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Deducing the emergent behavior of a material from the properties of its molecular or atomic constituents is one of the greatest challenges of condensed matter theory. Considering many-body systems with highly cooperative ground states renders this task even more challenging. Geometrically frustrated assemblies are comprised of ill-fitting constituents that are associated with two or more tendencies that cannot be simultaneously reconciled, and thus lack a stress free rest state. The ground state of frustrated assemblies is highly cooperative, leading them to exhibit super-extensive energy growth, filamentation, size limitation and exotic response properties. Such systems arise in naturally occurring structures in biology and organic chemistry as well as in manmade synthetic materials.

In this talk I will discuss how the intrinsic approach, in which matter is described only through local properties available to an observer within the material, overcomes the lack of a stress free rest state for frustrated assemblies and leads to a general framework. This framework in particular allows predicting the super-extensive energy exponent for sufficiently small systems. I will discuss its application to several specific systems exhibiting geometric frustration: growing elastic bodies, frustrated liquid crystals and twisted molecular crystals.

Collision Course:
Particle Physics meets Machine Learning
Modern machine learning has had an outsized impact on many scientific fields, and particle physics is no exception. What is special about particle physics, though, is the vast amount of theoretical and experimental knowledge that we already have about many problems in the field. In this colloquium, I present two cases studies involving quantum chromodynamics (QCD) at the Large Hadron Collider (LHC), highlighting the fascinating interplay between theoretical principles and machine learning strategies. First, by cataloging the space of all possible QCD measurements, we (re)discovered technology relevant for self-driving cars. Second, by quantifying the similarity between two LHC collisions, we unlocked a class of nonparametric machine learning techniques based on optimal transport. In addition to providing new quantitative insights into QCD, these techniques enable new ways to visualize data from the LHC.

According to the hierarchical predictive-coding framework, regularities in the environment are used by the nervous system for predicting the input, and deviations from this prediction are transmitted as ‘prediction errors’. However, regularities may be based on more than one dimension and may be based on different time windows. Multiple predictions, sometimes contradicting, may be formed simultaneously and it is not clear how the brain deals with this situation. I will present evidence from scalp and intracranial EEG (in humans) showing that different parts of auditory cortex and frontal cortices are involved in predictions in multiple time scales for the same events. These predictions do not obey a simple hierarchy.

B. Gross and D. Prasad _rst formulated their famous conjectures about
the restriction of representations of discrete series representations in
the original paper Discrete series of an orthogonal group G = SOn
when restricted to an orthogonal subgroup G0 = SOn

B. Gross and D. Prasad _rst formulated their famous conjectures about
the restriction of representations of discrete series representations in
the original paper Discrete series of an orthogonal group G = SOn
when restricted to an orthogonal subgroup G0 = SOn