MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced that they have developed a set of quantum algorithms for feedforward neural networks, breaking through the performance ...

Neuromorphic engineering draws inspiration from biological neural systems, which operate robustly despite significant variability, noise, and heterogeneity ...

It’s not often a math paper goes viral, but a new preprint from a theoretical physicist at Poland’s Jagiellonian University ...

Physics-informed neural networks (PINNs) have shown remarkable prospects in solving forward and inverse problems involving ...

Identification of each animal in a collective becomes possible even when individuals are never all visible simultaneously, enabling faster and more accurate analysis of collective behavior.

Quantum computing future explained through cryptography, optimization, and AI breakthroughs showing how quantum computing ...

Those changes will be contested, in math as in other academic disciplines wrestling with AI’s impact. As AI models become a ...

The multiple condition (MC)-retention model is an uncertainty-aware graph-based neural network that predicts liquid chromatography (LC) retention times across multiple column chem ...

A new review finds that AI is no longer being treated simply as a technical add-on for solar and wind prediction, but ...

When engineers at Sumitomo Riko needed to speed up the design cycle for automotive rubber and polymer components, they turned ...

Machine-learning-informed simulations of physical phenomena ranging from drifting bands (left), resonant ripples (center) and sharpening fronts (right) using a physics-informed neural network that ...

TSNC is being positioned as a practical path for developers who already ship BC-compressed assets and want to squeeze more data into the same storage, bandwidth, ...