Mirror Symmetry (Part II)
Contact organisers for access to meeting (Carmen Jorge-Diaz, Connor Behan or Sujay Nair)
Contact organisers for access to meeting (Carmen Jorge-Diaz, Connor Behan or Sujay Nair)
Contact organisers for access to meeting (Carmen Jorge-Diaz, Connor Behan or Sujay Nair)
Contact organisers for access to meeting (Carmen Jorge-Diaz, Connor Behan or Sujay Nair)
One of the main characterisations of word-hyperbolic groups is that they are the groups with a linear isoperimetric function. That is, for a compact 2-complex X, the hyperbolicity of its fundamental group is equivalent to the existence of a linear isoperimetric function for disc diagrams D -->X.
It is likewise known that hyperbolic groups have a linear annular isoperimetric function and a linear homological isoperimetric function. I will talk about these isoperimetric functions, and about a (previously unexplored) generalisation to all homotopy types of surface diagrams. This is joint work with Dani Wise.
The conformal bootstrap program for CFTs in d>2 dimensions is
based on well-defined rules and in principle it could be easily included
into rigorous mathematical physics. I will explain some interesting
conjectures which emerged from the program, but which so far lack rigorous
proof. No prior knowledge of CFTs or conformal bootstrap will be assumed.
How to evaluate meromorphic germs at their poles while preserving a
locality principle reminiscent of locality in QFT is a question that
lies at the heart of pQFT. It further arises in other disguises in
number theory, the combinatorics on cones and toric geometry. We
introduce an abstract notion of locality and a related notion of
mutually independent meromorphic germs in several variables. Much in the
spirit of Speer's generalised evaluators in the framework of analytic
renormalisation, the question then amounts to extending the ordinary
evaluation at a point to certain algebras of meromorphic germs, in
such a way that the extension factorises on mutually independent
germs. In the talk, we shall describe a family of such extended
evaluators and show that modulo a Galois type transformation, they
amount to a minimal subtraction scheme in several variables.
This talk is based on ongoing joint work with Li Guo and Bin Zhang.
Non-Hermitian random matrices with complex eigenvalues represent a truly two-dimensional (2D) Coulomb gas at inverse temperature beta=2. Compared to their Hermitian counter-parts they enjoy an enlarged bulk and edge universality. As an application to ecology we model large scale data of the approximately 2D distribution of buzzard nests in the Teutoburger forest observed over a period of 20 y. These birds of prey show a highly territorial behaviour. Their occupied nests are monitored annually and we compare these data with a one-component 2D Coulomb gas of repelling charges as a function of beta. The nearest neighbour spacing distribution of the nests is well described by fitting to beta as an effective repulsion parameter, that lies between the universal predictions of Poisson (beta=0) and random matrix statistics (beta=2). Using a time moving average and comparing with next-to-nearest neighbours we examine the effect of a population increase on beta and correlation length.
A quantum circuit defines a discrete-time evolution for a set of quantum spins/qubits, via a sequence of unitary 'gates’ coupling nearby spins. I will describe how random quantum circuits, where each gate is a random unitary matrix, serve as minimal models for various universal features of many-body dynamics. These include the dynamical generation of entanglement between distant spatial regions, and the quantum "butterfly effect". I will give a very schematic overview of mappings that relate averages in random circuits to the classical statistical mechanics of random paths. Time permitting, I will describe a new phase transition in the dynamics of a many-body wavefunction, due to repeated measurements by an external observer.
We provide a computational framework for approximating a class of structured matrices (e.g., block Toeplitz, block banded). Our approach has three steps: map the structured matrix to tensors, use tensor compression algorithms, and map the compressed tensors back to obtain two different matrix representations --- sum of Kronecker products and block low-rank format. The use of tensor decompositions enable us to uncover latent structure in the matrices and lead to computationally efficient algorithms. The resulting matrix approximations are memory efficient, easy to compute with, and preserve the error due to the tensor compression in the Frobenius norm. While our framework is quite general, we illustrate the potential of our method on structured matrices from three applications: system identification, space-time covariance matrices, and image deblurring.
Joint work with Misha Kilmer (Tufts University)
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