A modular machine learning tool for holistic and fine-grained behavioral analysis.

Artificial intelligence techniques offer promising avenues for exploring human body features from videos, yet no freely accessible tool has reliably provided holistic and fine-grained behavioral analyses to date. To address this, we developed a machine learning tool based on a two-level approach: a first lower-level processing using computer vision for extracting fine-grained and comprehensive behavioral features such as skeleton or facial points, gaze, and action units; a second level of machine learning classification coupled with explainability providing modularity, to determine which behavioral features are triggered by specific environments. To validate our tool, we filmed 16 participants across six conditions, varying according to the presence of a person ("Pers"), a sound ("Snd"), or silence ("Rest"), and according to emotional levels using self-referential ("Self") and control ("Ctrl") stimuli.

Pseudospectral particle-in-cell formulation with arbitrary charge and current-density time dependencies for the modeling of relativistic plasmas.

This paper introduces a formulation of the particle-in-cell (PIC) method for the modeling of relativistic plasmas, that leverages the ability of the pseudospectral analytical time-domain solver (PSATD) to handle arbitrary time dependencies of the charge and current densities during one PIC cycle (applied to second-order polynomial dependencies here). The formulation is applied to a modified set of Maxwell's equations that was proposed earlier in the context of divergence cleaning, and to recently proposed extensions of the PSATD-PIC algorithm. Detailed analysis and testings revealed that, under some condition, the formulation can expand the range of numerical parameters under which PIC simulations are stable and accurate when modeling relativistic plasmas such as, e.

Emittance preservation in a plasma-wakefield accelerator.

Radio-frequency particle accelerators are engines of discovery, powering high-energy physics and photon science, but are also large and expensive due to their limited accelerating fields. Plasma-wakefield accelerators (PWFAs) provide orders-of-magnitude stronger fields in the charge-density wave behind a particle bunch travelling in a plasma, promising particle accelerators of greatly reduced size and cost. However, PWFAs can easily degrade the beam quality of the bunches they accelerate.

Age-related differences in perception and coding of attractive odorants in mice.

Hedonic perception deeply changes with aging, significantly impacting health and quality of life in elderly. In young adult mice, an odor hedonic signature is represented along the antero-posterior axis of olfactory bulb, and transferred to the olfactory tubercle and ventral tegmental area, promoting approach behavior. Here, we show that while the perception of unattractive odorants was unchanged in older mice (22 months), the appreciation of some but not all attractive odorants declined.

Long-term olfactory enrichment promotes non-olfactory cognition, noradrenergic plasticity and remodeling of brain functional connectivity in older mice.

Brain functional and structural changes lead to cognitive decline during aging, but a high level of cognitive stimulation during life can improve cognitive performances in the older adults, forming the cognitive reserve. Noradrenaline has been proposed as a molecular link between environmental stimulation and constitution of the cognitive reserve. Taking advantage of the ability of olfactory stimulation to activate noradrenergic neurons of the locus coeruleus, we used repeated olfactory enrichment sessions over the mouse lifespan to enable the cognitive reserve buildup.