This article provides an overview of tensors, their properties, and their applications in
statistics. Tensors, also known as multidimensional arrays, are generalizations of matrices to …

Tensor decomposition has been demonstrated to be successful in a wide range of
applications, from neuroscience and wireless communications to social networks. In an …

Objective: In recent years, resting state fMRI has been widely utilized to understand the
functional organization of the brain for healthy and disease populations. Recent studies …

Phase synchronization is an important mechanism for the information processing of neurons
in the brain. Most of the current phase synchronization measures are bivariate and focus on …

In the study of brain functional connectivity networks, it is assumed that a network is built
from a data window in which activity is stationary. However, brain activity is non-stationary …

Recent studies show that the dynamics of electrophysiological functional connectivity is
attracting more and more interest since it is considered as a better representation of …

Efficient neuronal communication between brain regions through oscillatory synchronization
at certain frequencies is necessary for cognition. Such synchronized networks are transient …

With the development of technology in data collection and storage, new types of higher
order tensorial information streams are available in chemical and biological manufacturing …

Phase synchrony assessment across non-stationary multivariate signals is a useful way to
characterize the dynamical behavior of their underlying systems. Traditionally, phase …

Detecting anomalous traffic is a crucial task for network management. Although many
anomaly detection algorithms have been proposed recently, constrained by their matrix …