Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …

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 forecasting has been a widely explored task of great importance in many
applications. However, it is common that real-world time series data are recorded in a short …

Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that
is often approached using neurophysiological signals as the basis for building a reliable …

Time series analysis has proven to be a powerful method to characterize several
phenomena in biology, neuroscience and economics, and to understand some of their …

Rhythmic behavior represents one of the most striking and ubiquitous manifestations of
functional evolution for a wide class of natural and man-made systems. The emergence of …

Complexity science and systems thinking are increasingly recognized as relevant
paradigms for studying systems where biology, psychology, and socioenvironmental factors …

A central challenge in data-driven model discovery is the presence of hidden, or latent,
variables that are not directly measured but are dynamically important. Takens' theorem …

Persistence is an important characteristic of many complex systems in nature, related to how
long the system remains at a certain state before changing to a different one. The study of …

Network theory, as emerging from complex systems science, can provide critical predictive
power for mitigating the global warming crisis and other societal challenges. Here we …