Entropy

10 pages, 864 KB

Open AccessReview

How “Berry Phase” Analysis of Non-Adiabatic Non-Hermitian Systems Reflects Their Geometry

by Chris Jeynes

Entropy 2023, 25(2), 390; https://doi.org/10.3390/e25020390 - 20 Feb 2023

Cited by 2 | Viewed by 3132

There is currently great interest in systems represented by non-Hermitian Hamiltonians, including a wide variety of real systems that may be dissipative and whose behaviour can be represented by a “phase” parameter that characterises the way “exceptional points” (singularities of various sorts) determine [...] Read more.

There is currently great interest in systems represented by non-Hermitian Hamiltonians, including a wide variety of real systems that may be dissipative and whose behaviour can be represented by a “phase” parameter that characterises the way “exceptional points” (singularities of various sorts) determine the system. These systems are briefly reviewed here with an emphasis on their geometrical thermodynamics properties. Full article

(This article belongs to the Special Issue Geometry in Thermodynamics III)

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16 pages, 542 KB

Open AccessArticle

Round-Efficient Secure Inference Based on Masked Secret Sharing for Quantized Neural Network

by Weiming Wei, Chunming Tang and Yucheng Chen

Entropy 2023, 25(2), 389; https://doi.org/10.3390/e25020389 - 20 Feb 2023

Cited by 1 | Viewed by 2720

Abstract

Existing secure multiparty computation protocol from secret sharing is usually under this assumption of the fast network, which limits the practicality of the scheme on the low bandwidth and high latency network. A proven method is to reduce the communication rounds of the [...] Read more.

Existing secure multiparty computation protocol from secret sharing is usually under this assumption of the fast network, which limits the practicality of the scheme on the low bandwidth and high latency network. A proven method is to reduce the communication rounds of the protocol as much as possible or construct a constant-round protocol. In this work, we provide a series of constant-round secure protocols for quantized neural network (QNN) inference. This is given by masked secret sharing (MSS) in the three-party honest-majority setting. Our experiment shows that our protocol is practical and suitable for low-bandwidth and high-latency networks. To the best of our knowledge, this work is the first one where the QNN inference based on masked secret sharing is implemented. Full article

(This article belongs to the Special Issue Information Theory for Distributed Systems)

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14 pages, 3692 KB

Open AccessArticle

Attention-Based Convolutional Neural Network for Ingredients Identification

by Shi Chen, Ruixue Li, Chao Wang, Jiakai Liang, Keqiang Yue, Wenjun Li and Yilin Li

Entropy 2023, 25(2), 388; https://doi.org/10.3390/e25020388 - 20 Feb 2023

Cited by 1 | Viewed by 2278

Abstract

In recent years, with the development of artificial intelligence, smart catering has become one of the most popular research fields, where ingredients identification is a necessary and significant link. The automatic identification of ingredients can effectively reduce labor costs in the acceptance stage [...] Read more.

In recent years, with the development of artificial intelligence, smart catering has become one of the most popular research fields, where ingredients identification is a necessary and significant link. The automatic identification of ingredients can effectively reduce labor costs in the acceptance stage of the catering process. Although there have been a few methods for ingredients classification, most of them are of low recognition accuracy and poor flexibility. In order to solve these problems, in this paper, we construct a large-scale fresh ingredients database and design an end-to-end multi-attention-based convolutional neural network model for ingredients identification. Our method achieves an accuracy of 95.90% in the classification task, which contains 170 kinds of ingredients. The experiment results indicate that it is the state-of-the-art method for the automatic identification of ingredients. In addition, considering the sudden addition of some new categories beyond our training list in actual applications, we introduce an open-set recognition module to predict the samples outside the training set as the unknown ones. The accuracy of open-set recognition reaches 74.6%. Our algorithm has been deployed successfully in smart catering systems. It achieves an average accuracy of 92% in actual use and saves 60% of the time compared to manual operation, according to the statistics of actual application scenarios. Full article

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13 pages, 488 KB

Open AccessArticle

Generalized Toffoli Gate Decomposition Using Ququints: Towards Realizing Grover’s Algorithm with Qudits

by Anstasiia S. Nikolaeva, Evgeniy O. Kiktenko and Aleksey K. Fedorov

Entropy 2023, 25(2), 387; https://doi.org/10.3390/e25020387 - 20 Feb 2023

Cited by 13 | Viewed by 4854

Abstract

Qubits, which are the quantum counterparts of classical bits, are used as basic information units for quantum information processing, whereas underlying physical information carriers, e.g., (artificial) atoms or ions, admit encoding of more complex multilevel states—qudits. Recently, significant attention has been paid to [...] Read more.

Qubits, which are the quantum counterparts of classical bits, are used as basic information units for quantum information processing, whereas underlying physical information carriers, e.g., (artificial) atoms or ions, admit encoding of more complex multilevel states—qudits. Recently, significant attention has been paid to the idea of using qudit encoding as a way for further scaling quantum processors. In this work, we present an efficient decomposition of the generalized Toffoli gate on five-level quantum systems—so-called ququints—that use ququints’ space as the space of two qubits with a joint ancillary state. The basic two-qubit operation we use is a version of the controlled-phase gate. The proposed N-qubit Toffoli gate decomposition has

O (N)

asymptotic depth and does not use ancillary qubits. We then apply our results for Grover’s algorithm, where we indicate on the sizable advantage of using the qudit-based approach with the proposed decomposition in comparison to the standard qubit case. We expect that our results are applicable for quantum processors based on various physical platforms, such as trapped ions, neutral atoms, protonic systems, superconducting circuits, and others. Full article

(This article belongs to the Special Issue Quantum Information: From Fundamental Aspects to Practical Applications)

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17 pages, 6110 KB

Open AccessArticle

Effect of Gap Length and Partition Thickness on Thermal Boundary Layer in Thermal Convection

by Zhengyu Wang, Huilin Tong, Zhengdao Wang, Hui Yang, Yikun Wei and Yuehong Qian

Entropy 2023, 25(2), 386; https://doi.org/10.3390/e25020386 - 20 Feb 2023

Cited by 3 | Viewed by 2536

Abstract

Two-dimensional direct numerical simulations of partitioned thermal convection are performed using the thermal lattice Boltzmann method for the Rayleigh number (Ra) of 10⁹ and the Prandtl number (Pr) of 7.02 (water). The influence of the partition walls on [...] Read more.

Two-dimensional direct numerical simulations of partitioned thermal convection are performed using the thermal lattice Boltzmann method for the Rayleigh number (Ra) of 10⁹ and the Prandtl number (Pr) of 7.02 (water). The influence of the partition walls on the thermal boundary layer is mainly focused on. Moreover, to better describe the spatially nonuniform thermal boundary layer, the definition of the thermal boundary layer is extended. The numerical simulation results show that the gap length significantly affects the thermal boundary layer and Nusselt number (Nu). The gap length and partition wall thickness have a coupled effect on the thermal boundary layer and the heat flux. Based on the shape of the thermal boundary layer distribution, two different heat transfer models are identified at different gap lengths. This study provides a basis for improving the understanding of the effect of partitions on the thermal boundary layer in thermal convection. Full article

(This article belongs to the Special Issue Applications of CFD in Heat and Fluid Flow Processes)

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15 pages, 794 KB

Open AccessArticle

Combinatorics and Statistical Mechanics of Integer Partitions

by Themis Matsoukas

Entropy 2023, 25(2), 385; https://doi.org/10.3390/e25020385 - 20 Feb 2023

Cited by 2 | Viewed by 3796

Abstract

We study the set of integer partitions as a probability space that generates distributions and, in the asymptotic limit, obeys thermodynamics. We view ordered integer partition as a configuration of cluster masses and associate them with the distribution of masses it contains. We [...] Read more.

We study the set of integer partitions as a probability space that generates distributions and, in the asymptotic limit, obeys thermodynamics. We view ordered integer partition as a configuration of cluster masses and associate them with the distribution of masses it contains. We organized the set of ordered partitions into a table that forms a microcanonical ensemble and whose columns form a set of canonical ensembles. We define a functional of the distribution (selection functional) that establishes a probability measure on the distributions of the ensemble, study the combinatorial properties of this space, define its partition functions, and show that, in the asymptotic limit, this space obeys thermodynamics. We construct a stochastic process that we call exchange reaction and used it to sample the mean distribution by Mote Carlo simulation. We demonstrated that, with appropriate choice of the selection functional, we can obtain any distribution as the equilibrium distribution of the ensemble. Full article

(This article belongs to the Special Issue Generalized Statistical Thermodynamics II)

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11 pages, 337 KB

Open AccessArticle

Residence Time vs. Adjustment Time of Carbon Dioxide in the Atmosphere

by Peter Stallinga

Entropy 2023, 25(2), 384; https://doi.org/10.3390/e25020384 - 20 Feb 2023

Cited by 5 | Viewed by 19063

Abstract

We study the concepts of residence time vs. adjustment time time for carbon dioxide in the atmosphere. The system is analyzed with a two-box first-order model. Using this model, we reach three important conclusions: (1) The adjustment time is never larger than the [...] Read more.

We study the concepts of residence time vs. adjustment time time for carbon dioxide in the atmosphere. The system is analyzed with a two-box first-order model. Using this model, we reach three important conclusions: (1) The adjustment time is never larger than the residence time and can, thus, not be longer than about 5 years. (2) The idea of the atmosphere being stable at 280 ppm in pre-industrial times is untenable. (3) Nearly 90% of all anthropogenic carbon dioxide has already been removed from the atmosphere. Full article

(This article belongs to the Special Issue Thermodynamics Applied in Science of Climate Change)

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14 pages, 459 KB

Open AccessArticle

Statistical Topology—Distribution and Density Correlations of Winding Numbers in Chiral Systems

by Thomas Guhr

Entropy 2023, 25(2), 383; https://doi.org/10.3390/e25020383 - 20 Feb 2023

Cited by 1 | Viewed by 3076

Abstract

Statistical Topology emerged as topological aspects continue to gain importance in many areas of physics. It is most desirable to study topological invariants and their statistics in schematic models that facilitate the identification of universalities. Here, the statistics of winding numbers and of [...] Read more.

Statistical Topology emerged as topological aspects continue to gain importance in many areas of physics. It is most desirable to study topological invariants and their statistics in schematic models that facilitate the identification of universalities. Here, the statistics of winding numbers and of winding number densities are addressed. An introduction is given for readers with little background knowledge. Results that my collaborators and I obtained in two recent works on proper random matrix models for the chiral unitary and symplectic cases are reviewed, avoiding a technically detailed discussion. There is a special focus on the mapping of topological problems to spectral ones as well as on the first glimpse of universality. Full article

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

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16 pages, 549 KB

Open AccessArticle

Analysis and Optimization of a General Linking Matrix for JSCC Scheme Based on Double LDPC Codes

by Qiwang Chen, Zhiping Xu, Huihui Wu and Guofa Cai

Entropy 2023, 25(2), 382; https://doi.org/10.3390/e25020382 - 19 Feb 2023

Cited by 1 | Viewed by 2223

Abstract

A key component of the joint source-channel coding (JSCC) scheme based on double low-density parity-check (D-LDPC) codes is the introduction of a linking matrix between the source LDPC code and channel LDPC code, by which the decoding information including the source redundancy and [...] Read more.

A key component of the joint source-channel coding (JSCC) scheme based on double low-density parity-check (D-LDPC) codes is the introduction of a linking matrix between the source LDPC code and channel LDPC code, by which the decoding information including the source redundancy and channel state information can be transferred iteratively. However, the linking matrix is a fixed one-to-one mapping, i.e., an identity matrix in a conventional D-LDPC code system, which may not take full advantage of the decoding information. Therefore, this paper introduces a general linking matrix, i.e., a non-identity linking matrix, connecting the check nodes (CNs) of the source LDPC code and the variable nodes (VNs) of the channel LDPC code. Further, the encoding and decoding algorithms of the proposed D-LDPC coding system are generalized. A joint extrinsic information transfer (JEXIT) algorithm is derived for calculating the decoding threshold of the proposed system with a general linking matrix. In addition, several general linking matrices are optimized with the aid of the JEXIT algorithm. Finally, the simulation results demonstrate the superiority of the proposed D-LDPC coding system with general linking matrices. Full article

(This article belongs to the Special Issue Advances in Information and Coding Theory)

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14 pages, 2976 KB

Open AccessArticle

A Pedestrian Detection Network Model Based on Improved YOLOv5

by Ming-Lun Li, Guo-Bing Sun and Jia-Xiang Yu

Entropy 2023, 25(2), 381; https://doi.org/10.3390/e25020381 - 19 Feb 2023

Cited by 43 | Viewed by 5775

Abstract

Advanced object detection methods always face high algorithmic complexity or low accuracy when used in pedestrian target detection for the autonomous driving system. This paper proposes a lightweight pedestrian detection approach called the YOLOv5s-

G^{2}

network to address these issues. We apply [...] Read more.

Advanced object detection methods always face high algorithmic complexity or low accuracy when used in pedestrian target detection for the autonomous driving system. This paper proposes a lightweight pedestrian detection approach called the YOLOv5s-

G^{2}

network to address these issues. We apply Ghost and GhostC3 modules in the YOLOv5s-

G^{2}

network to minimize computational cost during feature extraction while keeping the network’s capability of extracting features intact. The YOLOv5s-

G^{2}

network improves feature extraction accuracy by incorporating the Global Attention Mechanism (GAM) module. This application can extract relevant information for pedestrian target identification tasks and suppress irrelevant information, improving the unidentified problem of occluded and small targets by replacing the GIoU loss function used in the bounding box regression with the

α

-CIoU loss function. The YOLOv5s-

G^{2}

network is evaluated on the WiderPerson dataset to ensure its efficacy. Our proposed YOLOv5s-

G^{2}

network offers a 1.0% increase in detection accuracy and a 13.2% decrease in Floating Point Operations (FLOPs) compared to the existing YOLOv5s network. As a result, the YOLOv5s-

G^{2}

network is preferable for pedestrian identification as it is both more lightweight and more accurate. Full article

(This article belongs to the Special Issue Entropy in Soft Computing and Machine Learning Algorithms II)

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17 pages, 6271 KB

Open AccessArticle

Improving Multiple Pedestrian Tracking in Crowded Scenes with Hierarchical Association

by Changcheng Xiao and Zhigang Luo

Entropy 2023, 25(2), 380; https://doi.org/10.3390/e25020380 - 19 Feb 2023

Cited by 6 | Viewed by 2650

Abstract

Recently, advances in detection and re-identification techniques have significantly boosted tracking-by-detection-based multi-pedestrian tracking (MPT) methods and made MPT a great success in most easy scenes. Several very recent works point out that the two-step scheme of first detection and then tracking is problematic [...] Read more.

Recently, advances in detection and re-identification techniques have significantly boosted tracking-by-detection-based multi-pedestrian tracking (MPT) methods and made MPT a great success in most easy scenes. Several very recent works point out that the two-step scheme of first detection and then tracking is problematic and propose using the bounding box regression head of an object detector to realize data association. In this tracking-by-regression paradigm, the regressor directly predicts each pedestrian’s location in the current frame according to its previous position. However, when the scene is crowded and pedestrians are close to each other, the small and partially occluded targets are easily missed. In this paper, we follow this pattern and design a hierarchical association strategy to obtain better performance in crowded scenes. To be specific, at the first association, the regressor is used to estimate the positions of obvious pedestrians. At the second association, we employ a history-aware mask to filter out the already occupied regions implicitly and look carefully at the remaining regions to find out the ignored pedestrians during the first association. We integrate the hierarchical association in a learning framework and directly infer the occluded and small pedestrians in an end-to-end way. We conduct extensive pedestrian tracking experiments on three public pedestrian tracking benchmarks from less crowded to crowded scenes, demonstrating the proposed strategy’s effectiveness in crowded scenes. Full article

(This article belongs to the Section Signal and Data Analysis)

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13 pages, 14450 KB

Open AccessArticle

Earthquake Nowcasting: Retrospective Testing in Greece

by Gerasimos Chouliaras, Efthimios S. Skordas and Nicholas V. Sarlis

Entropy 2023, 25(2), 379; https://doi.org/10.3390/e25020379 - 19 Feb 2023

Cited by 6 | Viewed by 8272

Abstract

Earthquake nowcasting (EN) is a modern method of estimating seismic risk by evaluating the progress of the earthquake (EQ) cycle in fault systems. EN evaluation is based on a new concept of time, termed ’natural time’. EN employs natural time, and uniquely estimates [...] Read more.

Earthquake nowcasting (EN) is a modern method of estimating seismic risk by evaluating the progress of the earthquake (EQ) cycle in fault systems. EN evaluation is based on a new concept of time, termed ’natural time’. EN employs natural time, and uniquely estimates seismic risk by means of the earthquake potential score (EPS), which has been found to have useful applications both regionally and globally. Amongst these applications, here we focused on Greece since 2019, for the estimation of the EPS for the largest-magnitude events,

M_{W}

(USGS) ≥ 6, that occurred during our study period: for example, the

M_{W} =

6.0 WNW-of-Kissamos EQ on 27 November 2019, the

M_{W} =

6.5 off-shore Southern Crete EQ on 2 May 2020, the

M_{W} =

7.0 Samos EQ on 30 October 2020, the

M_{W} =

6.3 Tyrnavos EQ on 3 March 2021, the

M_{W} =

6.0 Arkalohorion Crete EQ on 27 September 2021, and the

M_{W} =

6.4 Sitia Crete EQ on 12 October 2021. The results are promising, and reveal that the EPS provides useful information on impending seismicity. Full article

(This article belongs to the Special Issue Statistical Methods for Earthquake Hazard Assessment and Risk Analysis)

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13 pages, 1752 KB

Open AccessArticle

An Irreversible and Revocable Template Generation Scheme Based on Chaotic System

by Jinyuan Liu, Yong Wang, Kun Wang and Zhuo Liu

Entropy 2023, 25(2), 378; https://doi.org/10.3390/e25020378 - 18 Feb 2023

Cited by 5 | Viewed by 2181

Abstract

Face recognition technology has developed rapidly in recent years, and a large number of applications based on face recognition have emerged. Because the template generated by the face recognition system stores the relevant information of facial biometrics, its security is attracting more and [...] Read more.

Face recognition technology has developed rapidly in recent years, and a large number of applications based on face recognition have emerged. Because the template generated by the face recognition system stores the relevant information of facial biometrics, its security is attracting more and more attention. This paper proposes a secure template generation scheme based on a chaotic system. Firstly, the extracted face feature vector is permuted to eliminate the correlation within the vector. Then, the orthogonal matrix is used to transform the vector, and the state value of the vector is changed, while maintaining the original distance between the vectors. Finally, the cosine value of the included angle between the feature vector and different random vectors are calculated and converted into integers to generate the template. The chaotic system is used to drive the template generation process, which not only enhances the diversity of templates, but also has good revocability. In addition, the generated template is irreversible, and even if the template is leaked, it will not disclose the biometric information of users. Experimental results and theoretical analysis on the RaFD and Aberdeen datasets show that the proposed scheme has good verification performance and high security. Full article

(This article belongs to the Special Issue Image Encryption and Privacy Protection Based on Chaotic Systems)

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16 pages, 4826 KB

Open AccessArticle

Cryptocurrencies Are Becoming Part of the World Global Financial Market

by Marcin Wątorek, Jarosław Kwapień and Stanisław Drożdż

Entropy 2023, 25(2), 377; https://doi.org/10.3390/e25020377 - 18 Feb 2023

Cited by 32 | Viewed by 10841

Abstract

In this study the cross-correlations between the cryptocurrency market represented by the two most liquid and highest-capitalized cryptocurrencies: bitcoin and ethereum, on the one side, and the instruments representing the traditional financial markets: stock indices, Forex, commodities, on the other side, are measured [...] Read more.

In this study the cross-correlations between the cryptocurrency market represented by the two most liquid and highest-capitalized cryptocurrencies: bitcoin and ethereum, on the one side, and the instruments representing the traditional financial markets: stock indices, Forex, commodities, on the other side, are measured in the period: January 2020–October 2022. Our purpose is to address the question whether the cryptocurrency market still preserves its autonomy with respect to the traditional financial markets or it has already aligned with them in expense of its independence. We are motivated by the fact that some previous related studies gave mixed results. By calculating the q-dependent detrended cross-correlation coefficient based on the high frequency 10 s data in the rolling window, the dependence on various time scales, different fluctuation magnitudes, and different market periods are examined. There is a strong indication that the dynamics of the bitcoin and ethereum price changes since the March 2020 COVID-19 panic is no longer independent. Instead, it is related to the dynamics of the traditional financial markets, which is especially evident now in 2022, when the bitcoin and ethereum coupling to the US tech stocks is observed during the market bear phase. It is also worth emphasizing that the cryptocurrencies have begun to react to the economic data such as the Consumer Price Index readings in a similar way as traditional instruments. Such a spontaneous coupling of the so far independent degrees of freedom can be interpreted as a kind of phase transition that resembles the collective phenomena typical for the complex systems. Our results indicate that the cryptocurrencies cannot be considered as a safe haven for the financial investments. Full article

(This article belongs to the Special Issue Signatures of Maturity in Cryptocurrency Market)

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37 pages, 2262 KB

Open AccessArticle

Non-Local Parallel Processing and Database Settlement Using Multiple Teleportation Followed by Grover Post-Selection

by Francisco Delgado and Carlos Cardoso-Isidoro

Entropy 2023, 25(2), 376; https://doi.org/10.3390/e25020376 - 18 Feb 2023

Cited by 2 | Viewed by 2475

Abstract

Quantum information applications emerged decades ago, initially introducing a parallel development that mimicked the approach and development of classical computer science. However, in the current decade, novel computer-science concepts were rapidly extended to the fields of quantum processing, computation, and communication. Thus, areas [...] Read more.

Quantum information applications emerged decades ago, initially introducing a parallel development that mimicked the approach and development of classical computer science. However, in the current decade, novel computer-science concepts were rapidly extended to the fields of quantum processing, computation, and communication. Thus, areas such as artificial intelligence, machine learning, and neural networks have their quantum versions; furthermore, the quantum brain properties of learning, analyzing, and gaining knowledge are discussed. Quantum properties of matter conglomerates have been superficially explored in such terrain; however, the settlement of organized quantum systems able to perform processing can open a new pathway in the aforementioned domains. In fact, quantum processing involves certain requisites as the settlement of copies of input information to perform differentiated processing developed far away or in situ to diversify the information stored there. Both tasks at the end provide a database of outcomes with which to perform either information matching or final global processing with at least a subset of those outcomes. When the number of processing operations and input information copies is large, parallel processing (a natural feature in quantum computation due to the superposition) becomes the most convenient approach to accelerate the database settlement of outcomes, thus affording a time advantage. In the current study, we explored certain quantum features to realize a speed-up model for the entire task of processing based on a common information input to be processed, diversified, and finally summarized to gain knowledge, either in pattern matching or global information availability. By using superposition and non-local properties, the most valuable features of quantum systems, we realized parallel local processing to set a large database of outcomes and subsequently used post-selection to perform an ending global processing or a matching of information incoming from outside. We finally analyzed the details of the entire procedure, including its affordability and performance. The quantum circuit implementation, along with tentative applications, were also discussed. Such a model could be operated between large processing technological systems using communication procedures and also on a moderately controlled quantum matter conglomerate. Certain interesting technical aspects involving the non-local control of processing via entanglement were also analyzed in detail as an associated but notable premise. Full article

(This article belongs to the Special Issue Quantum Probability and Randomness IV)

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16 pages, 715 KB

Open AccessArticle

Manipulating Voice Attributes by Adversarial Learning of Structured Disentangled Representations

by Laurent Benaroya, Nicolas Obin and Axel Roebel

Entropy 2023, 25(2), 375; https://doi.org/10.3390/e25020375 - 18 Feb 2023

Cited by 3 | Viewed by 2848

Abstract

Voice conversion (VC) consists of digitally altering the voice of an individual to manipulate part of its content, primarily its identity, while maintaining the rest unchanged. Research in neural VC has accomplished considerable breakthroughs with the capacity to falsify a voice identity using [...] Read more.

Voice conversion (VC) consists of digitally altering the voice of an individual to manipulate part of its content, primarily its identity, while maintaining the rest unchanged. Research in neural VC has accomplished considerable breakthroughs with the capacity to falsify a voice identity using a small amount of data with a highly realistic rendering. This paper goes beyond voice identity manipulation and presents an original neural architecture that allows the manipulation of voice attributes (e.g., gender and age). The proposed architecture is inspired by the fader network, transferring the same ideas to voice manipulation. The information conveyed by the speech signal is disentangled into interpretative voice attributes by means of minimizing adversarial loss to make the encoded information mutually independent while preserving the capacity to generate a speech signal from the disentangled codes. During inference for voice conversion, the disentangled voice attributes can be manipulated and the speech signal can be generated accordingly. For experimental evaluation, the proposed method is applied to the task of voice gender conversion using the freely available VCTK dataset. Quantitative measurements of mutual information between the variables of speaker identity and speaker gender show that the proposed architecture can learn gender-independent representation of speakers. Additional measurements of speaker recognition indicate that speaker identity can be recognized accurately from the gender-independent representation. Finally, a subjective experiment conducted on the task of voice gender manipulation shows that the proposed architecture can convert voice gender with very high efficiency and good naturalness. Full article

(This article belongs to the Special Issue Information-Theoretic Approaches in Speech Processing and Recognition)

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15 pages, 1574 KB

Open AccessArticle

Effective Connectivity and Bias Entropy Improve Prediction of Dynamical Regime in Automata Networks

by Felipe Xavier Costa, Jordan C. Rozum, Austin M. Marcus and Luis M. Rocha

Entropy 2023, 25(2), 374; https://doi.org/10.3390/e25020374 - 18 Feb 2023

Cited by 9 | Viewed by 3070

Abstract

Biomolecular network dynamics are thought to operate near the critical boundary between ordered and disordered regimes, where large perturbations to a small set of elements neither die out nor spread on average. A biomolecular automaton (e.g., gene, protein) typically has high regulatory redundancy, [...] Read more.

Biomolecular network dynamics are thought to operate near the critical boundary between ordered and disordered regimes, where large perturbations to a small set of elements neither die out nor spread on average. A biomolecular automaton (e.g., gene, protein) typically has high regulatory redundancy, where small subsets of regulators determine activation via collective canalization. Previous work has shown that effective connectivity, a measure of collective canalization, leads to improved dynamical regime prediction for homogeneous automata networks. We expand this by (i) studying random Boolean networks (RBNs) with heterogeneous in-degree distributions, (ii) considering additional experimentally validated automata network models of biomolecular processes, and (iii) considering new measures of heterogeneity in automata network logic. We found that effective connectivity improves dynamical regime prediction in the models considered; in RBNs, combining effective connectivity with bias entropy further improves the prediction. Our work yields a new understanding of criticality in biomolecular networks that accounts for collective canalization, redundancy, and heterogeneity in the connectivity and logic of their automata models. The strong link we demonstrate between criticality and regulatory redundancy provides a means to modulate the dynamical regime of biochemical networks. Full article

(This article belongs to the Special Issue The Principle of Dynamical Criticality)

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18 pages, 3117 KB

Open AccessArticle

Dollar-Yuan Battle in the World Trade Network

by Célestin Coquidé, José Lages and Dima L. Shepelyansky

Entropy 2023, 25(2), 373; https://doi.org/10.3390/e25020373 - 17 Feb 2023

Cited by 6 | Viewed by 4855

Abstract

From the Bretton Woods agreement in 1944 till the present day, the US dollar has been the dominant currency in world trade. However, the rise of the Chinese economy has recently led to the emergence of trade transactions in Chinese yuan. Here, we [...] Read more.

From the Bretton Woods agreement in 1944 till the present day, the US dollar has been the dominant currency in world trade. However, the rise of the Chinese economy has recently led to the emergence of trade transactions in Chinese yuan. Here, we mathematically analyze how the structure of international trade flows would favor a country to trade whether in US dollar or in Chinese yuan. The trade currency preference of a country is modeled as a binary variable with the properties of a spin in an Ising model. The computation of this trade currency preference is based on the world trade network built from the 2010–2020 UN Comtrade data and is determined by two multiplicative factors: the relative weight of trade volume exchanged by the country with its direct trade partners and the relative weight of its trade partners in global international trade. The performed analysis, based on the convergence of the Ising spin interactions, shows that from 2010 to present a transition took place, and the majority of the world countries would now have a preference to trade in Chinese yuan if one only considers the world trade network structure. Full article

(This article belongs to the Special Issue Complexity in Economics and Finance: New Directions and Challenges)

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19 pages, 8715 KB

Open AccessArticle

Quantum Advantage of Thermal Machines with Bose and Fermi Gases

by Saikat Sur and Arnab Ghosh

Entropy 2023, 25(2), 372; https://doi.org/10.3390/e25020372 - 17 Feb 2023

Cited by 16 | Viewed by 4905

Abstract

In this article, we show that a quantum gas, a collection of massive, non-interacting, indistinguishable quantum particles, can be realized as a thermodynamic machine as an artifact of energy quantization and, hence, bears no classical analog. Such a thermodynamic machine depends on the [...] Read more.

In this article, we show that a quantum gas, a collection of massive, non-interacting, indistinguishable quantum particles, can be realized as a thermodynamic machine as an artifact of energy quantization and, hence, bears no classical analog. Such a thermodynamic machine depends on the statistics of the particles, the chemical potential, and the spatial dimension of the system. Our detailed analysis demonstrates the fundamental features of quantum Stirling cycles, from the viewpoint of particle statistics and system dimensions, that helps us to realize desired quantum heat engines and refrigerators by exploiting the role of quantum statistical mechanics. In particular, a clear distinction between the behavior of a Fermi gas and a Bose gas is observed in one dimension, rather than in higher dimensions, solely due to the innate differences in their particle statistics indicating the conspicuous role of a quantum thermodynamic signature in lower dimensions. Full article

(This article belongs to the Special Issue Quantum Thermodynamics: Fundamentals and Applications)

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19 pages, 410 KB

Open AccessArticle

An Ensemble and Multi-View Clustering Method Based on Kolmogorov Complexity

by Juan Zamora and Jérémie Sublime

Entropy 2023, 25(2), 371; https://doi.org/10.3390/e25020371 - 17 Feb 2023

Cited by 4 | Viewed by 2968

Abstract

The ability to build more robust clustering from many clustering models with different solutions is relevant in scenarios with privacy-preserving constraints, where data features have a different nature or where these features are not available in a single computation unit. Additionally, with the [...] Read more.

The ability to build more robust clustering from many clustering models with different solutions is relevant in scenarios with privacy-preserving constraints, where data features have a different nature or where these features are not available in a single computation unit. Additionally, with the booming number of multi-view data, but also of clustering algorithms capable of producing a wide variety of representations for the same objects, merging clustering partitions to achieve a single clustering result has become a complex problem with numerous applications. To tackle this problem, we propose a clustering fusion algorithm that takes existing clustering partitions acquired from multiple vector space models, sources, or views, and merges them into a single partition. Our merging method relies on an information theory model based on Kolmogorov complexity that was originally proposed for unsupervised multi-view learning. Our proposed algorithm features a stable merging process and shows competitive results over several real and artificial datasets in comparison with other state-of-the-art methods that have similar goals. Full article

(This article belongs to the Special Issue Pattern Recognition and Data Clustering in Information Theory)

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25 pages, 2869 KB

Open AccessArticle

Detecting Nonlinear Interactions in Complex Systems: Application in Financial Markets

by Akylas Fotiadis, Ioannis Vlachos and Dimitris Kugiumtzis

Entropy 2023, 25(2), 370; https://doi.org/10.3390/e25020370 - 17 Feb 2023

Cited by 7 | Viewed by 3396

Abstract

Emerging or diminishing nonlinear interactions in the evolution of a complex system may signal a possible structural change in its underlying mechanism. This type of structural break may exist in many applications, such as in climate and finance, and standard methods for change-point [...] Read more.

Emerging or diminishing nonlinear interactions in the evolution of a complex system may signal a possible structural change in its underlying mechanism. This type of structural break may exist in many applications, such as in climate and finance, and standard methods for change-point detection may not be sensitive to it. In this article, we present a novel scheme for detecting structural breaks through the occurrence or vanishing of nonlinear causal relationships in a complex system. A significance resampling test was developed for the null hypothesis (H₀) of no nonlinear causal relationships using (a) an appropriate Gaussian instantaneous transform and vector autoregressive (VAR) process to generate the resampled multivariate time series consistent with H₀; (b) the modelfree Granger causality measure of partial mutual information from mixed embedding (PMIME) to estimate all causal relationships; and (c) a characteristic of the network formed by PMIME as test statistic. The significance test was applied to sliding windows on the observed multivariate time series, and the change from rejection to no-rejection of H₀, or the opposite, signaled a non-trivial change of the underlying dynamics of the observed complex system. Different network indices that capture different characteristics of the PMIME networks were used as test statistics. The test was evaluated on multiple synthetic complex and chaotic systems, as well as on linear and nonlinear stochastic systems, demonstrating that the proposed methodology is capable of detecting nonlinear causality. Furthermore, the scheme was applied to different records of financial indices regarding the global financial crisis of 2008, the two commodity crises of 2014 and 2020, the Brexit referendum of 2016, and the outbreak of COVID-19, accurately identifying the structural breaks at the identified times. Full article

(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)

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18 pages, 352 KB

Open AccessArticle

Linear Codes from Two Weakly Regular Plateaued Balanced Functions

by Shudi Yang, Tonghui Zhang and Ping Li

Entropy 2023, 25(2), 369; https://doi.org/10.3390/e25020369 - 17 Feb 2023

Cited by 4 | Viewed by 2107

Abstract

Linear codes with a few weights have been extensively studied due to their wide applications in secret sharing schemes, strongly regular graphs, association schemes, and authentication codes. In this paper, we choose the defining sets from two distinct weakly regular plateaued balanced functions, [...] Read more.

Linear codes with a few weights have been extensively studied due to their wide applications in secret sharing schemes, strongly regular graphs, association schemes, and authentication codes. In this paper, we choose the defining sets from two distinct weakly regular plateaued balanced functions, based on a generic construction of linear codes. Then we construct a family of linear codes with at most five nonzero weights. Their minimality is also examined and the result shows that our codes are helpful in secret sharing schemes. Full article

(This article belongs to the Special Issue Advances in Information and Coding Theory)

16 pages, 747 KB

Open AccessArticle

Chaos and Predictability in Ionospheric Time Series

by Massimo Materassi, Tommaso Alberti, Yenca Migoya-Orué, Sandro Maria Radicella and Giuseppe Consolini

Entropy 2023, 25(2), 368; https://doi.org/10.3390/e25020368 - 17 Feb 2023

Cited by 7 | Viewed by 2483

Abstract

Modelling the Earth’s ionosphere is a big challenge, due to the complexity of the system. Different first principle models have been developed over the last 50 years, based on ionospheric physics and chemistry, mostly controlled by Space Weather conditions. However, it is not [...] Read more.

Modelling the Earth’s ionosphere is a big challenge, due to the complexity of the system. Different first principle models have been developed over the last 50 years, based on ionospheric physics and chemistry, mostly controlled by Space Weather conditions. However, it is not understood in depth if the residual or mismodelled component of the ionosphere’s behaviour is predictable in principle as a simple dynamical system, or is conversely so chaotic to be practically stochastic. Working on an ionospheric quantity very popular in aeronomy, we here suggest data analysis techniques to deal with the question of how chaotic and how predictable the local ionosphere’s behaviour is. In particular, we calculate the correlation dimension

D_{2}

and the Kolmogorov entropy rate

K_{2}

for two one-year long time series of data of vertical total electron content (vTEC), collected on the top of the mid-latitude GNSS station of Matera (Italy), one for the year of Solar Maximum 2001 and one for the year of Solar Minimum 2008. The quantity

D_{2}

is a proxy of the degree of chaos and dynamical complexity.

K_{2}

measures the speed of destruction of the time-shifted self-mutual information of the signal, so that

K_{2}^{- 1}

is a sort of maximum time horizon for predictability. The analysis of the

D_{2}

and

K_{2}

for the vTEC time series allows to give a measure of chaos and predictability of the Earth’s ionosphere, expected to limit any claim of prediction capacity of any model. The results reported here are preliminary, and must be intended only to demonstrate how the application of the analysis of these quantities to the ionospheric variability is feasible, and with a reasonable output. Full article

(This article belongs to the Special Issue Plasma Turbulence: Theory and Modelling)

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3 pages, 160 KB

Open AccessEditorial

Entropic and Complexity Measures in Atomic and Molecular Systems

by Juan Carlos Angulo and Sheila López-Rosa

Entropy 2023, 25(2), 367; https://doi.org/10.3390/e25020367 - 17 Feb 2023

Viewed by 1707

Abstract

Both entropy and complexity are central concepts for the understanding and development of Information Theory, playing an essential role in the increasingly numerous applications in a huge diversity of fields [...] Full article

(This article belongs to the Special Issue Entropic and Complexity Measures in Atomic and Molecular Systems)

17 pages, 573 KB

Open AccessArticle

A Physical Measure for Characterizing Crossover from Integrable to Chaotic Quantum Systems

by Chenguang Y. Lyu and Wen-Ge Wang

Entropy 2023, 25(2), 366; https://doi.org/10.3390/e25020366 - 17 Feb 2023

Viewed by 2289

Abstract

In this paper, a quantity that describes a response of a system’s eigenstates to a very small perturbation of physical relevance is studied as a measure for characterizing crossover from integrable to chaotic quantum systems. It is computed from the distribution of very [...] Read more.

In this paper, a quantity that describes a response of a system’s eigenstates to a very small perturbation of physical relevance is studied as a measure for characterizing crossover from integrable to chaotic quantum systems. It is computed from the distribution of very small, rescaled components of perturbed eigenfunctions on the unperturbed basis. Physically, it gives a relative measure to prohibition of level transitions induced by the perturbation. Making use of this measure, numerical simulations in the so-called Lipkin-Meshkov-Glick model show in a clear way that the whole integrability-chaos transition region is divided into three subregions: a nearly integrable regime, a nearly chaotic regime, and a crossover regime. Full article

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

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15 pages, 1455 KB

Open AccessArticle

Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks

by Vasily Lubashevskiy, Seval Yurtcicek Ozaydin and Fatih Ozaydin

Entropy 2023, 25(2), 365; https://doi.org/10.3390/e25020365 - 16 Feb 2023

Cited by 8 | Viewed by 2477

Abstract

Discovering communities in complex networks is essential in performing analyses, such as dynamics of political fragmentation and echo chambers in social networks. In this work, we study the problem of quantifying the significance of edges in a complex network, and propose a significantly [...] Read more.

Discovering communities in complex networks is essential in performing analyses, such as dynamics of political fragmentation and echo chambers in social networks. In this work, we study the problem of quantifying the significance of edges in a complex network, and propose a significantly improved version of the Link Entropy method. Using Louvain, Leiden and Walktrap methods, our proposal detects the number of communities in each iteration on discovering the communities. Running experiments on various benchmark networks, we show that our proposed method outperforms the Link Entropy method in quantifying edge significance. Considering also the computational complexities and possible defects, we conclude that Leiden or Louvain algorithms are the best choice for community number detection in quantifying edge significance. We also discuss designing a new algorithm for not only discovering the number of communities, but also computing the community membership uncertainties. Full article

(This article belongs to the Special Issue Dynamics and Entropy in Networked Systems)

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32 pages, 717 KB

Open AccessArticle

Distribution of the Age of Gossip in Networks

by Mohamed A. Abd-Elmagid and Harpreet S. Dhillon

Entropy 2023, 25(2), 364; https://doi.org/10.3390/e25020364 - 16 Feb 2023

Cited by 22 | Viewed by 2549

Abstract

We study a general setting of gossip networks in which a source node forwards its measurements (in the form of status updates) about some observed physical process to a set of monitoring nodes according to independent Poisson processes. Furthermore, each monitoring node sends [...] Read more.

We study a general setting of gossip networks in which a source node forwards its measurements (in the form of status updates) about some observed physical process to a set of monitoring nodes according to independent Poisson processes. Furthermore, each monitoring node sends status updates about its information status (about the process observed by the source) to the other monitoring nodes according to independent Poisson processes. We quantify the freshness of the information available at each monitoring node in terms of Age of Information (AoI). While this setting has been analyzed in a handful of prior works, the focus has been on characterizing the average (i.e., marginal first moment) of each age process. In contrast, we aim to develop methods that allow the characterization of higher-order marginal or joint moments of the age processes in this setting. In particular, we first use the stochastic hybrid system (SHS) framework to develop methods that allow the characterization of the stationary marginal and joint moment generating functions (MGFs) of age processes in the network. These methods are then applied to derive the stationary marginal and joint MGFs in three different topologies of gossip networks, with which we derive closed-form expressions for marginal or joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. Our analytical results demonstrate the importance of incorporating the higher-order moments of age processes in the implementation and optimization of age-aware gossip networks rather than just relying on their average values. Full article

(This article belongs to the Special Issue Age of Information: Concept, Metric and Tool for Network Control)

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21 pages, 6657 KB

Open AccessArticle

Routing Strategies for Isochronal-Evolution Random Matching Network

by Weicheng Lun, Qun Li, Zhi Zhu and Can Zhang

Entropy 2023, 25(2), 363; https://doi.org/10.3390/e25020363 - 16 Feb 2023

Cited by 1 | Viewed by 2220

Abstract

In order to abstract away a network model from some real-world networks, such as navigation satellite networks and mobile call networks, we proposed an Isochronal-Evolution Random Matching Network (IERMN) model. An IERMN is a dynamic network that evolves isochronally and has a collection [...] Read more.

In order to abstract away a network model from some real-world networks, such as navigation satellite networks and mobile call networks, we proposed an Isochronal-Evolution Random Matching Network (IERMN) model. An IERMN is a dynamic network that evolves isochronally and has a collection of edges that are pairwise disjoint at any point in time. We then investigated the traffic dynamics in IERMNs whose main research topic is packet transmission. When a vertex of an IERMN plans a path for a packet, it is permitted to delay the sending of the packet to make the path shorter. We designed a routing decision-making algorithm for vertices based on replanning. Since the IERMN has a specific topology, we developed two suitable routing strategies: the Least Delay Path with Minimum Hop (LDPMH) routing strategy and the Least Hop Path with Minimum Delay (LHPMD) routing strategy. An LDPMH is planned by a binary search tree and an LHPMD is planned by an ordered tree. The simulation results show that the LHPMD routing strategy outperformed the LDPMH routing strategy in terms of the critical packet generation rate, number of delivered packets, packet delivery ratio, and average posterior path lengths. Full article

(This article belongs to the Section Complexity)

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17 pages, 425 KB

Open AccessArticle

Efficient Equality Test on Identity-Based Ciphertexts Supporting Flexible Authorization

by Na Li

Entropy 2023, 25(2), 362; https://doi.org/10.3390/e25020362 - 15 Feb 2023

Cited by 4 | Viewed by 2189

Abstract

In the cloud, uploading encrypted data is the most effective way to ensure that the data are not leaked. However, data access control is still an open problem in cloud storage systems. To provide an authorization mechanism to limit the comparison of a [...] Read more.

In the cloud, uploading encrypted data is the most effective way to ensure that the data are not leaked. However, data access control is still an open problem in cloud storage systems. To provide an authorization mechanism to limit the comparison of a user’s ciphertexts with those of another, public key encryption supporting the equality test with four flexible authorizations (PKEET-FA) is presented. Subsequently, more functional identity-based encryption supporting the equality test (IBEET-FA) further combines identity-based encryption with flexible authorization. The bilinear pairing has always been intended to be replaced due to the high computational cost. Hence, in this paper, we use general trapdoor discrete log groups to construct a new and secure IBEET-FA scheme, which is more efficient. The computational cost for the encryption algorithm in our scheme was reduced to 43% of that of the scheme of Li et al. In Type 2 and 3 authorization algorithms, the computational cost of both was reduced to 40% of that of the scheme of Li et al. Furthermore, we give proof that our scheme is secure against one-wayness under the chosen identity and chosen ciphertext attacks (OW-ID-CCA), and indistinguishable against chosen identity and chosen ciphertext attacks (IND-ID-CCA). Full article

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19 pages, 1577 KB

Open AccessArticle

Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information

by Xiaoli Huang, Haibo Chen and Zheng Zhang

Entropy 2023, 25(2), 361; https://doi.org/10.3390/e25020361 - 15 Feb 2023

Cited by 2 | Viewed by 3218

Abstract

Hash is one of the most widely used methods for computing efficiency and storage efficiency. With the development of deep learning, the deep hash method shows more advantages than traditional methods. This paper proposes a method to convert entities with attribute information into [...] Read more.

Hash is one of the most widely used methods for computing efficiency and storage efficiency. With the development of deep learning, the deep hash method shows more advantages than traditional methods. This paper proposes a method to convert entities with attribute information into embedded vectors (FPHD). The design uses the hash method to quickly extract entity features, and uses a deep neural network to learn the implicit association between entity features. This design solves two main problems in large-scale dynamic data addition: (1) The linear growth of the size of the embedded vector table and the size of the vocabulary table leads to huge memory consumption. (2) It is difficult to deal with the problem of adding new entities to the retraining model. Finally, taking the movie data as an example, this paper introduces the encoding method and the specific algorithm flow in detail, and realizes the effect of rapid reuse of dynamic addition data model. Compared with three existing embedding algorithms that can fuse entity attribute information, the deep hash embedding algorithm proposed in this paper has significantly improved in time complexity and space complexity. Full article

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

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Entropy, Volume 25, Issue 2 (February 2023) – 211 articles

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