Molecular representation learning (MRL) has gained tremendous attention due to its critical
role in learning from limited supervised data for applications like drug design. In most MRL …

Many representative graph neural networks, $ eg $, GPR-GNN and ChebNet, approximate
graph convolutions with graph spectral filters. However, existing work either applies …

Designing spectral convolutional networks is a challenging problem in graph learning.
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …

We successfully synthesized Au−ZnO hybrid nanoparticles with a novel hexagonal pyramid-like
structure. The growth process of the as-prepared hybrid nanopyramids is clearly …

Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured
data. Recently, GCNs and subsequent variants have shown superior performance in …

Planthoppers are highly destructive pests in crop production worldwide. Brown planthopper
(BPH) causes the most serious damage of the rice crop globally among all rice pests. …

Autonomous agents have long been a research focus in academic and industry communities.
Previous research often focuses on training agents with limited knowledge within isolated …

UDP-glucose pyrophosphorylase (UGPase) catalyzes the reversible production of glucose-1-phosphate
and UTP to UDP-glucose and pyrophosphate. The rice (Oryza sativa) genome …

Direct reprogramming of human somatic cells into pluripotency has broad implications in
generating patient-specific induced pluripotent stem (iPS) cells for disease modeling and …

Engineering and breeding resistant plant varieties are the most effective and environmentally
friendly ways to control agricultural pests and improve crop performance. However, the …