Seminars and Colloquia by Series

Set Images and Convexity Properties of Convolutions for Sum Sets and Difference Sets

Series
Dissertation Defense
Time
Friday, June 23, 2023 - 14:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Chi-Nuo LeeGeorgia Tech

Many recent breakthroughs in additive combinatorics, such as results relating to Roth’s theorem or inverse sum set theorems, utilize a combination of Fourier analytical and physical methods. Physical methods refer to results relating to the physical space, such as almost-periodicity results regarding convolutions. This thesis focuses on the properties of convolutions.

Given a group G and sets A ⊆ G, we study the properties of the convolution for sum sets and difference sets, 1A ∗1A and 1A ∗1−A. Given x ∈ Gn, we study the set image of its sum set and difference set. We break down the study of set images into two cases, when x is independent, and when x is an arithmetic progression. In both cases, we provide some convexity result for the set image of both the sum set and difference set. For the case of the arithmetic progression, we prove convexity by first showing a recurrence relation for the distribution of the convolution.

Finally, we prove a smoothness property regarding 4-fold convolutions 1A ∗1A ∗1A ∗1A. We then construct different examples to better understand possible bounds for the smoothness property in the case of 2-fold convolutions 1A ∗ 1A.

Committee

Prof. Ernie Croot, Advisor

Prof. Michael Lacey

Prof. Josephine Yu

Prof. Anton Leykin

Prof. Will Perkins

Functional Ito Calculus for Lévy Processes (with a View Towards Mathematical Finance)

Series
Dissertation Defense
Time
Thursday, June 22, 2023 - 15:00 for 1 hour (actually 50 minutes)
Location
Skiles 006/Zoom
Speaker
Jorge Aurelio Víquez BolañosGeorgia Tech

Zoom link.  Meeting ID: 914 2801 6313, Passcode: 501018

We examine the relationship between Dupire's functional derivative and a variant of the functional derivative developed by Kim for analyzing functionals in systems with delay. Our findings demonstrate that if Dupire's space derivatives exist, differentiability in any continuous functional direction implies differentiability in any other direction, including the constant one. Additionally, we establish that co-invariant differentiable functionals can lead to a functional Ito formula in the Cont and Fournié path-wise setting under the right regularity conditions.

Next, our attention turns to functional extensions of the Meyer-Tanaka formula and the efforts made to characterize the zero-energy term for integral representations of functionals of semimartingales. Using Eisenbaum's idea for reversible semimartingales, we obtain an optimal integration formula for Lévy processes, which avoids imposing additional regularity requirements on the functional's space derivative, and extends other approaches using the stationary and martingale properties of Lévy processes.

Finally, we address the topic of integral representations for the delta of a path-dependent pay-off, which generalizes Benth, Di Nunno, and Khedher's framework for the approximation of functionals of jump-diffusions to cases where they may be driven by a process satisfying a path-dependent differential equation. Our results extend Jazaerli and Saporito's formula for the delta of functionals to the jump-diffusion case. We propose an adjoint formula for the horizontal derivative, hoping to obtain more tractable formulas for the Delta of strongly path-dependent functionals.

Committee 

  • Prof. Christian Houdré - School of Mathematics, Georgia Tech (advisor)
  • Prof. Michael Damron - School of Mathematics, Georgia Tech
  • Prof. Rachel Kuske - School of Mathematics, Georgia Tech
  • Prof. Andrzej Święch - School of Mathematics, Georgia Tech
  • Prof. José Figueroa-López - Department of Mathematics and Statistics, Washington University in St. Louis
  • Prof. Bruno Dupire - Department of Mathematics, New York University

Divisors and multiplicities under tropical and signed shadows

Series
Dissertation Defense
Time
Tuesday, June 20, 2023 - 09:30 for 1.5 hours (actually 80 minutes)
Location
Skiles 006 / Zoom
Speaker
Trevor GunnGeorgia Tech

Zoom link (Meeting ID: 941 5991 7033, Passcode: 328576)

I will present two projects related to tropical divisors and multiplicities. First, my work with Philipp Jell on fully-faithful tropicalizations in 3-dimensions. Second, my work with Andreas Gross on algebraic and combinatorial multiplicities for multivariate polynomials over the tropical and sign hyperfields.

The first part is about using piecewise linear functions to describe tropical curves in 3 dimensions and how the changes in those slopes (a divisor) lift to non-Archimedean curves. These divisors give an embedding of a curve in a 3-dimensional toric variety whose tropicalization is isometric to the so-called extended skeleton of the curve.

In part two, I describe how Baker and Lorscheid's theory of multiplicities over hyperfields can be extended to multivariate polynomials. One key result is a new proof/view of the work of Itenburg and Roy who used patchworking to construct some lower bounds on the number of positive roots of a system of polynomials given a particular sign arrangement. Another result is a collection of upper bounds for the same problem.

Committee:

  • Matt Baker (Advisor)
  • Josephine Yu
  • Oliver Lorscheid
  • Anton Leykin
  • Greg Blekherman

Improving and maximal inequalities in discrete harmonic analysis

Series
Dissertation Defense
Time
Wednesday, June 7, 2023 - 13:00 for 1 hour (actually 50 minutes)
Location
Skiles 006 & online
Speaker
Christina GiannitsiGeorgia Tech

►Presentation will be in hybrid format. Zoom link: https://gatech.zoom.us/j/99128737217?pwd=dllnNE1kSW1DZURrY1UycGxrazJtQT09

►Abstract: We study various averaging operators of discrete functions, inspired by number theory, in order to show they satisfy  $\ell^p$ improving and maximal bounds. The maximal bounds are obtained via sparse domination results for $p \in (1,2)$, which imply boundedness on $\ell ^p (w)$ for $p \in (1, \infty )$, for all weights $w$ in the Muckenhoupt $A_p$ class. 

We start by looking at averages along the integers weighted by the divisor function $d(n)$, and obtain a uniform, scale free $\ell^p$-improving estimate for $p \in (1,2)$. We also show that the associated maximal function satisfies $(p,p)$ sparse bounds for $p \in (1,2)$. We move on to study averages along primes in arithmetic progressions, and establish improving and maximal inequalities for these averages, that are uniform in the choice of progression. The uniformity over progressions imposes several novel elements on our approach. Lastly, we generalize our setting in the context of number fields, by considering averages over the Gaussian primes.

Finally, we explore the connections of our work to number theory:   Fix an interval $\omega \subset \mathbb{T}$. There is an integer $N_\omega $, so that every odd integer $n$ with $N(n)>N_\omega $ is a sum of three Gaussian primes with arguments in $\omega $.  This is the weak Goldbach conjecture. A density version of the strong Goldbach conjecture is proved, as well.

                                                   

►Members of the committee:
· Michael Lacey (advisor)
· Chris Heil
· Ben Krause
· Doron Lubinsky
· Shahaf Nitzan

Two Phases of Scaling Laws for Nearest Neighbor Classifiers

Series
Applied and Computational Mathematics Seminar
Time
Thursday, May 25, 2023 - 10:30 for 1 hour (actually 50 minutes)
Location
https://gatech.zoom.us/j/98355006347
Speaker
Jingzhao ZhangTsinghua University

Please Note: Special time & day. Remote only.

A scaling law refers to the observation that the test performance of a model improves as the number of training data increases. A fast scaling law implies that one can solve machine learning problems by simply boosting the data and the model sizes. Yet, in many cases, the benefit of adding more data can be negligible. In this work, we study the rate of scaling laws of nearest neighbor classifiers. We show that a scaling law can have two phases: in the first phase, the generalization error depends polynomially on the data dimension and decreases fast; whereas in the second phase, the error depends exponentially on the data dimension and decreases slowly. Our analysis highlights the complexity of the data distribution in determining the generalization error. When the data distributes benignly, our result suggests that nearest neighbor classifier can achieve a generalization error that depends polynomially, instead of exponentially, on the data dimension.

Symmetric nonnegative polynomials and sums of squares: mean roads to infinity

Series
Dissertation Defense
Time
Wednesday, May 24, 2023 - 11:00 for 1.5 hours (actually 80 minutes)
Location
Skiles 006
Speaker
Jose AcevedoGeorgia Tech
We study the limits of cones of symmetric nonnegative polynomials and symmetric sums of squares of fixed degree, when expressed in power-mean or monomial-mean basis. These limits correspond to forms with stable expression in power-mean polynomials that are globally nonnegative (resp. sums of squares) regardless of the number of variables. Using some elements of the representation theory of the symmetric group we introduce partial symmetry reduction to describe the limit cone of symmetric sums of squares, which simultaneously allows us to tropicalize its dual cone. Using tropical convexity to describe the tropicalization of the dual cone to symmetric nonnegative forms we then compare both tropicalizations, which turn out to be convex polyhedral cones. We then show that the cones are different for all degrees larger than 4. For even symmetric forms we show that the cones agree up to degree $8$, and are different starting at degree 10. We also find, via tropicalization, explicit examples of symmetric forms that are nonnegative but not sums of squares at the limit.

Quantitative Generalized CLT with Self-Decomposable Limiting Laws by Spectral Methods

Series
Stochastics Seminar
Time
Thursday, May 18, 2023 - 15:30 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Benjamin ArrasUniversité de Lille

In this talk, I will present new stability results for non-degenerate centered self-decomposable laws with finite second moment and for non-degenerate symmetric alpha-stable laws with alpha in (1,2). These stability results are based on Stein's method and closed forms techniques. As an application, explicit rates of convergence are obtained for several instances of the generalized CLTs. Finally, I will discuss the standard Cauchy case.

Dynamics of excitable cells: neurons and cardiomyocytes

Series
Other Talks
Time
Wednesday, May 10, 2023 - 11:00 for 1 hour (actually 50 minutes)
Location
PLOS (second floor of Howey)
Speaker
Roberto BarrioUniv. of Zaragoza
In recent years, much attention has been paid to the description of excitable media,
such as the dynamics of the brain and heart.
In both cases, the building blocks are excitable cells, neurons, and cardiomyocytes,
and a detailed look at the mathematics behind some of their mathematical models provides
a good starting point for answering some observed phenomena.
In this talk we show how some apparently  simple phenomena,
such as the spike-adding process,
have important consequences in the models and how various elements intervene behind their formation,
such as homoclinic bifurcations, fast-slow decompositions, "canards",
the completion of the Smale topological template, the formation of Morse surfaces
creating geometric bifurcations, etc.
Finally, we will illustrate its relevance in insect gait patterns and in the formation of cardiac arrhythmias.
 

Some Global Relaxation Methods for Quadratic and Semidefinite Programming

Series
Dissertation Defense
Time
Tuesday, May 9, 2023 - 11:00 for 1.5 hours (actually 80 minutes)
Location
Skiles 005 and ONLINE
Speaker
Shengding SunGeorgia Tech

Zoom link: https://gatech.zoom.us/meeting/96948840253

Quadratic programming and semidefinite programming are vital tools in discrete and continuous optimization, with broad applications. A major challenge is to develop methodologies and algorithms to solve instances with special structures. For this purpose, we study some global relaxation techniques to quadratic and semidefinite programming, and prove theoretical properties about their qualities. In the first half we study the negative eigenvalues of $k$-locally positive semidefinite matrices, which are closely related to the sparse relaxation of semidefinite programming. In the second half we study aggregations of quadratic inequalities, a tool that can be leveraged to obtain tighter relaxation to quadratic programming than the standard Shor relaxation. In particular, our results on finiteness of aggregations can potentially lead to efficient algorithms for certain classes of quadratic programming instances with two constraints.

A deter-mean-istic description of Stochastic Oscillators

Series
CDSNS Colloquium
Time
Friday, May 5, 2023 - 15:30 for 1 hour (actually 50 minutes)
Location
Online
Speaker
Alberto Pérez-CerveraUniversidad Complutense de Madrid, Spain

Link: https://gatech.zoom.us/j/91390791493?pwd=QnpaWHNEOHZTVXlZSXFkYTJ0b0Q0UT09

Abstract: The Parameterisation Method is a powerful body of theory to compute the invariant manifolds of a dynamical system by looking for a parameterization of them in such a way that the dynamics on this manifold expressed in the coordinates of such parameterization writes as simply as possible. This methodology was foreseen by Guillamon and Huguet [SIADS, 2009 & J. Math. Neurosci, 2013] as a possible way of extending the domain of accuracy of the phase-reduction of periodic orbits. This fruitful approach, known as phase-amplitude reduction, has been fully developed during the last decade and provides an essentially complete understanding of deterministic oscillatory dynamics.
In this talk, we pursue the "simpler as possible" philosophy underlying the Parameterisation Method to develop an analogous phase-amplitude approach to stochastic oscillators. Main idea of our approach is to find a change of variables such that the system, when transformed to these variables, expresses in the mean as the deterministic phase-amplitude description. Then, we take advantage of the simplicity of this approach, to develop interesting objects with the aim of further clarifying the stochastic oscillation.

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