Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks (GNNs)—AI systems used in applications from drug discovery to weather ...

Abstract: Predicting remaining useful life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a constant ...

Analogue engineering still relies heavily on manual intervention, but that is changing with the growing use of AI/ML.

From Kalshi and Polymarket to niche scientific platforms, traders are predicting the weather — and climate experts are ...

Abstract: Hybrid quantum–classical computing has become an attractive strategy for improving learning capability and predictive capacity in complex data environments. This work presents a ...

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two widely used neuroimaging techniques, with complementary strengths and weaknesses. Predicting fMRI activity from ...

Accurately identifying small molecule binding sites on proteins is fundamental to understanding protein function and enabling structure-based drug discovery, yet this critical step remains a major ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...