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DDC: 530 × Subject: entropy ×

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    Momentum space entanglement from the Wilsonian effective action
    (Woodbury, NY : Inst., 2022) Martins Costa, Matheus H.; van den Brink, Jeroen; Nogueira, Flavio S.; Krein, Gastão I.
    The entanglement between momentum modes of a quantum field theory at different scales is not as well studied as its counterpart in real space, despite the natural connection with the Wilsonian idea of integrating out the high-momentum degrees of freedom. Here, we push such a connection further by developing a novel method to calculate the Rényi and entanglement entropies between slow and fast modes, which is based on the Wilsonian effective action at a given scale. This procedure is applied to the perturbative regime of some scalar theories, comparing the lowest-order results with those from the literature and interpreting them in terms of Feynman diagrams. This method is easily generalized to higher-order or nonperturbative calculations. It has the advantage of avoiding matrix diagonalizations of other techniques.
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    A new color image encryption scheme using CML and a fractional-order chaotic system
    (San Francisco, CA : Public Library of Science (PLoS), 2015) Wu, X.; Li, Y.; Kurths, J.
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    Trends in recurrence analysis of dynamical systems
    (Les Ulis : EDP Sciences, 2023) Marwan, Norbert; Kraemer, K. Hauke
    The last decade has witnessed a number of important and exciting developments that had been achieved for improving recurrence plot-based data analysis and to widen its application potential. We will give a brief overview about important and innovative developments, such as computational improvements, alternative recurrence definitions (event-like, multiscale, heterogeneous, and spatio-temporal recurrences) and ideas for parameter selection, theoretical considerations of recurrence quantification measures, new recurrence quantifiers (e.g. for transition detection and causality detection), and correction schemes. New perspectives have recently been opened by combining recurrence plots with machine learning. We finally show open questions and perspectives for futures directions of methodical research.