A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles
were proposed in 2016 as prerequisites for proper data management and stewardship, with …

Artificial intelligence (AI)-based drug discovery has not yet completely addressed the
numerous and varied challenges posed by the inherent complexities and demands of …

Powerful detectors at modern experimental facilities routinely collect data at multiple GB/s.
Online analysis methods are needed to enable the collection of only interesting subsets of …

We present a critical analysis of physics-informed neural operators (PINOs) to solve partial
differential equations (PDEs) that are ubiquitous in the study and modeling of physics …

In the last decades, clinical laboratories have significantly advanced their technological
capabilities, through the use of interconnected systems and advanced software. Laboratory …

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively
train robust and generalized machine learning (ML) models without sharing sensitive (eg …

We introduce an end-to-end computational framework that allows for hyperparameter
optimization using the DeepHyper library, accelerated model training, and interpretable AI …

For an extended period, graphics processing units (GPUs) have stood as the exclusive
choice for training deep neural network (DNN) models. Over time, to serve the growing …

Artificial Intelligence (AI) has become a transformative technology across various sectors,
including aquaculture, which reached a global production of 185 million tonnes in 2022 …

The findable, accessible, interoperable, and reusable (FAIR) data principles provide a
framework for examining, evaluating, and improving how data is shared to facilitate scientific …