Mathematics

A probabilistic view of the box-ball system and other discrete integrable systems

Speaker: 
Makiko Sasada
Date: 
Thu, Dec 9, 2021
Location: 
Online
Conference: 
Pacific Workshop on Probability and Statistical Physics
Abstract: 

The box-ball system (BBS) was introduced by Takahashi and Satsuma as a simple discrete
model in which solitons (i.e. solitary waves) could be observed, and it has since been shown that it can be derived from the classical Korteweg-de-Vries equation, which describes waves in shallow water by a proper discretization. The last few years have seen a growing interest in the study of the BBS and related discrete integrable systems started from random initial conditions. Particular aims include characterizing measures that are invariant for the dynamics, exploring the soliton decompositions of random configurations, and establishing (generalized) hydrodynamic limits. In this talk, I will explain some recent progress in this direction.

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How round is a Jordan curve?

Speaker: 
Yilin Wang
Date: 
Thu, Dec 9, 2021
Location: 
Online
Conference: 
Pacific Workshop on Probability and Statistical Physics
Abstract: 

The Loewner energy for simple closed curves first arises from the small-parameter large deviations of Schramm-Loewner evolution (SLE), a family of random fractal curves modeling interfaces in 2D statistical mechanics. In a certain way, this energy measures the roundness of a Jordan curve, and we show that it is finite if and only if the curve is a Weil-Petersson quasicircle. This intriguing class of curves has more than 20 equivalent definitions arising in very different contexts, including Teichmueller theory, geometric function theory, hyperbolic geometry, spectral theory, and string theory, and has been studied since the eighties. The myriad of perspectives on this class of curves is both luxurious and mysterious. I will overview the links between Loewner energy and SLE, Weil-Petersson quasicircles, and other branches of math it touches on. I will also highlight how ideas from probability theory inspire new results on Weil-Petersson quasicircles and discuss further directions.

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Recent progress on random field Ising model

Speaker: 
Jian Ding
Date: 
Thu, Dec 9, 2021
Location: 
Online
Conference: 
Pacific Workshop on Probability and Statistical Physics
Abstract: 

Random field Ising model is a canonical example to study the effect of disorder on long
range order. In 70’s, Imry-Ma predicted that in the presence of weak disorder, the long-range
order persists at low temperatures in three dimensions and above but disappears in two
dimensions. In this talk, I will review mathematical development surrounding this prediction,
and I will focus on recent progress on exponential decay (joint with Jiaming Xia) and on
correlation length in two dimensions (joint with Mateo Wirth). In addition, I will describe a
recent general inequality for the Ising model (joint with Jian Song and Rongfeng Sun) which
has implications for the random field Ising model.

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Estimating transport distances via Stein's method

Speaker: 
Max Fathi
Date: 
Thu, Dec 2, 2021
Location: 
Zoom
Online
Conference: 
Kantorovich Initiative Seminar
Abstract: 

Stein’s method is a set of techniques for bounding distances between probability measures via integration-by-parts formulas. It was introduced by Stein in the 1980s for bouding the rate of convergence in central limit theorems, and has found many applications since then in probability, statistics and beyond. In this talk, I will present classical variants of this method in the context of estimating $L^1$ Wasserstein distances, and discuss some recent developments for $L^1$ Wasserstein distances.

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An Algebraic Approach on Fusions of Synchronization Models

Speaker: 
Hansol Park
Date: 
Wed, Dec 8, 2021
Location: 
PIMS, University of British Columbia
Zoom
Online
Conference: 
Emergent Research: The PIMS Postdoctoral Fellow Seminar
Abstract: 

In this talk, we study an algebraic approach to fusions of synchronization models. The Lohe tensor model is a generalized synchronization model which contains three synchronization models; the Kuramoto model(on the circle), the swarm sphere model(on the sphere), and the Lohe matrix model(on the unitary group). Since the Lohe tensor model contains any synchronization models defined on any rank and size of tensors, we use this model to study fusions of synchronization models. The final goal of the study is to present a fusion of multiple Lohe tensor models for different rank tensors and sizes. For this, we identify an admissible Cauchy problem to the Lohe tensor model with a characteristic symbol consisting of a size vector, a natural frequency tensor, a coupling strength tensor, and an initial admissible configuration. In this way, the collection of all admissible Cauchy problems for the Lohe tensor models is equivalent to the space of characteristic symbols. On the other hand, we introduce a binary operation which we call “fusion operation," as a binary operation between characteristic symbols. It turns out that the fusion operation satisfies associativity and admits an identity element in the space of characteristic symbols that naturally form a monoid. By the fusion operation, the weakly coupled system of multi tensor models can be obtained by applying the fusion operation of multiple characteristic symbols corresponding to the Lohe tensor models. As a concrete example, we consider a weak coupling of the swarm sphere model and the Lohe matrix model and provide a sufficient framework leading to emergent dynamics to this coupled model.

Speaker Biography

Hansol Park was born and raised in the Republic of Korea(South Korea). He got a Ph. D. in mathematics in 2021 from Seoul National University(Advisor: Prof. Seung-Yeal Ha). During his doctoral period, he tried to integrate various types of synchronization models. Currently, he is a PIMS Postdoc at Simon Fraser University under Prof. Razvan C. Fetecau. So far, most of his researches are related to particle systems with interactions. Recently, he is interested in variation methods (minimization problem) and information geometry.

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Statistical Network Models for Integrating Functional Connectivity with sMRI and PET Brain Imaging Data

Speaker: 
James D. Wilson
Date: 
Wed, Nov 24, 2021
Location: 
Online
Abstract: 

Network analysis is one of the prominent multivariate techniques used to study structural and functional connectivity of the brain. In a network model of the brain, vertices are used to represent voxels or regions of the brain, and edges between two nodes represent a physical or functional relationship between the two incident regions. Network investigations of connectivity have produced many important advances in our understanding of brain structure and function, including in domains of aging, learning and memory, cognitive control, emotion, and disease. Despite their use, network methodologies still face several important challenges. In this talk, I will focus on a particularly important challenge in the analysis of structural and functional connectivity: how does one jointly model the generative mechanisms of structural and functional connectivity with other modalities? I propose and describe a statistical network model, called the generalized exponential random graph model (GERGM), that flexibly characterizes the network topology of structural and functional connectivity and can readily integrate other modalities of data. The GERGM also directly enables the statistical testing of individual differences through the comparison of their fitted models. In applying the GERGM to the connectivity of healthy individuals from the Human Connectome Project, we find that the GERGM reveals remarkably consistent organizational properties guiding subnetwork architecture in the typically developing brain. We will discuss ongoing work of how to adapt these models to neuroimaging cohorts associated with the ADRC at the University of Pittsburgh, where the goal is to relate the dynamics of structural and functional connectivity with tau and amyloid – beta deposition in individuals across the Alzheimer’s continuum.

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Single-molecule insights for DNA/RNA/protein interactions and drug discovery and development: the next level of resolution, for the next era of genetic medicines

Speaker: 
Sabrina Leslie
Date: 
Wed, Nov 3, 2021
Location: 
PIMS, University of British Columbia
Zoom
Online
Conference: 
Mathematical Biology Seminar
Abstract: 

Molecular interactions lie at the core of biochemistry and biology, and their understanding is crucial to the advancement of biotechnology, therapeutics, and diagnostics. Most existing tools make “ensemble” measurements and report a single result, typically averaged over millions of molecules or more. These measurements can miss rare events, averaging out the natural variations or sub-populations within biological samples, and consequently obscure insights into multi-step and multi-state reactions. The ability to make and connect robust and quantitative measurements on multiple scales - single molecules, cellular complexes, cells, tissues - is a critical unmet need. In this talk, I will introduce a general method called “CLiC” imaging to image molecular interactions one molecule at time with precision and control, and under cell-like conditions. CLiC works by mechanically confining molecules to the field of view in an optical microscope, isolating them in nanofabricated features, and eliminates the complexity and potential biases inherent to tethering molecules. By imaging the trajectories of many single molecules simultaneously and in a dynamic manner, CLiC allows us to investigate and discover the design rules and mechanisms which govern how therapeutic molecules or molecular probes interact with target sites on nucleic acids; and how molecular cargo is released inside cells from lipid nanoparticles. In this talk, I will discuss applications of our imaging platform to better understand DNA, RNA, protein interactions, as well as emerging classes of genetic medicines, gene editing and drug delivery systems. I will highlight current and potential future applications to connect our observations from the level of single molecule to single cells, and opportunities for collaboration as we set up our labs at UBC.

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The Tumor Growth Paradox

Speaker: 
Thomas Hillen
Date: 
Wed, Nov 10, 2021
Location: 
PIMS, University of Alberta
Zoom
Conference: 
Mathematical Biology Seminar
Abstract: 

The tumor invasion paradox relates to the artifact that a cancer that is exposed to increased cell death (for example through radiation), might spread and grow faster than before. The presence of cancer stem cells can convincingly explain this effect. In my talk I will use non-local and local reaction-diffusion type models to look at tumor growth and invasion speeds. We can show that in certain situations the invasion speed increases with increasing death rate - an invasion paradox (joint work with A. Shyntar and M. Rhodes).

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Skeleta for Monomial Quiver Relations

Speaker: 
Jesse Huang
Date: 
Wed, Dec 1, 2021
Location: 
PIMS, University of Alberta
Zoom
Online
Conference: 
Emergent Research: The PIMS Postdoctoral Fellow Seminar
Abstract: 

I will introduce a skeleton obtained directly from monomial relations in a finite quiver without cycles, and relate the construction to some classical examples in mirror symmetry. This is work in progress with David Favero.

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Solving clustering problems via new swarm intelligent algorithms

Speaker: 
Vardan Narula
Date: 
Wed, Nov 17, 2021
Location: 
Online
Abstract: 

In this work, improved swarm intelligent algorithms, namely, Salp Swarm Optimization algorithm, whale optimization, and Grasshopper Optimization Algorithm are proposed for data clustering. Our proposed algorithms utilize the crossover operator to obtain an improvised version of the existing algorithms. The performance of our suggested algorithms is tested by comparing the proposed algorithms with standard swarm intelligent algorithms and other existing algorithms in the literature. Non-parametric statistical test, the Friedman test, is applied to show the superiority of our proposed algorithms over other existing algorithms in the literature. The performance of our algorithms outperforms the performance of other algorithms for the data clustering problem in terms of computational time and accuracy.

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