Cedric Chauve, Mathematics - SFU Tamon Stephen, Mathematics - SFU

Einsteins theory of General Relativity (GR) works remarkably well on Earth and inside our Solar System. However, there remain significant gaps in our understanding of gravity. One is the long- standing quest for a quantum theory of gravity. The other...

Spherical harmonics are the natural basis for square integrable functions on the sphere in the sense of Lebesgue. However, their analytic structure makes them formidable to work with numerically. In this talk, we will present rapid transformations...

In the simulation of complex physics, multiple model forms of varying fidelity and resolution are commonly available. In computational fluid dynamics, for example, common model fidelities include potential flow, inviscid Euler, Reynolds-averaged...

An intriguing application of Deep Learning research is the use of Deep Convolutional Neural Networks (DCNNs) for image processing, as seen for example in the Prisma app and the Deep Dream algorithm. At SFU SIAT's iVizLab, we are using DCNNs within...