Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/103254
Title: Multiscale structural complexity of natural patterns
Authors: Bagrov, A. A.
Iakovlev, I. A.
Iliasov, A. A.
Katsnelson, M. I.
Mazurenko, V. V.
Issue Date: 2020
Publisher: National Academy of Sciences
Citation: Multiscale structural complexity of natural patterns / A. A. Bagrov, I. A. Iakovlev, A. A. Iliasov, et al. — DOI 10.1073/pnas.2004976117 // Proceedings of the National Academy of Sciences of the United States of America. — 2020. — Vol. 117. — Iss. 48. — P. 30241-30251.
Abstract: Complexity of patterns is key information for human brain to differ objects of about the same size and shape. Like other innate human senses, the complexity perception cannot be easily quantified. We propose a transparent and universal machine method for estimating structural (effective) complexity of two-dimensional and three-dimensional patterns that can be straightforwardly generalized onto other classes of objects. It is based on multistep renormalization of the pattern of interest and computing the overlap between neighboring renormalized layers. This way, we can define a single number characterizing the structural complexity of an object. We apply this definition to quantify complexity of various magnetic patterns and demonstrate that not only does it reflect the intuitive feeling of what is “complex” and what is “simple” but also, can be used to accurately detect different phase transitions and gain information about dynamics of nonequilibrium systems. When employed for that, the proposed scheme is much simpler and numerically cheaper than the standard methods based on computing correlation functions or using machine learning techniques. © 2020 National Academy of Sciences. All rights reserved.
Keywords: PATTERN FORMATION | COMPLEXITY | RENORMALIZATION GROUP | IMAGE PROCESSING
ARTICLE
BODY PATTERNING
CORRELATION FUNCTION
HUMAN
IMAGE PROCESSING
MACHINE LEARNING
PHASE TRANSITION
QUANTITATIVE ANALYSIS
URI: http://hdl.handle.net/10995/103254
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85097210680
PURE ID: 20217034
0b24c150-d9c2-483e-ade4-d6d8e69f3d7b
ISSN: 278424
DOI: 10.1073/pnas.2004976117
metadata.dc.description.sponsorship: We thank Yuri Bakhtin, Victor Kleptsyn, Eugene Koonin, Denis Kosygin, Slava Rychkov, Stanislav Smirnov, and Tom Wester-hout for useful discussions and Elena Mazurenko for technical assistance in conducting food dye experiments. The work of A.A.B., I.A.I., and V.V.M. was supported by Russian Science Foundation Grant 18-12-00185. A.A.I. acknowledges financial support from Dutch Science Foundation Neder-landse Organisatie voor Wetenschappelijk Onderzoek (NWO)/Foundation for Fundamental Research on Matter Grant 16PR1024. M.I.K. acknowledges support from NWO Spinoza Prize. This work was partially supported by Knut and Alice Wallenberg Foundation Grant 2018.0060.
RSCF project card: 18-12-00185
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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