Mon, 26 Nov 2018
12:45
L3

Loop Amplitudes in the Scattering Equations Formalism

Ricardo Monteiro
(QMUL)
Abstract

 I will describe recent progress in the study of scattering amplitudes in gauge theory and gravity at loop level, using the formalism of the scattering equations. The scattering equations relate the kinematics of the scattering of massless particles to the moduli space of the sphere. Underpinned by ambitwistor string theory, this formalism provides new insights into the relation between tree-level and loop-level contributions to scattering amplitudes. In this talk, I will describe results up to two loops on how loop integrands can be constructed as forward-limits of trees. One application is the loop-level understanding of the colour-kinematics duality, a symmetry of perturbative gauge theory which relates it to perturbative gravity.

 

Mon, 15 Oct 2018
12:45
L3

Modular graph functions as iterated Eisenstein integrals

Erik Panzer
(Oxford)
Abstract

Superstring scattering amplitudes in genus one have a low-energy expansion in terms of certain real analytic modular forms, called modular graph functions (D'Hoger, Green, Gürdogan and Vanhove). I will sketch the proof that these functions belong to a family of iterated integrals of modular forms (a generalization of Eichler integrals), recently introduced by Francis Brown, which explains many of their properties. The main tools are elliptic multiple polylogarithms (Brown and Levin), single-valued versions thereof, and elliptic multiple zeta values (Enriquez).

Tue, 16 Oct 2018
16:00
L5

On decidability in local and global fields

Jochen Koenigsmann
(Oxford)
Abstract

This is a survey on recent advances in classical decidability issues for local and global fields and for some canonical infinite extensions of those.

Wed, 10 Oct 2018
16:00
C5

Cubulating Groups

Sam Shepherd
(Oxford University)
Abstract

Cubulating a group means finding a proper cocompact action on a CAT(0) cube complex. I will describe how cubulating a group tells us some nice properties of the group, and explain a general strategy for finding cubulations.

Wed, 10 Oct 2018
11:00
N3.12

Hilbert's 10th Problem: What We Know and What We Don't

Brian Tyrrell
(University of Oxford)
Abstract

In this talk I will introduce Hilbert's 10th Problem (H10) and the model-theoretic notions necessary to explore this problem from the perspective of mathematical logic. I will give a brief history of its proof, talk a little about its connection to decidability and definability, then close by speaking about generalisations of H10 - what has been proven and what has yet to be discovered.

Thu, 11 Oct 2018

16:00 - 17:00
L6

Polya’s Program for the Riemann Hypothesis and Related Problems

Ken Ono
(Emory)
Abstract

In 1927 Polya proved that the Riemann Hypothesis is equivalent to the hyperbolicity of Jensen polynomials for Riemann’s Xi-function. This hyperbolicity has only been proved for degrees d=1, 2, 3. For each d we prove the hyperbolicity of all but (perhaps) finitely many Jensen polynomials. We obtain a general theorem which models such polynomials by Hermite polynomials. This theorem also allows us to prove a conjecture of Chen, Jia, and Wang on the partition function. This result can be thought of as a proof of GUE for the Riemann zeta function in derivative aspect. This is joint work with Michael Griffin, Larry Rolen, and Don Zagier.
 

Thu, 18 Oct 2018

16:00 - 17:00
L6

Multizeta and related algebraic structures in the function field arithmetic

Dinesh Thakur
(Rochester)
Abstract

We will see some results and conjectures on the zeta and multizeta values in the function field context, and see how they relate to homological-homotopical objects, such as t-motives, iterated extensions, and to Hopf algebras, big Galois representations.

Tue, 23 Oct 2018

14:00 - 14:30
L5

A Bayesian Conjugate Gradient Method

Jon Cockayne
(University of Warwick)
Abstract

A fundamental task in numerical computation is the solution of large linear systems. The conjugate gradient method is an iterative method which offers rapid convergence to the solution, particularly when an effective preconditioner is employed. However, for more challenging systems a substantial error can be present even after many iterations have been performed. The estimates obtained in this case are of little value unless further information can be provided about the numerical error. In this paper we propose a novel statistical model for this numerical error set in a Bayesian framework. Our approach is a strict generalisation of the conjugate gradient method, which is recovered as the posterior mean for a particular choice of prior. The estimates obtained are analysed with Krylov subspace methods and a contraction result for the posterior is presented. The method is then analysed in a simulation study as well as being applied to a challenging problem in medical imaging.

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