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DTSTART;TZID=UTC:20211103T153000
DTEND;TZID=UTC:20211103T163000
DTSTAMP:20260613T195833
CREATED:20211031T172611Z
LAST-MODIFIED:20211031T172611Z
UID:6472-1635953400-1635957000@aarms.math.ca
SUMMARY:Atlantic Graph Theory Seminar: Jo Ellis-Monaghan (University of Amsterdam)
DESCRIPTION:2017 saw the centennial of William Tutte\, one of the greatest mathematicians of modern times.  One of the testimonies to Tutte’s genius is that nearly everything he did proved to be a catalyst\, triggering an explosion of further investigations and opening whole new vistas of mathematics.  The Tutte polynomial is one of many such examples in his legacy.   Here we will explore some of its salient properties and some of the many directions that propagated outward from the original Tutte polynomial.  These include several ways in which the Tutte polynomial may be defined and its universality\, as well as some of its combinatorial and algebraic properties.  We will showcase information encoded in the Tutte polynomial as evaluations and specializations\, as these inform nearly every aspect of combinatorics.   Furthermore\, the scope of the Tutte polynomial is continually broadening through generalizations of either its domain or parameter space\, and we will highlight some important examples\, and touch on its interrelations with other combinatorial polynomials.  We will conclude with its particularly fruitful connections with biology and the Potts model of statistical mechanics\, and offer some open questions.
URL:https://aarms.math.ca/event/atlantic-graph-theory-seminar-jo-ellis-monaghan-university-of-amsterdam/
LOCATION:Zoom seminar
CATEGORIES:AARMS Atlantic Graph Theory Seminar
ORGANIZER;CN="Jason Brown":MAILTO:jason.brown@dal.ca
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BEGIN:VEVENT
DTSTART;TZID=America/Halifax:20211105T160000
DTEND;TZID=America/Halifax:20211105T170000
DTSTAMP:20260613T195833
CREATED:20200904T115630Z
LAST-MODIFIED:20211103T174033Z
UID:6273-1636128000-1636131600@aarms.math.ca
SUMMARY:Dalhousie-AARMS AAMP Seminar: Nina Holden (ETH Zürich and the Courant Institute)
DESCRIPTION:Conformal invariance of percolation on random planar maps\nConformal invariance of critical percolation on the triangular lattice was proved by Smirnov. His proof is hard to extend to critical percolation on other lattices since his proof relies on a combinatorial identity which is only true on the triangular lattice. On random lattices known as random planar maps\, however\, it turns out that conformal invariance can be established. This was done in joint work with Sun\, building on our earlier works with Albenque\, Bernardi\, Garban\, Gwynne\, Lawler\, Li\, and Sepulveda. \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-2-2-3-2-2-2-2/
LOCATION:Zoom seminar
CATEGORIES:AAMP Seminar
ORGANIZER;CN="Suresh Eswarathasan":MAILTO:sr766936@dal.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20211109T110000
DTEND;TZID=UTC:20211109T120000
DTSTAMP:20260613T195833
CREATED:20211102T153203Z
LAST-MODIFIED:20211113T004815Z
UID:6474-1636455600-1636459200@aarms.math.ca
SUMMARY:AARMS Scientific Machine Learning Seminar: Hamid Usefi (MUN)
DESCRIPTION:Multicollinearity\, singular vectors\, and dimensionality reduction for high-dimensional datasets\nSingle nucleotide polymorphisms (SNPs) as building blocks of our DNA\, can determine the variations between people. It is believed that SNPs in genes that regulate DNA mismatch repair\, cell cycle regulation\, metabolism and immunity are associated with genetic susceptibility to cancer.  So\, SNPs are potential diagnostic and therapeutic biomarkers in many cancer types. This in part has prompted the rapid advancements in DNA sequencing  which makes it  possible both in terms of cost and time to genetically sequence a single suspect tissue. The number of SNPs in a disease dataset varies from tens of thousands to several million. So\, one of the bottlenecks of working with these genome datasets is their large-scale size that makes it difficult to render the data for meaningful analysis.  Furthermore\, in most diseases\, there are at most a couple of hundred SNPs associated with the disease. So\, simply put we would be looking for a needle in a haystack. \nMachine learning algorithms are gaining increasing attention and believe to have great potential in answering many questions in this respect. A common problem in machine learning and pattern recognition is the process of identifying the most relevant features\, specifically in dealing with high-dimensional datasets in bioinformatics. In this talk\, I will discuss some of our recent work on a new feature selection method\, called Singular-Vectors Feature Selection (SVFS). Part of this work is joint with my recently graduated PhD student Majid Afshar. It is stemmed from identifying linearly dependent columns of a matrix A. This problem can also be viewed as multicollinearity and subset selection in statistical modelling and arises in many contexts\, including regression\, ecology\, and machine learning. \nLet D = [A | b] be a labeled dataset\, where b is the class label and features (attributes or SNPs) are columns of matrix A; rows of A can be viewed as samples. We show with examples as well as a sketch of proof that the projector matrix P_A onto the null space of A can be used to partition the columns of A into clusters so that columns in a cluster correlate only with the columns in the same cluster. In the first step\, SVFS uses the projector P_D to find the cluster that contains b. We reduce the size of A by discarding features in the other clusters as irrelevant features. In the next step\, SVFS uses the P_A of reduced A to partition the remaining features into clusters and choose the most important features from each cluster. I will discuss the  performance of SVFS on genomic datasets compared to the state-of-the-art feature selection methods. \n[ recording ] \n\nMeeting link:  https://mun.webex.com/mun/j.php?MTID=m855b1e73549cf668f5b57190a7ef3eae\nMeeting number:   2633 483 6250\nMeeting password:  W3FisMnJa86\n\n\n 
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-hamid-usefi-mun/
LOCATION:WebEx seminar
CATEGORIES:AARMS Scientific Machine Learning Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20211117T153000
DTEND;TZID=UTC:20211117T163000
DTSTAMP:20260613T195833
CREATED:20211115T114239Z
LAST-MODIFIED:20211115T114239Z
UID:6506-1637163000-1637166600@aarms.math.ca
SUMMARY:Atlantic Graph Theory Seminar: Pavol Hell (SFU)
DESCRIPTION:I will discuss a few examples where considering loops leads to interesting insights\, often allowing unifying existing results. These examples will include cops and robbers games\, graph homomorphisms\, variants of interval and chordal graphs\,\nand versions of domination. \nJoin Zoom Meeting: link
URL:https://aarms.math.ca/event/atlantic-graph-theory-seminar-pavol-hell-sfu/
LOCATION:Zoom seminar
CATEGORIES:AARMS Atlantic Graph Theory Seminar
ORGANIZER;CN="Jason Brown":MAILTO:jason.brown@dal.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20211119
DTEND;VALUE=DATE:20211120
DTSTAMP:20260613T195833
CREATED:20211102T162849Z
LAST-MODIFIED:20211102T162849Z
UID:6479-1637280000-1637366399@aarms.math.ca
SUMMARY:University of New Brunswick Data Challenge
DESCRIPTION:The Data Challenge will bring together three competitive events\, the Open Data Visualization (6th annual)\, Data Analytics (2nd Edition)\, and Data Sprint (2nd Edition) on November 19\, 2021 in a hybrid format – virtually & in-person! Take up the challenge and demonstrate the power of data\, with the flexibility to take part remotely. Our previous edition saw 85 data-driven enthusiasts compete from across Canada in 30 teams. The event also saw 35 business experts\, mentors\, speakers & partners from diverse industries. \nParticipants and teams will have the chance to showcase their ability to tell a story-driven by data in three unique competitive formats. Teams can participate in all three competitive events. It is an ideal setting for citizens to get engaged\, to meet leaders in academia\, government\, and private organizations\, and to explore the world of data science. 
URL:https://aarms.math.ca/event/university-of-new-brunswick-data-challenge/
LOCATION:University of New Brunswick (Fredericton Campus)\, Fredericton\, New Brunswick\, Canada
CATEGORIES:AARMS sponsored events
ORGANIZER;CN="Nandi Kaul":MAILTO:nkaul@unb.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Halifax:20211119T160000
DTEND;TZID=America/Halifax:20211119T170000
DTSTAMP:20260613T195833
CREATED:20200904T115630Z
LAST-MODIFIED:20211111T152305Z
UID:6297-1637337600-1637341200@aarms.math.ca
SUMMARY:Dalhousie-AARMS AAMP Seminar: Amanda Young (Technical University of Munich)
DESCRIPTION:Title: A bulk gap in the presence of edge states for a HaldanepseudopotentialAbstract: In this talk\, we discuss a recent result on a bulk gap for atruncated Haldane pseudopotential with maximal half filling\, whichdescribes a strongly correlated system of spinless bosons in a cylindergeometry. For this Hamiltonian with either open or periodic boundaryconditions\, we prove a spectral gap above the highly degenerateground-state space which is uniform in the volume and particle number.Our proofs rely on identifying invariant subspaces to which we applygap-estimate methods previously developed only for quantum spinHamiltonians. In the case of open boundary conditions\, the lower boundon the spectral gap accurately reflects the presence of edge states\,which do not persist into the bulk. Customizing the gap technique to theinvariant subspace\, we avoid the edge states and establish a moreprecise estimate on the bulk gap in the case of periodic boundaryconditions. The same approach can also be applied to prove a bulk gapfor the analogously truncated 1/3-filled Haldane pseudopotential for thefractional quantum Hall effect. Based off joint work with S. Warzel. \n\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-2-2-3-2-2-2-3/
LOCATION:Zoom seminar
CATEGORIES:AAMP Seminar
ORGANIZER;CN="Suresh Eswarathasan":MAILTO:sr766936@dal.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20211123T110000
DTEND;TZID=UTC:20211123T120000
DTSTAMP:20260613T195833
CREATED:20211112T192613Z
LAST-MODIFIED:20211117T110237Z
UID:6497-1637665200-1637668800@aarms.math.ca
SUMMARY:AARMS Scientific Machine Learning Seminar: Ben Adcock (Simon Fraser University)
DESCRIPTION:Title: Tackling the curse: polynomial and deep neural network methods for function approximation in high dimensions \nAbstract: Many problems in computational science and engineering require the accurate approximation of a target function from data. This problem is rendered challenging by the high-dimensionality of the function\, the expense of generating function samples\, the presence of noise in the measurements\, and the fact that the target function may take values in a function space. Developing techniques that tackle these challenges without succumbing to the famous “curse of dimensionality” has been a long-standing problem. In the first part of this talk I will give a brief survey of a decade’s worth of progress on high-dimensional function approximation via sparse polynomial expansions. I will show how the proper use of nonlinear approximation theory and compressed sensing techniques leads to algorithms for high-dimensional approximation which\, unlike other approaches\, possess provably near-optimal error bounds and moderate sample complexities. In particular\, these techniques mitigate the curse of dimensionality to a substantial degree. The second part of the talk will be devoted to emerging approaches based on deep neural networks and deep learning. Such tools are beginning to garner substantial attention in the scientific computing community. Nonetheless\, I will present evidence of a key gap between current theory and practice. I will then discuss recent results showing that there exist deep neural networks that match the performance of best-in-class schemes\, and furthermore\, these can indeed be trained through realizable procedures. This highlights the potential of deep neural networks\, and sheds light on achieving robust\, reliable and overall improved practical performance. \nThis talk is based on joint work with Anyi Bao\, Simone Brugiapaglia\, Juan M. Cardenas\, Nick Dexter\, Sebastian Moraga\, Yi Sui and Clayton G. Webster. \nMeeting link:\nhttps://mun.webex.com/mun/j.php?MTID=m38cc6ce370de2b1c234e33feac5bb93a \nMeeting number: 2630 760 7617\nPassword: cJwPcrHM425
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-ben-adcock-sfu/
LOCATION:WebEx seminar
CATEGORIES:AARMS Scientific Machine Learning Seminar
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