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DTSTART;TZID=UTC:20220301T110000
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DTSTAMP:20260613T071512
CREATED:20220224T132859Z
LAST-MODIFIED:20220224T132859Z
UID:6613-1646132400-1646136000@aarms.math.ca
SUMMARY:AARMS Scientific Machine Learning Seminar: Stijn De Baerdemacker (UNB)
DESCRIPTION:Deep Learning Chemistry: extracting chemical features from Graph Convolutional Neural Networks\n\nThe 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 have been significant advances in the design of Machine Learning (ML) algorithms to interpolate chemical observables from precomputed data\, rather than engaging into costly computations.  At present\, the ML methods of choice in (quantum) chemistry are based upon Graph Convolutional Neural Networks (GCNN)\, as molecular compounds are easily reinterpreted into graph-based structures.\n\nIn this presentation\, I will discuss recent results from the QuNB group on the analysis of the feature space of SchNet\, a popular GCNN for quantum chemistry with high predictive power.  I will show that chemical information from chemical functional groups is learned and encoded in the feature space of the GCNN\, giving a strong quality hallmark of interpretability to the GCNN ML algorithm.\n\nJoint work from the QuNB group (UNB) with Guillaume Acke (Ghent University)\n\nWebex link: https://mun.webex.com/mun/j.php?MTID=m8b04b29275dfeb0c3617be2958e577ff
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-stijn-de-baerdemacker-unb/
CATEGORIES:AARMS Scientific Machine Learning Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220308T110000
DTEND;TZID=UTC:20220308T120000
DTSTAMP:20260613T071512
CREATED:20220228T160654Z
LAST-MODIFIED:20220228T160748Z
UID:6615-1646737200-1646740800@aarms.math.ca
SUMMARY:AARMS Scientific Machine Learning Seminar: Simone Brugiapaglia (Concordia University)
DESCRIPTION:The curse of dimensionality and the blessings of sparsity and Monte Carlo sampling: From polynomial approximation to deep learning in high dimensions \nIn 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.\n\nFirst\, we will consider the case of sparse polynomial approximation via compressed sensing. Focusing on the case where the target function is smooth\, but possibly highly anisotropic\, we will show how to obtain sample complexity bounds only mildly affected by the curse of dimensionality\, near-optimal accuracy guarantees\, stability to unknown errors corrupting the data\, and rigorous convergence rates of algebraic and exponential type.\n\nThen\, we will illustrate how the mathematical toolkit of sparse polynomial approximation can be employed to obtain a “practical existence theorem” for deep learning in the context of high-dimensional Hilbert-valued function approximation. This result shows not only the existence of neural networks with desirable approximation properties\, but also how to compute them via a suitable training procedure in order to achieve best-in-class performance guarantees.\n\nWe will conclude by discussing ongoing and future research directions.\n\n\nWebex link:\n\nhttps://mun.webex.com/mun/j.php?MTID=m3908ed63dfe6896d1e2421f4a3356bc9
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-simone-brugiapaglia-concordia-university/
LOCATION:WebEx seminar
CATEGORIES:AARMS Scientific Machine Learning Seminar
ORGANIZER;CN="Alexander Bihlo":MAILTO:abihlo@mun.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220309T153000
DTEND;TZID=UTC:20220309T163000
DTSTAMP:20260613T071512
CREATED:20220307T121100Z
LAST-MODIFIED:20220307T122041Z
UID:6619-1646839800-1646843400@aarms.math.ca
SUMMARY:Atlantic Graph Theory Seminar: Pjotr Buys (University of Amdsterdam)
DESCRIPTION: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 of all active parameters the activity-locus and I will show that it is equal to the zero-locus. I will use this framework to prove some fun things about the zero-locus. Although I have not yet figured out how to use this to answer any of the open questions posed by Jason\, I am hopeful it might be a step in the right direction. \n\nJoin Zoom Meeting: link
URL:https://aarms.math.ca/event/atlantic-graph-theory-seminar-pjotr-nuys-university-of-amdsterdam/
LOCATION:Online via Zoom
CATEGORIES:AARMS Atlantic Graph Theory Seminar
ORGANIZER;CN="Jason Brown":MAILTO:jason.brown@dal.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Halifax:20220311T160000
DTEND;TZID=America/Halifax:20220311T170000
DTSTAMP:20260613T071512
CREATED:20200904T115630Z
LAST-MODIFIED:20220308T140357Z
UID:6561-1647014400-1647018000@aarms.math.ca
SUMMARY:Dalhousie-AARMS AAMP Seminar: Justin Tzou (Macquarie U.)
DESCRIPTION:Title: Modeling and analysis of localized vegetation patterns on curved topography\n\nAbstract: 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 of the spot and determine how it is impacted by different aspects of the terrain. One such aspect is captured by the regular part of a certain Green’s function on the curved surface; I will briefly discuss how we numerically compute this quantity. Joint work with Leo Tzou.\nThe Dalhousie-AARMS Analysis-Applied Math-Physics Seminar takes place on Fridays from 4 – 5 pm Atlantic Time over Zoom.  If you would like to attend\, please email the organizers for connection details.
URL:https://aarms.math.ca/event/dalhousie-aarms-aamp-seminar-steven-lester-kings-college-london-2-2-3-2-2-2-4-2-2/
LOCATION:Zoom seminar
CATEGORIES:AAMP Seminar
ORGANIZER;CN="Suresh Eswarathasan":MAILTO:sr766936@dal.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220316T153000
DTEND;TZID=UTC:20220316T163000
DTSTAMP:20260613T071512
CREATED:20220314T110128Z
LAST-MODIFIED:20220314T110138Z
UID:6626-1647444600-1647448200@aarms.math.ca
SUMMARY:Atlantic Graph Theory Seminar: Theodore Kolokolnikov (Dalhousie)
DESCRIPTION: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 [3\,8]\, 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 generally\, we study random semi-regular graphs whose average degree is d\, not necessary an integer. This provides a natural generalization of a d-regular graph in the case of a non-integer d. We characterise their algebraic connectivity in terms of a root of a certain 6th-degree polynomial. Finally\, we construct a small-world-type network of average degree 2.5 with a relatively high algebraic connectivity. We also propose some related open problems and conjectures.
URL:https://aarms.math.ca/event/atlantic-graph-theory-seminar-theodore-kolokolnikov-dalhousie/
LOCATION:Online via Zoom
CATEGORIES:AARMS Atlantic Graph Theory Seminar
ORGANIZER;CN="Jason Brown":MAILTO:jason.brown@dal.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20220325
DTEND;VALUE=DATE:20220327
DTSTAMP:20260613T071512
CREATED:20220211T174511Z
LAST-MODIFIED:20220211T174703Z
UID:6597-1648166400-1648339199@aarms.math.ca
SUMMARY:Atlantic Canada Actuarial Student Conference
DESCRIPTION:We invite all Actuarial Science students from across the Atlantic region to join us for the 2022 Atlantic Canada Actuarial Student Conference! \nHosted 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.
URL:https://aarms.math.ca/event/atlantic-canada-actuarial-student-conference/
LOCATION:University of Prince Edward Island\, Charlottetown\, Prince Edward Island\, Canada
CATEGORIES:AARMS workshops and conferences
ORGANIZER;CN="Gabriel Pe%C3%B1alver":MAILTO:gopenalver@upei.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Halifax:20220325T160000
DTEND;TZID=America/Halifax:20220325T170000
DTSTAMP:20260613T071512
CREATED:20200904T115630Z
LAST-MODIFIED:20220211T135543Z
UID:6586-1648224000-1648227600@aarms.math.ca
SUMMARY:Dalhousie-AARMS AAMP Seminar: Manuela Girotti (Saint Mary's Uni.)
DESCRIPTION:Title: Asymptotic Analysis of the Interaction Between a Soliton and a Regular Gas of Solitons (a.k.a. Gulliver and the Lilliputians) \nAbstract: 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)\, describing classical results in the field. \n\nThe second part will focus on some new developments and growing interest into a special case of solitons defined as “solitonic gas” or “integrable turbulence”. In particular\, we will discuss a recent work on long-time asymptotic behaviour of such type of solitons. We will achieve our results by first framing the problem in the setting of a Riemann–Hilbert problem and then by rigorously analyzing it using the powerful technique of nonlinear steepest descent.\n\nThis is a joint work with Tamara Grava (U. Bristol/SISSA)\, Bob Jenkins (UCF)\, Ken McLaughlin (CSU) and Alexander Minakov (U. Karlova).\nThe Dalhousie-AARMS Analysis-Applied Math-Physics Seminar takes place on Fridays from 4 – 5 pm Atlantic Time over Zoom.  If you would like to attend\, please email the organizers for connection details.
URL:https://aarms.math.ca/event/dalhousie-aarms-aamp-seminar-steven-lester-kings-college-london-2-2-3-2-2-2-4-2-2-2/
LOCATION:Zoom seminar
CATEGORIES:AAMP Seminar
ORGANIZER;CN="Suresh Eswarathasan":MAILTO:sr766936@dal.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220329T110000
DTEND;TZID=UTC:20220329T120000
DTSTAMP:20260613T071512
CREATED:20220322T143839Z
LAST-MODIFIED:20220322T143839Z
UID:6631-1648551600-1648555200@aarms.math.ca
SUMMARY:AARMS Scientific Machine Learning Seminar: Scott MacLachlan (Memorial)
DESCRIPTION:Optimization and Learning in the Design of Preconditioners\n\nComputer 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 these systems are multigrid and domain decomposition methods\, which use multiscale representations of the systems to be solved to achieve linear-scaling complexity for the solution of these linear systems.  These preconditioners typically rely on heuristics in their construction\, to approximate solutions to underlying combinatorial (and other) optimization problems that specify parameters and other components of the preconditioners\, based on the discrete problem to which they are being applied.  In this talk\, I will discuss the use of advanced optimization and machine learning techniques to approximately solve these optimization problems and the impact these techniques can have on advanced preconditioner design.\n\n\nWebex link:\nhttps://mun.webex.com/mun/j.php?MTID=m8e73e9072ce63a9072b87874c1ee7cae
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-scott-maclachlan-memorial/
LOCATION:WebEx seminar
CATEGORIES:AARMS Scientific Machine Learning Seminar
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