History of art paintings through the lens of entropy and complexity

Sigaki, Higor Y. D.; Perc, Matjaž; Ribeiro, Haroldo V.

doi:10.1073/pnas.1800083115

Cited by 105 publications

(83 citation statements)

References 54 publications

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“…For a given value of H, the complexity C can assume values between a minimum and a maximum and provides important additional information about the correlational structure of A that is not properly carried out by the values of H. Mainly for this reason, we have used the diagram of C versus H (the so-called complexity-entropy plane) as a discriminating tool for investigating the liquid crystal textures. This framework has been successfully used in several applications with time series [34][35][36][37][38] and image analysis [29,[39][40][41]. In addition to their simplicity and intuitive meaning, these complexity measures are very fast and scalable from the computational point of view.…”

Section: Resultsmentioning

confidence: 99%

“…Our goal in this case is to identify the pitch of a cholesteric liquid crystal based on the values of H and C obtained from the textures. To do so, we create a dataset of textures composed of one hundred replicas for each pitch value η ∈ (15,17,19,21,23,25,27,29,40) nm, where η = 40 nm is large enough to mimic a nematic texture. The optical textures are numerically obtained by solving the model described in Appendix D, which is based on the Landau-de Gennes theory [32].…”

Section: Simulated Cholesteric Texturesmentioning

confidence: 99%

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Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Sigaki

Souza

et al. 2019

Phys. Rev. E

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Imaging techniques are essential tools for inquiring a number of properties from different materials. Liquid crystals are often investigated via optical and image processing methods. In spite of that, considerably less attention has been paid to the problem of extracting physical properties of liquid crystals directly from textures images of these materials. Here we present an approach that combines two physics-inspired image quantifiers (permutation entropy and statistical complexity) with machine learning techniques for extracting physical properties of nematic and cholesteric liquid crystals directly from their textures images. We demonstrate the usefulness and accuracy of our approach in a series of applications involving simulated and experimental textures, in which physical properties of these materials (namely: average order parameter, sample temperature, and cholesteric pitch length) are predicted with significant precision. Finally, we believe our approach can be useful in more complex liquid crystal experiments as well as for probing physical properties of other materials that are investigated via imaging techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Simulated Cholesteric Texturesmentioning

confidence: 99%

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Sigaki

Souza

et al. 2019

Phys. Rev. E

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“…n must be satisfied to obtain reliable statistics. Because of their simplicity, intuitive meaning, and scalability from the computational point of view, this framework has been successfully used in several applications with time series [29][30][31][32][33][34] and image analysis [35][36][37][38] .…”

Section: Permutation Entropy and Statistical Complexitymentioning

confidence: 99%

Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market

Sigaki

Perc

Ribeiro

2019

Sci Rep

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The efficient market hypothesis has far-reaching implications for financial trading and market stability. Whether or not cryptocurrencies are informationally efficient has therefore been the subject of intense recent investigation. Here, we use permutation entropy and statistical complexity over sliding time-windows of price log returns to quantify the dynamic efficiency of more than four hundred cryptocurrencies. We consider that a cryptocurrency is efficient within a time-window when these two complexity measures are statistically indistinguishable from their values obtained on randomly shuffled data. We find that 37% of the cryptocurrencies in our study stay efficient over 80% of the time, whereas 20% are informationally efficient in less than 20% of the time. Our results also show that the efficiency is not correlated with the market capitalization of the cryptocurrencies. A dynamic analysis of informational efficiency over time reveals clustering patterns in which different cryptocurrencies with similar temporal patterns form four clusters, and moreover, younger currencies in each group appear poised to follow the trend of their ‘elders’. The cryptocurrency market thus already shows notable adherence to the efficient market hypothesis, although data also reveals that the coming-of-age of digital currencies is in this regard still very much underway.

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“…Not only the data volume has grown, but also the investigated themes and degree of detail are nowadays unprecedented. While large and detailed data sets allow us to probe quantitative questions about topics as diverse as material sciences [1] and art [2], this complexity often challenges the methods and techniques available for executing the data analysis. Even the discrimination among simple data such as time series becomes challenging depending on the subject and amount of data involved in the task.…”

Section: Introductionmentioning

confidence: 99%

Characterizing stochastic time series with ordinal networks

Pessa

Ribeiro

2019

Phys. Rev. E

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Approaches for mapping time series to networks have become essential tools for dealing with the increasing challenges of characterizing data from complex systems. Among the different algorithms, the recently proposed ordinal networks stand out due to its simplicity and computational efficiency. However, applications of ordinal networks have been mainly focused on time series arising from nonlinear dynamical systems, while basic properties of ordinal networks related to simple stochastic processes remain poorly understood. Here, we investigate several properties of ordinal networks emerging from random time series, noisy periodic signals, fractional Brownian motion, and earthquake magnitude series. For ordinal networks of random series, we present an approach for building the exact form of the adjacency matrix, which in turn is useful for detecting non-random behavior in time series and the existence of missing transitions among ordinal patterns. We find that the average value of a local entropy, estimated from transition probabilities among neighboring nodes of ordinal networks, is more robust against noise addition than the standard permutation entropy. We show that ordinal networks can be used for estimating the Hurst exponent of time series with accuracy comparable with state-of-the-art methods. Finally, we argue that ordinal networks can detect sudden changes in Earth seismic activity caused by large earthquakes. arXiv:1910.01406v1 [physics.data-an]

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History of art paintings through the lens of entropy and complexity

Cited by 105 publications

References 54 publications

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market

Characterizing stochastic time series with ordinal networks

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