Neural networks have been extensively applied to short-term traffic prediction in the past
years. This study proposes a novel architecture of neural networks, Long Short-Term Neural …

Predicting large-scale transportation network traffic has become an important and challenging
topic in recent decades. Inspired by the domain knowledge of motion prediction, in which …

Understanding how congestion at one location can cause ripples throughout large-scale
transportation network is vital for transportation researchers and practitioners to pinpoint traffic …

We consider the problem of explaining the predictions of graph neural networks (GNNs),
which otherwise are considered as black boxes. Existing methods invariably focus on …

Tumour cells primarily utilize aerobic glycolysis for energy production, a phenomenon known
as the Warburg effect. Its mechanism is not well understood. The tumour suppressor gene …

A graphdiyne-based artificial synapse (GAS), exhibiting intrinsic short-term plasticity, has
been proposed to mimic biological signal transmission behavior. The impulse response of the …

Deep learning methods are achieving ever-increasing performance on many artificial intelligence
tasks. A major limitation of deep models is that they are not amenable to interpretability…

Phenotyping plays an important role in crop science research; the accurate and rapid
acquisition of phenotypic information of plants or cells in different environments is helpful for …

The Metaverse is a network of 3-D virtual worlds supporting social connections among its
users and enabling them to participate in activities mimicking real life. It merges physical and …

Large language models (LLMs) often contain misleading content, emphasizing the need to
align them with human values to ensure secure AI systems. Reinforcement learning from …