Quantum technologies from ultrasensitive sensors to next-generation information processors depend on the ability of quantum ...
Boron, a chemical element next to carbon in the periodic table, is known for its unique ability to form complex bond networks ...
An AI-driven computational toolkit, Gcoupler, integrates ligand design, statistical modeling, and graph neural networks to predict endogenous metabolites that allosterically modulate the GPCR–Gα ...
Understanding the shape or morphology of neurons and mapping the tree-like branches via which they receive signals from other ...
Abstract: Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs), which aim to cost-effectively validate medical device interventions using synthetic anatomical shapes ...
Abstract: Learning robust and effective representations of visual data is a fundamental task in computer vision. Traditionally, this is achieved by training models with labeled data which can be ...
Princeton researchers found that the brain excels at learning because it reuses modular “cognitive blocks” across many tasks.
While the concerns around AI in higher education were once focused on how it might lead to academic misconduct, the attention is shifting to how AI can support the process of learning. “We’ve seen a ...
Jean-Charles Pelland's work has been made possible by financial support from the ‘QUANTA: Evolution of Cognitive Tools for Quantification’ project, which has received funding from the European ...
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