Virtual

We are confronted with signals defined on the nodes of a graph in many applications.  Think for instance of a sensor network measuring temperature; or a social network, in which each person (node) has an opinion about a specific issue.  Graph signal processing (GSP) tries to device appropriate tools to process such data by generalizing classical methods from signal processing of time-series and images -- such as smoothing, filtering and interpolation -- to signals defined on graphs.  Typically, this involves leveraging the structure of the graph as encoded in the spectral properties of the graph Laplacian.

In other applications such as traffic network analysis, however, the signals of interest are naturally defined on the edges of a graph, rather than on the nodes. After a very brief recap of the central ideas of GSP, we examine why the standard tools from GSP may not be suitable for the analysis of such edge signals.  More specifically, we discuss how the underlying notion of a 'smooth signal' inherited from (the typically considered variants of) the graph Laplacian are not suitable when dealing with edge signals that encode flows.  To overcome this limitation we devise signal processing tools based on the Hodge-Laplacian and the associated discrete Hodge Theory for simplicial (and cellular) complexes.  We discuss applications of these ideas for signal smoothing, semi-supervised and active learning for edge-flows on discrete (or discretized) spaces.

In this talk we will study scattering amplitudes N=4 super-Yang-Mills theory. In this theory, scattering amplitudes are known to be functions of cluster variables of Gr(4,n) and certain algebraic functions of cluster variables. We will give an overview of how this cluster algebraic structure manifests, and will exploit it in an algorithm for computing symbol alphabets by solving matrix equations of the form C.Z = 0 associated with plabic graphs. These matrix equations associate functions on Gr(m,n) to parameterizations of certain cells of Gr_+ (k,n) indexed by plabic graphs. We are able to reproduce all known algebraic functions of cluster variables appearing in known symbol alphabets. We further show that it is possible to obtain all rational symbol letters (in fact all cluster variables) by solving C.Z = 0 if one allows C to be an arbitrary cluster parameterization of the top cell of Gr_+ (n-4,n).

I’ll review a new, simpler explanation for the large-scale properties of the
cosmos, presented with L. Boyle in our recent preprint arXiv:2201.07279. The
basic ingredients are elementary and well-known, namely Einstein’s theory of
gravity and Hawking’s method of computing gravitational entropy. The new
twist is provided by the boundary conditions we proposed for big bang-type
singularities, allowing conformal zeros but imposing CPT symmetry and

analyticity at the bang. These boundary conditions, which have significant
overlap with Penrose’s Weyl curvature hypothesis, allow gravitational
instantons for universes with Lambda, massless radiation and space
curvature, of either sign, from which we are able to infer a gravitational
entropy. We find the gravitational entropy can exceed the de Sitter entropy
and that, to the extent that it does, the most probable large-scale geometry
for the universe is flat, homogeneous and isotropic. I will briefly
summarise our earlier work showing how the gauge-fermion Lagrangian of the
standard model may be reconciled with Weyl symmetry and a small cosmological
constant, at leading order, provided there are precisely three generations
of fermions. The same mechanism generates scale-invariant primordial
perturbations. The cosmic dark matter consists of a right-handed neutrino.
In summary, we have taken significant steps towards a new, highly principled
and testable theory of cosmology.

We will consider the viscous Burgers driven by a localised one-dimensional control. The problem is considered in a bounded domain and is supplemented with the Dirichlet boundary condition. We will prove that any solution of the equation in question can be exponentially stabilised. Combining this result with an earlier result on local exact controllability we will show global exact controllability by a localised control. This is a joint work with A. Shirikyan.

A complex plane curve singularity gives rise to two objects: (1) a moduli space that representation theorists call an affine Springer fiber, and (2) a topological link up to isotopy. Roughly a decade ago, Oblomkov–Rasmussen–Shende conjectured a striking identity relating the homology of the affine Springer fiber to the so-called HOMFLYPT homology of the link. In unpublished writing, Shende speculated that it would follow from advances in nonabelian Hodge theory: the study of transcendental diffeomorphisms relating “Hitchin” and “Betti” moduli spaces. We make this dream precise by expressing HOMFLYPT homology in terms of the homology of a “Betti”-type space, which, we conjecture, deformation-retracts onto the affine Springer fiber. In doing so, we recast the whole story in terms of an arbitrary semisimple group. We give evidence for the nonabelian Hodge conjecture at the numerical level, using a mysterious formula that involves rational Cherednik algebras and the degrees of unipotent principal-series representations.

In the event of food-borne disease outbreaks, conventional epidemiological approaches to identify the causative food product are time-intensive and often inconclusive. Data-driven tools could help to reduce the number of products under suspicion by efficiently generating food-source hypotheses. We frame the problem of generating hypotheses about the food-source as one of identifying the source network from a set of food supply networks (e.g. vegetables, eggs) that most likely gave rise to the illness outbreak distribution over consumers at the terminal stage of the supply network. We introduce an information-theoretic measure that quantifies the degree to which an outbreak distribution can be explained by a supply network’s structure and allows comparison across networks. The method leverages a previously-developed food-borne contamination diffusion model and probability distribution for the source location in the supply chain, quantifying the amount of information in the probability distribution produced by a particular network-outbreak combination. We illustrate the method using supply network models from Germany and demonstrate its application potential for outbreak investigations through simulated outbreak scenarios and a retrospective analysis of a real-world outbreak.

Many real world graphs have edges correlated to the distance between them, but, in an inhomogeneous manner. While the Chung-Lu model and geometric random graph models both are elegant in their simplicity, they are insufficient to capture the complexity of these networks. For instance, the Chung-Lu model captures the inhomogeneity of the nodes but does not address the geometric nature of the nodes and simple geometric models treat names homogeneously.

In this talk, we develop a generalized geometric random graph model that preserves many graph-theoretic aspects of these models. Notably, each node is assigned a weight based on its desired expected degree; nodes are then adjacent based on a function of their weight and geometric distance. We will discuss the mathematical properties of this model. We also test the validity of this model on a graphical representation of the Drosophila Medulla connectome, a natural real-world inhomogeneous graph where spatial information is known.

This is joint work with Susama Agarwala, Johns Hopkins, Applied Physics Lab.

arXiv link: https://arxiv.org/abs/2109.00061

We propose a decentralised “local2global" approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or “patches") and training local representations for each patch independently. In a second step, we combine the local representations into a globally consistent representation by estimating the set of rigid motions that best align the local representations using information from the patch overlaps, via group synchronization.  A key distinguishing feature of local2global relative to existing work is that patches are trained independently without the need for the often costly parameter synchronisation during distributed training. This allows local2global to scale to large-scale industrial applications, where the input graph may not even fit into memory and may be stored in a distributed manner.

arXiv link: https://arxiv.org/abs/2107.12224v1

We use energy estimates to derive new bounds on the eigenvalues of a generic form of double saddle-point matrices, with and without regularization terms. Results related to inertia and algebraic multiplicity of eigenvalues are also presented. The analysis includes eigenvalue bounds for preconditioned matrices based on block-diagonal Schur complement-based preconditioners, and it is shown that in this case the eigenvalues are clustered within a few intervals bounded away from zero. The analytical observations are linked to a few multiphysics problems of interest. This is joint work with Susanne Bradley.

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I will talk about some recent joint work with H. Bui and J. Keating where we study the Ratios Conjecture for the family of quadratic L-functions over function fields. I will also discuss the closely related problem of obtaining upper bounds for negative moments of L-functions, which allows us to obtain partial results towards the Ratios Conjecture in the case of one over one, two over two and three over three L-functions.