Wed, 13 Nov 2024
16:00
L6

The McCullough-Miller space for RAAGs

Peio Gale
(Public University of Navarre)
Abstract

The McCullough-Miller space is a contractible simplicial complex that admits an action of the pure symmetric (outer) automorphisms of the free group, with stabilizers that are free abelian. This space has been used to derive several cohomological properties of these groups, such as computing their cohomology ring and proving that they are duality groups. In this talk, we will generalize the construction to right-angled Artin groups (RAAGs), and use it to obtain some interesting cohomological results about the pure symmetric (outer) automorphisms of RAAGs.

Characterising Cancer Cell Responses to Cyclic Hypoxia Using Mathematical Modelling
Celora, G Nixson, R Pitt-Francis, J Maini, P Byrne, H Bulletin of Mathematical Biology volume 86 issue 12 (06 Nov 2024)
Ultra-fast physics-based modeling of the elephant trunk
Kaczmarski, B Moulton, D Goriely, Z Goriely, A Kuhl, E
The unknotting number, hard unknot diagrams, and reinforcement learning.
Applebaum, T Blackwell, S Davies, A Edlich, T Juhász, A Lackenby, M Tomasev, N Zheng, D CoRR volume abs/2409.09032 (2024)
Community detection on directed networks with missing edges
Pedreschi, N Lambiotte, R Bovet, A (25 Oct 2024)
Fri, 29 Nov 2024
12:00
L2

Towards a mathematical definition of superstring scattering amplitudes

Alexander Polishchuk
(University of Oregon)
Abstract

This is a report on the ongoing joint project with Giovanni Felder and David Kazhdan. I'll describe a conjectural way to set up the integration of the superstring measure on the moduli space of supercurves, including a brief review of the necessary supergeometry. The main theorem is that this setup works for genus 2 with no punctures.

Thu, 06 Mar 2025

14:00 - 15:00
Lecture Room 3

Near-optimal hierarchical matrix approximation

Diana Halikias
(Cornell University)
Abstract

Can one recover a matrix from only matrix-vector products? If so, how many are needed? We will consider the matrix recovery problem for the class of hierarchical rank-structured matrices. This problem arises in scientific machine learning, where one wishes to recover the solution operator of a PDE from only input-output pairs of forcing terms and solutions. Peeling algorithms are the canonical method for recovering a hierarchical matrix from matrix-vector products, however their recursive nature poses a potential stability issue which may deteriorate the overall quality of the approximation. Our work resolves the open question of the stability of peeling. We introduce a robust version of peeling and prove that it achieves low error with respect to the best possible hierarchical approximation to any matrix, allowing us to analyze the performance of the algorithm on general matrices, as opposed to exactly hierarchical ones. This analysis relies on theory for low-rank approximation, as well as the surprising result that the Generalized Nystrom method is more accurate than the randomized SVD algorithm in this setting. 

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