Decision-making often involves trial and error, but conventional models assume we always act optimally based on past ...

Researchers from the University of Tokyo in collaboration with Aisin Corporation have demonstrated that universal scaling ...

The potential for these kinds of machines to reshape computer processing, increase energy efficiency, and revolutionize medical testing has scientists excited. But when do we consider these cells to ...

Researchers reveal how modeling the human brain’s hidden wiring could push AI beyond its current limits into human-like ...

Recent research has employed chemical reaction networks (CRNs), which harness biochemical processes for computations that translate interactions involving biochemical species into graphical form.

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

Intelligent Cyber-Physical Systems typically utilize sensors to gather a significant amount of raw data which often possesses temporal and high-dimensional characteristics. Ensuring the data ...

Mechanistic modeling with low-rank recurrent networks uncovers the relationship between network connectivity, neural dynamics, and selection modulation mechanisms in context-dependent computation.

The comparison results with other models show that the Variational Autoencoder neural network provides the best overall performance with a higher detection accuracy and a reasonable false positive ...

Spiking neural networks (SNNs), which are the next generation of artificial neural networks (ANNs), offer a closer mimicry to natural neural networks and hold promise for significant improvements in ...