Entropy

10 pages, 333 KiB

Open AccessReview

Hunting for Bileptons at Hadron Colliders

by Gennaro Corcella

Entropy 2024, 26(10), 850; https://doi.org/10.3390/e26100850 - 8 Oct 2024

I review possible signals at hadron colliders of bileptons, namely doubly charged vectors or scalars with lepton number

L = \pm 2

, as predicted by a 331 model, based on a [...] Read more.

I review possible signals at hadron colliders of bileptons, namely doubly charged vectors or scalars with lepton number

L = \pm 2

, as predicted by a 331 model, based on a

S U {(3)}_{c} \times S U {(3)}_{L} \times U {(1)}_{X}

symmetry. In particular, I account for a version of the 331 model wherein the embedding of the hypercharge is obtained with the addition of three exotic quarks and vector bileptons. Furthermore, a sextet of

S U {(3)}_{L}

, necessary to provide masses to leptons, yields an extra scalar sector, including a doubly charged Higgs, i.e., scalar bileptons. As bileptons are mostly produced in pairs at hadron colliders, their main signal is provided by two same-sign lepton pairs at high invariant mass. Nevertheless, they can also decay according to non-leptonic modes, such as a TeV-scale heavy quark, charged 4/3 or 5/3, plus a Standard Model quark. I explore both leptonic and non-leptonic decays and the sensitivity to the processes of the present and future hadron colliders. Full article

(This article belongs to the Special Issue Particle Theory and Theoretical Cosmology—Dedicated to Professor Paul Howard Frampton on the Occasion of His 80th Birthday)

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18 pages, 959 KiB

Open AccessArticle

Intelligent-Reflecting-Surface-Assisted Single-Input Single-Output Secure Transmission: A Joint Multiplicative Perturbation and Constructive Reflection Perspective

by Chaowen Liu, Anling Zeng, Fei Yu, Zhengmin Shi, Mingyang Liu and Boyang Liu

Entropy 2024, 26(10), 849; https://doi.org/10.3390/e26100849 - 8 Oct 2024

Abstract

Due to the inherent broadcasting nature and openness of wireless transmission channels, wireless communication systems are vulnerable to the eavesdropping of malicious attackers and usually encounter undesirable situations of information leakage. The problem may be more serious when a passive eavesdropping device is [...] Read more.

Due to the inherent broadcasting nature and openness of wireless transmission channels, wireless communication systems are vulnerable to the eavesdropping of malicious attackers and usually encounter undesirable situations of information leakage. The problem may be more serious when a passive eavesdropping device is directly connected to the transmitter of a single-input single-output (SISO) system. To deal with this urgent situation, a novel IRS-assisted physical-layer secure transmission scheme based on joint transmitter perturbation and IRS reflection (JPR) is proposed, such that the secrecy of wireless SISO systems can be comprehensively guaranteed regardless of whether the reflection-based jamming from the IRS to the eavesdropper is blocked or not. Moreover, to develop a trade-off between the achievable performance and implementation complexity, we propose both element-wise and group-wise reflected perturbation alignment (ERPA/GRPA)-based IRS reflection strategies, respectively. In order to evaluate the achievable performance, we analyze the ergodic secrecy rate (ESR) and secrecy outage probability (SOP) of the SISO secure systems with the ERPA/GRPA-based JPRs, respectively. Finally, by characterizing the simulated and numerical ESR and SOP performance results, our proposed scheme is compared with the benchmark scheme of random phase-based reflection, which strongly demonstrates the effectiveness of our proposed scheme. Full article

(This article belongs to the Section Multidisciplinary Applications)

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17 pages, 736 KiB

Open AccessArticle

Functional Hypergraphs of Stock Markets

by Jerry Jones David, Narayan G. Sabhahit, Sebastiano Stramaglia, T. Di Matteo, Stefano Boccaletti and Sarika Jalan

Entropy 2024, 26(10), 848; https://doi.org/10.3390/e26100848 - 8 Oct 2024

Abstract

In stock markets, nonlinear interdependencies between various companies result in nontrivial time-varying patterns in stock prices. A network representation of these interdependencies has been successful in identifying and understanding hidden signals before major events like stock market crashes. However, these studies have revolved [...] Read more.

In stock markets, nonlinear interdependencies between various companies result in nontrivial time-varying patterns in stock prices. A network representation of these interdependencies has been successful in identifying and understanding hidden signals before major events like stock market crashes. However, these studies have revolved around the assumption that correlations are mediated in a pairwise manner, whereas, in a system as intricate as this, the interactions need not be limited to pairwise only. Here, we introduce a general methodology using information-theoretic tools to construct a higher-order representation of the stock market data, which we call functional hypergraphs. This framework enables us to examine stock market events by analyzing the following functional hypergraph quantities: Forman–Ricci curvature, von Neumann entropy, and eigenvector centrality. We compare the corresponding quantities of networks and hypergraphs to analyze the evolution of both structures and observe features like robustness towards events like crashes during the course of a time period. Full article

(This article belongs to the Special Issue Robustness and Resilience of Complex Networks)

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20 pages, 10233 KiB

Open AccessArticle

Development and Experimental Study of Supercritical Flow Payload for Extravehicular Mounting on TZ-6

by Liang Guo, Li Duan, Xuemei Zou, Yang Gao, Xiang Zhang, Yewang Su, Jia Wang, Di Wu and Qi Kang

Entropy 2024, 26(10), 847; https://doi.org/10.3390/e26100847 - 8 Oct 2024

Abstract

This paper provides a detailed description of the development and experimental results of the supercritical flow experiment payload carried on the TZ-6 cargo spacecraft, as well as a systematic verification of the out-of-cabin deployment experiment. The technical and engineering indicators of the payload [...] Read more.

This paper provides a detailed description of the development and experimental results of the supercritical flow experiment payload carried on the TZ-6 cargo spacecraft, as well as a systematic verification of the out-of-cabin deployment experiment. The technical and engineering indicators of the payload deployment experiment are analyzed, and the functional modules of the payload are shown. The paper provides a detailed description of the design, installation location, size, weight, temperature, illumination, pressure, radiation, control, command reception, telemetry data, downlink data, and experimental procedures for the out-of-cabin payload in the extreme conditions of space. The paper presents the annular liquid surface state and temperature oscillation signals obtained from the space experiment and conducts ground matching experiments to verify the results, providing scientific references for the design and condition setting of space experiments and comparisons for the experimental results to obtain the flow field structure under supercritical conditions. The paper provides a specific summary and discussion of the space fluid science experiment project, providing useful references for future long-term in-orbit scientific research using cargo spacecraft. Full article

(This article belongs to the Section Complexity)

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14 pages, 1311 KiB

Open AccessArticle

Decision Transformer-Based Efficient Data Offloading in LEO-IoT

by Pengcheng Xia, Mengfei Zang, Jie Zhao, Ting Ma, Jie Zhang, Changxu Ni, Jun Li and Yiyang Ni

Entropy 2024, 26(10), 846; https://doi.org/10.3390/e26100846 - 7 Oct 2024

Abstract

Recently, the Internet of Things (IoT) has witnessed rapid development. However, the scarcity of computing resources on the ground has constrained the application scenarios of IoT. Low Earth Orbit (LEO) satellites have drawn people’s attention due to their broader coverage and shorter transmission [...] Read more.

Recently, the Internet of Things (IoT) has witnessed rapid development. However, the scarcity of computing resources on the ground has constrained the application scenarios of IoT. Low Earth Orbit (LEO) satellites have drawn people’s attention due to their broader coverage and shorter transmission delay. They are capable of offloading more IoT computing tasks to mobile edge computing (MEC) servers with lower latency in order to address the issue of scarce computing resources on the ground. Nevertheless, it is highly challenging to share bandwidth and power resources among multiple IoT devices and LEO satellites. In this paper, we explore the efficient data offloading mechanism in the LEO satellite-based IoT (LEO-IoT), where LEO satellites forward data from the terrestrial to the MEC servers. Specifically, by optimally selecting the forwarding LEO satellite for each IoT task and allocating communication resources, we aim to minimize the data offloading latency and energy consumption. Particularly, we employ the state-of-the-art Decision Transformer (DT) to solve this optimization problem. We initially obtain a pre-trained DT through training on a specific task. Subsequently, the pre-trained DT is fine-tuned by acquiring a small quantity of data under the new task, enabling it to converge rapidly, with less training time and superior performance. Numerical simulation results demonstrate that in contrast to the classical reinforcement learning approach (Proximal Policy Optimization), the convergence speed of DT can be increased by up to three times, and the performance can be improved by up to 30%. Full article

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

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11 pages, 2277 KiB

Open AccessArticle

Anomalous Non-Hermitian Open-Boundary Spectrum

by Xi-Xi Bao, Gang-Feng Guo, Lei Tan and Wu-Ming Liu

Entropy 2024, 26(10), 845; https://doi.org/10.3390/e26100845 - 7 Oct 2024

Abstract

For a long time, it was presumed that continuum bands could be readily encompassed by open-boundary spectra, irrespective of the system’s modest dimensions. However, our findings reveal a nuanced picture: under open-boundary conditions, the proliferation of complex eigenvalues progresses in a sluggish, oscillating [...] Read more.

For a long time, it was presumed that continuum bands could be readily encompassed by open-boundary spectra, irrespective of the system’s modest dimensions. However, our findings reveal a nuanced picture: under open-boundary conditions, the proliferation of complex eigenvalues progresses in a sluggish, oscillating manner as the system expands. Consequently, even in larger systems, the overlap between continuum bands and open-boundary eigenvalues becomes elusive, with the surprising twist that the count of these complex eigenvalues may actually diminish with increasing system size. This counterintuitive trend underscores that the pursuit of an ideal, infinite-sized system scenario does not necessarily align with enlarging the system size. Notably, despite the inherent non-Hermiticity of our system, the eigenstates distribute themselves in a manner reminiscent of Bloch waves. These discoveries hold potential significance for both theoretical explorations and experimental realizations of non-Hermitian systems. Full article

(This article belongs to the Section Statistical Physics)

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16 pages, 275 KiB

Open AccessArticle

A Cross-Entropy Approach to the Domination Problem and Its Variants

by Ryan Burdett, Michael Haythorpe and Alex Newcombe

Entropy 2024, 26(10), 844; https://doi.org/10.3390/e26100844 - 6 Oct 2024

Abstract

The domination problem and three of its variants (total domination, 2-domination, and secure domination) are considered. These problems have various real-world applications, including error correction codes, ad hoc routing for wireless networks, and social network analysis, among others. However, each of them is [...] Read more.

The domination problem and three of its variants (total domination, 2-domination, and secure domination) are considered. These problems have various real-world applications, including error correction codes, ad hoc routing for wireless networks, and social network analysis, among others. However, each of them is NP-hard to solve to provable optimality, making fast heuristics for these problems desirable. There are a wealth of highly developed heuristics and approximation algorithms for the domination problem; however, such heuristics are much less common for variants of the domination problem. We redress this gap in the literature by proposing a novel implementation of the cross-entropy method that can be applied to any sensible variant of domination. We present results from experiments that demonstrate that this approach can produce good results in an efficient manner even for larger graphs and that it works roughly as well for any of the domination variants considered. Full article

(This article belongs to the Special Issue Entropy-Centric Intelligent Computation with Graph: In Pursuit of Advanced Computational Theories, Methods, and Applications)

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16 pages, 3583 KiB

Open AccessArticle

BHT-QAOA: The Generalization of Quantum Approximate Optimization Algorithm to Solve Arbitrary Boolean Problems as Hamiltonians

by Ali Al-Bayaty and Marek Perkowski

Entropy 2024, 26(10), 843; https://doi.org/10.3390/e26100843 - 6 Oct 2024

Abstract

A new methodology is introduced to solve classical Boolean problems as Hamiltonians, using the quantum approximate optimization algorithm (QAOA). This methodology is termed the “Boolean-Hamiltonians Transform for QAOA” (BHT-QAOA). Because a great deal of research and studies are mainly focused on solving combinatorial [...] Read more.

A new methodology is introduced to solve classical Boolean problems as Hamiltonians, using the quantum approximate optimization algorithm (QAOA). This methodology is termed the “Boolean-Hamiltonians Transform for QAOA” (BHT-QAOA). Because a great deal of research and studies are mainly focused on solving combinatorial optimization problems using QAOA, the BHT-QAOA adds an additional capability to QAOA to find all optimized approximated solutions for Boolean problems, by transforming such problems from Boolean oracles (in different structures) into Phase oracles, and then into the Hamiltonians of QAOA. From such a transformation, we noticed that the total utilized numbers of qubits and quantum gates are dramatically minimized for the generated Hamiltonians of QAOA. In this article, arbitrary Boolean problems are examined by successfully solving them with our BHT-QAOA, using different structures based on various logic synthesis methods, an IBM quantum computer, and a classical optimization minimizer. Accordingly, the BHT-QAOA will provide broad opportunities to solve many classical Boolean-based problems as Hamiltonians, for the practical engineering applications of several algorithms, digital synthesizers, robotics, and machine learning, just to name a few, in the hybrid classical-quantum domain. Full article

(This article belongs to the Special Issue The Future of Quantum Machine Learning and Quantum AI)

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21 pages, 360 KiB

Open AccessArticle

Statistics in Service of Metascience: Measuring Replication Distance with Reproducibility Rate

by Erkan O. Buzbas and Berna Devezer

Entropy 2024, 26(10), 842; https://doi.org/10.3390/e26100842 - 5 Oct 2024

Abstract

Motivated by the recent putative reproducibility crisis, we discuss the relationship between the replicability of scientific studies, the reproducibility of results obtained in these replications, and the philosophy of statistics. Our approach focuses on challenges in specifying scientific studies for scientific inference via [...] Read more.

Motivated by the recent putative reproducibility crisis, we discuss the relationship between the replicability of scientific studies, the reproducibility of results obtained in these replications, and the philosophy of statistics. Our approach focuses on challenges in specifying scientific studies for scientific inference via statistical inference and is complementary to classical discussions in the philosophy of statistics. We particularly consider the challenges in replicating studies exactly, using the notion of the idealized experiment. We argue against treating reproducibility as an inherently desirable property of scientific results, and in favor of viewing it as a tool to measure the distance between an original study and its replications. To sensibly study the implications of replicability and results reproducibility on inference, such a measure of replication distance is needed. We present an effort to delineate such a framework here, addressing some challenges in capturing the components of scientific studies while identifying others as ongoing issues. We illustrate our measure of replication distance by simulations using a toy example. Rather than replications, we present purposefully planned modifications as an appropriate tool to inform scientific inquiry. Our ability to measure replication distance serves scientists in their search for replication-ready studies. We believe that likelihood-based and evidential approaches may play a critical role towards building statistics that effectively serve the practical needs of science. Full article

(This article belongs to the Special Issue Entropy, Statistical Evidence, and Scientific Inference: Evidence Functions in Theory and Applications)

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9 pages, 278 KiB

Open AccessArticle

Improved PEG-Based Construction of Analog Fountain Codes

by Xue Li and Qin Huang

Entropy 2024, 26(10), 841; https://doi.org/10.3390/e26100841 - 5 Oct 2024

Abstract

This paper proposes an improved progressive edge-growth (PEG) construction of analog fountain codes (AFCs). During edge selection, it simultaneously allocates weight coefficients in descending order. Analysis shows that our proposed construction reduces the probability of large weight coefficients involved in harmful short cycles. [...] Read more.

This paper proposes an improved progressive edge-growth (PEG) construction of analog fountain codes (AFCs). During edge selection, it simultaneously allocates weight coefficients in descending order. Analysis shows that our proposed construction reduces the probability of large weight coefficients involved in harmful short cycles. Simulation results indicate that it has good block error rate (BLER) in short block length regime. Full article

(This article belongs to the Special Issue Advanced New Physical Layer Technologies for Next-Generation Wireless Communications)

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27 pages, 2445 KiB

Open AccessReview

From Geometry of Hamiltonian Dynamics to Topology of Phase Transitions: A Review

by Giulio Pettini, Matteo Gori and Marco Pettini

Entropy 2024, 26(10), 840; https://doi.org/10.3390/e26100840 - 5 Oct 2024

Abstract

In this review work, we outline a conceptual path that, starting from the numerical investigation of the transition between weak chaos and strong chaos in Hamiltonian systems with many degrees of freedom, comes to highlight how, at the basis of equilibrium phase transitions, [...] Read more.

In this review work, we outline a conceptual path that, starting from the numerical investigation of the transition between weak chaos and strong chaos in Hamiltonian systems with many degrees of freedom, comes to highlight how, at the basis of equilibrium phase transitions, there must be major changes in the topology of submanifolds of the phase space of Hamiltonian systems that describe systems that exhibit phase transitions. In fact, the numerical investigation of Hamiltonian flows of a large number of degrees of freedom that undergo a thermodynamic phase transition has revealed peculiar dynamical signatures detected through the energy dependence of the largest Lyapunov exponent, that is, of the degree of chaoticity of the dynamics at the phase transition point. The geometrization of Hamiltonian flows in terms of geodesic flows on suitably defined Riemannian manifolds, used to explain the origin of deterministic chaos, combined with the investigation of the dynamical counterpart of phase transitions unveils peculiar geometrical changes of the mechanical manifolds in correspondence to the peculiar dynamical changes at the phase transition point. Then, it turns out that these peculiar geometrical changes are the effect of deeper topological changes of the configuration space hypersurfaces

\sum_{v} = V_{N}^{- 1} (v)

as well as of the manifolds

{M_{v} = V_{N}^{- 1} ((- \infty, v])}_{v} \in R

bounded by the ∑_v. In other words, denoting by vc the critical value of the average potential energy density at which the phase transition takes place, the members of the family

{\sum_{v}}_{v < v_{c}}

are not diffeomorphic to those of the family

{\sum_{v}}_{v > v_{c}}

; additionally, the members of the family

{M_{v}}_{v > v_{c}}

are not diffeomorphic to those of

{M_{v}}_{v > v_{c}}

. The topological theory of the deep origin of phase transitions allows a unifying framework to tackle phase transitions that may or may not be due to a symmetry-breaking phenomenon (that is, with or without an order parameter) and to finite/small N systems. Full article

(This article belongs to the Special Issue On the Role of Geometric and Entropic Arguments in Physics: From Classical Thermodynamics to Quantum Mechanics)

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16 pages, 747 KiB

Open AccessArticle

Mixed Mutual Transfer for Long-Tailed Image Classification

by Ning Ren, Xiaosong Li, Yanxia Wu and Yan Fu

Entropy 2024, 26(10), 839; https://doi.org/10.3390/e26100839 - 2 Oct 2024

Abstract

Real-world datasets often follow a long-tailed distribution, where a few majority (head) classes contain a large number of samples, while many minority (tail) classes contain significantly fewer samples. This imbalance creates an information disparity between head and tail classes, which can negatively impact [...] Read more.

Real-world datasets often follow a long-tailed distribution, where a few majority (head) classes contain a large number of samples, while many minority (tail) classes contain significantly fewer samples. This imbalance creates an information disparity between head and tail classes, which can negatively impact the performance of deep networks. Some transfer knowledge techniques attempt to mitigate this gap by generating additional minority samples, either through oversampling the tail classes or transferring knowledge from the head classes to the tail classes. However, these methods often restrict the diversity of the generated minority samples, primarily focusing on transferring information only to the tail classes. This paper introduces a simple yet effective method for long-tailed classification, called mixed mutual transfer (MMT), which facilitates the mutual transfer of knowledge between head and tail classes by blending samples. The core idea of our method is to create new samples by blending head and tail samples. Head samples are selected using a uniform sampler that retains the long-tailed distribution, while tail samples are selected using a differential sampler that reverses the long-tailed distribution to alleviate imbalance. Our approach aims to diversify both tail and head classes. During the training phase, we utilize the generated samples to update the original dataset for training deep networks. Mixed mutual transfer simultaneously enhances the performance of both head and tail classes. Experimental results on various class-imbalanced datasets show that the proposed method significantly outperforms existing methods, demonstrating its effectiveness in improving the performance of long-tailed deep networks. Full article

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

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28 pages, 347 KiB

Open AccessArticle

Conditional Values in Quantum Mechanics

by Leon Cohen

Entropy 2024, 26(10), 838; https://doi.org/10.3390/e26100838 - 30 Sep 2024

Abstract

We consider the local value of an operator for a given position or momentum and, more generally on the value of another arbitrary observable. We develop a general approach that is based on breaking up

A ψ (x)

as [...] Read more.

We consider the local value of an operator for a given position or momentum and, more generally on the value of another arbitrary observable. We develop a general approach that is based on breaking up

A ψ (x)

as

\frac{A ψ (x)}{ψ (x)} = {(\frac{A ψ (x)}{ψ (x)})}_{R} + i {(\frac{A ψ (x)}{ψ (x)})}_{I}

where

A

is the operator whose local value we seek and

ψ (x)

is the position wave function. We show that the real part is related to the conditional value for a given position and the imaginary part is related to the standard deviation of the conditional value. We show that the uncertainty of an operator can be expressed in two parts that depend on the real and imaginary parts. In the case of the position representation, the expression for the uncertainty of an operator shows that there are two fundamental contributions, one due to the amplitude of the wave function and the other due to the phase. We obtain the equation of motion for the conditional values, and in particular, we generalize the Ehrenfest theorem by deriving a local version of the theorem. We give a number of examples, including the local value of momentum, kinetic energy, and Hamiltonian. We also discuss other approaches for obtaining a conditional value in quantum mechanics including using quasi-probability distributions and the characteristic function approach, among others. Full article

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

24 pages, 970 KiB

Open AccessArticle

Bias in O-Information Estimation

by Johanna Gehlen, Jie Li, Cillian Hourican, Stavroula Tassi, Pashupati P. Mishra, Terho Lehtimäki, Mika Kähönen, Olli Raitakari, Jos A. Bosch and Rick Quax

Entropy 2024, 26(10), 837; https://doi.org/10.3390/e26100837 - 30 Sep 2024

Abstract

Higher-order relationships are a central concept in the science of complex systems. A popular method of attempting to estimate the higher-order relationships of synergy and redundancy from data is through O-information. It is an information–theoretic measure composed of Shannon entropy terms that quantifies [...] Read more.

Higher-order relationships are a central concept in the science of complex systems. A popular method of attempting to estimate the higher-order relationships of synergy and redundancy from data is through O-information. It is an information–theoretic measure composed of Shannon entropy terms that quantifies the balance between redundancy and synergy in a system. However, bias is not yet taken into account in the estimation of the O-information of discrete variables. In this paper, we explain where this bias comes from and explore it for fully synergistic, fully redundant, and fully independent simulated systems of

n = 3

variables. Specifically, we explore how the sample size and number of bins affect the bias in the O-information estimation. The main finding is that the O-information of independent systems is severely biased towards synergy if the sample size is smaller than the number of jointly possible observations. This could mean that triplets identified as highly synergistic may in fact be close to independent. A bias approximation based on the Miller–Maddow method is derived for O-information. We find that for systems of

n = 3

variables the bias approximation can partially correct for the bias. However, simulations of fully independent systems are still required as null models to provide a benchmark of the bias of O-information. Full article

(This article belongs to the Special Issue Topological Data Analysis Meets Information Theory. New Perspectives for the Analysis of Higher-Order Interactions in Complex Systems)

12 pages, 1792 KiB

Open AccessArticle

Information Bottleneck Driven Deep Video Compression—IBOpenDVCW

by Timor Leiderman and Yosef Ben Ezra

Entropy 2024, 26(10), 836; https://doi.org/10.3390/e26100836 - 30 Sep 2024

Abstract

Video compression remains a challenging task despite significant advancements in end-to-end optimized deep networks for video coding. This study, inspired by information bottleneck (IB) theory, introduces a novel approach that combines IB theory with wavelet transform. We perform a comprehensive analysis of information [...] Read more.

Video compression remains a challenging task despite significant advancements in end-to-end optimized deep networks for video coding. This study, inspired by information bottleneck (IB) theory, introduces a novel approach that combines IB theory with wavelet transform. We perform a comprehensive analysis of information and mutual information across various mother wavelets and decomposition levels. Additionally, we replace the conventional average pooling layers with a discrete wavelet transform creating more advanced pooling methods to investigate their effects on information and mutual information. Our results demonstrate that the proposed model and training technique outperform existing state-of-the-art video compression methods, delivering competitive rate-distortion performance compared to the AVC/H.264 and HEVC/H.265 codecs. Full article

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

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16 pages, 3652 KiB

Open AccessArticle

Information FOMO: The Unhealthy Fear of Missing Out on Information—A Method for Removing Misleading Data for Healthier Models

by Ethan Pickering and Themistoklis P. Sapsis

Entropy 2024, 26(10), 835; https://doi.org/10.3390/e26100835 - 30 Sep 2024

Abstract

Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Bayesian experimental design, that identifies critically important information within a dataset while ignoring data that are [...] Read more.

Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Bayesian experimental design, that identifies critically important information within a dataset while ignoring data that are either misleading or bring unnecessary complexity to the surrogate model of choice. Our method improves sample-wise error convergence and eliminates instances where more data lead to worse performance and instabilities of the surrogate model, often termed sample-wise “double descent”. We find these instabilities are a result of the complexity of the underlying map and are linked to extreme events and heavy tails. Our approach has two key features. First, the selection algorithm dynamically couples the chosen model and data. Data is chosen based on its merits towards improving the selected model, rather than being compared strictly against other data. Second, a natural convergence of the method removes the need for dividing the data into training, testing, and validation sets. Instead, the selection metric inherently assesses testing and validation error through global statistics of the model. This ensures that key information is never wasted in testing or validation. The method is applied using both Gaussian process regression and deep neural network surrogate models. Full article

(This article belongs to the Special Issue An Information-Theoretical Perspective on Complex Dynamical Systems)

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23 pages, 670 KiB

Open AccessArticle

Distributed Adaptive Optimization Algorithm for High-Order Nonlinear Multi-Agent Stochastic Systems with Lévy Noise

by Hui Yang, Qing Sun and Jiaxin Yuan

Entropy 2024, 26(10), 834; https://doi.org/10.3390/e26100834 - 30 Sep 2024

Abstract

An adaptive neural network output-feedback control strategy is proposed in this paper for the distributed optimization problem (DOP) of high-order nonlinear stochastic multi-agent systems (MASs) driven by Lévy noise. On the basis of the penalty-function method, the consensus constraint is removed and the [...] Read more.

An adaptive neural network output-feedback control strategy is proposed in this paper for the distributed optimization problem (DOP) of high-order nonlinear stochastic multi-agent systems (MASs) driven by Lévy noise. On the basis of the penalty-function method, the consensus constraint is removed and the global objective function (GOF) is reconstructed. The stability of the system is analyzed by combining the generalized Itô’s formula with the Lyapunov function method. Moreover, the command filtering mechanism is introduced to solve the “complexity explosion” problem in the process of designing virtual controller, and the filter errors are compensated by introducing compensating signals. The proposed algorithm has been proved that the outputs of all agents converge to the optimal solution of the DOP with bounded errors. The simulation results demonstrate the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Information Theory in Control Systems, 2nd Edition)

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26 pages, 1223 KiB

Open AccessReview

Active Inference in Psychology and Psychiatry: Progress to Date?

by Paul B. Badcock and Christopher G. Davey

Entropy 2024, 26(10), 833; https://doi.org/10.3390/e26100833 - 30 Sep 2024

Abstract

The free energy principle is a formal theory of adaptive self-organising systems that emerged from statistical thermodynamics, machine learning and theoretical neuroscience and has since been translated into biologically plausible ‘process theories’ of cognition and behaviour, which fall under the banner of ‘active [...] Read more.

The free energy principle is a formal theory of adaptive self-organising systems that emerged from statistical thermodynamics, machine learning and theoretical neuroscience and has since been translated into biologically plausible ‘process theories’ of cognition and behaviour, which fall under the banner of ‘active inference’. Despite the promise this theory holds for theorising, research and practical applications in psychology and psychiatry, its impact on these disciplines has only now begun to bear fruit. The aim of this treatment is to consider the extent to which active inference has informed theoretical progress in psychology, before exploring its contributions to our understanding and treatment of psychopathology. Despite facing persistent translational obstacles, progress suggests that active inference has the potential to become a new paradigm that promises to unite psychology’s subdisciplines, while readily incorporating the traditionally competing paradigms of evolutionary and developmental psychology. To date, however, progress towards this end has been slow. Meanwhile, the main outstanding question is whether this theory will make a positive difference through applications in clinical psychology, and its sister discipline of psychiatry. Full article

(This article belongs to the Special Issue From Functional Imaging to Free Energy—Dedicated to Professor Karl Friston on the Occasion of His 65th Birthday)

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24 pages, 34952 KiB

Open AccessArticle

A Novel Parameter-Variabled and Coupled Chaotic System and Its Application in Image Encryption with Plaintext-Related Key Concealment

by Zuxi Wang, Siyang Wang, Zhong Chen and Boyun Zhou

Entropy 2024, 26(10), 832; https://doi.org/10.3390/e26100832 - 30 Sep 2024

Abstract

The design of a chaotic system and pseudo-random sequence generation method with excellent performance and its application in image encryption have always been attractive and challenging research fields. In this paper, a new model of parameter-variabled coupled chaotic system (PVCCS) is established by [...] Read more.

The design of a chaotic system and pseudo-random sequence generation method with excellent performance and its application in image encryption have always been attractive and challenging research fields. In this paper, a new model of parameter-variabled coupled chaotic system (PVCCS) is established by interaction coupling between parameters and states of multiple low-dimensional chaotic systems, and a new way to construct more complex hyperchaotic systems from simple low-dimensional systems is obtained. At the same time, based on this model and dynamical DNA codings and operations, a new pseudo-random sequence generation method (PSGM-3DPVCCS/DNA) is proposed, and it is verified that the generated pseudo-random sequence of PSGM-3DPVCCS/DNA has excellent random characteristics. Furthermore, this paper designs a novel pixel chain diffusion image encryption algorithm based on the proposed parameter-variabled coupled chaotic system (PVCCS) in which the hash value of plaintext image is associated with the initial key to participate in the encryption process so that the encryption key is closely associated with plaintext, which improves the security of the algorithm and effectively resists the differential cryptanalysis risk. In addition, an information hiding method is designed to hide the hash value of plaintext image in ciphertext image so that the hash value does not need to be transmitted in each encryption, and the initial key can be reused, which solves the key management problem in application and improves the application efficiency of the encryption algorithm. The experimental analysis shows that the chaotic system constructed in this paper is creative and universal and has more excellent chaotic characteristics than the original low-dimensional system. The sequence generated by the pseudo-random sequence generation method has excellent pseudo-random characteristics and security, and the image encryption algorithm can effectively resist differential cryptanalysis risk, showing advanced encryption performance. Full article

(This article belongs to the Section Complexity)

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