Past

Let S be an orientable surface of finite type. Using Pho-On's infinite unicorn paths, we prove the hyperfiniteness of the orbit equivalence relation coming from the action of the mapping class group of S on the Gromov boundary of the arc graph of S. This is joint work with Marcin Sabok.

We give a brief introduction to Floer homotopy, from the Seiberg-Witten point of view.  We will then discuss Manolescu's version of finite-dimensional approximation for rational homology spheres.  We prove that a version of finite-dimensional approximation for the Seiberg-Witten equations associates equivariant spectra to a large class of three-manifolds.  In the process we will also associate, to a cobordism of three-manifolds, a map between spectra.  We give some applications to intersection forms of four-manifolds with boundary. This is joint work with Hirofumi Sasahira. 

In a broad class of gravity theories, the equations of motion for vacuum compactifications give a curvature bound on the Ricci tensor minus a multiple of the Hessian of the warping function. Using results in so-called Bakry-Émery geometry, I will show how to put rigorous general bounds on the KK scale in gravity compactifications in terms of the reduced Planck mass or the internal diameter.
If time permits, I will reexamine in this light the local behavior in type IIA for the class of supersymmetric solutions most promising for scale separation. It turns out that the local O6-plane behavior cannot be smoothed out as in other local examples; it generically turns into a formal partially smeared O4.

In this session we will host a Q&A with current researchers who have recently gone through successful applications as well as more senior staff who have been on interview panels and hiring committees for postdoctoral positions in mathematics. The session will be a chance to get varied perspectives on the application process and find out about the different types of academic positions to apply for.

The panel members will be Candy Bowtell, Luci Basualdo Bonatto, Mohit Dalwadi, Ben Fehrman and Frances Kirwan. 

Anabelian geometry asks how much we can say about a variety from its fundamental group. In 1997 Shinichi Mochizuki, using p-adic hodge theory, proved a fundamental anabelian result for the case of p-adic fields. In my talk I will discuss representation theoretical data which can be reconstructed from an absolute Galois group of a field, and also types of representations that cannot be constructed solely from a Galois group. I will also sketch how these types of ideas can potentially give many new results about p-adic Galois representations.

Geometrical questions commonly arise in clinical practice: for example, what is the optimal shape for a particular medical device? or what shapes of anatomical structures are indicative of pathological events? In this talk we explore two disparate clinical applications of geometrical underpinning: (A) how to design the optimal device for kidney stone removal surgery? and (B) what blood vessel shapes are associated with biomechanical failure? (A) Flexible ureteroscopy is a minimally invasive treatment for the removal of kidney stones by irrigating dust-like stone fragments with a saline solution. Finding the optimal ureteroscope tip shape for efficient flushing of stone fragments is a pertinent but complex question. We represent the renal pelvis (the main hollow cavity within the kidney) as a 2D cavity and employ adjoint-based shape optimisation to identify tip geometries that shrink the size of recirculation zones thereby reducing stone washout times. (B) The aorta is the largest blood vessel in the body, with an archetypal arched “candy-cane” shape and is responsible for transporting blood from the heart to the rest of the body. Aortic dissection, in which the inner layer of the aorta tears, can lead to frank rupture and is often rapidly fatal. Accurate clinical assessment of dissection risk from a CT scan of a patient’s thorax is paramount to patient survival. We apply statistical shape analysis, coupled with hemodynamic simulations, to identify pathological shape features of the aortic arch and to elucidate mechanistic underpinnings of aortic dissection.

Abstract, lecture notes and the manuscript for Lecture 1

References
[1] F. Bethuel, H. Brezis, F. Helein, Ginzburg-Landau vortices, Birkhauser, Boston, 1994.
[2] H. Brezis, L. Nirenberg, Degree theory and BMO. I. Compact manifolds without boundaries,
Selecta Math. (N.S.) 1 (1995), 197{263.
[3] R. Ignat, R.L. Jerrard, Renormalized energy between vortices in some Ginzburg-Landau models
on 2-dimensional Riemannian manifolds, Arch. Ration. Mech. Anal. 239 (2021), 1577{1666.
[4] R. Ignat, L. Nguyen, V. Slastikov, A. Zarnescu, On the uniqueness of minimisers of Ginzburg-
Landau functionals, Ann. Sci. Ec. Norm. Super. 53 (2020), 589{613.
[5] R.L. Jerrard, Lower bounds for generalized Ginzburg-Landau functionals, SIAM J. Math. Anal.
30 (1999), 721-746.
[6] R.L. Jerrard, H.M. Soner, The Jacobian and the Ginzburg-Landau energy, Calc. Var. PDE 14
(2002), 151-191.
[7] E. Sandier, Lower bounds for the energy of unit vector elds and applications J. Funct. Anal.
152 (1998), 379-403.
[8] E. Sandier, S. Serfaty, Vortices in the magnetic Ginzburg-Landau model, Birkhauser, 2007.

We survey some of the applications of generalized indiscernible sequences, both in model theory and in structural Ramsey theory.  Given structures $A$ and $B$, a semi-retraction is a pair of  quantifier-free type respecting maps $f: A \rightarrow B$ and $g: B \rightarrow A$ such that $g \circ f: A \rightarrow A$ is quantifier-free type preserving, i.e. an embedding.  In the case that $A$ and $B$ are locally finite ordered structures, if $A$ is a semi-retraction of $B$ and the age of $B$ has the Ramsey property, then the age of $A$ has the Ramsey property.

I will discuss the following papers in my talk:
(1) Protecting consumers from collusive prices due to AI, 2020 with E. Calvano, V. Denicolò, J. Harrington, S.  Pastorello.  Nov 27, 2020, SCIENCE, cover featured article.
(2) Artificial intelligence, algorithmic pricing and collusion, 2020 with E. Calvano, V. Denicolò, S. Pastorello. AMERICAN ECONOMIC REVIEW,  Oct. 2020.
(3) Algorithmic Collusion with Imperfect Monitoring, 2021, with E. Calvano, V. Denicolò, S.  Pastorello

We introduce a new method for high-dimensional, online changepoint detection in settings where a $p$-variate Gaussian data stream may undergo a change in mean. The procedure works by performing likelihood ratio tests against simple alternatives of different scales in each coordinate, and then aggregating test statistics across scales and coordinates. The algorithm is online in the sense that both its storage requirements and worst-case computational complexity per new observation are independent of the number of previous observations. We prove that the patience, or average run length under the null, of our procedure is at least at the desired nominal level, and provide guarantees on its response delay under the alternative that depend on the sparsity of the vector of mean change. Simulations confirm the practical effectiveness of our proposal, which is implemented in the R package 'ocd', and we also demonstrate its utility on a seismology data set.

In this talk, we discuss the feasibility of algorithms based on deep artificial neural networks (DNN) for the solution of high-dimensional PDEs, such as those arising from stochastic control and games. In the first part, we show that in certain cases, DNNs can break the curse of dimensionality in representing high-dimensional value functions of stochastic control problems. We then exploit policy iteration to reduce the associated nonlinear PDEs into a sequence of linear PDEs, which are then further approximated via a multilayer feedforward neural network ansatz. We establish that in suitable settings the numerical solutions and their derivatives converge globally, and further demonstrate that this convergence is superlinear, by interpreting the algorithm as an inexact Newton iteration. Numerical experiments on Zermelo's navigation problem and on consensus control of interacting particle systems are presented to demonstrate the effectiveness of the method. This is joint work with Kazufumi Ito, Christoph Reisinger and Wolfgang Stockinger.

Controlling film flows has long been a central target for fluid dynamicists due to its numerous applications, in fields from heat exchangers to biochemical recovery, to semiconductor manufacture. However, despite its significance in the literature, most analyses have focussed on the “forward” problem: what effect a given control has on the flow. Often these problems are already complex, incorporating the - generally multiphysical - interplay of hydrodynamic phenomena with the mechanism of control. Indeed, many systems still defy meaningful agreement between models and experiments.
 
The inverse problem - determining a suitable control scheme for producing a specified flow - is considerably harder, and much more computationally expensive (often involving thousands of calculations of the forward problem). Performing such calculations for the full Navier-Stokes problem is generally prohibitive.

We examine the use of electric fields as a control mechanism. Solving the forward problem involves deriving a low-order model that turns out to be accurate even deep into the shortwave regime. We show that the weakly-nonlinear problem is Kuramoto-Sivashinsky-like, allowing for greater analytical traction. The fully nonlinear problem can be solved numerically via the use of a rapid solver, enabling solution of both the forward and adjoint problems on sub-second timescales, allowing for both terminal and regulation optimal control studies to be implemented. Finally, we examine the feasibility of controlling direct numerical simulations using these techniques.

The conformal dimension of a hyperbolic group is a powerful numeric quasi-isometry invariant associated to its boundary.

As an invariant it is finer than the topological dimension and allows us to distinguish between groups with homeomorphic boundaries.

I will start by talking about what conformal geometry even is, before discussing how this connects to studying the boundaries of hyperbolic groups.

I will probably end by saying how jolly hard it is to compute.

A link for this talk will be sent to our mailing list a day or two in advance.  If you are not on the list and wish to be sent a link, please contact @email.

We present an exponential improvement to the lower bound on diagonal Ramsey numbers for any fixed number of colors greater than two.
This is a joint work with David Conlon.
 

In 1972 Robert May argued that (generic) complex systems become unstable to small displacements from equilibria as the system complexity increases. His analytical model and outlook was linear. I will talk about a “minimal” non-linear extension of May’s model – a nonlinear autonomous system of N ≫ 1 degrees of freedom randomly coupled by both relaxational (’gradient’) and non-relaxational (’solenoidal’) random interactions. With the increasing interaction strength such systems undergo an abrupt transition from a trivial phase portrait with a single stable equilibrium into a topologically non-trivial regime where equilibria are on average exponentially abundant, but typically all of them are unstable, unless the dynamics is purely gradient. When the interaction strength increases even further the stable equilibria eventually become on average exponentially abundant unless the interaction is purely solenoidal. One can investigate these transitions with the help of the Kac-Rice formula for counting zeros of random functions and theory of random matrices applied to the real elliptic ensemble with some of the mathematical problems remaining open. This talk is based on collaborative work with Gerard Ben Arous and Yan Fyodorov.

How does a colony of ants find the shortest path between its nest and a source of food without any means of communication other than the pheromones each ant leave behind itself?
In this joint work with Daniel Kious (Bath) and Bruno Schapira (Marseille), we introduce a new probabilistic model for this phenomenon. In this model, the nest and the source of food are two marked nodes in a finite graph. Ants perform successive random walks from the nest to the food, and the distribution of the $n$th walk depends on the trajectories of the $(n-1)$ previous walks through some linear reinforcement mechanism.
Using stochastic approximation methods, couplings with Pólya urns, and the electric conductances method for random walks on graphs (which I will explain on some simple examples), we prove that, depending on the exact reinforcement rule, the ants may or may not always find the shortest path(s) between their nest and the source food.

Let G be a real or a p-adic connected reductive group. We will recall the description of the connected components of the tempered dual of G in terms of certain subalgebras of its reduced C*-algebra.

Each connected component comes with a torus equipped with a finite group action. We will see that, under a certain geometric assumption on the structure of stabilizers for that action (that is always satisfied for real groups), the component has a simple geometric structure which encodes the reducibility of the associate parabolically induced representations.

We will provide a characterization of the connected components for which the geometric assumption is satisfied, in the case when G is a symplectic group.

This is a joint work with Alexandre Afgoustidis.

The problem of identifying geometric structure in heterogeneous, high-dimensional data is a cornerstone of representation learning. In this talk, we study the problem of data geometry from the perspective of Discrete Geometry. We focus specifically on the analysis of relational data, i.e., data that is given as a graph or can be represented as such.

We start by reviewing discrete notions of curvature, where we focus especially on discrete Ricci curvature. Then we discuss the problem of embeddability: For downstream machine learning and data science applications, it is often beneficial to represent data in a continuous space, i.e., Euclidean, Hyperbolic or Spherical space. How can we decide on a suitable representation space? While there exists a large body of literature on the embeddability of canonical graphs, such as lattices or trees, the heterogeneity of real-world data limits the applicability of these classical methods. We discuss a combinatorial approach for evaluating embeddability, where we analyze nearest-neighbor structures and local neighborhood growth rates to identify the geometric priors of suitable embedding spaces. For canonical graphs, the algorithm’s prediction provably matches classical results. As for large, heterogeneous graphs, we introduce an efficiently computable statistic that approximates the algorithm’s decision rule. We validate our method over a range of benchmark data sets and compare with recently published optimization-based embeddability methods. 

A semiprime is a natural number which can be written as the product of two primes. Using elementary methods, we'll explore an asymptotic expansion for the counting function of semiprimes $\pi_2(x)$, which generalises previous findings of Landau, Delange and Tenenbaum.  We'll also obtain an efficient way of computing the constants involved. In the end, we'll look towards possible generalisations for products of $k$ primes.

In this talk, we will consider multidimensional fast-slow dynamical systems in discrete-time with random initial conditions but otherwise completely deterministic dynamics. The question we will investigate is whether the slow variable converges in law to a stochastic process under a suitable scaling limit. We will be particularly interested in the case when the limiting dynamic is superdiffusive, i.e. it coincides in law with the solution of a Marcus SDE driven by a discontinuous stable Lévy process. Under certain assumptions, we will show that generically convergence does not hold in any Skorokhod topology but does hold in a generalisation of the Skorokhod strong M1 topology which we define using so-called path functions. Our methods are based on a combination of ergodic theory and ideas arising from (but not using) rough paths. We will finally show that our assumptions are satisfied for a class of intermittent maps of Pomeau-Manneville type. 

The geodesic flow on hyperbolic finite-volume hyperbolic manifolds is a particularly well-studied dynamical system; this is in part due to its connection to other important dynamical systems on the manifold, as well as orbital counting and other number-theoretic problems related to discrete subgroups of orthogonal groups. In recent years, there has been some interest in generalizing many of the properties of the geodesic flow on finite-volume manifolds to the infinite-volume setting. I will discuss joint work with Hee Oh in which we establish exponential mixing of the geodesic flow on infinite-volume geometrically finite hyperbolic manifolds with large enough critical exponent. Patterson-Sullivan densities and Burger-Roblin measures, the Lax-Phillips spectral gap for the Laplace operator on infinite volume geometrically finite hyperbolic manifolds, and complementary series representations are all involved in both the statement and proof of our result, and I will try to explain how these different objects are related in this setting.

Counting coherent sheaves on Calabi--Yau fourfolds is a subject in its infancy. An evidence of this is given by how little is known about perhaps the simplest case - counting ideal sheaves of length $n$. On the other hand, the parallel story for surfaces while with many open questions has seen many new results, especially in the direction of understanding virtual integrals over Quot-schemes. Motivated by the conjectures of Cao--Kool and Nekrasov, we study virtual integrals over Hilbert schemes of points of top Chern classes $c_n(L^{[n]})$ and their K-theoretic refinements. Unlike lower-dimensional sheaf-counting theories, one also needs to pay attention to orientations. In this, we rely on the conjectural wall-crossing framework of Joyce. The same methods can be used for Quot-schemes of surfaces and we obtain a generalization of the work of Arbesfeld--Johnson--Lim--Oprea--Pandharipande for a trivial curve class. As a result, there is a correspondence between invariants for surfaces and fourfolds in terms of a universal transformation.

Manifolds with G2 structure allow almost contact structures. In this talk I will discuss various aspects of such structures, and their effect on certain supersymmetric configurations in string and M-theory.

This is based on recent work with Xenia de la Ossa and Matthew Magill.

I will give a pedagogical introduction to the Cardy-like limit of the superconformal index of N=4 SYM and generic N=1 SCFTs, highlighting its role in the holographic dual black hole microstate counting problem.

Active matter systems, ranging from liquid crystals to populations of cells and animals, exhibit complex collective behavior characterized by pattern formation and dynamic phase transitions. However, quantitative analysis of these systems is challenging, especially for heterogeneous populations of varying sizes, and typically requires expertise in formulating problem-specific order parameters. I will describe an alternative approach, using a combination of topological data analysis and machine learning, to investigate emergent behaviors in self-organizing populations of interacting discrete agents.

The first studies of the friction at the base of glacier were done by the pioneers Weertman and Lliboutry in the 1950s, who proposed theories under assumptions that have not been questioned for decades. Among these assumptions, the most questionable are the 2d geometry of the bumps, the pure sliding at the ice-bed interface and the hypothesis of stationary water pressure. In this seminar, I will present recent works using local modelling of basal friction with the finite element method that explore the validity of the proposed friction laws when these assumptions do not hold any more. 

The Temperley-Lieb algebra is a diagrammatic algebra - defined on a basis of "string diagrams" with multiplication given by "joining the diagrams together".  It first arose as an algebra of operators in statistical mechanics but quickly found application in knot theory (where Jones used it to define his famed polynomial) and the representation theory of $sl_2$.  From the outset, the representation theory of the Temperley-Lieb algebra itself has been of interest from a physics viewpoint and in characteristic zero it is well understood.  In this talk we will explore the representation theory over mixed characteristic (i.e. over positive characteristic fields and specialised at a root of unity).  This gentle introduction will take the listener through the beautifully fractal-like structure of the algebras and their cell modules with plenty of examples.

Residual networks (ResNets) have displayed impressive results in pattern recognition and, recently, have garnered considerable theoretical interest due to a perceived link with neural ordinary differential equations (neural ODEs). This link relies on the convergence of network weights to a smooth function as the number of layers increases. We investigate the properties of weights trained by stochastic gradient descent and their scaling with network depth through detailed numerical experiments. We observe the existence of scaling regimes markedly different from those assumed in neural ODE literature. Depending on certain features of the network architecture, such as the smoothness of the activation function, one may obtain an alternative ODE limit, a stochastic differential equation or neither of these. These findings cast doubts on the validity of the neural ODE model as an adequate asymptotic description of deep ResNets and point to an alternative class of differential equations as a better description of the deep network limit.
 

We revisit rough paths and signatures from a geometric and "smooth model" perspective. This provides a lean framework to understand and formulate key concepts of the theory, including recent insights on higher-order translation, also known as renormalization of rough paths. This first part is joint work with C Bellingeri (TU Berlin), and S Paycha (U Potsdam). In a second part, we take a semimartingale perspective and more specifically analyze the structure of expected signatures when written in exponential form. Following Bonnier-Oberhauser (2020), we call the resulting objects signature cumulants. These can be described - and recursively computed - in a way that can be seen as unification of previously unrelated pieces of mathematics, including Magnus (1954), Lyons-Ni (2015), Gatheral and coworkers (2017 onwards) and Lacoin-Rhodes-Vargas (2019). This is joint work with P Hager and N Tapia.

We develop a trust-region method for minimizing the sum of a smooth term f and a nonsmooth term h, both of which can be nonconvex. Each iteration of our method minimizes a possibly nonconvex model of f+h in a trust region. The model coincides with f+h in value and subdifferential at the center. We establish global convergence to a first-order stationary point when f satisfies a smoothness condition that holds, in particular, when it has Lipschitz-continuous gradient, and h is proper and lower semi-continuous. The model of h is required to be proper, lower-semi-continuous and prox-bounded. Under these weak assumptions, we establish a worst-case O(1/ε^2) iteration complexity bound that matches the best known complexity bound of standard trust-region methods for smooth optimization. We detail a special instance in which we use a limited-memory quasi-Newton model of f and compute a step with the proximal gradient method, resulting in a practical proximal quasi-Newton method. We describe our Julia implementations and report numerical results on inverse problems from sparse optimization and signal processing. Our trust-region algorithm exhibits promising performance and compares favorably with linesearch proximal quasi-Newton methods based on convex models.

This is joint work with Aleksandr Aravkin and Robert Baraldi.

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A link for this talk will be sent to our mailing list a day or two in advance.  If you are not on the list and wish to be sent a link, please contact @email.

Noise is generated in an aerodynamic setting when flow turbulence encounters a structural edge, such as at the sharp trailing edge of an aerofoil. The generation of this noise is unavoidable, however this talk addresses various ways in which it may be mitigated through altering the design of the edge. The alterations are inspired by natural silent fliers: owls. A short review of how trailing-edge noise is modelled will be given, followed by a discussion of two independent adaptations; serrations, and porosity. The mathematical impacts of the adaptations to the basic trailing-edge model will be presented, along with the physical implications they have on noise generation and control.

In this talk I will introduce the study of lattices in locally compact groups through their actions CAT(0) spaces. This is an extremely rich class of groups including S-arithmetic groups acting on products of symmetric spaces and buildings, right angled Artin and Coxeter groups acting on polyhedral complexes, Burger-Mozes simple groups acting on products of trees, and the recent CAT(0) but non biautomatic groups of Leary and Minasyan. If time permits I will discuss some of my recent work related to the Leary-Minasyan groups.

There are many open conjectures about the algebraic behaviour of transcendental functions in arithmetic geometry, one of which is the Existential Closedness problem. In this talk I will review recent developments made on this question: the cases where we have unconditional existence of solutions, the conditional existence of generic solutions (depending on the conjecture of periods and Zilber-Pink), and even a few cases of unconditional existence of generic solutions. Many of the results I will mention are joint work with (different subsets of) Vahagn Aslanyan, Jonathan Kibry, Sebastián Herrero, and Roy Zhao. 

https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZGRiMTM1ZjQtZWNi…

I will introduce the notion of moduli spaces of curves and specifically genus 0 curves. They are in general not compact, and we will discuss the most common way to compactify them. In particular, I will try to explain the construction of Mbar_{0,5}, together with how to classify the boundary, how it is related to a moduli space of tropical curves, and how to do intersection theory on this space.

In this talk, I will discuss AdS super gluon scattering amplitudes in various spacetime dimensions. These amplitudes are dual to correlation functions in a variety of non-maximally supersymmetric CFTs, such as the 6d E-string theory, 5d Seiberg exceptional theories, etc. I will introduce a powerful method based on symmetries and consistency conditions, and show that it fixes all the infinitely many four-point amplitudes at tree level. I will also point out many interesting properties and structures of these amplitudes, which include the flat space limit, Parisi-Sourlas-like dimensional reduction, hidden conformal symmetry, and a color-kinematic duality in AdS. Along the way, I will also review some earlier progress and the relation with this work. I will conclude with a brief discussion of various open problems. 

We discuss the behavior of geodesics in the continuous models of random geometry known as the Brownian map and the Brownian plane. We say that a point $x$ is a geodesic star with $m$ arms if $x$ is the endpoint of $m$ disjoint geodesics. We prove that the set of all geodesic stars with $m$ arms has dimension $5-m$, for $m=1,2,3,4$. This complements recents results of Miller and Qian, who derived upper bounds for these dimensions.

A group of projection based approaches for solving large-scale linear systems is known for its speed and simplicity. For example, Kaczmarz algorithm iteratively projects the previous approximation x_k onto the solution spaces of the next equation in the system. An elegant proof of the exponential convergence of this method, using correct randomization of the process, was given in 2009 by Strohmer and Vershynin, and succeeded by many extensions and generalizations. I will discuss our newly developed variants of these methods that successfully avoid large and potentially adversarial corruptions in the linear system. I specifically focus on the random matrix and high-dimensional probability results that play a crucial role in proving convergence of such methods. Based on the joint work with Jamie Haddock, Deanna Needell, and Will Swartworth.

Riemannian optimization is a powerful and active area of research that studies the optimization of functions defined on manifolds with structure. A class of functions of interest is the set of geodesically convex functions, which are functions that are convex when restricted to every geodesic. In this talk, we will present an accelerated first-order method, nearly achieving the same rates as accelerated gradient descent in the Euclidean space, for the optimization of smooth and g-convex or strongly g-convex functions defined on the hyperbolic space or a subset of the sphere. We will talk about accelerated optimization of another non-convex problem, defined in the Euclidean space, that we solve as a proxy. Additionally, for any Riemannian manifold of bounded sectional curvature, we will present reductions from optimization methods for smooth and g-convex functions to methods for smooth and strongly g-convex functions and vice versa.

This talk is based on the paper https://arxiv.org/abs/2012.03618.

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How much does the chromatic number of the random graph $G(n, 1/2)$ vary? Shamir and Spencer proved that it is contained in some sequence of intervals of length about $n^{1/2}$. Alon improved this slightly to $n^{1/2} / \log n$. Until recently, however, no lower bounds on the fluctuations of the chromatic number of $G(n, 1/2)$ were known, even though the question was raised by Bollobás many years ago. I will talk about the main ideas needed to prove that, at least for infinitely many $n$, the chromatic number of $G(n, 1/2)$ is not concentrated on fewer than $n^{1/2-o(1)}$ consecutive values.
I will also discuss the Zigzag Conjecture, made recently by Bollobás, Heckel, Morris, Panagiotou, Riordan and Smith: this proposes that the correct concentration interval length 'zigzags' between $n^{1/4+o(1)}$ and $n^{1/2+o(1)}$, depending on $n$.
Joint work with Oliver Riordan.

The complementarity and substitutability between products are essential concepts in retail and marketing. Qualitatively, two products are said to be substitutable if a customer can replace one product by the other, while they are complementary if they tend to be bought together. In this article, we take a network perspective to help automatically identify complements and substitutes from sales transaction data. Starting from a bipartite product-purchase network representation, with both transaction nodes and product nodes, we develop appropriate null models to infer significant relations, either complements or substitutes, between products, and design measures based on random walks to quantify their importance. The resulting unipartite networks between products are then analysed with community detection methods, in order to find groups of similar products for the different types of relationships. The results are validated by combining observations from a real-world basket dataset with the existing product hierarchy, as well as a large-scale flavour compound and recipe dataset.

arXiv link: https://arxiv.org/abs/2103.02042

We propose a randomized algorithm for solving a linear system $Ax = b$ with a highly numerically rank-deficient coefficient matrix $A$ with nearly consistent right-hand side possessing a small-norm solution. Our algorithm finds a small-norm solution with small residual in $O(N_r + nrlogn + r^3 )$ operations, where $r$ is the numerical rank of $A$ and $N_r$ is the cost of multiplying an $n\times r$ matrix to $A$. 

Joint work with Marcus Webb (Manchester). 

A link for this talk will be sent to our mailing list a day or two in advance.  If you are not on the list and wish to be sent a link, please contact @email.

The index of a saddle point of a smooth function is the number of descending directions of the saddle. While the index can usually be retrieved by counting the number of negative eigenvalues of the Hessian at the critical point, we may not have the luxury of having second derivatives in data deriving from practical applications. To address this problem, we develop a computational pipeline for estimating the index of a non-degenerate saddle point without explicitly computing the Hessian. In our framework, we only require a sufficiently dense sample of level sets of the function near the saddle point. Using techniques in Morse theory and Topological Data Analysis, we show how the shape of saddle points can help us infer the index of the saddle. Furthermore, we derive an explicit upper bound on the density of point samples necessary for inferring the index depending on the curvature of level sets.