Mon, 26 Oct 2020

16:00 - 17:00
Virtual

From curves to arithmetic geometry: Parshin's trick

Jay Swar
Abstract

In 1983, Faltings proved Mordell's conjecture on the finiteness of $K$-points on curves of genus >1 defined over a number field $K$ by proving the finiteness of isomorphism classes of isogenous abelian varieties over $K$. The "first" major step from Mordell's conjecture to what Faltings did came 15 years earlier when Parshin showed that a certain conjecture of Shafarevich would imply Mordell's conjecture. In this talk, I'll focus on motivating and sketching Parshin's argument in an accessible manner and provide some heuristics on how to get from Faltings' finiteness statement to the Shafarevich conjecture.

Wed, 09 Sep 2020

16:00 - 17:00

An elementary proof of RH for curves over finite fields

Jared Duker Lichtman
Abstract

The Riemann hypothesis (RH) is one of the great open problems in mathematics. It arose from the study of prime numbers in an analytic context, and—as often occurs in mathematics—developed analogies in an algebraic setting, leading to the influential Weil conjectures. RH for curves over finite fields was proven in the 1940’s by Weil using algebraic-geometric methods. In this talk, we discuss an alternate proof of this result by Stepanov (and Bombieri), using only elementary properties of polynomials. Over the decades, the proof has been whittled down to a 5 page gem! Time permitting, we also indicate connections to exponential sums and the original RH.
 

Mon, 19 Oct 2020

16:00 - 17:00

Khovanskii's Theorem and Effective Results on Sumset Structure

Michael Curran
Abstract

A remarkable theorem due to Khovanskii asserts that for any finite subset $A$ of an abelian group, the cardinality of the $h$-fold sumset $hA$ grows like a polynomial for all sufficiently large $h$. However, neither the polynomial nor what sufficiently large means are understood in general. We obtain an effective version of Khovanskii's theorem for any $A \subset \mathbb{Z}$ whose convex hull is a simplex; previously such results were only available for $d = 1$. Our approach also gives information about the structure of $hA$, answering a recent question posed by Granville and Shakan. The work is joint with Leo Goldmakher at Williams College.

Mon, 12 Oct 2020

16:00 - 17:00
Virtual

Classical and elliptic polylogarithms

Nil Matthes
(Oxford)
Abstract

The Dirichlet class number formula gives an expression for the residue at s=1 of the Dedekind zeta function of a number field K in terms of certain quantities associated to K. Among those is the regulator of K, a certain determinant involving logarithms of units in K. In the 1980s, Don Zagier gave a conjectural expression for the values at integers s $\geq$ 2 in terms of "higher regulators", with polylogarithms in place of logarithms. The goal of this talk is to give an algebraic-geometric interpretation of these polylogarithms. Time permitting, we will also discuss a similar picture for Hasse--Weil L-functions of elliptic curves.
 

Tue, 20 Oct 2020

14:15 - 15:15
Virtual

Subspace arrangements and the representation theory of rational Cherednik algebras

Stephen Griffeth
(Universidad de Talca)
Abstract

I will explain how the representation theory of rational Cherednik algebras interacts with the commutative algebra of certain subspace arrangements arising from the reflection arrangement of a complex reflection group. Potentially, the representation theory allows one to study both qualitative questions (e.g., is the arrangement Cohen-Macaulay or not?) and quantitative questions (e.g., what is the Hilbert series of the ideal of the arrangement, or even, what are its graded Betti numbers?), by applying the tools (such as orthogonal polynomials, Kazhdan-Lusztig characters, and Dirac cohomology) that representation theory provides. This talk is partly based on joint work with Susanna Fishel and Elizabeth Manosalva.

Mon, 02 Nov 2020

16:00 - 17:00

Stochastic Ricci flow on surfaces

JULIEN DUBEDAT
(Columbia University)
Abstract

The Ricci flow on a surface is an intrinsic evolution of the metric converging to a constant curvature metric within the conformal class. It can be seen as an infinite-dimensional gradient flow. We introduce a natural 'Langevin' version of that flow, thus constructing an SPDE with invariant measure expressed in terms of Liouville Conformal Field Theory.
Joint work with Hao Shen (Wisconsin).

 

Tue, 10 Nov 2020

14:15 - 15:15
Virtual

What is a unipotent representation?

Lucas Mason-Brown
(Oxford University)
Abstract

Let $G$ be a connected reductive algebraic group, and let $G(\mathbb{F}_q)$ be its group of $\mathbb{F}_q$-rational points. Denote by $\mathrm{Irr}(G(\mathbb{F}_q))$ the set of (equivalence classes) of irreducible finite-dimensional representations. Deligne and Lusztig defined a finite subset $$\mathrm{Unip}(G(\mathbb{F}_q)) \subset \mathrm{Irr}_{\mathrm{fd}}(G(\mathbb{F}_q))$$ 
of unipotent representations. These representations play a distinguished role in the representation theory of $G(\mathbb{F}_q)$. In particular, the classification of $\mathrm{Irr}_{\mathrm{fd}}(G(\mathbb{F}_q))$ reduces to the classification of $\mathrm{Unip}(G(\mathbb{F}_q))$. 

Now replace $\mathbb{F}_q$ with a local field $k$ and replace $\mathrm{Irr}_{\mathrm{fd}}(G(\mathbb{F}_q))$ with $\mathrm{Irr}_{\mathrm{u}}(G(k))$ (irreducible unitary representations). Vogan has predicted the existence of a finite subset 
$$\mathrm{Unip}(G(k)) \subset \mathrm{Irr}_{\mathrm{u}}(G(k))$$ 
which completes the following analogy
$$\mathrm{Unip}(G(k)) \text{ is to } \mathrm{Irr}_{\mathrm{u}}(G(k)) \text{ as } \mathrm{Unip}(G(\mathbb{F}_q)) \text{ is to } \mathrm{Irr}_{\mathrm{fd}}(G(\mathbb{F}_q)).$$
In this talk I will propose a definition of $\mathrm{Unip}(G(k))$ when $k = \mathbb{C}$. The definition is geometric and case-free. The representations considered include all of Arthur's, but also many others. After sketching the definition and cataloging its properties, I will explain a classification of $\mathrm{Unip}(G(\mathbb{C}))$, generalizing the well-known result of Barbasch-Vogan for Arthur's representations. Time permitting, I will discuss some speculations about the case of $k=\mathbb{R}$.

This talk is based on forthcoming joint work with Ivan Loseu and Dmitryo Matvieievskyi.

Tue, 13 Oct 2020

14:15 - 15:15
Virtual

The Dirac inequality, Weyl groups, and isolated unitary representations

Dan Ciubotaru
(Oxford University)
Abstract

In the classical setting of real semisimple Lie groups, the Dirac inequality (due to Parthasarathy) gives a necessary condition that the infinitesimal character of an irreducible unitary representation needs to satisfy in terms of the restriction of the representation to the maximal compact subgroup. A similar tool was introduced in the setting of representations of p-adic groups in joint work with Barbasch and Trapa, where the necessary unitarity condition is phrased in terms of the semisimple parameter in the Kazhdan-Lusztig parameterization and the hyperspecial parahoric restriction. I will present several consequences of this inequality to the problem of understanding the unitary dual of the p-adic group, in particular, how it can be used in order to exhibit several isolated "extremal" unitary representations and to compute precise "spectral gaps" for them.

Thu, 22 Oct 2020

14:00 - 15:00
Virtual

Classifier-based Distribution-Dissimilarities: From Maximum Mean Discrepancies to Adversarial Examples

Carl-Johann Simon-Gabriel
(ETH Zurich)
Further Information

datasig.ox.ac.uk/events

Abstract

Any binary classifier (or score-function) can be used to define a dissimilarity between two distributions of points with positive and negative labels. Actually, many well-known distribution-dissimilarities are classifier-based dissimilarities: the total variation, the KL- or JS-divergence, the Hellinger distance, etc. And many recent popular generative modelling algorithms compute or approximate these distribution-dissimilarities by explicitly training a classifier: eg GANs and their variants. After a brief introduction to these classifier-based dissimilarities, I will focus on the influence of the classifier's capacity. I will start with some theoretical considerations illustrated on maximum mean discrepancies --a weak form of total variation that has grown popular in machine learning-- and then focus on deep feed-forward networks and their vulnerability to adversarial examples. We will see that this vulnerability is already rooted in the design and capacity of our current networks, and will discuss ideas to tackle this vulnerability in future.

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