Seminars and Colloquia by Series

Affine Grassmannians in motivic homotopy theory

Series
Geometry Topology Seminar
Time
Monday, November 12, 2018 - 14:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Tom BachmannMIT
It is a classical theorem in algebraic topology that the loop space of a suitable Lie group can be modeled by an infinite dimensional variety, called the loop Grassmannian. It is also well known that there is an algebraic analog of loop Grassmannians, known as the affine Grassmannian of an algebraic groop (this is an ind-variety). I will explain how in motivic homotopy theory, the topological result has the "expected" analog: the Gm-loop space of a suitable algebraic group is A^1-equivalent to the affine Grassmannian.

Capture small-noise-induced rare events in differential equations: from variation to sampling

Series
Applied and Computational Mathematics Seminar
Time
Monday, November 12, 2018 - 13:55 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Prof. Xiaoliang WanLouisiana State University
In this talk, we will discuss some computational issues when applying the large deviation theory to study small-noise-induced rare events in differential equations. We focus on two specific problems: the most probable transition path for an ordinary differential equation and the asymptotically efficient simulation of rare events for an elliptic problem. Both problems are related to the large deviation theory. From a computational point of view, the former one is a variational problem while the latter one is a sampling problem. For the first problem, we have developed an hp adaptive minimum action method, and for the second problem, we will present an importance sampling estimator subject to a sufficient and necessary condition for its asymptotic efficiency.

Models of unstable motivic homotopy theory

Series
Geometry Topology Seminar Pre-talk
Time
Monday, November 12, 2018 - 13:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Tom BachmannMIT
I will review various ways of modeling the homotopy theory of spaces: several model categories of simplicial sheaves and simplicial presheaves, and related infinity categorical constructions.

A formula with some applications to the theory of Lyapunov exponents (Cancelled)

Series
Dynamical Systems Working Seminar
Time
Friday, November 9, 2018 - 15:05 for 1 hour (actually 50 minutes)
Location
Skiles 156
Speaker
Rui HanGeorgia Tech
We prove an elementary formula about the average expansion of certain products of 2 by 2 matrices. This permits us to quickly re-obtain an inequality by M. Herman and a theorem by Dedieu and Shub, both concerning Lyapunov exponents. This is a work of A. Avila and J. Bochi. https://link.springer.com/article/10.1007/BF02785853

Locally decodable codes and arithmetic progressions in random settings

Series
Combinatorics Seminar
Time
Friday, November 9, 2018 - 15:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Sivakanth GopiMicrosoft Research Redmond
(1) A set D of natural numbers is called t-intersective if every positive upper density subset A of natural numbers contains a (t+1)-length arithmetic progression (AP) whose common differences is in D. Szemeredi's theorem states that the set of all natural numbers is t-intersective for every t. But there are other non-trivial examples like {p-1: p prime}, {1^k,2^k,3^k,\dots} for any k etc. which are t-intersective for every t. A natural question to study is at what density random subsets of natural numbers become t-intersective? (2) Let X_t be the number of t-APs in a random subset of Z/NZ where each element is selected with probability p independently. Can we prove precise estimates on the probability that X_t is much larger than its expectation? (3) Locally decodable codes (LDCs) are error correcting codes which allow ultra fast decoding of any message bit from a corrupted encoding of the message. What is the smallest encoding length of such codes? These seemingly unrelated problems can be addressed by studying the Gaussian width of images of low degree polynomial mappings, which seems to be a fundamental tool applicable to many such problems. Adapting ideas from existing LDC lower bounds, we can prove a general bound on Gaussian width of such sets which reproves the known LDC lower bounds and also implies new bounds for the above mentioned problems. Our bounds are still far from conjectured bounds which suggests that there is plenty of room for improvement. If time permits, we will discuss connections to type constants of injective tensor products of Banach spaces (or chernoff bounds for tensors in simpler terms). Joint work with Jop Briet.

Randomness vs Quantumness

Series
ACO Student Seminar
Time
Friday, November 9, 2018 - 13:05 for 30 minutes
Location
Skiles 005
Speaker
Lance FortnowSchool of Computer Science, Georgia Tech
Often the popular press talks about the power of quantum computing coming from its ability to perform several computations simultaneously. We’ve already had a similar capability from probabilistic machines. This talk will explore the relationship between quantum and randomized computation, how they are similar and how they differ, and why quantum can work exponentially faster on some but far from all computational problems. We’ll talk about some open problems in quantum complexity that can help shed light on the similarities and differences between randomness and “quantumness”. This talk will not assume any previous knowledge of quantum information or quantum computing.

The Clemens conjecture

Series
Student Algebraic Geometry Seminar
Time
Thursday, November 8, 2018 - 13:30 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Stephen McKeanGeorgia Tech
In 1986, Herb Clemens conjectured that on a general quintic threefold, there are finitely many rational curves of any given degree. In this talk, we will give a survey of what is known about this conjecture. We will also highlight the connections between enumerative geometry and physics that arise in studying the quintic threefold.

Finding small simple cycle separators for 2-connected planar graphs

Series
Graph Theory Working Seminar
Time
Wednesday, November 7, 2018 - 16:30 for 1.5 hours (actually 80 minutes)
Location
Skiles 006
Speaker
Michael WigalGeorgia Tech
For a graph on $n$ vertices, a vertex partition $A,B,C$ is a $f(n)$-vertex separator if $|C| \le f(n)$ and $|A|,|B| \le \frac{2}{3}n$ and $(A,B) = \emptyset$. A theorem from Gary Miller states for an embedded 2-connected planar graph with maximum face size $d$ there exists a simple cycle such that it is vertex separator of size at most $2\sqrt{dn}$. This has applications in divide and conquer algorithms.

Transition lines for Almost Mathieu Operator

Series
Math Physics Seminar
Time
Wednesday, November 7, 2018 - 16:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Fan YangGeorgia Tech
I will talk about what happens on the spectral transition lines for the almost Mathieu operator. This talk is based on joint works with Svetlana Jitomirskaya and Qi Zhou. For both transition lines \{\beta(\alpha)=\ln{\lambda}\} and \{\gamma(\alpha,\theta)=\ln{\lambda}\} in the positive Lyapunov exponent regime, we show purely point spectrum/purely singular continuous spectrum for dense subsets of frequencies/phases.

Analysis and recovery of high-dimensional data with low-dimensional structures

Series
High Dimensional Seminar
Time
Wednesday, November 7, 2018 - 12:52 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Wenjing LiaoGeorgia Tech

High-dimensional data arise in many fields of contemporary science and introduce new challenges in statistical learning and data recovery. Many datasets in image analysis and signal processing are in a high-dimensional space but exhibit a low-dimensional structure. We are interested in building efficient representations of these data for the purpose of compression and inference, and giving performance guarantees depending on the intrinsic dimension of data. I will present two sets of problems: one is related with manifold learning; the other arises from imaging and signal processing where we want to recover a high-dimensional, sparse vector from few linear measurements. In the first problem, we model a data set in $R^D$ as samples from a probability measure concentrated on or near an unknown $d$-dimensional manifold with $d$ much smaller than $D$. We develop a multiscale adaptive scheme to build low-dimensional geometric approximations of the manifold, as well as approximating functions on the manifold. The second problem arises from source localization in signal processing where a uniform array of sensors is set to collect propagating waves from a small number of sources. I will present some theory and algorithms for the recovery of the point sources with high precision.

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