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Dynamics and triggers of misinformation on vaccines.
PLoS One
January 2025
Centro Ricerche Enrico Fermi, Rome, Italy.
The Covid-19 pandemic has sparked renewed attention to the risks of online misinformation, emphasizing its impact on individuals' quality of life through the spread of health-related myths and misconceptions. In this study, we analyze 6 years (2016-2021) of Italian vaccine debate across diverse social media platforms (Facebook, Instagram, Twitter, YouTube), encompassing all major news sources-both questionable and reliable. We first use the symbolic transfer entropy analysis of news production time-series to dynamically determine which category of sources, questionable or reliable, causally drives the agenda on vaccines.
View Article and Find Full Text PDFVCSAP: Online reinforcement learning exploration method based on visitation count of state-action pairs.
Neural Netw
January 2025
Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Changchun 130012, China; College of Computer Science and Technology, Jilin University, Changchun 130012, China; College of Software, Jilin University, Changchun 130012, China. Electronic address:
In the domain of online reinforcement learning, strategies that leverage inherent rewards for exploration tend to achieve commendable outcomes within contexts characterized by deceptive or sparse rewards. Counting through the visitation of states is an efficient count-based exploration method to get the proper intrinsic reward. However, only the novelty of the states encountered by the agent is considered in this exploration method, resulting in the over-exploration of a certain state-action pair and falling into a locally optimal solution.
View Article and Find Full Text PDFA Bayesian dynamic stopping method for evoked response brain-computer interfacing.
Front Hum Neurosci
December 2024
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands.
Introduction: As brain-computer interfacing (BCI) systems transition fromassistive technology to more diverse applications, their speed, reliability, and user experience become increasingly important. Dynamic stopping methods enhance BCI system speed by deciding at any moment whether to output a result or wait for more information. Such approach leverages trial variance, allowing good trials to be detected earlier, thereby speeding up the process without significantly compromising accuracy.
View Article and Find Full Text PDFApplications of Entropy in Data Analysis and Machine Learning: A Review.
Entropy (Basel)
December 2024
Centro de Investigación Operativa, Universidad Miguel Hernández de Elche, 03202 Elche, Spain.
Since its origin in the thermodynamics of the 19th century, the concept of entropy has also permeated other fields of physics and mathematics, such as Classical and Quantum Statistical Mechanics, Information Theory, Probability Theory, Ergodic Theory and the Theory of Dynamical Systems. Specifically, we are referring to the classical entropies: the Boltzmann-Gibbs, von Neumann, Shannon, Kolmogorov-Sinai and topological entropies. In addition to their common name, which is historically justified (as we briefly describe in this review), another commonality of the classical entropies is the important role that they have played and are still playing in the theory and applications of their respective fields and beyond.
View Article and Find Full Text PDFSymbolic dynamics of joint brain states during dyadic coordination.
Chaos
January 2025
Department of Cognitive Sciences, University of California, Irvine, California 92617, USA.
We propose a novel approach to investigate the brain mechanisms that support coordination of behavior between individuals. Brain states in single individuals defined by the patterns of functional connectivity between brain regions are used to create joint symbolic representations of brain states in two or more individuals to investigate symbolic dynamics that are related to interactive behaviors. We apply this approach to electroencephalographic data from pairs of subjects engaged in two different modes of finger-tapping coordination tasks (synchronization and syncopation) under different interaction conditions (uncoupled, leader-follower, and mutual) to explore the neural mechanisms of multi-person motor coordination.
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