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February 2022
Dalhousie-AARMS AAMP Seminar: Micah Milinovich (U. Mississippi)
Title: Fourier optimization, prime gaps, and zeta zeros Abstract: There are many situations where one imposes certain conditions on a function and its Fourier transform and then wants to optimize a certain quantity. I will describe two ways these types of Fourier optimization problems can arise in the context of the explicit formula, which relates the primes to the zeros of the Riemann zeta-function. Using information from the zeros to study the primes, I will show how one can prove the strongest known estimates in the classical problem of bounding the maximum gap between consecutive…
Find out more »AARMS Scientific Machine Learning Seminar: Alison Malcom (Memorial University)
Using Normalizing Flows for Seismic Data Interpolation Normalizing Flows are a type of neural network that allow us to map one probability distribution into another. The advantage of such a technique is that they allow us to relate a simple distribution, like a Gaussian, to a more complicated distribution that may be more difficult to estimate and sample from. In uncertainty quantification for inverse problems, we are trying to estimate one of these more complicated distributions, thus normalizing flows can…
Find out more »March 2022
AARMS Scientific Machine Learning Seminar: Stijn De Baerdemacker (UNB)
Deep Learning Chemistry: extracting chemical features from Graph Convolutional Neural Networks The task of quantum chemistry is to compute chemical properties of molecular compounds from the fundamental laws of quantum mechanics. This poses a tremendous challenge as the most accurate methods also come with a perniciously scaling computational cost, limiting systematic and meaningful calculations mostly to medium-small molecules. This is an undesirable situation as there is a high potential for computationally driven discovery of chemical compounds. For this reason, there…
Find out more »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…
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