Background Increasing utilization of long-term outpatient ambulatory electrocardiographic
(ECG) monitoring continues to drive the need for improved ECG interpretation algorithms …

Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative
analysis of imaging data for biological and biomedical applications. Many bioimage analysis …

The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous
impact on patients and healthcare systems around the world. In the fight against this novel …

How to represent a jet is at the core of machine learning on jet physics. Inspired by the
notion of point clouds, we propose a new approach that considers a jet as an unordered set …

With the advances in artificial intelligence (AI), data‐driven algorithms are becoming
increasingly popular in the medical domain. However, due to the nonlinear and complex …

Multimode fibers (MMFs) are an example of a highly scattering medium, which scramble the
coherent light propagating within them to produce seemingly random patterns. Thus, for …

Leveraging the chemical data available in legacy formats such as publications and patents
is a significant challenge for the community. Automated reaction mining offers a promising …

The standard probabilistic perspective on machine learning gives rise to empirical risk-
minimization tasks that are frequently solved by stochastic gradient descent (SGD) and …

Automated construction of surface geometries of cardiac structures from volumetric medical
images is important for a number of clinical applications. While deep-learning-based …

Molecular dynamics is a powerful tool for studying the thermodynamics and kinetics of
complex molecular events. However, these simulations can rarely sample the required time …