Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

This article investigates the problem of enhancing underwater visual observations for the
purpose of accurate underwater object detection. Most existing underwater visual …

We introduce a new semantic communication mechanism-SemanticRL, whose key idea is to
preserve the semantic information instead of strictly securing the bit-level precision. Unlike …

Conventional image classification methods usually require a large number of training
samples for the training model. However, in practical scenarios, the amount of available …

PET is a noninvasive molecular imaging modality that is increasingly popular in oncology,
neurology, cardiology, and other fields. 1–3 Accurate quantitation of PET radiotracer uptake …

Recently, the success of non-additive steganography has demonstrated that asymmetric
distortion can remarkably improve security performance compared with symmetric cost …

It has been demonstrated that the blurring process reduces the high-frequency information
of the original sharp image, so the main challenge for image deblurring is to reconstruct high …

In recent years, brain science and neuroscience have greatly propelled the innovation of
computer science. In particular, knowledge from the neurobiology and neuropsychology of …