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Adjusted Kolmogorov Complexity of Binary Words with Empirical Entropy Normalization [PDF]
EntropyKolmogorov complexity of a finite binary word reflects both algorithmic structure and the empirical distribution of symbols appearing in the word. Words with symbol frequencies far from one half belong to smaller combinatorial classes and therefore ...
Brani Vidakovic
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Freedom under algorithms: how unpredictable and asocial management erodes free choice [PDF]
Frontiers in Artificial IntelligenceThis article examines the impact of algorithmic management on individual freedom. To orient this exploration, I draw on the (feminist) conception of liberty as the choosing subject.
Robert Donoghue
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Axioms, 2023 This expository paper advocates an approach to physics in which “typicality” is identified with a suitable form of algorithmic randomness. To this end various theorems from mathematics and physics are reviewed.
Klaas Landsman
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Algorithmic Randomness and Capacity of Closed Sets [PDF]
Logical Methods in Computer Science, 2011 We investigate the connection between measure, capacity and algorithmic randomness for the space of closed sets. For any computable measure m, a computable capacity T may be defined by letting T(Q) be the measure of the family of closed sets K which have
Douglas Cenzer +3 more
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Algorithmic randomness for Doob's martingale convergence theorem in continuous time [PDF]
Logical Methods in Computer Science, 2014 We study Doob's martingale convergence theorem for computable continuous time martingales on Brownian motion, in the context of algorithmic randomness. A characterization of the class of sample points for which the theorem holds is given. Such points are
Bjørn Kjos-Hanssen +2 more
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Computational Creativity and Aesthetics with Algorithmic Information Theory
Entropy, 2021 We build an analysis based on the Algorithmic Information Theory of computational creativity and extend it to revisit computational aesthetics, thereby, improving on the existing efforts of its formulation.
Tiasa Mondol, Daniel G. Brown
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Frontiers in Computational Neuroscience, 2023 Being able to objectively characterize the intrinsic complexity of behavioral patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems.
Hector Zenil +4 more
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Black Holes and Complexity via Constructible Universe
Universe, 2020 The relation of randomness and classical algorithmic computational complexity is a vast and deep subject by itself. However, already, 1-randomness sequences call for quantum mechanics in their realization.
Jerzy Król, Paweł Klimasara
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Re-Consolidating First-Order Masking Schemes
Transactions on Cryptographic Hardware and Embedded Systems, 2020 Application of masking, known as the most robust and reliable countermeasure to side-channel analysis attacks, on various cryptographic algorithms has dedicated a lion’s share of research to itself.
Aein Rezaei Shahmirzadi, Amir Moradi
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Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity [PDF]
Royal Society Open Science, 2018 Natural selection explains how life has evolved over millions of years from more primitive forms. The speed at which this happens, however, has sometimes defied formal explanations when based on random (uniformly distributed) mutations.
Santiago Hernández-Orozco +2 more
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