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Discreteness Unravels the Black Hole Information Puzzle: Insights from a Quantum Gravity Toy Model
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Graph Partitions in Chemistry
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Parity-Time Symmetric Holographic Principle
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Dynamical Analyses Show That Professional Archers Exhibit Tighter, Finer and More Fluid Dynamical Control Than Neophytes
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Complexity Synchronization of Organ Networks
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 is provided to authors approximately 20.4 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2023).
- 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:
2.7 (2022);
5-Year Impact Factor:
2.6 (2022)
Latest Articles
Stable and Fast Deep Mutual Information Maximization Based on Wasserstein Distance
Entropy 2023, 25(12), 1607; https://doi.org/10.3390/e25121607 (registering DOI) - 30 Nov 2023
Abstract
Deep learning is one of the most exciting and promising techniques in the field of artificial intelligence (AI), which drives AI applications to be more intelligent and comprehensive. However, existing deep learning techniques usually require a large amount of expensive labeled data, which
[...] Read more.
Deep learning is one of the most exciting and promising techniques in the field of artificial intelligence (AI), which drives AI applications to be more intelligent and comprehensive. However, existing deep learning techniques usually require a large amount of expensive labeled data, which limit the application and development of deep learning techniques, and thus it is imperative to study unsupervised machine learning. The learning of deep representations by mutual information estimation and maximization (Deep InfoMax or DIM) method has achieved unprecedented results in the field of unsupervised learning. However, in the DIM method, to restrict the encoder to learn more normalized feature representations, an adversarial network learning method is used to make the encoder output consistent with a priori positively distributed data. As we know, the model training of the adversarial network learning method is difficult to converge, because there is a logarithmic function in the loss function of the cross-entropy measure, and the gradient of the model parameters is susceptible to the “gradient explosion” or “gradient disappearance” phenomena, which makes the training of the DIM method extremely unstable. In this regard, we propose a Wasserstein distance-based DIM method to solve the stability problem of model training, and our method is called the WDIM. Subsequently, the training stability of the WDIM method and the classification ability of unsupervised learning are verified on the CIFAR10, CIFAR100, and STL10 datasets. The experiments show that our proposed WDIM method is more stable to parameter updates, has faster model convergence, and at the same time, has almost the same accuracy as the DIM method on the classification task of unsupervised learning. Finally, we also propose a reflection of future research for the WDIM method, aiming to provide a research idea and direction for solving the image classification task with unsupervised learning.
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(This article belongs to the Section Information Theory, Probability and Statistics)
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Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature Selection
Entropy 2023, 25(12), 1606; https://doi.org/10.3390/e25121606 (registering DOI) - 30 Nov 2023
Abstract
In multiview data clustering, consistent or complementary information in the multiview data can achieve better clustering results. However, the high dimensions, lack of labeling, and redundancy of multiview data certainly affect the clustering effect, posing a challenge to multiview clustering. A clustering algorithm
[...] Read more.
In multiview data clustering, consistent or complementary information in the multiview data can achieve better clustering results. However, the high dimensions, lack of labeling, and redundancy of multiview data certainly affect the clustering effect, posing a challenge to multiview clustering. A clustering algorithm based on multiview feature selection clustering (MFSC), which combines similarity graph learning and unsupervised feature selection, is designed in this study. During the MFSC implementation, local manifold regularization is integrated into similarity graph learning, with the clustering label of similarity graph learning as the standard for unsupervised feature selection. MFSC can retain the characteristics of the clustering label on the premise of maintaining the manifold structure of multiview data. The algorithm is systematically evaluated using benchmark multiview and simulated data. The clustering experiment results prove that the MFSC algorithm is more effective than the traditional algorithm.
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(This article belongs to the Special Issue Pattern Recognition and Data Clustering in Information Theory)
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Cohesion: A Measure of Organisation and Epistemic Uncertainty of Incoherent Ensembles
Entropy 2023, 25(12), 1605; https://doi.org/10.3390/e25121605 (registering DOI) - 30 Nov 2023
Abstract
This paper offers a measure of how organised a system is, as defined by self-consistency. Complex dynamics such as tipping points and feedback loops can cause systems with identical initial parameters to vary greatly by their final state. These systems can be called
[...] Read more.
This paper offers a measure of how organised a system is, as defined by self-consistency. Complex dynamics such as tipping points and feedback loops can cause systems with identical initial parameters to vary greatly by their final state. These systems can be called non-ergodic or incoherent. This lack of consistency (or replicability) of a system can be seen to drive an additional form of uncertainty, beyond the variance that is typically considered. However, certain self-organising systems can be shown to have some self-consistency around these tipping points, when compared with systems that find no consistent final states. Here, we propose a measure of this self-consistency that is used to quantify our confidence in the outcomes of agent-based models, simulations or experiments of dynamical systems, which may or may not contain multiple attractors.
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(This article belongs to the Special Issue Information and Self-Organization III)
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Phase-Specific Damage Tolerance of a Eutectic High Entropy Alloy
Entropy 2023, 25(12), 1604; https://doi.org/10.3390/e25121604 - 30 Nov 2023
Abstract
Phase-specific damage tolerance was investigated for the AlCoCrFeNi2.1 high entropy alloy with a lamellar microstructure of L12 and B2 phases. A microcantilever bending technique was utilized with notches milled in each of the two phases as well as at the phase
[...] Read more.
Phase-specific damage tolerance was investigated for the AlCoCrFeNi2.1 high entropy alloy with a lamellar microstructure of L12 and B2 phases. A microcantilever bending technique was utilized with notches milled in each of the two phases as well as at the phase boundary. The L12 phase exhibited superior bending strength, strain hardening, and plastic deformation, while the B2 phase showed limited damage tolerance during bending due to micro-crack formation. The dimensionalized stiffness (DS) of the L12 phase cantilevers were relatively constant, indicating strain hardening followed by increase in stiffness at the later stages and, therefore, indicating plastic failure. In contrast, the B2 phase cantilevers showed a continuous drop in stiffness, indicating crack propagation. Distinct differences in micro-scale deformation mechanisms were reflected in post-compression fractography, with L12-phase cantilevers showing typical characteristics of ductile failure, including the activation of multiple slip planes, shear lips at the notch edge, and tearing inside the notch versus quasi-cleavage fracture with cleavage facets and a river pattern on the fracture surface for the B2-phase cantilevers.
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(This article belongs to the Special Issue Advances in High-Entropy Alloys)
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Asymmetric Entanglement-Assisted Quantum MDS Codes Constructed from Constacyclic Codes
by
, , , , , , and
Entropy 2023, 25(12), 1603; https://doi.org/10.3390/e25121603 - 30 Nov 2023
Abstract
Due to the asymmetry of quantum errors, phase-shift errors are more likely to occur than qubit-flip errors. Consequently, there is a need to develop asymmetric quantum error-correcting (QEC) codes that can safeguard quantum information transmitted through asymmetric channels. Currently, a significant body of
[...] Read more.
Due to the asymmetry of quantum errors, phase-shift errors are more likely to occur than qubit-flip errors. Consequently, there is a need to develop asymmetric quantum error-correcting (QEC) codes that can safeguard quantum information transmitted through asymmetric channels. Currently, a significant body of literature has investigated the construction of asymmetric QEC codes. However, the asymmetry of most QEC codes identified in the literature is limited by the dual-containing condition within the Calderbank-Shor-Steane (CSS) framework. This limitation restricts the exploration of their full potential in terms of asymmetry. In order to enhance the asymmetry of asymmetric QEC codes, we utilize entanglement-assisted technology and exploit the algebraic structure of cyclotomic cosets of constacyclic codes to achieve this goal. In this paper, we generalize the decomposition method of the defining set for constacyclic codes and apply it to count the number of pre-shared entangled states in order to construct four new classes of asymmetric entanglement-assisted quantum maximal-distance separable (EAQMDS) codes that satisfy the asymmetric entanglement-assisted quantum Singleton bound. Compared with the codes existing in the literature, the lengths of the constructed EAQMDS codes and the number of pre-shared entangled states are more general, and the codes constructed in this paper have greater asymmetry.
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(This article belongs to the Special Issue Quantum Shannon Theory and Its Applications)
Open AccessArticle
Market Impact Analysis of Financial Literacy among A-Share Market Investors: An Agent-Based Model
Entropy 2023, 25(12), 1602; https://doi.org/10.3390/e25121602 - 29 Nov 2023
Abstract
Financial literacy has become increasingly crucial in today’s complex financial markets. This paper explores the impact of financial literacy on the stock market by establishing an artificial financial market that aligns with the characteristics of the Chinese A-share market using agent-based modeling. The
[...] Read more.
Financial literacy has become increasingly crucial in today’s complex financial markets. This paper explores the impact of financial literacy on the stock market by establishing an artificial financial market that aligns with the characteristics of the Chinese A-share market using agent-based modeling. The study incorporates financial literacy into investors’ mixed beliefs and simulates their behavior in the market. The results show that improving individual investors’ financial literacy can improve market quality and investor performance, as well as reduce the unequal distribution of wealth to some extent. However, the phenomenon of speculative trading and irrational behavior in the market can pose potential risks that require regulatory measures. Thus, policy recommendations to improve individual investors’ financial literacy and establish corresponding regulatory measures are proposed.
Full article
(This article belongs to the Special Issue Complexity in Economics and Finance: New Directions and Challenges)
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Open AccessArticle
Random Lasers as Social Processes Simulators
by
, , , , , and
Entropy 2023, 25(12), 1601; https://doi.org/10.3390/e25121601 - 29 Nov 2023
Abstract
In this work, we suggest a quantum-like simulator concept to study social processes related to the solution of NP-hard problems. The simulator is based on the solaser model recently proposed by us in the framework of information cascade growth and echo chamber formation
[...] Read more.
In this work, we suggest a quantum-like simulator concept to study social processes related to the solution of NP-hard problems. The simulator is based on the solaser model recently proposed by us in the framework of information cascade growth and echo chamber formation in social network communities. The simulator is connected with the random laser approach that we examine in the A and D-class (superradiant) laser limits. Novel network-enforced cooperativity parameters of decision-making agents, which may be measured as a result of the solaser simulation, are introduced and justified for social systems. The innovation diffusion in complex networks is discussed as one of the possible impacts of our proposal.
Full article
(This article belongs to the Special Issue Quantum Models of Cognition and Decision-Making II)
Open AccessPerspective
Postulating the Unicity of the Macroscopic Physical World
Entropy 2023, 25(12), 1600; https://doi.org/10.3390/e25121600 - 29 Nov 2023
Abstract
We argue that a clear view of quantum mechanics is obtained by considering that the unicity of the macroscopic world is a fundamental postulate of physics, rather than an issue that must be mathematically justified or demonstrated. This postulate allows for a framework
[...] Read more.
We argue that a clear view of quantum mechanics is obtained by considering that the unicity of the macroscopic world is a fundamental postulate of physics, rather than an issue that must be mathematically justified or demonstrated. This postulate allows for a framework in which quantum mechanics can be constructed in a complete mathematically consistent way. This is made possible by using general operator algebras to extend the mathematical description of the physical world toward macroscopic systems. Such an approach goes beyond the usual type-I operator algebras used in standard textbook quantum mechanics. This avoids a major pitfall, which is the temptation to make the usual type-I formalism ’universal’. This may also provide a meta-framework for both classical and quantum physics, shedding new light on ancient conceptual antagonisms and clarifying the status of quantum objects. Beyond exploring remote corners of quantum physics, we expect these ideas to be helpful to better understand and develop quantum technologies.
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(This article belongs to the Special Issue Quantum Correlations, Contextuality, and Quantum Nonlocality)
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What Is in a Simplicial Complex? A Metaplex-Based Approach to Its Structure and Dynamics
Entropy 2023, 25(12), 1599; https://doi.org/10.3390/e25121599 - 29 Nov 2023
Abstract
Geometric realization of simplicial complexes makes them a unique representation of complex systems. The existence of local continuous spaces at the simplices level with global discrete connectivity between simplices makes the analysis of dynamical systems on simplicial complexes a challenging problem. In this
[...] Read more.
Geometric realization of simplicial complexes makes them a unique representation of complex systems. The existence of local continuous spaces at the simplices level with global discrete connectivity between simplices makes the analysis of dynamical systems on simplicial complexes a challenging problem. In this work, we provide some examples of complex systems in which this representation would be a more appropriate model of real-world phenomena. Here, we generalize the concept of metaplexes to embrace that of geometric simplicial complexes, which also includes the definition of dynamical systems on them. A metaplex is formed by regions of a continuous space of any dimension interconnected by sinks and sources that works controlled by discrete (graph) operators. The definition of simplicial metaplexes given here allows the description of the diffusion dynamics of this system in a way that solves the existing problems with previous models. We make a detailed analysis of the generalities and possible extensions of this model beyond simplicial complexes, e.g., from polytopal and cell complexes to manifold complexes, and apply it to a real-world simplicial complex representing the visual cortex of a macaque.
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(This article belongs to the Special Issue Models, Topology and Inference of Multilayer and Higher-Order Networks)
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Bridging Extremes: The Invertible Bimodal Gumbel Distribution
Entropy 2023, 25(12), 1598; https://doi.org/10.3390/e25121598 - 29 Nov 2023
Abstract
This paper introduces a novel three-parameter invertible bimodal Gumbel distribution, addressing the need for a versatile statistical tool capable of simultaneously modeling maximum and minimum extremes in various fields such as hydrology, meteorology, finance, and insurance. Unlike previous bimodal Gumbel distributions available in
[...] Read more.
This paper introduces a novel three-parameter invertible bimodal Gumbel distribution, addressing the need for a versatile statistical tool capable of simultaneously modeling maximum and minimum extremes in various fields such as hydrology, meteorology, finance, and insurance. Unlike previous bimodal Gumbel distributions available in the literature, our proposed model features a simple closed-form cumulative distribution function, enhancing its computational attractiveness and applicability. This paper elucidates the behavior and advantages of the invertible bimodal Gumbel distribution through detailed mathematical formulations, graphical illustrations, and exploration of distributional characteristics. We illustrate using financial data to estimate Value at Risk (VaR) from our suggested model, considering maximum and minimum blocks simultaneously.
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(This article belongs to the Special Issue Stochastic Models and Statistical Inference: Analysis and Applications)
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Open AccessFeature PaperArticle
Kernel-Based Independence Tests for Causal Structure Learning on Functional Data
Entropy 2023, 25(12), 1597; https://doi.org/10.3390/e25121597 - 28 Nov 2023
Abstract
Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over
[...] Read more.
Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert–Schmidt independence criterion (hsic) and its d-variate version (d-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert–Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data.
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(This article belongs to the Special Issue Causality and Complex Systems)
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Open AccessFeature PaperReview
Self-Organisation of Prediction Models
Entropy 2023, 25(12), 1596; https://doi.org/10.3390/e25121596 - 28 Nov 2023
Abstract
Living organisms are active open systems far from thermodynamic equilibrium. The ability to behave actively corresponds to dynamical metastability: minor but supercritical internal or external effects may trigger major substantial actions such as gross mechanical motion, dissipating internally accumulated energy reserves. Gaining a
[...] Read more.
Living organisms are active open systems far from thermodynamic equilibrium. The ability to behave actively corresponds to dynamical metastability: minor but supercritical internal or external effects may trigger major substantial actions such as gross mechanical motion, dissipating internally accumulated energy reserves. Gaining a selective advantage from the beneficial use of activity requires a consistent combination of sensual perception, memorised experience, statistical or causal prediction models, and the resulting favourable decisions on actions. This information processing chain originated from mere physical interaction processes prior to life, here denoted as structural information exchange. From there, the self-organised transition to symbolic information processing marks the beginning of life, evolving through the novel purposivity of trial-and-error feedback and the accumulation of symbolic information. The emergence of symbols and prediction models can be described as a ritualisation transition, a symmetry-breaking kinetic phase transition of the second kind previously known from behavioural biology. The related new symmetry is the neutrally stable arbitrariness, conventionality, or code invariance of symbols with respect to their meaning. The meaning of such symbols is given by the structural effect they ultimately unleash, directly or indirectly, by deciding on which actions to take. The early genetic code represents the first symbols. The genetically inherited symbolic information is the first prediction model for activities sufficient for survival under the condition of environmental continuity, sometimes understood as the “final causality” property of the model.
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(This article belongs to the Special Issue Information and Self-Organization III)
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Theory and Application of Zero Trust Security: A Brief Survey
Entropy 2023, 25(12), 1595; https://doi.org/10.3390/e25121595 - 28 Nov 2023
Abstract
As cross-border access becomes more frequent, traditional perimeter-based network security models can no longer cope with evolving security requirements. Zero trust is a novel paradigm for cybersecurity based on the core concept of “never trust, always verify”. It attempts to protect against security
[...] Read more.
As cross-border access becomes more frequent, traditional perimeter-based network security models can no longer cope with evolving security requirements. Zero trust is a novel paradigm for cybersecurity based on the core concept of “never trust, always verify”. It attempts to protect against security risks related to internal threats by eliminating the demarcations between the internal and external network of traditional network perimeters. Nevertheless, research on the theory and application of zero trust is still in its infancy, and more extensive research is necessary to facilitate a deeper understanding of the paradigm in academia and the industry. In this paper, trust in cybersecurity is discussed, following which the origin, concepts, and principles related to zero trust are elaborated on. The characteristics, strengths, and weaknesses of the existing research are analysed in the context of zero trust achievements and their technical applications in Cloud and IoT environments. Finally, to support the development and application of zero trust in the future, the concept and its current challenges are analysed.
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(This article belongs to the Special Issue Information Security and Data Privacy)
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Novel Noise Injection Scheme to Guarantee Zero Secrecy Outage under Imperfect CSI
Entropy 2023, 25(12), 1594; https://doi.org/10.3390/e25121594 - 28 Nov 2023
Abstract
The paper proposes a novel artificial noise (AN) injection strategy in multiple-input single-output multiple-antenna-eavesdropper (MISOME) systems under imperfect channel estimation at the legitimate channel to achieve zero secrecy outage probability under any circumstance. The zero secrecy outage is proved to always be achievable
[...] Read more.
The paper proposes a novel artificial noise (AN) injection strategy in multiple-input single-output multiple-antenna-eavesdropper (MISOME) systems under imperfect channel estimation at the legitimate channel to achieve zero secrecy outage probability under any circumstance. The zero secrecy outage is proved to always be achievable regardless of the eavesdropper’s number of antennas or location when the pair secrecy and codeword rates are chosen properly. The results show that when there is perfect channel state information, the zero-outage secrecy throughput increases with the transmit power, which is important for secrecy design. Additionally, an analysis of the secrecy throughput and secrecy energy efficiency gives further insight into the effectiveness of the proposed scheme.
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(This article belongs to the Special Issue Information Security and Data Privacy)
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Solar Flare 1/f Fluctuations from Amplitude-Modulated Five-Minute Oscillation
by
and
Entropy 2023, 25(12), 1593; https://doi.org/10.3390/e25121593 - 28 Nov 2023
Abstract
We first report that the solar flare time sequence exhibits a fluctuation characterized by its power spectral density being inversely proportional to the signal frequency. This is the 1/f fluctuation, or pink noise, observed ubiquitously in nature. Using GOES16 data, we found that
[...] Read more.
We first report that the solar flare time sequence exhibits a fluctuation characterized by its power spectral density being inversely proportional to the signal frequency. This is the 1/f fluctuation, or pink noise, observed ubiquitously in nature. Using GOES16 data, we found that low-energy flares ( ) display 1/f fluctuations, whereas high-energy flares ( ) show a flat spectrum. Furthermore, we found that the timing sequence of the flares reveals clearer 1/f fluctuations. These observations suggest that the solar flare 1/f fluctuations are associated with low-energy phenomena. We investigated the origin of these 1/f fluctuations based on our recent hypothesis: 1/f fluctuations arise from amplitude modulation and demodulation. We propose that this amplitude modulation is encoded by the resonance with the solar five-minute oscillation (SFO) and demodulated by magnetic reconnections. We partially demonstrate this scenario by analyzing the SFO eigenmodes resolving the frequency degeneration in the azimuthal order number m using the solar rotation and resonance. Given the robust nature of 1/f fluctuations, we speculated that the solar flare 1/f fluctuations may be inherited by the various phenomena around the Sun, such as the sunspot numbers and cosmic rays. In addition, we draw parallels between solar flares and earthquakes, both exhibiting 1/f fluctuations. Interestingly, the analysis applied to solar flares can also be adapted to earthquakes if we read the SFO as Earth’s free oscillation and magnetic reconnections as fault ruptures. Moreover, we point out the possibility that the same analysis also applies to the activity of a black hole/disk system if we read the SFO as the quasi-periodic oscillation of a black hole.
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(This article belongs to the Special Issue Complexity and Statistical Physics Approaches to Earthquakes)
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Stochastic Adder Circuits with Improved Entropy Output
by
and
Entropy 2023, 25(12), 1592; https://doi.org/10.3390/e25121592 - 28 Nov 2023
Abstract
Random pulse computing (RPC), the third paradigm along with digital and quantum computing, draws inspiration from biology, particularly the functioning of neurons. Here, we study information processing in random pulse computing circuits intended for the summation of numbers. Based on the information-theoretic merits
[...] Read more.
Random pulse computing (RPC), the third paradigm along with digital and quantum computing, draws inspiration from biology, particularly the functioning of neurons. Here, we study information processing in random pulse computing circuits intended for the summation of numbers. Based on the information-theoretic merits of entropy budget and relative Kolmogorov–Sinai entropy, we investigate the prior art and propose new circuits: three deterministic adders with significantly improved output entropy and one exact nondeterministic adder that requires much less additional entropy than the previous art. All circuits are realized and tested experimentally, using quantum entropy sources and reconfigurable logic devices. Not only the proposed circuits yield a precise mathematical result and have output entropy near maximum, which satisfies the need for building a programmable random pulse computer, but also they provide affordable hardware options for generating additional entropy.
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(This article belongs to the Section Information Theory, Probability and Statistics)
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A New Transformation Technique for Reducing Information Entropy: A Case Study on Greyscale Raster Images
by
, , , , , , , and
Entropy 2023, 25(12), 1591; https://doi.org/10.3390/e25121591 - 27 Nov 2023
Abstract
This paper proposes a new string transformation technique called Move with Interleaving (MwI). Four possible ways of rearranging 2D raster images into 1D sequences of values are applied, including scan-line, left-right, strip-based, and Hilbert arrangements. Experiments on 32 benchmark greyscale raster images of
[...] Read more.
This paper proposes a new string transformation technique called Move with Interleaving (MwI). Four possible ways of rearranging 2D raster images into 1D sequences of values are applied, including scan-line, left-right, strip-based, and Hilbert arrangements. Experiments on 32 benchmark greyscale raster images of various resolutions demonstrated that the proposed transformation reduces information entropy to a similar extent as the combination of the Burrows–Wheeler transform followed by the Move-To-Front or the Inversion Frequencies. The proposed transformation MwI yields the best result among all the considered transformations when the Hilbert arrangement is applied.
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(This article belongs to the Section Information Theory, Probability and Statistics)
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Stability Analysis of a Delayed Rumor Propagation Model with Nonlinear Incidence Incorporating Impulsive Vaccination
Entropy 2023, 25(12), 1590; https://doi.org/10.3390/e25121590 - 27 Nov 2023
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The presence of information asymmetry can hinder the public’s ability to make well-informed decisions, resulting in unwarranted suspicion and the widespread dissemination of rumors. Therefore, it is crucial to provide individuals with consistent and dependable scientific education. Regular popular science education is considered
[...] Read more.
The presence of information asymmetry can hinder the public’s ability to make well-informed decisions, resulting in unwarranted suspicion and the widespread dissemination of rumors. Therefore, it is crucial to provide individuals with consistent and dependable scientific education. Regular popular science education is considered a periodic impulsive intervention to mitigate the impact of information asymmetry and promote a more informed and discerning public. Drawing on these findings, this paper proposes a susceptible-hesitant-infected-refuting-recovered (SHIDR) rumor-spreading model to explain the spread of rumors. The model incorporates elements such as time delay, nonlinear incidence, and refuting individuals. Firstly, by applying the comparison theorem of an impulsive differential equation, we calculate two thresholds for rumor propagation. Additionally, we analyze the conditions of global attractiveness of the rumor-free periodic solution. Furthermore, we consider the condition for the rumor’s permanence. Finally, numerical simulations are conducted to validate the accuracy of our findings. The results suggest that increasing the proportion of impulsive vaccination, reducing the impulsive period, or prolonging the delay time can effectively suppress rumors.
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Open AccessArticle
Nonequilibrium Effects on Information Recoverability of the Noisy Channels
Entropy 2023, 25(12), 1589; https://doi.org/10.3390/e25121589 - 27 Nov 2023
Abstract
We investigated the impact of nonequilibrium conditions on the transmission and recovery of information through noisy channels. By measuring the recoverability of messages from an information source, we demonstrate that the ability to recover information is connected to the nonequilibrium behavior of the
[...] Read more.
We investigated the impact of nonequilibrium conditions on the transmission and recovery of information through noisy channels. By measuring the recoverability of messages from an information source, we demonstrate that the ability to recover information is connected to the nonequilibrium behavior of the information flow, particularly in terms of sequential information transfer. We discovered that the mathematical equivalence of information recoverability and entropy production characterizes the dissipative nature of information transfer. Our findings show that both entropy production (or recoverability) and mutual information increase monotonically with the nonequilibrium strength of information dynamics. These results suggest that the nonequilibrium dissipation cost can enhance the recoverability of noise messages and improve the quality of information transfer. Finally, we propose a simple model to test our conclusions and found that the numerical results support our findings.
Full article
(This article belongs to the Collection Disorder and Biological Physics)
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Part-Aware Point Cloud Completion through Multi-Modal Part Segmentation
Entropy 2023, 25(12), 1588; https://doi.org/10.3390/e25121588 - 27 Nov 2023
Abstract
Point cloud completion aims to generate high-resolution point clouds using incomplete point clouds as input and is the foundational task for many 3D visual applications. However, most existing methods suffer from issues related to rough localized structures. In this paper, we attribute these
[...] Read more.
Point cloud completion aims to generate high-resolution point clouds using incomplete point clouds as input and is the foundational task for many 3D visual applications. However, most existing methods suffer from issues related to rough localized structures. In this paper, we attribute these problems to the lack of attention to local details in the global optimization methods used for the task. Thus, we propose a new model, called PA-NET, to guide the network to pay more attention to local structures. Specifically, we first use textual embedding to assist in training a robust point assignment network, enabling the transformation of global optimization into the co-optimization of local and global aspects. Then, we design a novel plug-in module using the assignment network and introduce a new loss function to guide the network’s attention towards local structures. Numerous experiments were conducted, and the quantitative results demonstrate that our method achieves novel performance on different datasets. Additionally, the visualization results show that our method efficiently resolves the issue of poor local structures in the generated point cloud.
Full article
(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing II)
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Entropy, Future Internet, Healthcare, MAKE, Sensors
Communications Challenges in Health and Well-Being
Topic Editors: Dragana Bajic, Konstantinos Katzis, Gordana GardasevicDeadline: 30 November 2023
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Energies, Entropy, Thermo
Research Frontier in Renewable Energy Systems
Topic Editors: T. M. Indra Mahlia, Behzad RismanchiDeadline: 10 December 2023
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Entropy, Fractal Fract, MCA, Mathematics, Symmetry
HAT: Hamiltonian Systems—Applications and Theory
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Complex Systems and Network Science
Topic Editors: Massimo Marchiori, Latora VitoDeadline: 31 December 2023

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Entropy
Foundations of Quantum Mechanics: Reversibility and Time Arrow in Quantum Theory
Guest Editors: Federico Holik, Gustavo Martín Bosyk, Ana MajteyDeadline: 30 November 2023
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Entropy
Applied Probability, Information Theory and Applications
Guest Editors: Dimitris Kugiumtzis, George TsaklidisDeadline: 15 December 2023
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Bio-Neuro Informatics Models and Algorithms
Guest Editors: Saikat Gochhait, Victor B. KazantsevDeadline: 20 December 2023
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Entropy
Coexistence of Complexity Metrics and Machine-Learning Approaches for Understanding Complex Biological Phenomena
Guest Editors: Leonidas P. Karakatsanis, Dimitrios S. MonosDeadline: 31 December 2023
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Entropy-Based Applied Cryptography and Enhanced Security for Future IT Environments
Collection Editor: Luis Javier Garcia Villalba
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Wavelets, Fractals and Information Theory
Collection Editor: Carlo Cattani
Topical Collection in
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Foundations of Statistical Mechanics
Collection Editor: Antonio M. Scarfone