The scarcity of high-quality annotated medical imaging datasets is a major problem that
collides with machine learning applications in the field of medical imaging analysis and …

Deep learning, a state-of-the-art machine learning approach, has shown outstanding
performance over traditional machine learning in identifying intricate structures in complex …

We present network embedding algorithms that capture information about a node from the
local distribution over node attributes around it, as observed over random walks following an …

Advancements in neural machinery have led to a wide range of algorithmic solutions for
molecular property prediction. Two classes of models in particular have yielded promising …

Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly
impacting molecule design, property prediction, and synthesis optimization. This review …

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver
registration for image-pairs works by patch-wise prediction of a deformation model based …

Antimicrobial peptides (AMPs) are a valuable source of antimicrobial agents and a potential
solution to the multi-drug resistance problem. In particular, short-length AMPs have been …

The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an
imperative to develop accurate and efficient diagnostic tools for the disease as medical …

Electroencephalogram (EEG) signals contain vital information on the electrical activities of
the brain and are widely used to aid epilepsy analysis. A challenging element of epilepsy …