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After describing elastic interactions and the discovery of the odderon by the TOTEM and D0 Collaborations, we will discuss new kinematical regimes where the gluon density in the proton or Pb can be very large which leads to new equations in Quantum Chromodynamics.  We will also present the latest expectations on beyond standard model physics and especially the production of axion-like particles using intact protons after the collision.  We will finish by describing briefly the Low Gain Avalanche Detectors and their applications in medicine for cancer treatment and with NASA to measure cosmic rays. 

Abstract:
Understanding how network function constrains neural connectivity is a central challenge in neuroscience. An influential approach is to train neural networks with gradient descent on cognitive tasks and characterize the resulting connectivity. A key limitation is that the resulting structure depends on the details of the training procedure. Here we propose a complementary normative approach based on the maximum entropy principle for network connectivity, independent of any particular learning algorithm. We describe connectivity as a probability distribution over single-neuron weights, express task requirements as constraints on this distribution, and determine the unique distribution maximizing Shannon entropy subject to these constraints. A weight scale parameter controls the balance between randomness and task-induced structure. We apply this framework to context-dependent input-selection tasks in 2-layer feed-forward networks, and show that maximum entropy inference becomes analytically tractable by mapping nonlinear networks onto gain-modulated linear models. Starting from an a priori homogeneous distribution, we find that maximizing entropy under task constraints leads to the emergence of populations of neurons, each defined by its pattern of contextual gain modulation. Increasing the number of contexts drives a transition from context-specialized to unspecialized, random populations. Increasing the weight scale drives a parallel transition from structured to random stimulus selectivity. Strikingly, this maximum entropy connectivity matches both qualitatively and quantitatively the structure of networks trained with gradient descent across different learning regimes. Our results suggest that the interplay between task constraints and entropy maximization provides a fundamental principle for understanding the relationship between structure and function in neural networks.