Entropy最新文献_第4页

An Information-Theoretic Proof of a Hypercontractive Inequality. 超契约不等式的信息论证明。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-11 DOI: 10.3390/e26110966

Ehud Friedgut

引用次数: 0

Correction: Herrera Romero, R.; Bastarrachea-Magnani, M.A. Phase and Amplitude Modes in the Anisotropic Dicke Model with Matter Interactions. Entropy 2024, 26, 574. 更正：Herrera Romero, R.; Bastarrachea-Magnani, M.A. 《具有物质相互作用的各向异性迪克模型中的相位和振幅模式》。熵 2024, 26, 574.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-11 DOI: 10.3390/e26110965

Ricardo Herrera Romero, Miguel Angel Bastarrachea-Magnani

引用次数: 0

Entropy of Neuronal Spike Patterns. 神经元尖峰模式的熵

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-11 DOI: 10.3390/e26110967

Artur Luczak

{"title":"Entropy of Neuronal Spike Patterns.","authors":"Artur Luczak","doi":"10.3390/e26110967","DOIUrl":"10.3390/e26110967","url":null,"abstract":"Neuronal spike patterns are the fundamental units of neural communication in the brain, which is still not fully understood. Entropy measures offer a quantitative framework to assess the variability and information content of these spike patterns. By quantifying the uncertainty and informational content of neuronal patterns, entropy measures provide insights into neural coding strategies, synaptic plasticity, network dynamics, and cognitive processes. Here, we review basic entropy metrics and then we provide examples of recent advancements in using entropy as a tool to improve our understanding of neuronal processing. It focuses especially on studies on critical dynamics in neural networks and the relation of entropy to predictive coding and cortical communication. We highlight the necessity of expanding entropy measures from single neurons to encompass multi-neuronal activity patterns, as cortical circuits communicate through coordinated spatiotemporal activity patterns, called neuronal packets. We discuss how the sequential and partially stereotypical nature of neuronal packets influences the entropy of cortical communication. Stereotypy reduces entropy by enhancing reliability and predictability in neural signaling, while variability within packets increases entropy, allowing for greater information capacity. This balance between stereotypy and variability supports both robustness and flexibility in cortical information processing. We also review challenges in applying entropy to analyze such spatiotemporal neuronal spike patterns, notably, the \"curse of dimensionality\" in estimating entropy for high-dimensional neuronal data. Finally, we discuss strategies to overcome these challenges, including dimensionality reduction techniques, advanced entropy estimators, sparse coding schemes, and the integration of machine learning approaches. Thus, this work summarizes the most recent developments on how entropy measures contribute to our understanding of principles underlying neural coding.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"26 11","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11592492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727187","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

Efficient Search Algorithms for Identifying Synergistic Associations in High-Dimensional Datasets. 在高维数据集中识别协同关联的高效搜索算法

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-11 DOI: 10.3390/e26110968

Cillian Hourican, Jie Li, Pashupati P Mishra, Terho Lehtimäki, Binisha H Mishra, Mika Kähönen, Olli T Raitakari, Reijo Laaksonen, Liisa Keltikangas-Järvinen, Markus Juonala, Rick Quax

{"title":"Efficient Search Algorithms for Identifying Synergistic Associations in High-Dimensional Datasets.","authors":"Cillian Hourican, Jie Li, Pashupati P Mishra, Terho Lehtimäki, Binisha H Mishra, Mika Kähönen, Olli T Raitakari, Reijo Laaksonen, Liisa Keltikangas-Järvinen, Markus Juonala, Rick Quax","doi":"10.3390/e26110968","DOIUrl":"10.3390/e26110968","url":null,"abstract":"In recent years, there has been a notably increased interest in the study of multivariate interactions and emergent higher-order dependencies. This is particularly evident in the context of identifying synergistic sets, which are defined as combinations of elements whose joint interactions result in the emergence of information that is not present in any individual subset of those elements. The scalability of frameworks such as partial information decomposition (PID) and those based on multivariate extensions of mutual information, such as O-information, is limited by combinational explosion in the number of sets that must be assessed. In order to address these challenges, we propose a novel approach that utilises stochastic search strategies in order to identify synergistic triplets within datasets. Furthermore, the methodology is extensible to larger sets and various synergy measures. By employing stochastic search, our approach circumvents the constraints of exhaustive enumeration, offering a scalable and efficient means to uncover intricate dependencies. The flexibility of our method is illustrated through its application to two epidemiological datasets: The Young Finns Study and the UK Biobank Nuclear Magnetic Resonance (NMR) data. Additionally, we present a heuristic for reducing the number of synergistic sets to analyse in large datasets by excluding sets with overlapping information. We also illustrate the risks of performing a feature selection before assessing synergistic information in the system.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"26 11","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11592859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727185","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

Evidential Analysis: An Alternative to Hypothesis Testing in Normal Linear Models. 证据分析：正态线性模型假设检验的替代方法。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-10 DOI: 10.3390/e26110964

Brian Dennis, Mark L Taper, José M Ponciano

引用次数: 0

Sensitivity of Bayesian Networks to Noise in Their Parameters. 贝叶斯网络对其参数噪声的敏感性。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-09 DOI: 10.3390/e26110963

Agnieszka Onisko, Marek J Druzdzel

{"title":"Sensitivity of Bayesian Networks to Noise in Their Parameters.","authors":"Agnieszka Onisko, Marek J Druzdzel","doi":"10.3390/e26110963","DOIUrl":"10.3390/e26110963","url":null,"abstract":"There is a widely spread belief in the Bayesian network (BN) community that the overall accuracy of results of BN inference is not too sensitive to the precision of their parameters. We present the results of several experiments in which we put this belief to a test in the context of medical diagnostic models. We study the deterioration of accuracy under random symmetric noise but also biased noise that represents overconfidence and underconfidence of human experts.Our results demonstrate consistently, across all models studied, that while noise leads to deterioration of accuracy, small amounts of noise have minimal effect on the diagnostic accuracy of BN models. Overconfidence, common among human experts, appears to be safer than symmetric noise and much safer than underconfidence in terms of the resulting accuracy. Noise in medical laboratory results and disease nodes as well as in nodes forming the Markov blanket of the disease nodes has the largest effect on accuracy. In light of these results, knowledge engineers should moderately worry about the overall quality of the numerical parameters of BNs and direct their effort where it is most needed, as indicated by sensitivity analysis.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"26 11","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11592589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727236","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

Permutation Entropy: An Ordinal Pattern-Based Resilience Indicator for Industrial Equipment. 置换熵：基于序列模式的工业设备弹性指标。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-08 DOI: 10.3390/e26110961

Christian Salas, Orlando Durán, José Ignacio Vergara, Adolfo Arata

{"title":"Permutation Entropy: An Ordinal Pattern-Based Resilience Indicator for Industrial Equipment.","authors":"Christian Salas, Orlando Durán, José Ignacio Vergara, Adolfo Arata","doi":"10.3390/e26110961","DOIUrl":"10.3390/e26110961","url":null,"abstract":"In a highly dynamic and complex environment where risks and uncertainties are inevitable, the ability of a system to quickly recover from disturbances and maintain optimal performance is crucial for ensuring operational continuity and efficiency. In this context, resilience has become an increasingly important topic in the field of engineering and the management of productive systems. However, there is no single quantitative indicator of resilience that allows for the measurement of this characteristic in a productive system. This study proposes the use of permutation entropy of ordinal patterns in time series as an indicator of resilience in industrial equipment and systems. Based on the definition of resilience, the developed method enables precise and efficient assessment of a system's ability to withstand and recover from disturbances. The methodology includes the identification of ordinal patterns and their analysis through the calculation of a permutation entropy indicator to characterize the dynamics of industrial systems. Case studies are presented and the results are compared with other resilience models existing in the literature, aiming to demonstrate the effectiveness of the proposed approach. The results are promising and highlight a highly applicable and simple indicator for resilience in industrial systems.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"26 11","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11592844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142726927","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

Machine Learning-Assisted Hartree-Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom. 用于中性镱原子能级计算的机器学习辅助哈特里-福克方法。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-08 DOI: 10.3390/e26110962

Kaichen Ma, Chen Yang, Junyao Zhang, Yunfei Li, Gang Jiang, Junjie Chai

引用次数: 0

Generalized Gaussian Distribution Improved Permutation Entropy: A New Measure for Complex Time Series Analysis. 广义高斯分布改进的置换熵：复杂时间序列分析的新测量方法

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-07 DOI: 10.3390/e26110960

Kun Zheng, Hong-Seng Gan, Jun Kit Chaw, Sze-Hong Teh, Zhe Chen

{"title":"Generalized Gaussian Distribution Improved Permutation Entropy: A New Measure for Complex Time Series Analysis.","authors":"Kun Zheng, Hong-Seng Gan, Jun Kit Chaw, Sze-Hong Teh, Zhe Chen","doi":"10.3390/e26110960","DOIUrl":"10.3390/e26110960","url":null,"abstract":"To enhance the performance of entropy algorithms in analyzing complex time series, generalized Gaussian distribution improved permutation entropy (GGDIPE) and its multiscale variant (MGGDIPE) are proposed in this paper. First, the generalized Gaussian distribution cumulative distribution function is employed for data normalization to enhance the algorithm's applicability across time series with diverse distributions. The algorithm further processes the normalized data using improved permutation entropy, which maintains both the absolute magnitude and temporal correlations of the signals, overcoming the equal value issue found in traditional permutation entropy (PE). Simulation results indicate that GGDIPE is less sensitive to parameter variations, exhibits strong noise resistance, accurately reveals the dynamic behavior of chaotic systems, and operates significantly faster than PE. Real-world data analysis shows that MGGDIPE provides markedly better separability for RR interval signals, EEG signals, bearing fault signals, and underwater acoustic signals compared to multiscale PE (MPE) and multiscale dispersion entropy (MDE). Notably, in underwater target recognition tasks, MGGDIPE achieves a classification accuracy of 97.5% across four types of acoustic signals, substantially surpassing the performance of MDE (70.5%) and MPE (62.5%). Thus, the proposed method demonstrates exceptional capability in processing complex time series.","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/PMC11592916/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727207","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

On the Negative Result Experiments in Quantum Mechanics. 论量子力学中的负结果实验。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2024-11-07 DOI: 10.3390/e26110958

Kenichi Konishi

引用次数: 0