Topic: power spectra

Paco Villaescusa examines how simulations and artificial intelligence change the extraction of physical knowledge from precision cosmological surveys. The talk asks how much information lies in large-scale structure beyond power spectra, including nonlinear scales and higher-order statistics, and introduces the Quijote and Backlight simulation programmes. It then addresses the uncertain astrophysical processes that complicate small-scale inference through CAMELS, a framework combining cosmology, galaxy formation and machine learning. DREAMS is presented as a route to predicting dark-matter obs

Characterizing brain oscillatory activity at the single-subject level is a key concern in personalized medicine. A recent publication has presented a normative map of the brain’s natural or typical frequencies of oscillation that could be used for detecting potential deviations in healthcare. However, this map represents brain activity in a voxel-by-voxel basis from high-density recordings of a large sample of healthy volunteers, compared to the low-density recordings usually employed on patients. Thus, accounting for less data to construct the single-subject map, a significant portion of nois

Phantom limb pain (PLP) is a condition where pain is perceived as arising from a missing limb. Despite the high prevalence of PLP among individuals with amputation, the underlying mechanisms of this painful condition are poorly understood. The aim of the research presented here is to analyse high-resolution EEG data to differentiate oscillatory patterns in amputees who experience PLP from two control groups, amputees without PLP and individuals without amputation, and generate novel insights into the pathophysiology of PLP. This work expands on a previous investigation on the use of common spa