This method demonstrates strong robustness and generalizability across various experimental setups, load conditions, and fault types/severities. We have conducted comprehensive validation experiments, ...

Abstract: Permutation entropy (PE) is a widely used metric for quantifying the complexity of time series data. Recent efforts have extended PE to graph signals, resulting in the graph permutation ...

L. G. J. M. Voltarelli, A. A. B. Pessa, L. Zunino, R. S. Zola, E. K. Lenzi, M. Perc, H. V. Ribeiro, Characterizing unstructured data with the nearest neighbor ...

Scientists have simultaneously broken a temperature record, overturned a long-held theory and utilized a new laser spectroscopy method for dense plasmas in a groundbreaking article published today in ...

Understanding the high-pressure phase behavior of carbon dioxide–hydrocarbon mixtures is of considerable interest owing to their wide range of applications. Under certain conditions, these systems are ...

In machine learning, making informed decisions about splitting data is critical for building accurate models. Entropy, a concept from information theory, helps us quantify the uncertainty in a dataset ...

Abstract: As a powerful tool for measuring complexity and randomness, multivariate multi-scale permutation entropy (MMPE) has been widely applied to the feature representation and extraction of ...

# This small notebook implements, in [Python 3](https://docs.python.org/3/), several algorithms aiming at a simple task: # given a certain list, generate *all* the ...

In this tutorial for graduate students and postdocs, John Baez and Tai-Danae Bradley (GC alum, class of 2020) will introduce material related to the upcoming symposium, Categorical Semantics of ...

ABSTRACT: We have defined the environmental interface through the exchange processes between media forming this interface. Considering the environmental interface as a complex system we elaborated the ...