Disentangling Sources of Momentum Fluctuations in Xe+Xe and Pb+Pb Collisions with the ATLAS Detector.

High-energy nuclear collisions create a quark-gluon plasma, whose initial condition and subsequent expansion vary from event to event, impacting the distribution of the eventwise average transverse momentum [P([p_{T}])]. Disentangling the contributions from fluctuations in the nuclear overlap size (geometrical component) and other sources at a fixed size (intrinsic component) remains a challenge. This problem is addressed by measuring the mean, variance, and skewness of P([p_{T}]) in ^{208}Pb+^{208}Pb and ^{129}Xe+^{129}Xe collisions at sqrt[s_{NN}]=5.

LDAPrototype: a model selection algorithm to improve reliability of latent Dirichlet allocation.

Latent Dirichlet allocation (LDA) is a popular method for analyzing large text corpora, but it suffers from instability due to its reliance on random initialization. This results in different outcomes for replicated runs, hindering reproducibility. To address this, we introduce LDAPrototype, a new approach for selecting the most representative LDA run from multiple replications on the same dataset.

Isolated posterior stabilization of the pelvic ring in type III/IV fragility fractures of the pelvis are beneficial compared to 360° antero-posterior surgical approaches. A dual-center cohort analysis.

Background: Fragility fractures of the pelvis (FFP) in elderly patients are an increasing concern due to their association with osteoporosis and the aging population. These fractures significantly affect patients' mobility and quality of life. This study evaluates different surgical techniques in patients suffering from FFP to provide standardized recommendations for treatment strategies.

Generative modeling of the Circle of Willis using 3D-StyleGAN.

The circle of Willis (CoW) is a network of cerebral arteries with significant inter-individual anatomical variations. Deep learning has been used to characterize and quantify the status of the CoW in various applications for the diagnosis and treatment of cerebrovascular disease. In medical imaging, the performance of deep learning models is limited by the diversity and size of training datasets.