Self-powered neurostimulation via biomaterials and bioelectronics innovation has emerged
as a compelling approach to explore, repair, and modulate neural systems. This review …

Graph contrastive learning (GCL) has emerged as a dominant technique for graph
representation learning which maximizes the mutual information between paired graph …

Sign language recognition (SLR) is a weakly supervised task that annotates sign videos as
textual glosses. Recent studies show that insufficient training caused by the lack of large …

Spatiotemporal predictive learning aims to generate future frames by learning from historical
frames. In this paper, we investigate existing methods and present a general framework of …

Recent years have witnessed the prosperity of pre-training graph neural networks (GNNs)
for molecules. Typically, atom types as node attributes are randomly masked and GNNs are …

Based on the foundation of Large Language Models (LLMs), Multilingual LLMs (MLLMs)
have been developed to address the challenges faced in multilingual natural language …

Pre-trained transformer models shine in many natural language processing tasks and
therefore are expected to bear the representation of the input sentence or text meaning …

Deep learning has achieved remarkable success in learning representations for molecules,
which is crucial for various biochemical applications, ranging from property prediction to …

Enzyme design plays a crucial role in both industrial production and biology. However, this
field faces challenges due to the lack of comprehensive benchmarks and the complexity of …

The utilization of word embeddings—powerful models computed through Neural Network
architectures that encode words as vectors—has witnessed rapid growth across various …