Deep learning for laser beam imprinting.

Methods of ablation imprints in solid targets are widely used to characterize focused X-ray laser beams due to a remarkable dynamic range and resolving power. A detailed description of intense beam profiles is especially important in high-energy-density physics aiming at nonlinear phenomena. Complex interaction experiments require an enormous number of imprints to be created under all desired conditions making the analysis demanding and requiring a huge amount of human work.

Robust deep learning pipeline for PVC beats localization.

Background: Premature ventricular contraction (PVC) is among the most frequently occurring types of arrhythmias. Existing approaches for automated PVC identification suffer from a range of disadvantages related to hand-crafted features and benchmarking on datasets with a tiny sample of PVC beats.

Objective: The main objective is to address the drawbacks described above in the proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats.