Bershadsky--Cecotti--Ooguri--Vafa torsion in Landau--Ginzburg models

Speaker: 

Guangbo Xu

Institution: 

SUNY Stony Brook

Time: 

Tuesday, October 9, 2018 - 3:00pm to 4:00pm

Host: 

Location: 

306 Rowland Hall

In the celebrated work of Bershadsky--Cecotti--Ooguri--Vafa the genus one string partition function in the B-model is identified with certain analytic torsion of the Hodge Laplacian on a K\"ahler manifold. In a joint work with Shu Shen (IMJ-PRG) and Jianqing Yu (USTC) we study the analogous torsion in Landau--Ginzburg models. I will explain the corresponding index theorem based on the asymptotic expansion of the heat kernel of the Schr\"odinger operator. I will also explain the rigorous definition of the BCOV torsion for homogeneous polynomials on ${\mathbb C}^N$. Lastly I will explain the conjecture stating that in the Calabi--Yau case the BCOV torsion solves the holomorphic anomaly equation for marginal deformations.

Section problems

Speaker: 

Lei Chen

Institution: 

Caltech

Time: 

Monday, December 3, 2018 - 4:00pm to 5:00pm

Host: 

Location: 

RH 340P

In this talk, I will discuss a direction of study in topology: Section problems. There are many variations of the problem: Nielsen realization problems, sections of a surface bundle, sections of a bundle with special property (e.g. nowhere zero vector eld). I will discuss some techniques including homology, Thurston-Nielsen classication and dynamics. Also I will share many open problems. Some of the result are joint work with Nir Gadish, Justin Lanier and Nick Salter.

Symmetric Tensor Decompositions

Speaker: 

Jiawang Nie

Institution: 

UCSD

Time: 

Monday, April 15, 2019 - 4:00pm to 5:00pm

Host: 

Location: 

RH 306

For symmetric tensors, there exist linear relations of recursive patterns among their entries. Such a relation can be represented by a polynomial, which is called a generating polynomial.   A symmetric tensor decomposition can be determined by a set of generating polynomials, which can be represented by a generating matrix. Generally, a symmetric tensor decomposition can be determined by a generating matrix satisfying certain conditions. Based on them, an efficient method is given for computing symmetric tensor decompositions.  

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