Mon, 27 Nov 2017
12:45
L3

D-brane masses and the motivic Hodge conjecture

Albrecht Klemm
(Bonn)
Abstract

We consider the one parameter mirror families W of the Calabi-Yau 3-folds with Picard-Fuchs  equations of hypergeometric type. By mirror symmetry the  even D-brane masses of orginial Calabi-Yau manifolds M can be identified with four periods with respect to an integral symplectic basis of $H_3(W,\mathbb{Z})$ at the point of maximal unipotent monodromy. We establish that the masses of the D4 and D2 branes at the conifold are given by the two algebraically independent values of the L-function of the weight four holomorphic Hecke eigenform with eigenvalue one of $\Gamma_0(N)$. For the quintic in  $\mathbb{P}^4$ it this Hecke eigenform of $\Gamma_0(25)$ was as found by Chad Schoen.  It was discovered  by de la Ossa, Candelas and Villegas that  its  coefficients $a_p$ count the number of  solutions of  the mirror quinitic at the conifold over the finite number field $\mathbb{F}_p$ . Using the theory of periods and quasi-periods of $\Gamma_0(N)$ and the special geometry pairing on Calabi-Yau 3 folds we can fix further values in the connection matrix between the maximal unipotent monodromy point and the conifold point.  

 
 
 
 
Mon, 06 Nov 2017
12:45
L3

On the Vafa-Witten theory on closed four-manifolds

Yuuji Tanaka
(Oxford)
Abstract

We discuss mathematical studies on the Vafa-Witten theory, one of topological twists of N=4 super Yang-Mills theory in four dimensions, from the viewpoints of both differential and algebraic geometry. After mentioning backgrounds and motivation, we describe some issues to construct mathematical theory of this Vafa-Witten one, and explain possible ways to sort them out by analytic and algebro-geometric methods, the latter is joint work with Richard Thomas.

 
On the reducibility of induced representations for classical p-adic groups and related affine Hecke algebras
Ciubotaru, D Heiermann, V Israel Journal of Mathematics volume 231 issue 1 379-417 (07 May 2019)
Mon, 30 Oct 2017
14:30
L6

Rainbow Matchings in Properly Edge-Coloured Multigraphs

Liana Yepremyan
(Oxford University)
Abstract

Aharoni and Berger conjectured that in any bipartite multigraph that is properly edge-coloured by n colours with at least n+1 edges of each colour there must be a matching that uses each colour exactly once (such a matching is called rainbow). This conjecture recently have been proved asymptotically by Pokrovskiy. In this talk, I will consider the same question without the bipartiteness assumption. It turns out that in any multigraph with bounded edge multiplicities, that is properly edge-coloured by n colours with at least n+o(n) edges of each colour, there must be a matching of size n-O(1) that uses each colour at most once. This is joint work with Peter Keevash.

Thu, 08 Mar 2018

16:00 - 17:00
L4

Statistical Learning for Portfolio Tail Risk Measurement

Mike Ludkovski
(University of California Santa Barbara)
Abstract


We consider calculation of VaR/TVaR capital requirements when the underlying economic scenarios are determined by simulatable risk factors. This problem involves computationally expensive nested simulation, since evaluating expected portfolio losses of an outer scenario (aka computing a conditional expectation) requires inner-level Monte Carlo. We introduce several inter-related machine learning techniques to speed up this computation, in particular by properly accounting for the simulation noise. Our main workhorse is an advanced Gaussian Process (GP) regression approach which uses nonparametric spatial modeling to efficiently learn the relationship between the stochastic factors defining scenarios and corresponding portfolio value. Leveraging this emulator, we develop sequential algorithms that adaptively allocate inner simulation budgets to target the quantile region. The GP framework also yields better uncertainty quantification for the resulting VaR/\TVaR estimators that reduces bias and variance compared to existing methods.  Time permitting, I will highlight further related applications of statistical emulation in risk management.
This is joint work with Jimmy Risk (Cal Poly Pomona). 
 

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