We explore the impact of strong gravitational fields on neutrino decoherence. To this aim, we
employ the density matrix formalism to describe the propagation of neutrino wave packets …

Neutrino flavor conversion studies based on astrophysical environments usually implement
neutrino mixings, neutrino interactions with matter, and neutrino self-interactions. In …

We introduce LightOn's Optical Processing Unit (OPU), the first photonic AI accelerator chip
available on the market for at-scale Non von Neumann computations, reaching 1500 …

Access to large pre-trained models of varied architectures, in many different languages, is
central to the democratization of NLP. We introduce PAGnol, a collection of French GPT …

We explore the impact of nonstandard interactions on neutrino propagation in accretion disks
around binary neutron star merger remnants. We show flavor evolution can be significantly …

Recent work has identified simple empirical scaling laws for language models, linking compute
budget, dataset size, model size, and autoregressive modeling loss. The validity of these …

Proteins are made of atoms constantly fluctuating, but can occasionally undergo large-scale
changes. Such transitions are of biological interest, linking the structure of a protein to its …

Since the discovery of neutrino oscillations in vacuum, it has been shown that the presence
of a matter background can greatly modify the flavor evolution. The inclusion of neutrino self-…

Encoder-only transformer models such as BERT offer a great performance-size tradeoff for
retrieval and classification tasks with respect to larger decoder-only models. Despite being …