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March 2022
AARMS Scientific Machine Learning Seminar: Simone Brugiapaglia (Concordia University)
The curse of dimensionality and the blessings of sparsity and Monte Carlo sampling: From polynomial approximation to deep learning in high dimensions In data science and scientific computing, the approximation of high-dimensional functions from pointwise samples is a ubiquitous task, which is made intrinsically difficult by the so-called curse of dimensionality. In this talk, we will illustrate how to alleviate the curse thanks to the "blessings" of sparsity and Monte Carlo sampling. First, we will consider the case of sparse…
Find out more »Atlantic Graph Theory Seminar: Pjotr Buys (University of Amdsterdam)
About a year ago Jason Brown spoke in our seminar (of the university of Amsterdam) about the two-terminal reliability polynomial and left us with some questions about the closure of the complex zeros of all such polynomials (the zero-locus). In this talk I will define a way to capture, for a certain parameter, whether the set of all two-terminal reliability polynomials behaves chaotically around this parameter or not, i.e. whether this parameter is active or passive. I call the set…
Find out more »Dalhousie-AARMS AAMP Seminar: Justin Tzou (Macquarie U.)
Title: Modeling and analysis of localized vegetation patterns on curved topography Abstract: We propose a two-component reaction-advection-diffusion model for vegetation density and soil water concentration on a curved terrain which accounts for downhill flow of soil water, spatially dependent effective evaporation of soil water, and vertical rainfall on a curved surface. In the limit of slow diffusion of vegetation, we construct a one-spot localized solution corresponding to one patch of a periodic spotted vegetation pattern. We derive an ODE for the motion…
Find out more »Atlantic Graph Theory Seminar: Theodore Kolokolnikov (Dalhousie)
We study the algebraic connectivity for several classes of random semi-regular graphs. For large random semi-regular bipartite graphs, we explicitly compute both their algebraic connectivity and as well as the full spectrum distribution. For an integer d in , we find families of random semi-regular graphs that have higher algebraic connectivity than a random d-regular graphs with the same number of vertices and edges. On the other hand, we show that regular graphs beat semi-regular graphs when d >8. More…
Find out more »Atlantic Canada Actuarial Student Conference
We invite all Actuarial Science students from across the Atlantic region to join us for the 2022 Atlantic Canada Actuarial Student Conference! Hosted in beautiful downtown Charlottetown, this year’s event will provide students with an opportunity to meet others from the region, gain insightful knowledge about the actuarial field from industry professionals, and provide networking opportunities through a career fair. It is a fantastic opportunity to find potential internships or full-time positions in French and English with several sponsors.
Find out more »Dalhousie-AARMS AAMP Seminar: Manuela Girotti (Saint Mary’s Uni.)
Title: Asymptotic Analysis of the Interaction Between a Soliton and a Regular Gas of Solitons (a.k.a. Gulliver and the Lilliputians) Abstract: N. Zabusky coined the word "soliton" in 1965 to describe a curious feature he and M. Kruskal observed in their numerical simulations of the initial-value problem for a simple nonlinear PDE. The first part of the talk will be a broad introduction to the theory of solitons/solitary waves and integrable PDEs (the KdV and modified KdV equation in particular),…
Find out more »AARMS Scientific Machine Learning Seminar: Scott MacLachlan (Memorial)
Optimization and Learning in the Design of Preconditioners Computer simulation algorithms are a major tool in many areas of science and industry, particularly in areas where the behaviour of fluids or complex materials governs the physical processes of interest. A typical core of these tools is the numerical approximation of the solution to coupled nonlinear systems of partial differential equations, relying on nonlinear and linear solvers, such as Newton’s method and preconditioned Krylov iterations. Among the most effective preconditioners for…
Find out more »April 2022
AARMS Scientific Machine Learning Seminar: Geoffrey McGregor (University of Northern British Columbia)
Conservative Hamiltonian Monte Carlo Markov Chain Monte Carlo (MCMC) methods enable us to extract meaningful statistics from complex distributions which frequently appear in parameter estimation, Bayesian statistics, statistical mechanics and machine learning. Similar to how flipping a coin, or rolling a dice, allows us to sample from the corresponding distributions underlying these processes, MCMC methods enable us to sample from more complex distributions. The sample statistics of the sequence generated by MCMC will converge to those of the target distribution, or…
Find out more »Atlantic Graph Theory Seminar: John Engbers (Marquette University)
Extremal questions for vertex colorings of graphs For graphs $G$ and $H$, an $H$-coloring of $G$ is a map from the vertices of $G$ to the vertices of $H$ so that an edge in $G$ is mapped to an edge in $H$. The graph $H$ can be thought of as the allowable coloring scheme: its vertices are the colors used and its edges indicating colors that can appear on the endpoints of an edge in $G$. When the graph $H$…
Find out more »AARMS Scientific Machine Learning Seminar: Michael W. Dunham (Department of Earth Sciences, Memorial University)
Semisupervised machine learning algorithms and their application to geoscience classification problems In recent years, many disciplines have been challenged with trying to efficiently extract meaning, or value, out of large datasets. Technological advances have improved data storage capabilities as well as how data can be obtained (e.g., real-time data). Manually interpreting data that are exponentially growing in volume has obvious management and analysis challenges. Machine learning is a solution to these challenges. Machine learning algorithms teach computers to recognize patterns…
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