Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Abstract The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI)
records the temporal dynamics of intrinsic functional networks in the brain. However, existing …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (\emph {eg,} social …

Irritable bowel syndrome (IBS) is a common clinical label for medically unexplained
gastrointestinal symptoms, recently described as a disturbance of the microbiota-gut-brain …

Self-supervised learning techniques are celebrating immense success in natural language
processing (NLP) by enabling models to learn from broad language data at unprecedented …

In mental state decoding, researchers aim to identify the set of mental states (eg,
experiencing happiness or fear) that can be reliably identified from the activity patterns of a …

Brain neural activity decoding is an important branch of neuroscience research and a key
technology for the brain–computer interface (BCI). Researchers initially developed simple …

Task fMRI provides an opportunity to analyze the working mechanisms of the human brain
during specific experimental paradigms. Deep learning models have increasingly been …