Journal Description

Entropy

Entropy is an international and interdisciplinary peer-reviewed open access journal of entropy and information studies, published monthly online by MDPI. The International Society for the Study of Information (IS4SI) and Spanish Society of Biomedical Engineering (SEIB) are affiliated with Entropy and their members receive a discount on the article processing charge.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), MathSciNet, Inspec, PubMed, PMC, Astrophysics Data System, and other databases.
Journal Rank: JCR - Q2 (Physics, Multidisciplinary) / CiteScore - Q1 (Mathematical Physics)
Rapid Publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 18.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2022).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our editors and authors say about Entropy.
Companion journals for Entropy include: Foundations, Thermo and MAKE.

Impact Factor: 2.738 (2021) ; 5-Year Impact Factor: 2.642 (2021)

Imprint Information Journal Flyer Open Access ISSN: 1099-4300

Latest Articles

Open AccessArticle

Catastrophe Theory Applied to Neuropsychological Data: Nonlinear Effects of Depression on Financial Capacity in Amnestic Mild Cognitive Impairment and Dementia

Dimitrios Stamovlasis

Vaitsa Giannouli

Julie Vaiopoulou

and

Magda Tsolaki

Entropy 2022, 24(8), 1089; https://doi.org/10.3390/e24081089 (registering DOI) - 07 Aug 2022

Abstract

Financial incapacity is one of the cognitive deficits observed in amnestic mild cognitive impairment and dementia, while the combined interference of depression remains unexplored. The objective of this research is to investigate and propose a nonlinear model that explains empirical data better than ordinary linear ones and elucidates the role of depression. Four hundred eighteen (418) participants with a diagnosis of amnestic MCI with varying levels of depression were examined with the Geriatric Depression Scale (GDS-15), the Functional Rating Scale for Symptoms of Dementia (FRSSD), and the Legal Capacity for Property Law Transactions Assessment Scale (LCPLTAS). Cusp catastrophe analysis was applied to the data, which suggested that the nonlinear model was superior to the linear and logistic alternatives, demonstrating depression contributes to a bifurcation effect. Depressive symptomatology induces nonlinear effects, that is, beyond a threshold value sudden decline in financial capacity is observed. Implications for theory and practice are discussed. Full article

(This article belongs to the Special Issue Complexity Science in Human Change: Research, Models, Clinical Applications)

Open AccessArticle

Multiscale Analysis of Runoff Complexity in the Yanhe Watershed

Xintong Liu

and

Hongrui Zhao

Entropy 2022, 24(8), 1088; https://doi.org/10.3390/e24081088 (registering DOI) - 07 Aug 2022

Abstract

Runoff complexity is an important indicator reflecting the sustainability of a watershed ecosystem. In order to explore the multiscale characteristics of runoff complexity and analyze its variation and influencing factors in the Yanhe watershed in China during the period 1991–2020, we established a new analysis method for watershed runoff complexity based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method for the decomposition of multiscale characteristics and the refined composite multiscale entropy (RCMSE) method for the quantification of the system complexity. The results show that runoff and its components all present multiscale complexity characteristics that are different from random signals, and the intermediate frequency modes contribute the most to runoff complexity. The runoff complexity of the Yanhe watershed has decreased gradually since 1991, and 2010 was a turning point of runoff complexity, when it changed from a decline to an increase, indicating that the ecological sustainability of this basin has improved since 2010, which was mainly related to the ecological restoration measures of the Grain for Green Project. This study expands the research perspective for analyzing the variation characteristics of runoff at the multiscale, and provides a reference for the study of watershed ecological sustainability and ecological management. Full article

(This article belongs to the Topic Ecosystem Monitoring: Collective Species and Environmental Information)

Open AccessArticle

A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention

and

Entropy 2022, 24(8), 1087; https://doi.org/10.3390/e24081087 (registering DOI) - 06 Aug 2022

Abstract

In recent years, deep learning has been applied to intelligent fault diagnosis and has achieved great success. However, the fault diagnosis method of deep learning assumes that the training dataset and the test dataset are obtained under the same operating conditions. This condition can hardly be met in real application scenarios. Additionally, signal preprocessing technology also has an important influence on intelligent fault diagnosis. How to effectively relate signal preprocessing to a transfer diagnostic model is a challenge. To solve the above problems, we propose a novel deep transfer learning method for intelligent fault diagnosis based on Variational Mode Decomposition (VMD) and Efficient Channel Attention (ECA). In the proposed method, the VMD adaptively matches the optimal center frequency and finite bandwidth of each mode to achieve effective separation of signals. To fuse the mode features more effectively after VMD decomposition, ECA is used to learn channel attention. The experimental results show that the proposed signal preprocessing and feature fusion module can increase the accuracy and generality of the transfer diagnostic model. Moreover, we comprehensively analyze and compare our method with state-of-the-art methods at different noise levels, and the results show that our proposed method has better robustness and generalization performance. Full article

(This article belongs to the Special Issue Fault Diagnosis Methods Based on Information Theory or Machine Learning: From Theory to Application)

Open AccessArticle

Towards Generalizing the Information Theory for Neural Communication

János Végh

and

�?dám József Berki

Entropy 2022, 24(8), 1086; https://doi.org/10.3390/e24081086 (registering DOI) - 05 Aug 2022

Abstract

Neuroscience extensively uses the information theory to describe neural communication, among others, to calculate the amount of information transferred in neural communication and to attempt the cracking of its coding. There are fierce debates on how information is represented in the brain and during transmission inside the brain. The neural information theory attempts to use the assumptions of electronic communication; despite the experimental evidence that the neural spikes carry information on non-discrete states, they have shallow communication speed, and the spikes’ timing precision matters. Furthermore, in biology, the communication channel is active, which enforces an additional power bandwidth limitation to the neural information transfer. The paper revises the notions needed to describe information transfer in technical and biological communication systems. It argues that biology uses Shannon’s idea outside of its range of validity and introduces an adequate interpretation of information. In addition, the presented time-aware approach to the information theory reveals pieces of evidence for the role of processes (as opposed to states) in neural operations. The generalized information theory describes both kinds of communication, and the classic theory is the particular case of the generalized theory. Full article

(This article belongs to the Special Issue Applications of Information Theory in Neuroscience)

Open AccessArticle

Localization Detection Based on Quantum Dynamics

Kazue Kudo

Entropy 2022, 24(8), 1085; https://doi.org/10.3390/e24081085 (registering DOI) - 05 Aug 2022

Abstract

Detecting many-body localization (MBL) typically requires the calculation of high-energy eigenstates using numerical approaches. This study investigates methods that assume the use of a quantum device to detect disorder-induced localization. Numerical simulations for small systems demonstrate how the magnetization and twist overlap, which can be easily obtained from the measurement of qubits in a quantum device, changing from the thermal phase to the localized phase. The twist overlap evaluated using the wave function at the end of the time evolution behaves similarly to the one evaluated with eigenstates in the middle of the energy spectrum under a specific condition. The twist overlap evaluated using the wave function after time evolution for many disorder realizations is a promising probe for detecting MBL in quantum computing approaches. Full article

(This article belongs to the Special Issue Quantum Chaos—Dedicated to Professor Giulio Casati on the Occasion of His 80th Birthday)

Open AccessArticle

MIGAN: Mutual-Interaction Graph Attention Network for Collaborative Filtering

Ahlem Drif

and

Hocine Cherifi

Entropy 2022, 24(8), 1084; https://doi.org/10.3390/e24081084 (registering DOI) - 05 Aug 2022

Abstract

Many web platforms now include recommender systems. Network representation learning has been a successful approach for building these efficient recommender systems. However, learning the mutual influence of nodes in the network is challenging. Indeed, it carries collaborative signals accounting for complex user-item interactions on user decisions. For this purpose, in this paper, we develop a Mutual Interaction Graph Attention Network “MIGAN”, a new algorithm based on self-supervised representation learning on a large-scale bipartite graph (BGNN). Experimental investigation with real-world data demonstrates that MIGAN compares favorably with the baselines in terms of prediction accuracy and recommendation efficiency. Full article

(This article belongs to the Special Issue Selected Papers from the Tenth International Conference on Complex Networks & Their Applications)

Open AccessArticle

On the Eigenvalues of the Fermionic Angular Eigenfunctions in the Kerr Metric

Davide Batic

Suzan Hamad Abdul Karim

and

Marek Nowakowski

Entropy 2022, 24(8), 1083; https://doi.org/10.3390/e24081083 (registering DOI) - 05 Aug 2022

Abstract

In view of a result recently published in the context of the deformation theory of linear Hamiltonian systems, we reconsider the eigenvalue problem associated with the angular equation arising after the separation of the Dirac equation in the Kerr metric, and we show how a quasilinear first order PDE for the angular eigenvalues can be derived efficiently. We also prove that it is not possible to obtain an ordinary differential equation for the eigenvalues when the role of the independent variable is played by the particle energy or the black hole mass. Finally, we construct new perturbative expansions for the eigenvalues in the Kerr case and obtain an asymptotic formula for the eigenvalues in the case of a Kerr naked singularity. Full article

(This article belongs to the Section Astrophysics, Cosmology, and Black Holes)

Open AccessArticle

Ladderpath Approach: How Tinkering and Reuse Increase Complexity and Information

Yu Liu

Zengru Di

and

Philip Gerlee

Entropy 2022, 24(8), 1082; https://doi.org/10.3390/e24081082 (registering DOI) - 05 Aug 2022

Abstract

The notion of information and complexity are important concepts in many scientific fields such as molecular biology, evolutionary theory and exobiology. Many measures of these quantities are either difficult to compute, rely on the statistical notion of information, or can only be applied to strings. Based on assembly theory, we propose the notion of a ladderpath, which describes how an object can be decomposed into hierarchical structures using repetitive elements. From the ladderpath, two measures naturally emerge: the ladderpath-index and the order-index, which represent two axes of complexity. We show how the ladderpath approach can be applied to both strings and spatial patterns and argue that all systems that undergo evolution can be described as ladderpaths. Further, we discuss possible applications to human language and the origin of life. The ladderpath approach provides an alternative characterization of the information that is contained in a single object (or a system) and could aid in our understanding of evolving systems and the origin of life in particular. Full article

(This article belongs to the Section Complexity)

Open AccessArticle

On Some Extension of Intuitionistic Fuzzy Synthetic Measures for Two Reference Points and Entropy Weights

Ewa Roszkowska

Bartłomiej Jefmański

and

Marta Kusterka-Jefmańska

Entropy 2022, 24(8), 1081; https://doi.org/10.3390/e24081081 (registering DOI) - 05 Aug 2022

Abstract

In this paper, a novel Double Intuitionistic Fuzzy Synthetic Measure (DIFSM), based on intuitionistic fuzzy values for handling multi-criteria decision-making problems used to rank alternatives, is presented. In the studies, intuitionistic fuzzy sets (IFSs) represented uncertain, imprecise information or human judgment. The intuitionistic fuzzy sets can also reflect the approval, rejection, and hesitation of decision-makers. The degrees of satisfiability and non-satisfiability and uncertainty of each alternative with respect to a set of criteria are described by membership functions, non-membership functions, and hesitancy indexes, respectively. The aggregation algorithm DIFSM is inspired by Hellwig’s method based on two reference points: ideal point (pattern) and anti-ideal point (anti-pattern), measuring distances between the alternative and ideal point and distance between the ideal and anti-ideal point. The proposed methods take into consideration the entropy-based weights of criteria. An illustrative example is given to demonstrate the practicality and effectiveness of the proposed approach. Additionally, the comparative analysis results, using the DIFSM and the Intuitionistic Fuzzy TOPSIS-based framework, are presented. Full article

(This article belongs to the Special Issue Decision Optimization in Information Theory and Game Theory)

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Open AccessArticle

Rényi Entropy in Statistical Mechanics

Jesús Fuentes

and

Jorge Gonçalves

Entropy 2022, 24(8), 1080; https://doi.org/10.3390/e24081080 (registering DOI) - 05 Aug 2022

Abstract

Rényi entropy was originally introduced in the field of information theory as a parametric relaxation of Shannon (in physics, Boltzmann–Gibbs) entropy. This has also fuelled different attempts to generalise statistical mechanics, although mostly skipping the physical arguments behind this entropy and instead tending to introduce it artificially. However, as we will show, modifications to the theory of statistical mechanics are needless to see how Rényi entropy automatically arises as the average rate of change of free energy over an ensemble at different temperatures. Moreover, this notion is extended by considering distributions for isospectral, non-isothermal processes, resulting in relative versions of free energy, in which the Kullback–Leibler divergence or the relative version of Rényi entropy appear within the structure of the corrections to free energy. These generalisations of free energy recover the ordinary thermodynamic potential whenever isothermal processes are considered. Full article

(This article belongs to the Special Issue Rényi Entropy: Sixty Years Later)

Open AccessFeature PaperArticle

Information Theoretic Methods for Variable Selection—A Review

Jan Mielniczuk

Entropy 2022, 24(8), 1079; https://doi.org/10.3390/e24081079 - 04 Aug 2022

Abstract

We review the principal information theoretic tools and their use for feature selection, with the main emphasis on classification problems with discrete features. Since it is known that empirical versions of conditional mutual information perform poorly for high-dimensional problems, we focus on various ways of constructing its counterparts and the properties and limitations of such methods. We present a unified way of constructing such measures based on truncation, or truncation and weighing, for the Möbius expansion of conditional mutual information. We also discuss the main approaches to feature selection which apply the introduced measures of conditional dependence, together with the ways of assessing the quality of the obtained vector of predictors. This involves discussion of recent results on asymptotic distributions of empirical counterparts of criteria, as well as advances in resampling. Full article

(This article belongs to the Special Issue Information Theoretic Criteria: New Theoretical Developments and Applications)

Open AccessArticle

Degrees of Freedom of a K-User Interference Channel in the Presence of an Instantaneous Relay

Ali H. Abdollahi Bafghi

Mahtab Mirmohseni

and

Masoumeh Nasiri-Kenari

Entropy 2022, 24(8), 1078; https://doi.org/10.3390/e24081078 - 04 Aug 2022

Abstract

In this paper, we study the degrees of freedom (DoF) of a frequency-selective K-user interference channel in the presence of an instantaneous relay (IR) with multiple receiving and transmitting antennas. We investigate two scenarios based on the IR antennas’ cooperation ability. First, we assume that the IR receiving and transmitting antennas can coordinate with each other and that the transmitted signal of each transmitting antenna can depend on the received signals of all receiving antennas, and we derive lower and upper bounds for the sum DoF of this model. In an interference alignment scheme, we divide receivers into two groups called clean and dirty receivers. We design our scheme such that a part of the messages of clean receivers can be de-multiplexed at the IR. Thus, the IR can use these message streams for an interference cancellation at the clean receivers. Next, we consider an IR, the antennas of which do not have coordination with each other and where the transmitted signal of each transmitting antenna depends only on the received signal of its corresponding receiving antenna. We also derive lower and upper bounds for the sum DoF for this model of IR. We show that the achievable sum DoF decreases considerably compared with the coordinated case. In both of these models, our schemes achieve the maximum K sum DoF if the number of transmitting and receiving antennas is more than a finite threshold. Full article

(This article belongs to the Special Issue Information Theoretic Methods for Future Communication Systems)

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Open AccessArticle

Probabilistic Hierarchical Quantum Information Splitting of Arbitrary Multi-Qubit States

Jie Tang

Song-Ya Ma

and

Qi Li

Entropy 2022, 24(8), 1077; https://doi.org/10.3390/e24081077 - 04 Aug 2022

Abstract

By utilizing the non-maximally entangled four-qubit cluster states as the quantum channel, we first propose a hierarchical quantum information splitting scheme of arbitrary three-qubit states among three agents with a certain probability. Then we generalize the scheme to arbitrary multi-qubit states. Hierarchy is reflected on the different abilities of agents to restore the target state. The high-grade agent only needs the help of one low-grade agent, while the low-grade agent requires all the other agents’ assistance. The designated receiver performs positive operator-valued measurement (POVM) which is elaborately constructed with the aid of Hadamard matrix. It is worth mentioning that a general expression of recovery operation is derived to disclose the relationship with measurement outcomes. Moreover, the scheme is extended to multiple agents by means of the symmetry of cluster states. Full article

(This article belongs to the Special Issue Quantum Computation and Quantum Information)

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Open AccessArticle

Testing for Serial Correlation in Autoregressive Exogenous Models with Possible GARCH Errors

and

Entropy 2022, 24(8), 1076; https://doi.org/10.3390/e24081076 - 04 Aug 2022

Abstract

Autoregressive exogenous, hereafter ARX, models are widely adopted in time series-related domains as they can be regarded as the combination of an autoregressive process and a predictive regression. Within a more complex structure, extant diagnostic checking methods face difficulties in remaining validity in many conditions existing in real applications, such as heteroscedasticity and error correlations exhibited between the ARX model itself and its exogenous processes. For these reasons, we propose a new serial correlation test method based on the profile empirical likelihood. Simulation results, as well as two real data examples, show that our method has a good performance in all mentioned conditions. Full article

(This article belongs to the Special Issue Statistical Theory and Modeling of Rare, Extreme Events: Entropy and Information Theory)

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Open AccessArticle

Waveform Design for Multi-Target Detection Based on Two-Stage Information Criterion

Yu Xiao

and

Xiaoxiang Hu

Entropy 2022, 24(8), 1075; https://doi.org/10.3390/e24081075 - 03 Aug 2022

Abstract

Parameter estimation accuracy and average sample number (ASN) reduction are important to improving target detection performance in sequential hypothesis tests. Multiple-input multiple-output (MIMO) radar can balance between parameter estimation accuracy and ASN reduction through waveform diversity. In this study, we propose a waveform design method based on a two-stage information criterion to improve multi-target detection performance. In the first stage, the waveform is designed to estimate the target parameters based on the criterion of single-hypothesis mutual information (MI) maximization under the constraint of the signal-to-noise ratio (SNR). In the second stage, the objective function is designed based on the criterion of MI minimization and Kullback–Leibler divergence (KLD) maximization between multi-hypothesis posterior probabilities, and the waveform is chosen from the waveform library of the first-stage parameter estimation. Furthermore, an adaptive waveform design algorithm framework for multi-target detection is proposed. The simulation results reveal that the waveform design based on the two-stage information criterion can rapidly detect the target direction. In addition, the waveform design based on the criterion of dual-hypothesis MI minimization can improve the parameter estimation performance, whereas the design based on the criterion of dual-hypothesis KLD maximization can improve the target detection performance. Full article

(This article belongs to the Special Issue Information Theory in Signal Processing and Image Processing)

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Open AccessArticle

Four-Objective Optimization for an Irreversible Porous Medium Cycle with Linear Variation in Working Fluid’s Specific Heat

and

Entropy 2022, 24(8), 1074; https://doi.org/10.3390/e24081074 - 03 Aug 2022

Abstract

Considering that the specific heat of the working fluid varies linearly with its temperature, this paper applies finite time thermodynamic theory and NSGA-II to conduct thermodynamic analysis and multi-objective optimization for irreversible porous medium cycle. The effects of working fluid’s variable-specific heat characteristics, heat transfer, friction and internal irreversibility losses on cycle power density and ecological function characteristics are analyzed. The relationship between power density and ecological function versus compression ratio or thermal efficiency are obtained. When operating in the circumstances of maximum power density, the thermal efficiency of the porous medium cycle engine is higher and its size is less than when operating in the circumstances of maximum power output, and it is also more efficient when operating in the circumstances of maximum ecological function. The four objectives of dimensionless power density, dimensionless power output, thermal efficiency and dimensionless ecological function are optimized simultaneously, and the Pareto front with a set of solutions is obtained. The best results are obtained in two-objective optimization, targeting power output and thermal efficiency, which indicates that the optimal results of the multi-objective are better than that of one-objective. Full article

(This article belongs to the Special Issue Heat-Powered Systems)

Open AccessArticle

An Enhanced Belief Propagation Flipping Decoder for Polar Codes with Stepping Strategy

and

Entropy 2022, 24(8), 1073; https://doi.org/10.3390/e24081073 - 03 Aug 2022

Abstract

The Belief Propagation (BP) algorithm has the advantages of high-speed decoding and low latency. To improve the block error rate (BLER) performance of the BP-based algorithm, the BP flipping algorithm was proposed. However, the BP flipping algorithm attempts numerous useless flippings for improving the BLER performance. To reduce the number of decoding attempts needed without any loss of BLER performance, in this paper a metric is presented to evaluate the likelihood that the bits would correct the BP flipping decoding. Based on this, a BP-Step-Flipping (BPSF) algorithm is proposed which only traces the unreliable bits in the flip set (FS) to flip and skips over the reliable ones. In addition, a threshold

β

is applied when the magnitude of the log–likelihood ratio (LLR) is small, and an enhanced BPSF (EBPSF) algorithm is presented to lower the BLER. With the same FS, the proposed algorithm can reduce the average number of iterations efficiently. Numerical results show the average number of iterations for EBPSF-1 decreases by 77.5% when N = 256, compared with the BP bit-flip-1 (BPF-1) algorithm at

E_{b} / N_{0}

= 1.5 dB. Full article

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Open AccessArticle

Analysis of the Element-Free Galerkin Method with Penalty for Stokes Problems

Tao Zhang

and

Xiaolin Li

Entropy 2022, 24(8), 1072; https://doi.org/10.3390/e24081072 - 03 Aug 2022

Abstract

The element-free Galerkin (EFG) method with penalty for Stokes problems is proposed and analyzed in this work. A priori error estimates of the penalty method, which is used to deal with Dirichlet boundary conditions, are derived to illustrate its validity in a continuous sense. Based on a feasible assumption, it is proved that there is a unique weak solution in the modified weak form of penalized Stokes problems. Then, the error bounds with the penalty factor for the EFG discretization are derived, which provide a rationale for choosing an efficient penalty factor. Numerical examples are given to confirm the theoretical results. Full article

(This article belongs to the Special Issue Finite Element Methods for the Navier-Stokes Equations and MHD Equations)

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Open AccessFeature PaperArticle

A Bayesian Motivated Two-Sample Test Based on Kernel Density Estimates

Naveed Merchant

and

Jeffrey D. Hart

Entropy 2022, 24(8), 1071; https://doi.org/10.3390/e24081071 - 03 Aug 2022

Abstract

A new nonparametric test of equality of two densities is investigated. The test statistic is an average of log-Bayes factors, each of which is constructed from a kernel density estimate. Prior densities for the bandwidths of the kernel estimates are required, and it is shown how to choose priors so that the log-Bayes factors can be calculated exactly. Critical values of the test statistic are determined by a permutation distribution, conditional on the data. An attractive property of the methodology is that a critical value of 0 leads to a test for which both type I and II error probabilities tend to 0 as sample sizes tend to ∞. Existing results on Kullback–Leibler loss of kernel estimates are crucial to obtaining these asymptotic results, and also imply that the proposed test works best with heavy-tailed kernels. Finite sample characteristics of the test are studied via simulation, and extensions to multivariate data are straightforward, as illustrated by an application to bivariate connectionist data. Full article

(This article belongs to the Section Information Theory, Probability and Statistics)

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Open AccessArticle

On the Inherent Instability of Biocognition: Toward New Probability Models and Statistical Tools

Rodrick Wallace

Irina Leonova

and

Saikat Gochhait

Entropy 2022, 24(8), 1070; https://doi.org/10.3390/e24081070 - 03 Aug 2022

Abstract

A central conundrum enshrouds biocognition: almost all such phenomena are inherently unstable and must be constantly controlled by external regulatory machinery to ensure proper function, in much the same sense that blood pressure and the ‘stream of consciousness’ require persistent delicate regulation for the survival of higher organisms. Here, we derive the Data Rate Theorem of control theory that characterizes such instability via the Rate Distortion Theorem of information theory for adiabatically stationary nonergodic systems. We then outline a novel approach to building new statistical tools for data analysis based on those theorems, focusing on groupoid symmetry-breaking phase transitions characterized by Fisher Zero analogs. Full article

(This article belongs to the Special Issue Selected Papers of International Conference on Bio-Neuro Informatics Models and Algorithms 2022)

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Topic in Entropy, Sensors

Advances in Biomedical Engineering from the Annual Conference of SEIB 2021 Topic Editors: Raúl Alcaraz, Elisabete Aramendi, Raimon Jane, Gema García-Sáez, Gema Prats-Boluda, Javier Reina-Tosina, Roberto Hornero, Patricia Sánchez-González
Deadline: 30 September 2022

Topic in Algorithms, Entropy, Fractal Fract, Mathematics, Physics

Mathematical Modeling in Physical Sciences Topic Editors: Dimitrios Vlachos, George Kastis
Deadline: 15 November 2022

Topic in Entropy, Algorithms, BDCC, Data

Complex Data Analytics and Computing with Real-World Applications Topic Editors: S. Ejaz Ahmed, Shuangge Steven Ma, Peter X.K. Song
Deadline: 22 November 2022

Topic in Entropy, Fractal Fract, Dynamics, Mathematics, Computation, Axioms

Advances in Nonlinear Dynamics: Methods and Applications Topic Editors: Ravi P. Agarwal, Maria Alessandra Ragusa
Deadline: 5 December 2022