Digital systems are expected to navigate real-world environments, understand multimedia content, and make high-stakes ...

Deep learning has emerged as a transformative paradigm in modern computational science, leveraging neural networks to approximate complex functions across a variety of domains. Central to this ...

Artificial intelligence (AI) is increasingly prevalent, integrated into phone apps, search engines and social media platforms as well as supporting myriad research applications. Of particular interest ...

The future of conflict prediction relies on combining technical ability, institutional governance and ethical responsibility.

Most contemporary artificial intelligence (AI) systems learn to complete tasks via machine learning and deep learning. Machine learning is a computational approach that allows models to uncover ...

The tiny worm Caenorhabditis elegans has a brain just about the width of a human hair. Yet this animal’s itty-bitty organ coordinates and computes complex movements as the worm forages for food. “When ...

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

A new review examines how insertion and deletion (indel) errors disrupt data synchronization in modern communication systems. By surveying both traditional and Deep Learning-driven approaches, the ...

• Leaf vein network geometry can predict levels of resource transport, defence and mechanical support that operate at different spatial scales. However, it is challenging to quantify network ...