Brain-inspired chips can slash AI energy use by as much as 100-fold, but the road to mainstream deployment is far from guaranteed.

Well-known compressed sensing (CS) is widely used in image acquisition and reconstruction. However, accurately reconstructing images from measurements at low sampling rates remains a considerable ...

In addition, prior networks often overlook the regularization term separately, which is an absent factor in these networks. In this study, we focus on leveraging the power of CNN and transformer ...

A Transformer-based neural network that decodes movement intentions in real time from EEG, EMG, and IMU signals. It classifies intended actions and sends control signals to external actuators, such as ...

To address these challenges, we propose a Noise-Consistent hypeRgraph AutoEncoder framework with denoising strategies, termed NCRAE, aimed at achieving robust node embeddings in ceRNA regulatory ...