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Group Structure as a Foundation for Entropies Entropy (IF 2.7) Pub Date : 2024-03-18 Henrik Jeldtoft Jensen, Piergiulio Tempesta
Entropy can signify different things. For instance, heat transfer in thermodynamics or a measure of information in data analysis. Many entropies have been introduced, and it can be difficult to ascertain their respective importance and merits. Here, we consider entropy in an abstract sense, as a functional on a probability space, and we review how being able to handle the trivial case of non-interacting
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(Re)Construction of Quantum Space-Time: Transcribing Hilbert into Configuration Space Entropy (IF 2.7) Pub Date : 2024-03-18 Karl Svozil
Space-time in quantum mechanics is about bridging Hilbert and configuration space. Thereby, an entirely new perspective is obtained by replacing the Newtonian space-time theater with the image of a presumably high-dimensional Hilbert space, through which space-time becomes an epiphenomenon construed by internal observers.
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Game Theoretic Clustering for Finding Strong Communities Entropy (IF 2.7) Pub Date : 2024-03-18 Chao Zhao, Ali Al-Bashabsheh, Chung Chan
We address the challenge of identifying meaningful communities by proposing a model based on convex game theory and a measure of community strength. Many existing community detection methods fail to provide unique solutions, and it remains unclear how the solutions depend on initial conditions. Our approach identifies strong communities with a hierarchical structure, visualizable as a dendrogram, and
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A Numerical Study of Quantum Entropy and Information in the Wigner–Fokker–Planck Equation for Open Quantum Systems Entropy (IF 2.7) Pub Date : 2024-03-14 Arash Edrisi, Hamza Patwa, Jose A. Morales Escalante
Kinetic theory provides modeling of open quantum systems subject to Markovian noise via the Wigner–Fokker–Planck equation, which is an alternate of the Lindblad master equation setting, having the advantage of great physical intuition as it is the quantum equivalent of the classical phase space description. We perform a numerical inspection of the Wehrl entropy for the benchmark problem of a harmonic
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Exploration of Resonant Modes for Circular and Polygonal Chladni Plates Entropy (IF 2.7) Pub Date : 2024-03-15 Amira Val Baker, Mate Csanad, Nicolas Fellas, Nour Atassi, Ia Mgvdliashvili, Paul Oomen
In general, sound waves propagate radially outwards from a point source. These waves will continue in the same direction, decreasing in intensity, unless a boundary condition is met. To arrive at a universal understanding of the relation between frequency and wave propagation within spatial boundaries, we explore the maximum entropy states that are realized as resonant modes. For both circular and
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Exact Results for Non-Newtonian Transport Properties in Sheared Granular Suspensions: Inelastic Maxwell Models and BGK-Type Kinetic Model Entropy (IF 2.7) Pub Date : 2024-03-15 Rubén Gómez González, Vicente Garzó
The Boltzmann kinetic equation for dilute granular suspensions under simple (or uniform) shear flow (USF) is considered to determine the non-Newtonian transport properties of the system. In contrast to previous attempts based on a coarse-grained description, our suspension model accounts for the real collisions between grains and particles of the surrounding molecular gas. The latter is modeled as
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Analysis of Quantum Steering Measures Entropy (IF 2.7) Pub Date : 2024-03-14 Lucas Maquedano, Ana C. S. Costa
The effect of quantum steering describes a possible action at a distance via local measurements. In the last few years, several criteria have been proposed to detect this type of correlation in quantum systems. However, there are few approaches presented in order to measure the degree of steerability of a given system. In this work, we are interested in investigating possible ways to quantify quantum
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Three-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation Entropy (IF 2.7) Pub Date : 2024-03-14 Yutaro Yamada, Fred Weiying Zhang, Yuval Kluger, Ilker Yildirim
Ensuring robustness of image classifiers against adversarial attacks and spurious correlation has been challenging. One of the most effective methods for adversarial robustness is a type of data augmentation that uses adversarial examples during training. Here, inspired by computational models of human vision, we explore a synthesis of this approach by leveraging a structured prior over image formation:
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Differential Entropy-Based Fault-Detection Mechanism for Power-Constrained Networked Control Systems Entropy (IF 2.7) Pub Date : 2024-03-14 Alejandro J. Rojas
In this work, we consider the design of power-constrained networked control systems (NCSs) and a differential entropy-based fault-detection mechanism. For the NCS design of the control loop, we consider faults in the plant gain and unstable plant pole locations, either due to natural causes or malicious intent. Since the power-constrained approach utilized in the NCS design is a stationary approach
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Restoring the Fluctuation–Dissipation Theorem in Kardar–Parisi–Zhang Universality Class through a New Emergent Fractal Dimension Entropy (IF 2.7) Pub Date : 2024-03-14 Márcio S. Gomes-Filho, Pablo de Castro, Danilo B. Liarte, Fernando A. Oliveira
The Kardar–Parisi–Zhang (KPZ) equation describes a wide range of growth-like phenomena, with applications in physics, chemistry and biology. There are three central questions in the study of KPZ growth: the determination of height probability distributions; the search for ever more precise universal growth exponents; and the apparent absence of a fluctuation–dissipation theorem (FDT) for spatial dimension
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Magnetic Black Hole Thermodynamics in an Extended Phase Space with Nonlinear Electrodynamics Entropy (IF 2.7) Pub Date : 2024-03-14 Sergey Il’ich Kruglov
We study Einstein’s gravity coupled to nonlinear electrodynamics with two parameters in anti-de Sitter spacetime. Magnetically charged black holes in an extended phase space are investigated. We obtain the mass and metric functions and the asymptotic and corrections to the Reissner–Nordström metric function when the cosmological constant vanishes. The first law of black hole thermodynamics in an extended
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CNN-HT: A Two-Stage Algorithm Selection Framework Entropy (IF 2.7) Pub Date : 2024-03-14 Siyi Xu, Wenwen Liu, Chengpei Wu, Junli Li
The No Free Lunch Theorem tells us that no algorithm can beat other algorithms on all types of problems. The algorithm selection structure is proposed to select the most suitable algorithm from a set of algorithms for an unknown optimization problem. This paper introduces an innovative algorithm selection approach called the CNN-HT, which is a two-stage algorithm selection framework. In the first stage
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Fundamental Limits of Coded Caching in Request-Robust D2D Communication Networks Entropy (IF 2.7) Pub Date : 2024-03-12 Wuqu Wang, Zhe Tao, Nan Liu, Wei Kang
D2D coded caching, originally introduced by Ji, Caire, and Molisch, significantly improves communication efficiency by applying the multi-cast technology proposed by Maddah-Ali and Niesen to the D2D network. Most prior works on D2D coded caching are based on the assumption that all users will request content at the beginning of the delivery phase. However, in practice, this is often not the case. Motivated
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Intra-Beam Interference Mitigation for the Downlink Transmission of the RIS-Assisted Hybrid Millimeter Wave System Entropy (IF 2.7) Pub Date : 2024-03-13 Lou Zhao, Yuliang Zhang, Minjie Zhang, Chunshan Liu
Millimeter-wave (mmWave) communication systems leverage the directional beamforming capabilities of antenna arrays equipped at the base stations (BS) to counteract the inherent high propagation path loss characteristic of mmWave channels. In downlink mmWave transmissions, i.e., from the BS to users, distinguishing users within the same beam direction poses a significant challenge. Additionally, digital
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An n-Dimensional Chaotic Map with Application in Reversible Data Hiding for Medical Images Entropy (IF 2.7) Pub Date : 2024-03-13 Yuli Yang, Ruiyun Chang, Xiufang Feng, Peizhen Li, Yongle Chen, Hao Zhang
The drawbacks of a one-dimensional chaotic map are its straightforward structure, abrupt intervals, and ease of signal prediction. Richer performance and a more complicated structure are required for multidimensional chaotic mapping. To address the shortcomings of current chaotic systems, an n-dimensional cosine-transform-based chaotic system (nD-CTBCS) with a chaotic coupling model is suggested in
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Events as Elements of Physical Observation: Experimental Evidence Entropy (IF 2.7) Pub Date : 2024-03-13 J. Gerhard Müller
It is argued that all physical knowledge ultimately stems from observation and that the simplest possible observation is that an event has happened at a certain space–time location X→=x→,t. Considering historic experiments, which have been groundbreaking in the evolution of our modern ideas of matter on the atomic, nuclear, and elementary particle scales, it is shown that such experiments produce as
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Patterns in Temporal Networks with Higher-Order Egocentric Structures Entropy (IF 2.7) Pub Date : 2024-03-13 Beatriz Arregui-García, Antonio Longa, Quintino Francesco Lotito, Sandro Meloni, Giulia Cencetti
The analysis of complex and time-evolving interactions, such as those within social dynamics, represents a current challenge in the science of complex systems. Temporal networks stand as a suitable tool for schematizing such systems, encoding all the interactions appearing between pairs of individuals in discrete time. Over the years, network science has developed many measures to analyze and compare
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Correction: Zhou et al. Optimal Flow Distribution of Military Supply Transportation Based on Network Analysis and Entropy Measurement. Entropy 2018, 20, 446 Entropy (IF 2.7) Pub Date : 2024-03-11 Wei Zhou, Jin Chen, Bingqing Ding
In the original publication [...]
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Constrained Reweighting of Distributions: An Optimal Transport Approach Entropy (IF 2.7) Pub Date : 2024-03-11 Abhisek Chakraborty, Anirban Bhattacharya, Debdeep Pati
We commonly encounter the problem of identifying an optimally weight-adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the moments, tail behavior, shapes, number of modes, etc., of the resulting weight-adjusted empirical distribution. In this article, we substantially enhance the flexibility
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Multipole Approach to the Dynamical Casimir Effect with Finite-Size Scatterers Entropy (IF 2.7) Pub Date : 2024-03-12 Lucas Alonso, Guilherme C. Matos, François Impens, Paulo A. Maia Neto, Reinaldo de Melo e Souza
A mirror subjected to a fast mechanical oscillation emits photons out of the quantum vacuum—a phenomenon known as the dynamical Casimir effect (DCE). The mirror is usually treated as an infinite metallic surface. Here, we show that, in realistic experimental conditions (mirror size and oscillation frequency), this assumption is inadequate and drastically overestimates the DCE radiation. Taking the
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To Compress or Not to Compress—Self-Supervised Learning and Information Theory: A Review Entropy (IF 2.7) Pub Date : 2024-03-12 Ravid Shwartz Ziv, Yann LeCun
Deep neural networks excel in supervised learning tasks but are constrained by the need for extensive labeled data. Self-supervised learning emerges as a promising alternative, allowing models to learn without explicit labels. Information theory has shaped deep neural networks, particularly the information bottleneck principle. This principle optimizes the trade-off between compression and preserving
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A Kalman Filtering Algorithm for Measurement Interruption Based on Polynomial Interpolation and Taylor Expansion Entropy (IF 2.7) Pub Date : 2024-03-10 Jianhua Cheng, Zili Wang, Bing Qi, He Wang
Combined SINS/GPS navigation systems have been widely used. However, when the traditional combined SINS/GPS navigation system travels between tall buildings, in the shade of trees, or through tunnels, the GPS encounters frequent signal blocking, which leads to the interruption of GPS signals, and as a result, the combined SINS/GPS-based navigation method degenerates into a pure inertial guidance system
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Network Higher-Order Structure Dismantling Entropy (IF 2.7) Pub Date : 2024-03-11 Peng Peng, Tianlong Fan, Linyuan Lü
Diverse higher-order structures, foundational for supporting a network’s “meta-functions”, play a vital role in structure, functionality, and the emergence of complex dynamics. Nevertheless, the problem of dismantling them has been consistently overlooked. In this paper, we introduce the concept of dismantling higher-order structures, with the objective of disrupting not only network connectivity but
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Thermodynamic Insights into Symmetry Breaking: Exploring Energy Dissipation across Diverse Scales Entropy (IF 2.7) Pub Date : 2024-03-05 Andrés Arango-Restrepo, J. Miguel Rubi
Symmetry breaking is a phenomenon that is observed in various contexts, from the early universe to complex organisms, and it is considered a key puzzle in understanding the emergence of life. The importance of this phenomenon is underscored by the prevalence of enantiomeric amino acids and proteins.The presence of enantiomeric amino acids and proteins highlights its critical role. However, the origin
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Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network Entropy (IF 2.7) Pub Date : 2024-03-07 Zhan Chen, Wenxing Fu, Ruitao Zhang, Yangwang Fang, Zhun Xiao
The problem of state estimation based on bearing-only sensors is increasingly important while existing research on distributed filtering solutions is rather limited. Therefore, this paper proposed the novel distributed cubature information filtering (DCIF) method for addressing the state estimation challenge in bearing-only sensor networks. Firstly, the system model of the bearing-only sensor network
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Impact of Moving Walls and Entropy Generation on Doubly Diffusive Mixed Convection of Casson Fluid in Two-Sided Driven Enclosure Entropy (IF 2.7) Pub Date : 2024-03-10 Sivanandam Sivasankaran, Marimuthu Bhuvaneswari, Abdullah K. Alzahrani
In this study, numerical simulations are conducted with the goal of exploring the impact of the direction of the moving wall, solute and thermal transport, and entropy production on doubly diffusive convection in a chamber occupied by a Casson liquid. Wall movement has a significant impact on convective flow, which, in turn, affects the rate of mass and heat transfer; this sparked our interest in conducting
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Deceptive Information Retrieval Entropy (IF 2.7) Pub Date : 2024-03-10 Sajani Vithana, Sennur Ulukus
We introduce the problem of deceptive information retrieval (DIR), in which a user wishes to download a required file out of multiple independent files stored in a system of databases while deceiving the databases by making the databases’ predictions on the user-required file index incorrect with high probability. Conceptually, DIR is an extension of private information retrieval (PIR). In PIR, a user
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Analysis of Self-Gravitating Fluid Instabilities from the Post-Newtonian Boltzmann Equation Entropy (IF 2.7) Pub Date : 2024-03-10 Gilberto M. Kremer
Self-gravitating fluid instabilities are analysed within the framework of a post-Newtonian Boltzmann equation coupled with the Poisson equations for the gravitational potentials of the post-Newtonian theory. The Poisson equations are determined from the knowledge of the energy–momentum tensor calculated from a post-Newtonian Maxwell–Jüttner distribution function. The one-particle distribution function
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It Ain’t Necessarily So: Ludwig Boltzmann’s Darwinian Notion of Entropy Entropy (IF 2.7) Pub Date : 2024-03-08 Steven Gimbel
Ludwig Boltzmann’s move in his seminal paper of 1877, introducing a statistical understanding of entropy, was a watershed moment in the history of physics. The work not only introduced quantization and provided a new understanding of entropy, it challenged the understanding of what a law of nature could be. Traditionally, nomological necessity, that is, specifying the way in which a system must develop
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Multi-Modal Temporal Hypergraph Neural Network for Flotation Condition Recognition Entropy (IF 2.7) Pub Date : 2024-03-08 Zunguan Fan, Yifan Feng, Kang Wang, Xiaoli Li
Efficient flotation beneficiation heavily relies on accurate flotation condition recognition based on monitored froth video. However, the recognition accuracy is hindered by limitations of extracting temporal features from froth videos and establishing correlations between complex multi-modal high-order data. To address the difficulties of inadequate temporal feature extraction, inaccurate online condition
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Ensemble Classifier Based on Interval Modeling for Microarray Datasets Entropy (IF 2.7) Pub Date : 2024-03-08 Urszula Bentkowska, Wojciech Gałka, Marcin Mrukowicz, Aleksander Wojtowicz
The purpose of the study is to propose a multi-class ensemble classifier using interval modeling dedicated to microarray datasets. An approach of creating the uncertainty intervals for the single prediction values of constituent classifiers and then aggregating the obtained intervals with the use of interval-valued aggregation functions is used. The proposed heterogeneous classification employs Random
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Replica Field Theory for a Generalized Franz–Parisi Potential of Inhomogeneous Glassy Systems: New Closure and the Associated Self-Consistent Equation Entropy (IF 2.7) Pub Date : 2024-03-08 Hiroshi Frusawa
On approaching the dynamical transition temperature, supercooled liquids show heterogeneity over space and time. Static replica theory investigates the dynamical crossover in terms of the free energy landscape (FEL). Two kinds of static approaches have provided a self-consistent equation for determining this crossover, similar to the mode coupling theory for glassy dynamics. One uses the Morita–Hiroike
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Temporal Properties of Self-Prioritization Entropy (IF 2.7) Pub Date : 2024-03-09 Zhuoen Lu, Xun He, Dewei Yi, Jie Sui
Using electroencephalogram (EEG), we tested the hypothesis that the association of a neutral stimulus with the self would elicit ultra-fast neural responses from early top-down feedback modulation to late feedforward periods for cognitive processing, resulting in self-prioritization in information processing. In two experiments, participants first learned three associations between personal labels
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Convergence of Relative Entropy for Euler–Maruyama Scheme to Stochastic Differential Equations with Additive Noise Entropy (IF 2.7) Pub Date : 2024-03-06 Yuan Yu
For a family of stochastic differential equations driven by additive Gaussian noise, we study the asymptotic behaviors of its corresponding Euler–Maruyama scheme by deriving its convergence rate in terms of relative entropy. Our results for the convergence rate in terms of relative entropy complement the conventional ones in the strong and weak sense and induce some other properties of the Euler–Maruyama
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Mechanisms for Robust Local Differential Privacy Entropy (IF 2.7) Pub Date : 2024-03-06 Milan Lopuhaä-Zwakenberg, Jasper Goseling
We consider privacy mechanisms for releasing data X=(S,U), where S is sensitive and U is non-sensitive. We introduce the robust local differential privacy (RLDP) framework, which provides strong privacy guarantees, while preserving utility. This is achieved by providing robust privacy: our mechanisms do not only provide privacy with respect to a publicly available estimate of the unknown true distribution
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The Dynamic Spatial Structure of Flocks Entropy (IF 2.7) Pub Date : 2024-03-07 Nicholas J. Russell, Kevin R. Pilkiewicz, Michael L. Mayo
Studies of collective motion have heretofore been dominated by a thermodynamic perspective in which the emergent “flocked” phases are analyzed in terms of their time-averaged orientational and spatial properties. Studies that attempt to scrutinize the dynamical processes that spontaneously drive the formation of these flocks from initially random configurations are far more rare, perhaps owing to the
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Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper Entropy (IF 2.7) Pub Date : 2024-03-07 Prasoon Kumar Vinodkumar, Dogus Karabulut, Egils Avots, Cagri Ozcinar, Gholamreza Anbarjafari
The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its
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Methods to Calculate Entropy Generation Entropy (IF 2.7) Pub Date : 2024-03-07 Jude A. Osara, Michael D. Bryant
Entropy generation, formulated by combining the first and second laws of thermodynamics with an appropriate thermodynamic potential, emerges as the difference between a phenomenological entropy function and a reversible entropy function. The phenomenological entropy function is evaluated over an irreversible path through thermodynamic state space via real-time measurements of thermodynamic states.
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Style-Enhanced Transformer for Image Captioning in Construction Scenes Entropy (IF 2.7) Pub Date : 2024-03-01 Kani Song, Linlin Chen, Hengyou Wang
Image captioning is important for improving the intelligence of construction projects and assisting managers in mastering construction site activities. However, there are few image-captioning models for construction scenes at present, and the existing methods do not perform well in complex construction scenes. According to the characteristics of construction scenes, we label a text description dataset
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Maximum Geometric Quantum Entropy Entropy (IF 2.7) Pub Date : 2024-03-01 Fabio Anza, James P. Crutchfield
Any given density matrix can be represented as an infinite number of ensembles of pure states. This leads to the natural question of how to uniquely select one out of the many, apparently equally-suitable, possibilities. Following Jaynes’ information-theoretic perspective, this can be framed as an inference problem. We propose the Maximum Geometric Quantum Entropy Principle to exploit the notions of
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Walking with the Atoms in a Chemical Bond: A Perspective Using Quantum Phase Transition Entropy (IF 2.7) Pub Date : 2024-03-03 Sabre Kais
Phase transitions happen at critical values of the controlling parameters, such as the critical temperature in classical phase transitions, and system critical parameters in the quantum case. However, true criticality happens only at the thermodynamic limit, when the number of particles goes to infinity with constant density. To perform the calculations for the critical parameters, a finite-size scaling
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Quadratic Growth of Out-of-Time-Ordered Correlators in Quantum Kicked Rotor Model Entropy (IF 2.7) Pub Date : 2024-03-03 Guanling Li, Wenlei Zhao
We investigate both theoretically and numerically the dynamics of out-of-time-ordered correlators (OTOCs) in quantum resonance conditions for a kicked rotor model. We employ various operators to construct OTOCs in order to thoroughly quantify their commutation relation at different times, therefore unveiling the process of quantum scrambling. With the help of quantum resonance condition, we have deduced
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Information Theoretic Study of COVID-19 Genome Entropy (IF 2.7) Pub Date : 2024-03-01 Philippe Jacquet
In this paper, we analyse the genome sequence of COVID-19 on a information point of view, and we compare that with past and present genomes. We use the powerful tool of joint complexity in order to quantify the similarities measured between the various potential parent genomes. The tool has a computing complexity of several orders of magnitude below the classic Smith–Waterman algorithm and would allow
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A Blockwise Bootstrap-Based Two-Sample Test for High-Dimensional Time Series Entropy (IF 2.7) Pub Date : 2024-03-01 Lin Yang
We propose a two-sample testing procedure for high-dimensional time series. To obtain the asymptotic distribution of our ℓ∞-type test statistic under the null hypothesis, we establish high-dimensional central limit theorems (HCLTs) for an α-mixing sequence. Specifically, we derive two HCLTs for the maximum of a sum of high-dimensional α-mixing random vectors under the assumptions of bounded finite
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Dynamical Analysis of an Improved Bidirectional Immunization SIR Model in Complex Network Entropy (IF 2.7) Pub Date : 2024-03-02 Shixiang Han, Guanghui Yan, Huayan Pei, Wenwen Chang
In order to investigate the impact of two immunization strategies—vaccination targeting susceptible individuals to reduce their infection rate and clinical medical interventions targeting infected individuals to enhance their recovery rate—on the spread of infectious diseases in complex networks, this study proposes a bilinear SIR infectious disease model that considers bidirectional immunization.
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Comparing Causal Bayesian Networks Estimated from Data Entropy (IF 2.7) Pub Date : 2024-03-02 Sisi Ma, Roshan Tourani
The knowledge of the causal mechanisms underlying one single system may not be sufficient to answer certain questions. One can gain additional insights from comparing and contrasting the causal mechanisms underlying multiple systems and uncovering consistent and distinct causal relationships. For example, discovering common molecular mechanisms among different diseases can lead to drug repurposing
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(Non)Resonance Bonds in Molecular Dynamics Simulations: A Case Study concerning C60 Fullerenes Entropy (IF 2.7) Pub Date : 2024-02-28 Jacek Siódmiak
In the case of certain chemical compounds, especially organic ones, electrons can be delocalized between different atoms within the molecule. These resulting bonds, known as resonance bonds, pose a challenge not only in theoretical descriptions of the studied system but also present difficulties in simulating such systems using molecular dynamics methods. In computer simulations of such systems, it
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Quantum Implementation of the SAND Algorithm and Its Quantum Resource Estimation for Brute-Force Attack Entropy (IF 2.7) Pub Date : 2024-02-29 Hongyu Wu, Xiaoning Feng, Jiale Zhang
The SAND algorithm is a family of lightweight AND-RX block ciphers released by DCC in 2022. Our research focuses on assessing the security of SAND with a quantum computation model. This paper presents the first quantum implementation of SAND (including two versions of SAND, SAND-64 and SAND-128). Considering the depth-times-width metric, the quantum circuit implementation of the SAND algorithm demonstrates
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Multipartite Entanglement: A Journey through Geometry Entropy (IF 2.7) Pub Date : 2024-02-29 Songbo Xie, Daniel Younis, Yuhan Mei, Joseph H. Eberly
Genuine multipartite entanglement is crucial for quantum information and related technologies, but quantifying it has been a long-standing challenge. Most proposed measures do not meet the “genuine” requirement, making them unsuitable for many applications. In this work, we propose a journey toward addressing this issue by introducing an unexpected relation between multipartite entanglement and hypervolume
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A Nonlinear Local Approximation Approach for Catchment Classification Entropy (IF 2.7) Pub Date : 2024-02-29 Shakera K. Khan, Bellie Sivakumar
Catchment classification plays an important role in many applications associated with water resources and environment. In recent years, several studies have applied the concepts of nonlinear dynamics and chaos for catchment classification, mainly using dimensionality measures. The present study explores prediction as a measure for catchment classification, through application of a nonlinear local approximation
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An Effective Flux Framework for Linear Irreversible Heat Engines: Case Study of a Thermoelectric Generator Entropy (IF 2.7) Pub Date : 2024-02-29 Jasleen Kaur, Ramandeep S. Johal
We consider an autonomous heat engine in simultaneous contact with a hot and a cold reservoir and describe it within a linear irreversible framework. In a tight-coupling approximation, the rate of entropy generation is effectively written in terms of a single thermal flux that is a homogeneous function of the hot and cold fluxes. The specific algebraic forms of the effective flux are deduced for scenarios
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Machine Learning Classification of Event-Related Brain Potentials during a Visual Go/NoGo Task Entropy (IF 2.7) Pub Date : 2024-02-29 Anna Bryniarska, José A. Ramos, Mercedes Fernández
Machine learning (ML) methods are increasingly being applied to analyze biological signals. For example, ML methods have been successfully applied to the human electroencephalogram (EEG) to classify neural signals as pathological or non-pathological and to predict working memory performance in healthy and psychiatric patients. ML approaches can quickly process large volumes of data to reveal patterns
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Ralph Kenna’s Scaling Relations in Critical Phenomena Entropy (IF 2.7) Pub Date : 2024-02-29 Leïla Moueddene, Arnaldo Donoso, Bertrand Berche
In this note, we revisit the scaling relations among “hatted critical exponents”, which were first derived by Ralph Kenna, Des Johnston, and Wolfhard Janke, and we propose an alternative derivation for some of them. For the scaling relation involving the behavior of the correlation function, we will propose an alternative form since we believe that the expression is erroneous in the work of Ralph and
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Fault Diagnosis Method for Rolling Bearings Based on Grey Relation Degree Entropy (IF 2.7) Pub Date : 2024-02-29 Yulin Mao, Jianghui Xin, Liguo Zang, Jing Jiao, Cheng Xue
Aiming at the difficult problem of extracting fault characteristics and the low accuracy of fault diagnosis throughout the full life cycle of rolling bearings, a fault diagnosis method for rolling bearings based on grey relation degree is proposed in this paper. Firstly, the subtraction-average-based optimizer is used to optimize the parameters of the variational mode decomposition algorithm. Secondly
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Entanglement of Temporal Sections as Quantum Histories and Their Quantum Correlation Bounds Entropy (IF 2.7) Pub Date : 2024-02-26 Marcin Nowakowski
In this paper, we focus on the underlying quantum structure of temporal correlations and show their peculiar nature which differentiates them from spatial quantum correlations. With a growing interest in the representation of quantum states as topological objects, we consider quantum history bundles based on the temporal manifold and show the source of the violation of monogamous temporal Bell-like
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On the Precise Link between Energy and Information Entropy (IF 2.7) Pub Date : 2024-02-27 Cameron Witkowski, Stephen Brown, Kevin Truong
We present a modified version of the Szilard engine, demonstrating that an explicit measurement procedure is entirely unnecessary for its operation. By considering our modified engine, we are able to provide a new interpretation of Landauer’s original argument for the cost of erasure. From this view, we demonstrate that a reset operation is strictly impossible in a dynamical system with only conservative
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Enhanced Heterogeneous Graph Attention Network with a Novel Multilabel Focal Loss for Document-Level Relation Extraction Entropy (IF 2.7) Pub Date : 2024-02-28 Yang Chen, Bowen Shi
Recent years have seen a rise in interest in document-level relation extraction, which is defined as extracting all relations between entities in multiple sentences of a document. Typically, there are multiple mentions corresponding to a single entity in this context. Previous research predominantly employed a holistic representation for each entity to predict relations, but this approach often overlooks
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Bitcoin Money Laundering Detection via Subgraph Contrastive Learning Entropy (IF 2.7) Pub Date : 2024-02-28 Shiyu Ouyang, Qianlan Bai, Hui Feng, Bo Hu
The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences
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Opinion Models, Election Data, and Political Theory Entropy (IF 2.7) Pub Date : 2024-02-28 Matthias Gsänger, Volker Hösel, Christoph Mohamad-Klotzbach, Johannes Müller
A unifying setup for opinion models originating in statistical physics and stochastic opinion dynamics are developed and used to analyze election data. The results are interpreted in the light of political theory. We investigate the connection between Potts (Curie–Weiss) models and stochastic opinion models in the view of the Boltzmann distribution and stochastic Glauber dynamics. We particularly find
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Ensemble and Pre-Training Approach for Echo State Network and Extreme Learning Machine Models Entropy (IF 2.7) Pub Date : 2024-02-28 Lingyu Tang, Jun Wang, Mengyao Wang, Chunyu Zhao
The echo state network (ESN) is a recurrent neural network that has yielded state-of-the-art results in many areas owing to its rapid learning ability and the fact that the weights of input neurons and hidden neurons are fixed throughout the learning process. However, the setting procedure for initializing the ESN’s recurrent structure may lead to difficulties in designing a sound reservoir that matches