14:30
Measurable circle squaring
Abstract
14:30
Finitely forcible limits of graphs and permutations
Abstract
Graphons and permutons are analytic objects associated with convergent sequences of graphs and permutations, respectively. Problems from extremal combinatorics and theoretical computer science led to a study of graphons and permutons determined by finitely many substructure densities, which are referred to as finitely forcible. The talk will contain several results on finite forcibility, focusing on the relation between finite forcibility of graphons and permutons. We also disprove a conjecture of Lovasz and Szegedy about the dimension of the space of typical vertices of finitely forcible graphons. The talk is based on joint work with Roman Glebov, Andrzej Grzesik and Dan Kral.
A panoramic view of infrared singularities
Abstract
The study of infrared singularities, due to the emission of “soft” (low momentum) gauge bosons, remains a highly active research area in a variety of quantum field theories. After motivating both phenomenological and formal reasons as to why we should care about IR singularities, this talk will review their structure in QED, QCD and quantum gravity, examining the similarities and differences between these three contexts. The role of Wilson lines will be examined, which provide a useful unifying language. Finally, I will examine recent work on moving beyond the soft approximation, and why this might be useful.
14:30
Decompositions of large graphs into small subgraphs
Abstract
A fundamental theorem of Wilson states that, for every graph $F$, every sufficiently large $F$-divisible clique has an $F$-decomposition. Here $G$ has an $F$-decomposition if the edges of $G$ can be covered by edge-disjoint copies of $F$ (and $F$-divisibility is a trivial necessary condition for this). We extend Wilson's theorem to graphs which are allowed to be far from complete (joint work with B. Barber, D. Kuhn, A. Lo).
I will also discuss some results and open problems on decompositions of dense graphs and hypergraphs into Hamilton cycles and perfect matchings.
14:15
Spatial localization in temperature-dependent viscosity convection
Abstract
Studies of thermal convection in planetary interiors have largely focused on convection above the critical Rayleigh number. However, convection in planetary mantles and crusts can also occur under subcritical conditions. Subcritical convection exhibits phenomena which do not exist above the critical Rayleigh number. One such phenomenon is spatial localization characterized by the formation of stable, spatially isolated convective cells. Spatial localization occurs in a broad range of viscosity laws including temperature-dependent viscosity and power-law viscosity and may explain formation of some surface features observed on rocky and icy bodies in the Solar System.
Nomura-OMI Seminar: Optimal exit under moral hazard
Abstract
We revisit the optimal exit problem by adding a moral hazard problem where a firm owner contracts out with an agent to run a project. We analyse the optimal contracting problem between the owner and the agent in a Brownian framework, when the latter modifies the project cash-flows with an hidden action. The analysis leads to the resolution of a constrained optimal stopping problem that we solve explicitly.
Machine learning using Hawkes processes and concentration for matrix martingales
Abstract
We consider the problem of unveiling the implicit network structure of user interactions in a social network, based only on high-frequency timestamps. Our inference is based on the minimization of the least-squares loss associated with a multivariate Hawkes model, penalized by $\ell_1$ and trace norms. We provide a first theoretical analysis of the generalization error for this problem, that includes sparsity and low-rank inducing priors. This result involves a new data-driven concentration inequality for matrix martingales in continuous time with observable variance, which is a result of independent interest. The analysis is based on a new supermartingale property of the trace exponential, based on tools from stochastic calculus. A consequence of our analysis is the construction of sharply tuned $\ell_1$ and trace-norm penalizations, that leads to a data-driven scaling of the variability of information available for each users. Numerical experiments illustrate the strong improvements achieved by the use of such data-driven penalizations.