Volcanic fissure localisation and lava delta formation: Modelling of volcanic flows undergoing rheological evolution
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Abstract
16:00
New Lower Bounds For Cap Sets
Abstract
A cap set is a subset of $\mathbb{F}_3^n$ with no solutions to $x + y + z = 0$ other than when $x = y = z$, or equivalently no non-trivial $3$-term arithmetic progressions. The cap set problem asks how large a cap set can be, and is an important problem in additive combinatorics and combinatorial number theory. In this talk, I will introduce the problem, give some background and motivation, and describe how I was able to provide the first progress in 20 years on the lower bound for the size of a maximal cap set. Building on a construction of Edel, we use improved computational methods and new theoretical ideas to show that, for large enough $n$, there is always a cap set in $\mathbb{F}_3^n$ of size at least $2.218^n$. I will then also discuss recent developments, including an extension of this result by Google DeepMind.
17:00
Logging the World - Oliver Johnson
During the pandemic, you may have seen graphs of data plotted on strange-looking (logarithmic) scales. Oliver will explain some of the basics and history of logarithms, and show why they are a natural tool to represent numbers ranging from COVID data to Instagram followers. In fact, we’ll see how logarithms can even help us understand information itself in a mathematical way.
Oliver Johnson is Professor of Information Theory in the School of Mathematics at the University of Bristol. His research involves randomness and uncertainty, and includes collaborations with engineers, biologists and computer scientists. During the pandemic he became a commentator on the daily COVID numbers, through his Twitter account and through appearances on Radio 4 and articles for the Spectator. He is the author of the book Numbercrunch (2023), which is designed to help a general audience understand the value of maths as a toolkit for making sense of the world.
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The lecture will be broadcast on the Oxford Mathematics YouTube Channel on Wednesday 06 March at 5-6pm and any time after (no need to register for the online version).
The Oxford Mathematics Public Lectures are generously supported by XTX Markets.
Topology and dynamics on the space of subgroups
Abstract
The space of subgroups of a countable group is a compact topological space which encodes many of the properties of its non-free actions. We will discuss some approaches to studying the Cantor-Bendixson decomposition of this space in the context of hyperbolic groups and groups which act (nicely) on trees. We will also give some conditions under which the conjugation action on the perfect kernel is highly topologically transitive and see how this can be applied to find new examples of groups (including all virtually compact special groups) which admit faithful transitive amenable actions. This is joint work with Damien Gaboriau.
16:00
90 years of pointwise ergodic theory
Abstract
This talk will cover the greatest hits of pointwise ergodic theory, beginning with Birkhoff's theorem, then Bourgain's work, and finishing with more modern directions.
16:00
Parity of ranks of abelian surfaces
Abstract
Hypercontractivity on compact Lie groups, and some applications
Abstract
We present two ways of obtaining hypercontractive inequalities for low-degree functions on compact Lie groups: one based on Ricci curvature bounds, the Bakry-Emery criterion and the representation theory of compact Lie groups, and another based on a (very different) probabilistic coupling approach. As applications we make progress on a question of Gowers concerning product-free subsets of the special unitary groups, and we also obtain 'mixing' inequalities for the special unitary groups, the special orthogonal groups, the spin groups and the compact symplectic groups. We expect that the latter inequalities will have applications in physics.
Based on joint work with Guy Kindler (HUJI), Noam Lifshitz (HUJI) and Dor Minzer (MIT).
Universal characteristics of deep neural network loss surfaces from random matrix theory
Abstract
Neural networks are the most practically successful class of models in modern machine learning, but there are considerable gaps in the current theoretical understanding of their properties and success. Several authors have applied models and tools from random matrix theory to shed light on a variety of aspects of neural network theory, however the genuine applicability and relevance of these results is in question. Most works rely on modelling assumptions to reduce large, complex matrices (such as the Hessians of neural networks) to something close to a well-understood canonical RMT ensemble to which all the sophisticated machinery of RMT can be applied to yield insights and results. There is experimental work, however, that appears to contradict these assumptions. In this talk, we will explore what can be derived about neural networks starting from RMT assumptions that are much more general than considered by prior work. Our main results start from justifiable assumptions on the local statistics of neural network Hessians and make predictions about their spectra than we can test experimentally on real-world neural networks. Overall, we will argue that familiar ideas from RMT universality are at work in the background, producing practical consequences for modern deep neural networks.