Daniela Besozzi

TL;DR: This work presents coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade, defined according to both mass-action kinetics and Hill functions, and shows that GPU-accelerated parallel simulations of this model can increase the computational performances up to a 181× speedup compared to the corresponding sequential simulations.

TL;DR: Evaluated CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/cross-dataset generalization.

TL;DR: This work proposes a novel self-tuning algorithm—called Fuzzy Self-Tuning PSO (FST-PSO)—which exploits FL to calculate the inertia, cognitive and social factor, minimum and maximum velocity independently for each particle, thus realizing a complete settings-free version of PSO.

TL;DR: A collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures.

TL;DR: This work introduces all necessary definitions of dynamical probabilistic P systems, describes the functioning of the parallel and stochastic algorithm used in computer simulation, and evaluates its time complexity.