IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-07 DOI: 10.3390/e26110959

Victor Nawa, Saralees Nadarajah

{"title":"Exact Expressions for Kullback-Leibler Divergence for Univariate Distributions.","authors":"Victor Nawa, Saralees Nadarajah","doi":"10.3390/e26110959","DOIUrl":"10.3390/e26110959","url":null,"abstract":"The Kullback-Leibler divergence (KL divergence) is a statistical measure that quantifies the difference between two probability distributions. Specifically, it assesses the amount of information that is lost when one distribution is used to approximate another. This concept is crucial in various fields, including information theory, statistics, and machine learning, as it helps in understanding how well a model represents the underlying data. In a recent study by Nawa and Nadarajah, a comprehensive collection of exact expressions for the Kullback-Leibler divergence was derived for both multivariate and matrix-variate distributions. This work is significant as it expands on our existing knowledge of KL divergence by providing precise formulations for over sixty univariate distributions. The authors also ensured the accuracy of these expressions through numerical checks, which adds a layer of validation to their findings. The derived expressions incorporate various special functions, highlighting the mathematical complexity and richness of the topic. This research contributes to a deeper understanding of KL divergence and its applications in statistical analysis and modeling.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"26 11","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11592849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Algorithmic Agent Perspective and Computational Neuropsychiatry: From Etiology to Advanced Therapy in Major Depressive Disorder. 算法代理视角与计算神经精神病学：从病因学到重度抑郁症的高级疗法》。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-06 DOI: 10.3390/e26110953

Giulio Ruffini, Francesca Castaldo, Edmundo Lopez-Sola, Roser Sanchez-Todo, Jakub Vohryzek

{"title":"The Algorithmic Agent Perspective and Computational Neuropsychiatry: From Etiology to Advanced Therapy in Major Depressive Disorder.","authors":"Giulio Ruffini, Francesca Castaldo, Edmundo Lopez-Sola, Roser Sanchez-Todo, Jakub Vohryzek","doi":"10.3390/e26110953","DOIUrl":"10.3390/e26110953","url":null,"abstract":"Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using the Kolmogorov theory (KT) of consciousness, we developed a foundational model where algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression, defined in this context by a state of persistently low valence, may arise from various factors-including inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, we map the agent model to brain circuits and functional networks, framing potential etiological routes and linking with depression biotypes. Finally, we explore how brain stimulation, psychotherapy, and plasticity-enhancing compounds such as psychedelics can synergistically repair neural circuits and optimize therapies using personalized computational models.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"26 11","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11592617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Key Nodes for the Influence Spread Using a Machine Learning Approach. 利用机器学习方法识别影响传播的关键节点

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-06 DOI: 10.3390/e26110955

Mateusz Stolarski, Adam Piróg, Piotr Bródka

{"title":"Identifying Key Nodes for the Influence Spread Using a Machine Learning Approach.","authors":"Mateusz Stolarski, Adam Piróg, Piotr Bródka","doi":"10.3390/e26110955","DOIUrl":"10.3390/e26110955","url":null,"abstract":"The identification of key nodes in complex networks is an important topic in many network science areas. It is vital to a variety of real-world applications, including viral marketing, epidemic spreading and influence maximization. In recent years, machine learning algorithms have proven to outperform the conventional, centrality-based methods in accuracy and consistency, but this approach still requires further refinement. What information about the influencers can be extracted from the network? How can we precisely obtain the labels required for training? Can these models generalize well? In this paper, we answer these questions by presenting an enhanced machine learning-based framework for the influence spread problem. We focus on identifying key nodes for the Independent Cascade model, which is a popular reference method. Our main contribution is an improved process of obtaining the labels required for training by introducing \"Smart Bins\" and proving their advantage over known methods. Next, we show that our methodology allows ML models to not only predict the influence of a given node, but to also determine other characteristics of the spreading process-which is another novelty to the relevant literature. Finally, we extensively test our framework and its ability to generalize beyond complex networks of different types and sizes, gaining important insight into the properties of these methods.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"26 11","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11592689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning. 基于监督对比学习的蒸汽轮机叶片裂纹故障分类与识别研究。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-06 DOI: 10.3390/e26110956

Qinglei Zhang, Laifeng Tang, Jiyun Qin, Jianguo Duan, Ying Zhou

{"title":"Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning.","authors":"Qinglei Zhang, Laifeng Tang, Jiyun Qin, Jianguo Duan, Ying Zhou","doi":"10.3390/e26110956","DOIUrl":"10.3390/e26110956","url":null,"abstract":"Steam turbine blades may crack, break, or suffer other failures due to high temperatures, high pressures, and high-speed rotation, which seriously threatens the safety and reliability of the equipment. The signal characteristics of different fault types are slightly different, making it difficult to accurately classify the faults of rotating blades directly through vibration signals. This method combines a one-dimensional convolutional neural network (1DCNN) and a channel attention mechanism (CAM). 1DCNN can effectively extract local features of time series data, while CAM assigns different weights to each channel to highlight key features. To further enhance the efficacy of feature extraction and classification accuracy, a projection head is introduced in this paper to systematically map all sample features into a normalized space, thereby improving the model's capacity to distinguish between distinct fault types. Finally, through the optimization of a supervised contrastive learning (SCL) strategy, the model can better capture the subtle differences between different fault types. Experimental results show that the proposed method has an accuracy of 99.61%, 97.48%, and 96.22% in the classification task of multiple crack fault types at three speeds, which is significantly better than Multilayer Perceptron (MLP), Residual Network (ResNet), Momentum Contrast (MoCo), and Transformer methods.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"26 11","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11593184/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hybrid Quantum-Classical Model for Stock Price Prediction Using Quantum-Enhanced Long Short-Term Memory. 利用量子增强型长短期记忆预测股价的量子-经典混合模型。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-06 DOI: 10.3390/e26110954

Kimleang Kea, Dongmin Kim, Chansreynich Huot, Tae-Kyung Kim, Youngsun Han

{"title":"A Hybrid Quantum-Classical Model for Stock Price Prediction Using Quantum-Enhanced Long Short-Term Memory.","authors":"Kimleang Kea, Dongmin Kim, Chansreynich Huot, Tae-Kyung Kim, Youngsun Han","doi":"10.3390/e26110954","DOIUrl":"10.3390/e26110954","url":null,"abstract":"The stock markets have become a popular topic within machine learning (ML) communities, with one particular application being stock price prediction. However, accurately predicting the stock market is a challenging task due to the various factors within financial markets. With the introduction of ML, prediction techniques have become more efficient but computationally demanding for classical computers. Given the rise of quantum computing (QC), which holds great promise for being exponentially faster than current classical computers, it is natural to explore ML within the QC domain. In this study, we leverage a hybrid quantum-classical ML approach to predict a company's stock price. We integrate classical long short-term memory (LSTM) with QC, resulting in a new variant called QLSTM. We initially validate the proposed QLSTM model by leveraging an IBM quantum simulator running on a classical computer, after which we conduct predictions using an IBM real quantum computer. Thereafter, we evaluate the performance of our model using the root mean square error (RMSE) and prediction accuracy. Additionally, we perform a comparative analysis, evaluating the prediction performance of the QLSTM model against several other classical models. Further, we explore the impacts of hyperparameters on the QLSTM model to determine the best configuration. Our experimental results demonstrate that while the classical LSTM model achieved an RMSE of 0.0693 and a prediction accuracy of 0.8815, the QLSTM model exhibited superior performance, achieving values of 0.0602 and 0.9736, respectively. Furthermore, the QLSTM outperformed other classical models in both metrics.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"26 11","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11592896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142726622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harnessing Nth Root Gates for Energy Storage. 利用 Nth 根门进行能量存储。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-06 DOI: 10.3390/e26110952

Elliot John Fox, Marcela Herrera, Ferdinand Schmidt-Kaler, Irene D'Amico

引用次数: 0

Information Propagation in Hypergraph-Based Social Networks. 基于超图的社交网络中的信息传播

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-06 DOI: 10.3390/e26110957

Hai-Bing Xiao, Feng Hu, Peng-Yue Li, Yu-Rong Song, Zi-Ke Zhang

{"title":"Information Propagation in Hypergraph-Based Social Networks.","authors":"Hai-Bing Xiao, Feng Hu, Peng-Yue Li, Yu-Rong Song, Zi-Ke Zhang","doi":"10.3390/e26110957","DOIUrl":"10.3390/e26110957","url":null,"abstract":"Social networks, functioning as core platforms for modern information dissemination, manifest distinctive user clustering behaviors and state transition mechanisms, thereby presenting new challenges to traditional information propagation models. Based on hypergraph theory, this paper augments the traditional SEIR model by introducing a novel hypernetwork information dissemination SSEIR model specifically designed for online social networks. This model accurately represents complex, multi-user, high-order interactions. It transforms the traditional single susceptible state (S) into active (Sa) and inactive (Si) states. Additionally, it enhances traditional information dissemination mechanisms through reaction process strategies (RP strategies) and formulates refined differential dynamical equations, effectively simulating the dissemination and diffusion processes in online social networks. Employing mean field theory, this paper conducts a comprehensive theoretical derivation of the dissemination mechanisms within the SSEIR model. The effectiveness of the model in various network structures was verified through simulation experiments, and its practicality was further validated by its application on real network datasets. The results show that the SSEIR model excels in data fitting and illustrating the internal mechanisms of information dissemination within hypernetwork structures, further clarifying the dynamic evolutionary patterns of information dissemination in online social hypernetworks. This study not only enriches the theoretical framework of information dissemination but also provides a scientific theoretical foundation for practical applications such as news dissemination, public opinion management, and rumor monitoring in online social networks.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"26 11","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11593332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New Families of Frequency-Hopping Sequence Sets with a Low-Hit-Zone. 具有低命中区的跳频序列组新族。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-05 DOI: 10.3390/e26110948

Limengnan Zhou, Hanzhou Wu

引用次数: 0

Quantum Information Scrambling in Adiabatically Driven Critical Systems. 绝热驱动临界系统中的量子信息扰乱。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-05 DOI: 10.3390/e26110951

Ricardo Puebla, Fernando J Gómez-Ruiz

{"title":"Quantum Information Scrambling in Adiabatically Driven Critical Systems.","authors":"Ricardo Puebla, Fernando J Gómez-Ruiz","doi":"10.3390/e26110951","DOIUrl":"10.3390/e26110951","url":null,"abstract":"Quantum information scrambling refers to the spread of the initially stored information over many degrees of freedom of a quantum many-body system. Information scrambling is intimately linked to the thermalization of isolated quantum many-body systems, and has been typically studied in a sudden quench scenario. Here, we extend the notion of quantum information scrambling to critical quantum many-body systems undergoing an adiabatic evolution. In particular, we analyze how the symmetry-breaking information of an initial state is scrambled in adiabatically driven integrable systems, such as the Lipkin-Meshkov-Glick and quantum Rabi models. Following a time-dependent protocol that drives the system from symmetry-breaking to a normal phase, we show how the initial information is scrambled, even for perfect adiabatic evolutions, as indicated by the expectation value of a suitable observable. We detail the underlying mechanism for quantum information scrambling, its relation to ground- and excited-state quantum phase transitions, and quantify the degree of scrambling in terms of the number of eigenstates that participate in the encoding of the initial symmetry-breaking information. While the energy of the final state remains unaltered in an adiabatic protocol, the relative phases among eigenstates are scrambled, and so is the symmetry-breaking information. We show that a potential information retrieval, following a time-reversed protocol, is hindered by small perturbations, as indicated by a vanishingly small Loschmidt echo and out-of-time-ordered correlators. The reported phenomenon is amenable for its experimental verification, and may help in the understanding of information scrambling in critical quantum many-body systems.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"26 11","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11592705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Thermodynamics of the Primordial Universe. 原始宇宙的热力学。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-05 DOI: 10.3390/e26110947

David Silva Pereira, João Ferraz, Francisco S N Lobo, José Pedro Mimoso

{"title":"Thermodynamics of the Primordial Universe.","authors":"David Silva Pereira, João Ferraz, Francisco S N Lobo, José Pedro Mimoso","doi":"10.3390/e26110947","DOIUrl":"10.3390/e26110947","url":null,"abstract":"This review delves into the pivotal primordial stage of the universe, a period that holds the key to understanding its current state. To fully grasp this epoch, it is essential to consider three fundamental domains of physics: gravity, particle physics, and thermodynamics. The thermal history of the universe recreates the extreme high-energy conditions that are critical for exploring the unification of the fundamental forces, making it a natural laboratory for high-energy physics. This thermal history also offers valuable insights into how the laws of thermodynamics have governed the evolution of the universe's constituents, shaping them into the forms we observe today. Focusing on the Standard Cosmological Model (SCM) and the Standard Model of Particles (SM), this paper provides an in-depth analysis of thermodynamics in the primordial universe. The structure of the study includes an introduction to the SCM and its strong ties to thermodynamic principles. It then explores equilibrium thermodynamics in the context of the expanding universe, followed by a detailed analysis of out-of-equilibrium phenomena that were pivotal in shaping key events during the early stages of the universe's evolution.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"26 11","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11593139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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