Contrastive learning has been widely used in graph representation learning, which extracts node or graph representations by contrasting positive and negative node pairs. It requires node ...

27. "Average Album Length 1964 - 2024" via reddit "As the visualization says, I averaged the lengths of the 50 most popular (i.e. most frequently logged, not most highly rated) albums for each ...

EEG-based emotion recognition is crucial for understanding human affective states, offering valuable insights into diverse fields like mental health monitoring and humancomputer interaction. Recent ...

Skeleton-based recognition of human actions has received attention in recent years because of the popularity of 3-D acquisition sensors. Existing studies use 3-D skeleton data from video clips ...

Knowledge graph reasoning (KGR) seeks to infer new factual triples from existing knowledge graphs (KGs). Recent methods have unified transductive and inductive reasoning by learning entity-independent ...

Developing a distributed bipartite optimal consensus scheme while ensuring user-predefined performance is essential in practical applications. Existing approaches to this problem typically require a ...

Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier edges. However, for ...

Domain-adaptive vehicle re-identification is a challenging task that aims to transfer the knowledge from a labeled source domain to an unlabeled target domain for effective vehicle re-identification.

This article studies the adaptive optimal control problem for continuous-time nonlinear systems described by differential equations. A key strategy is to exploit the value iteration (VI) method ...

Maritime radar detectors developed using deep learning technology have demonstrated promising performance in the clutter environment. However, real clutter environments are usually time-varying, and ...

In industrial applications, the degradation rate of equipment is often accompanied by stochasticity due to constant changes in operating conditions and loads, making the degradation process tend to ...

The operational state of harmonic drives demonstrates nonlinear and nonstationary characteristics, which pose challenges for traditional methods to extract features. Graph neural networks (GNNs) have ...