I will describe a family of 2d TQFTs, due to Moore-Tachikawa, which take values in a category whose objects are Lie groups and whose morphisms are holomorphic symplectic varieties. They link many interesting aspects of geometry, such as moduli spaces of solutions to Nahm equations, hyperkähler reduction, and geometric invariant theory.

What does abstract nonsense (category theory) have to do with the apparently opposite proverbial concreteness of physics? In this talk I will try to convey the importance of understanding physical theories from a compositional and structural perspective, where the fundamental logic of interaction between systems becomes the real protagonist. Firstly, we will see how different classes of symmetric monoidal categories can be used to model physical processes in a very natural and intuitive way. We will then explore the claim that category theory is not only useful in providing a unified framework, but it can be also used to perfect and modify preexistent models. In this direction, I will show how the introduction of a trace in the symmetric monoidal category describing QIT can be used to talk about quantum interactions induced by cyclic causal relationships.

A recent calculation of the multi-Higgs boson production in scalar theories
with spontaneous symmetry breaking has demonstrated the fast growth of the
cross section with the Higgs multiplicity at sufficiently large energies,
called “Higgsplosion”. It was argued that “Higgsplosion” solves the Higgs
hierarchy and fine-tuning problems. The phenomena looks quite exciting,
however in my talk I will present arguments that: a) the formula for
“Higgsplosion” has a limited applicability and inconsistent with unitarity
of the Standard Model; b) that the contribution from “Higgsplosion” to the
imaginary part of the Higgs boson propagator cannot be re-summed in order to
furnish a solution of the Higgs hierarchy and fine-tuning problems.

Based on our recent paper https://arxiv.org/abs/1808.05641 (A. Belyaev, F. Bezrukov, D. Ross)

Clustering is a very important task in data analytics and is usually addressed using (i) statistical tools based on maximum likelihood estimators for mixture models, (ii) techniques based on network models such as the stochastic block model, or (iii) relaxations of the K-means approach based on semi-definite programming (or even simpler spectral approaches). Statistical approaches of type (i) often suffer from not being solvable with sufficient guarantees, because of the non-convexity of the underlying cost function to optimise. The other two approaches (ii) and (iii) are amenable to convex programming but do not usually scale to large datasets. In the big data setting, one usually needs to resort to data subsampling, a preprocessing stage also known as "coreset selection". We will present this last approach and the problem of selecting a coreset for the special cases of K-means and spectral-type relaxations.