Past

I will discuss various possible ways a global symmetry can act on operators in a quantum field theory. The possible actions on q-dimensional operators are referred to as q-charges of the symmetry. Crucially, there exist generalized higher-charges already for an ordinary global symmetry described by a group G. The usual charges are 0-charges, describing the action of the symmetry group G on point-like local operators, which are well-known to correspond to representations of G. We find that there is a neat generalization of this fact to higher-charges: i.e. q-charges are (q+1)-representations of G. I will also discuss q-charges for generalized global symmetries, including not only invertible higher-form and higher-group symmetries, but also non-invertible categorical symmetries. This talk is based on a recent (arXiv: 2304.02660) and upcoming works with Sakura Schafer-Nameki.

For week 4's @email session we welcome the SIAM-IMA student chapter, running their annual Three Minute Thesis competition.

The Three Minute Thesis competition challenges graduate students to present their research in a clear and engaging manner within a strict time limit of three minutes. Each presenter will be allowed to use only one static slide to support their presentation, and the panel of esteemed judges (details TBC) will evaluate the presentations based on criteria such as clarity, pacing, engagement, enthusiasm, and impact. Each presenter will receive a free mug and there is £250 in cash prizes for the winners. If you're a graduate student, sign up here (https://oxfordsiam.com/3mt) by Friday of week 3 to take part! And if not, come along to support your DPhil friends and colleagues, and to learn about the exciting maths being done by our research students.

I will survey on the cluster structure of random geometric graphs in a regime that is less discussed in the literature. The statistics of interest include the number of k-components, the number of components, the number of vertices in the giant component, and the connectivity threshold. We show LLN and normal/Poisson approximation by Stein's method. Based on recent joint works with Mathew Penrose (Bath).

The giant planets, Jupiter and Saturn, all host several satellites that contain vast liquid water reservoirs beneath their frozen surfaces. These Ocean Worlds and some of the most compelling targets for future exploration of the solar system due to their potential for hosting habitable subsurface environments. The internal dynamics of these bodies is, as yet, largely unknown.

A key process that shapes the internal and orbital evolution of these systems is tides and the resultant dissipation of heat. I will review how these global ocean’s dynamically respond to the tide-generating potentials that are relevant in tightly-packed planetary systems, including the physics and mathematical techniques used to model global tidal flow. Oceanic dissipation rates due to tides will be estimated, including the effect of a thick global ice layer above the ocean and tides raised by neighbouring moons. I will end on recent work regarding the generation of weak mean flows via periodic tidal forcing.

Image-based readouts of biology are information-rich and inexpensive. Yet historically, bespoke data collection methods and the intrinsically unstructured nature of image data have made these assays difficult to work with at scale. This presentation will discuss advances made at Recursion to industrialise the use of cellular imaging to decode biology and drive drug discovery. First, the use of deep learning allows the transformation of unstructured images into biologically meaningful representations, enabling a 'map of biology' relating genetic and chemical perturbations to scale drug discovery. Second, building such a map at whole-genome scale led to the discovery of a "proximity bias" globally confounding CRISPR-Cas9-based functional genomics screens. Finally, I will discuss how publicly-shared resources from Recursion, including the RxRx3 dataset and MolRec application, enable downstream research both on cellular images themselves and on deep learning-derived embeddings, making advanced image analysis more accessible to researchers worldwide.

The study of cohomology of infinite-dimensional Lie algebras was started by Gel'fand and Fuchs in the late 1960s. Since then, significant progress has been made, mainly focusing on the Witt algebra (the Lie algebra of vector fields on the punctured affine line) and some of its subalgebras. In this talk, I will explain the basics of Lie algebra cohomology and sketch the computation of the first cohomology group of certain subalgebras of the Witt algebra known as submodule-subalgebras. Interestingly, these cohomology groups are, in some sense, controlled by the cohomology of the Witt algebra. This can be explained by the fact that the Witt algebra can be abstractly reconstructed from any of its submodule-subalgebras, which can be described as a universal property satisfied by the Witt algebra.

I shall present a new flexible method showing that every countable model of PA admits a pointwise definable end-extension, one in which every point is definable without parameters. Also, any model of PA of size at most continuum admits an extension that is Leibnizian, meaning that any two distinct points are separated by some expressible property. Similar results hold in set theory, where one can also achieve V=L in the extension, or indeed any suitable theory holding in an inner model of the original model.

Many problems in number theory are equivalent to determining all of the rational points on some curve or family of curves. In general, finding all the rational points on any given curve is a challenging (even unsolved!) problem. 

The focus of this talk is rational points on so-called Erdős-Selfridge curves. A deep conjecture of Sander, still unproven in many cases, predicts all of the rational points on these curves. 

I will describe work-in-progress proving new cases of Sander's conjecture, and sketch some ideas in the proof. The core of the proof is a `mass increment argument,' which is loosely inspired by various increment arguments in additive combinatorics. The main ingredients are a mixture of combinatorial ideas and quantitative estimates in Diophantine geometry.

We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree-based models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting one-day-ahead RVs using past intraday RVs as predictors, and highlight interesting time-of-day effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs.

Linear poroelasticity models have important applications in biology and geophysics. In particular, the well-known Biot consolidation model describes the coupled interaction between the linear response of a porous elastic medium saturated with fluid and a diffusive fluid flow within it, assuming small deformations. This is the starting point for modeling human organs in computational medicine and for modeling the mechanics of permeable
rock in geophysics. Finite element methods for Biot’s consolidation model have been widely studied over the past four decades.
In the first part of the talk, we discuss a posteriori error estimators for locking-free mixed finite element approximation of Biot’s consolidation model. The simplest of these is a conventional residual-based estimator. We establish bounds relating the estimated and true errors, and show that these are independent of the physical parameters. The other two estimators require the solution of local problems. These local problem estimators are also shown to be reliable, efficient and robust. Numerical results are presented that
validate the theoretical estimates, and illustrate the effectiveness of the estimators in guiding adaptive solution algorithms.
In the second part of talk, we discuss a novel locking-free stochastic Galerkin mixed finite element method for the Biot consolidation model with uncertain Young’s modulus and hydraulic conductivity field. After introducing a five-field mixed variational formulation of the standard Biot consolidation model, we discuss stochastic Galerkin mixed finite element approximation, focusing on the issue of well-posedness and efficient linear algebra for the discretized system. We introduce a new preconditioner for use with MINRES and
establish eigenvalue bounds. Finally, we present specific numerical examples to illustrate the efficiency of our numerical solution approach.

Finally, we discuss some remarks related to non-conforming approximation of Biot’s consolidation model.

References:
1. A. Khan, D. J. Silvester, Robust a posteriori error estimation for mixed finite
element approximation of linear poroelsticity, IMA Journal of Numerical Analysis, Oxford University Press, 41 (3), 2021, 2000-2025.
2. A. Khan, C. E. Powell, Parameter-robust stochastic Galerkin approxination for linear poroelasticity with uncertain inputs, SIAM Journal on Scientific Computing (SISC), 43 (4), 2021, B855-B883.
3. A. Khan, P. Zanotti, A nonsymmetric approach and a quasi-optimal and robust discretization for the Biot’s model. Mathematics of Computation, 91 (335), 2022, 1143-1170.
4. V. Anaya, A. Khan, D. Mora, R. Ruiz-Baier, Robust a posteriori error analysis for rotation-based formulations of the elasticity/poroelasticity coupling, SIAM Journal
on Scientific Computing (SISC), 2022.

My talk will have two parts. First, I will tell you how a single cell produces light to survive; then, I will explain how a huddle of chloroplasts in cells form glasses to optimize plant life. Part I: Bioluminescence (light generation in living organisms) has mesmerized humans since thousands of years ago. I will first go over the recent progress in experimental and mathematical biophysics of single-cell bioluminescence (PRL 125 (2), 028102, 2020) and then will go beyond and present a lab-scale experiment and a continuum model of bioluminescent breaking waves. Part II: To remain efficient during photosynthesis, plants can re-arrange the internal structure of cells by the active motion of chloroplasts. I will show that the chloroplasts can behave like a densely packed light-sensitive active matter, whose non-gaussian athermal fluctuations can lead to various self-organization scenarios, including glassy dynamics under dim lights (PNAS 120 (3), 2216497120, 2023). To this end, I will also present a simple model that captures the dynamic of these biological glasses.

The virtual Haken theorem is one of the most influential recent results in 3-manifold theory. The statement dates back to Waldhausen, who conjectured that every aspherical closed 3-manifold has a finite cover containing an essential embedded closed surface. The proof is usually attributed to Agol, although his virtual special theorem is only the last piece of the puzzle. This talk is dedicated to the unsung heroes of virtual Haken, the mathematicians whose invaluable work helped turning this conjecture into a theorem. We will trace the history of a mathematical thread that connects Thurston-Perelman's geometrisation to Agol's final contribution, surveying Kahn-Markovic's surface subgroup theorem, Bergeron-Wise's cubulation of 3-manifold groups, Haglund-Wise's special cube complexes, Wise's work on quasi-convex hierarchies and Agol-Groves-Manning's weak separation theorem.

In 1986, Katznelson and Tzafriri proved that, if $T$ is a power-bounded operator on a Banach space $X$, and the spectrum of $T$ meets the unit circle only at 1, then $\|T^n(I-T)\| \to 0$ as $n\to\infty$. Actually, they went further and proved that $\|T^nf(T)\| \to 0$ if $T$ and $f$ satisfy certain conditions. Soon afterward, analogous results were obtained for bounded $C_0$-semigroups $(T(t))_{t\ge0}$. Further extensions and variants were proved later. I will speak about several extensions to the Katznelson-Tzafriri theorem(s), including in particular a recent result(s) obtained by David Seifert and myself.

COURSE_PROPOSAL (12)_0.pdf

The space of unordered tuples. The notion of differentiability and the theory of metric Sobolev in the context of multi-valued functions. Multivalued maximum principle and Holder regularity. Estimates on the Hausdorff dimension of the singular set of Dir-minimizing functions. If time permits: mass minimizing currents and their link with Dir-minimizers. 

A parabolic representation of the free group  is one in which the images of both generators are parabolic elements of $PSL(2,\IC)$. The Riley slice is a closed subset ${\cal R}\subset \IC$ which is a model for the moduli space of parabolic, discrete and faithful representations. The complement of the Riley slice is a bounded Jordan domain within which there are isolated points, accumulating only at the boundary, corresponding to parabolic discrete and faithful representations of rigid subgroups of $PSL(2,\IC)$. Recent work of Aimi, Akiyoshi, Lee, Oshika, Parker, Lee, Sakai, Sakuma \& Yoshida, have topologically identified all these groups. Here we give the first  substantive properties of the nondiscrete representations using ergodic properties of the action of a polynomial semigroup and identifying the Riley slice as the ``Julia set’’ of this dynamical system. We prove a supergroup density theorem: given any irreducible parabolic representation of $F_2$ whatsoever, {\em any}  non-discrete parabolic representation has an arbitrarily small perturbation which contains that group as a conjugate.  Using these ideas we then show that there are nondiscrete parabolic representations with an arbitrarily large number of distinct Nielsen classes of parabolic generators.

 Wide Neural Networks are well known for their Gaussian Process behaviour. Based upon this fact, an initialisation scheme for the weights and biases of a network preserving some geometrical properties of the input data is presented — The edge-of-chaos. This talk will introduce such a scheme before briefly mentioning a recent contribution related to the edge-of-chaos dynamics of wide randomly initialized low-rank feedforward networks. Formulae for the optimal weight and bias variances are extended from the full-rank to low-rank setting and are shown to follow from multiplicative scaling. The principle second order effect, the variance of the input-output Jacobian, is derived and shown to increase as the rank to width ratio decreases. These results inform practitioners how to randomly initialize feedforward networks with a reduced number of learnable parameters while in the same ambient dimension, allowing reductions in the computational cost and memory constraints of the associated network.

Let $p_c$ and $q_c$ be the threshold and the expectation threshold, respectively, of an increasing family $F$ of subsets of a finite set $X$. Recently, Park and Pham proved Kahn–Kalai conjecture stating that a not-too-large multiple of $q_c$ is an upper bound on $p_c$. In the talk, I will present a slight improvement to the Park–Pham theorem, which is obtained from transferring the threshold result from the small $p$ regime to general $p$. Based on joint work with Oliver Riordan.

Complex networks usually exhibit a rich architecture organized over multiple intertwined scales. Information pathways are expected to pervade these scales reflecting structural insights that are not manifest from analyses of the network topology. Moreover, small-world effects correlate with the different network hierarchies complicating identifying coexisting mesoscopic structures and functional cores. We present a communicability analysis of effective information pathways throughout complex networks based on information diffusion to shed further light on these issues. This leads us to formulate a new renormalization group scheme for heterogeneous networks. The Renormalization Group is the cornerstone of the modern theory of universality and phase transitions, a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its network counterpart is particularly challenging due to correlations between intertwined scales. The Laplacian RG picture for complex networks defines both the supernodes concept à la Kadanoff, and the equivalent momentum space procedure à la Wilson for graphs.

The pro-C completion of a free profinite group on an infinite set of generators is a profinite group of a greater rank. However, it is still not known whether it is a free profinite group too.  We will discuss this question, present a positive answer for some special varieties, and show partial results regarding the general case. In addition, we present the infinite tower of profinite completions, which leads to a generalisation for completions of higher orders. 

Placeholder entry; date+time TBC. 

Abstract for talk: In this talk, we present a systematic discretization of the Bernstein-Gelfand-Gelfand (BGG) diagrams and complexes over cubical meshes of arbitrary dimension via the use of tensor-product structures of one-dimensional piecewise-polynomial spaces, such as spline and finite element spaces. We demonstrate the construction of the Hessian, the elasticity, and div-div complexes as examples for our construction.

PDEs frequently exhibit certain physical structures that guide their behaviour, e.g. energy/helicity dissipation, Hamiltonians, and material conservation. Preserving these structures during numerical discretisation is essential.

Although the finite-element method has proven powerful in constructing such models, incorporating inhomogeneous(/non-zero) boundary conditions has been a significant challenge. We propose a technique that addresses this issue, deriving structure-preserving models for diverse inhomogeneous problems.

Moreover, this technique enables the derivation of novel structure-preserving timesteppers for time-dependent problems. Analogies can be drawn with the other workhorse of modern structure-preserving methods: symplectic integrators.