Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …

Entropy is a powerful tool for the analysis of time series, as it allows describing the
probability distributions of the possible state of a system, and therefore the information …

This paper intends to provide an overview on recent development of knowledge transfer for
rotary machine fault diagnosis (RMFD) by using different transfer learning techniques. After …

Mutual information (MI) is a powerful method for detecting relationships between data sets.
There are accurate methods for estimating MI that avoid problems with “binning” when both …

In the past several years studies have started to appear comparing the accuracies of various
science mapping approaches. These studies primarily compare the cluster solutions …

Tests that infer the ancestral origin of a DNA sample have considerable potential in the
development of forensic tools that can help to guide crime investigation. We have developed …

One of the most important aspects of time series is their degree of stochasticity vs. chaoticity.
Since the discovery of chaotic maps, many algorithms have been proposed to discriminate …

We discuss a way of characterizing probability distributions, complementing that provided by
the celebrated notion of information measure, with reference to a measure of complexity that …

Nonlinear time series analysis is an active field of research that studies the structure of
complex signals in order to derive information of the process that generated those series, for …

Many analyses on configurable software systems are intractable when confronted with
colossal and highly-constrained configuration spaces. These analyses could instead use …