Abstract Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools sha** the
healthcare sector. This review considers twelve key aspects of AI in clinical practice: 1) …

Single-atom catalysts (SACs) represent the ultimate size limit of nanoscale catalysts,
combining the advantages of homogeneous and heterogeneous catalysts. SACs have …

Synthetic image datasets offer unmatched advantages for designing and evaluating deep
neural networks: they make it possible to (i) render as many data samples as needed,(ii) …

Data sharing is crucial for open science and reproducible research, but the legal sharing of
clinical data requires the removal of protected health information from electronic health …

Generalization and invariance are two essential properties of machine learning models.
Generalization captures a model's ability to classify unseen data while invariance measures …

The structure and morphology of supported nanoparticle catalysts play important roles in
many industrial reactions. Recent progress has identified key aspects of structure–activity …

Object-oriented reinforcement learning (OORL) is a promising way to improve the sample
efficiency and generalization ability over standard RL. Recent works that try to solve OORL …

In recent years, machine learning methods have been widely used to predict the long-term
settlement of tunnels. However, data-driven models for long-term settlement prediction often …

Symmetry is omnipresent in nature and perceived by the visual system of many species, as it
facilitates detecting ecologically important classes of objects in our environment. Yet, the …

Recognizing that a multiple-degree-of-freedom mass–spring–damper system can be viewed
as a group of connected nodes, this paper presents a novel computational framework that …