IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0263796

Akhila Henry, Rajan Sundaravaradhan, Nithin Nagaraj

{"title":"Simplified neurochaos learning architectures for data classification.","authors":"Akhila Henry, Rajan Sundaravaradhan, Nithin Nagaraj","doi":"10.1063/5.0263796","DOIUrl":"https://doi.org/10.1063/5.0263796","url":null,"abstract":"Developing machine learning algorithms that can classify datasets with higher accuracy and efficiency is crucial in practical applications. Neurochaos learning (NL) is a recently proposed algorithm that is inspired by the chaotic firing of neurons in the brain. NL has shown promise in recent times both in terms of classification accuracy and in the number of samples needed for training. In this study, we propose a novel simplification of the neurochaos learning algorithm by reducing the number of features needed for classification and also reducing the number of hyperparameters needed to be tuned. By using a single feature of the chaotic neural traces (orbit generated by chaotic map) of NL and by using only one hyperparameter, we demonstrate a significant boost in run time of the algorithm while retaining comparable classification accuracy. This single feature could either be the mean of the chaotic neural traces (Tracemean) or the Fluctuation Index (FI) of the chaotic neural traces. The classifier itself could either be a simple cosine similarity (Tracemean ChaosNet, FI ChaosNet) or any of the classical machine learning (ML) classifiers (Tracemean+ML, FI+ML). We compare the performance of these newly proposed simplified NL algorithms on ten publicly available datasets. The proposed simplified NL architectures in this study are able to efficiently classify datasets while taking much less run time. The fact that only a single hyperparameter needs to be tuned in both architectures (Tracemean ChaosNet and FI ChaosNet) makes them very attractive for practical applications with the ease of interpretability.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 6","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial locking of chimera states to frequency heterogeneity in nonlocally coupled oscillators. 非局域耦合振荡器中嵌合体状态对频率异质性的空间锁定。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0266425

Petar Mircheski, Hiroya Nakao

引用次数: 0

Imre M. Jánosi (1963-2023).

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0274421

Marcus W Beims, Pedro G Lind, Thorsten Pöschel, Támas Tél, Miklós Vincze, Dietrich E Wolf

引用次数: 0

Application of the direct interaction approximation in turbulence theory to generalized stochastic models. 湍流理论中直接相互作用近似在广义随机模型中的应用。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0260737

B K Shivamoggi, N Tuovila

{"title":"Application of the direct interaction approximation in turbulence theory to generalized stochastic models.","authors":"B K Shivamoggi, N Tuovila","doi":"10.1063/5.0260737","DOIUrl":"https://doi.org/10.1063/5.0260737","url":null,"abstract":"The purpose of this paper is to seek mathematical insights into Kraichnan's direct-interaction approximation (DIA) in turbulence theory via its application to generalized stochastic models. Previous developments [R. H. Kraichnan, J. Math. Phys. 2, 124-148 (1961); Phys. Fluids 8, 575-598 (1965); B. K. Shivamoggi et al., J. Math. Anal. Appl. 229, 639-658 (1999); B. K. Shivamoggi and N. Tuovila, Chaos 29, 063124 (2019)] were based on the Boltzmann-Gibbs prescription for the underlying entropy measure, which exhibits the extensivity property and is suited for ergodic systems. Here, we proceed further and consider the introduction of an influence bias discriminating rare and frequent events explicitly, as it behooves non-ergodic systems by a using a Tsallis type [C. Tsallis, J. Stat. Phys. 52, 479-487 (1988)] autocorrelation model with an underlying non-extensive entropy measure. As an example of this development, we consider a linear damped stochastic oscillator system and explore the Markovian and non-Markovian regimes separately using Keller's perturbative and the DIA non-perturbative procedures. We find that, in the asymptotic regimes of short-range (white-noise) and long-range (black-noise) autocorrelations, the Tsallis model yields the same result as the Uhlenbeck-Ornstein model. Furthermore, the non-perturbative aspects excluded by Keller's perturbative procedure are found to be negligible in these asymptotic regimes. During the course of this investigation, we also exhibit some apparently novel mathematical properties of the stochastic models in question-the gamma distribution and the Tsallis non-extensive entropy.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 6","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Complex network analysis of zebrafish locomotion based on time series and visibility graphs. 基于时间序列和可见性图的斑马鱼运动复杂网络分析。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0253756

Zhen Wang, Jian Gao

{"title":"Complex network analysis of zebrafish locomotion based on time series and visibility graphs.","authors":"Zhen Wang, Jian Gao","doi":"10.1063/5.0253756","DOIUrl":"https://doi.org/10.1063/5.0253756","url":null,"abstract":"Zebrafish are increasingly being used as a model organism to study various biological processes, including both normal and pathological conditions. Understanding zebrafish locomotor behavior is essential for gaining insights into human movement disorders. Despite an abundance of research on zebrafish locomotion, studies utilizing time series data remain limited. In this study, we employ the visibility graph method to examine how container size influences zebrafish locomotor characteristics under normal conditions. We further characterize specific behavioral indicators under normal, panic, and intoxication conditions. Our findings highlight the effectiveness of this method in identifying the behavioral states of individual zebrafish irrespective of container size. Notably, under normal conditions, the step series of individuals in containers of varying sizes consistently exhibit a non-trivial, strongly correlated pattern. These patterns are characterized by hub nodes that display long-range correlations in their positions within the step series. For other time series, including direction-changing series under normal conditions and both step and direction-changing series under panic and intoxication conditions, the strong patterns are trivial. In these cases, hub nodes do not form motifs, and the positions of motifs within the series exhibit randomness.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 6","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global patterns of extreme temperature teleconnections using climate network analysis. 利用气候网络分析的极端温度遥相关的全球模式。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0276151

Yuhao Feng, Jun Meng, Jingfang Fan

{"title":"Global patterns of extreme temperature teleconnections using climate network analysis.","authors":"Yuhao Feng, Jun Meng, Jingfang Fan","doi":"10.1063/5.0276151","DOIUrl":"https://doi.org/10.1063/5.0276151","url":null,"abstract":"Extreme weather events, rare yet profoundly impactful, are often accompanied by severe conditions. Increasing global temperatures are poised to exacerbate these events, resulting in greater human casualties, economic losses, and ecological destruction. Complex global climate interactions, known as teleconnections, can lead to widespread repercussions triggered by localized extreme weather. Understanding these teleconnection patterns is crucial for weather forecasting, enhancing safety, and advancing climate science. Here, we employ climate network analysis to uncover teleconnection patterns associated with extreme day-to-day temperature differences, including both extreme warming and cooling events occurring on a daily basis. Our study results demonstrate that the distances of significant teleconnections initially conform to a power-law decay, signifying a decline in connectivity with distance. However, this power-law decay tendency breaks beyond a certain threshold distance, suggesting the existence of long-distance connections. Additionally, we uncover a greater prevalence of long-distance connectivity among extreme cooling events compared to extreme warming events. The global pattern of teleconnections is, in part, likely driven by the mechanism of Rossby waves, which serve as a rapid conduit for inducing correlated fluctuations in both pressure and temperature. These results enhance our understanding of the multiscale nature of climate teleconnections and hold significant implications for improving weather forecasting and assessing climate risks in a warming world.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 6","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144316001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rogue waves: Theory, methods, and applications-30 years after the Draupner wave. 异常波：理论、方法和应用——德劳普纳波后30年。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0282887

Zhenya Yan, Boris A Malomed, Kwok Wing Chow, Guoqiang Zhang, Weifang Weng

引用次数: 0

The impact of feedbacks on evolutionary game dynamics in structured populations. 结构化种群中反馈对进化博弈动态的影响。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0278673

Jiaquan Huang, Yuying Zhu, Dawei Zhao, Chengyi Xia, Matjaž Perc

{"title":"The impact of feedbacks on evolutionary game dynamics in structured populations.","authors":"Jiaquan Huang, Yuying Zhu, Dawei Zhao, Chengyi Xia, Matjaž Perc","doi":"10.1063/5.0278673","DOIUrl":"10.1063/5.0278673","url":null,"abstract":"In this paper, we investigate how the structure of a lattice network influences the outcomes of an evolutionary game that includes environmental feedback. Specifically, we study how the number of neighbors each individual has affects population strategies under three different update rules: birth-death, death-birth, and imitation. Our results show both similarities and differences among these update rules. The similarity is that individuals generally prefer defection when the environment is rich but tend to cooperate more often when the environment is poor. The key difference arises between the birth-death and imitation update rules compared to the death-birth rule. Under the birth-death and imitation rules, the number of neighbors affects cooperation levels only when the system reaches a boundary equilibrium, whereas under the death-birth rule, the number of neighbors does not have this effect. Additionally, compared to those populations without structured networks, we find that lattice network structures help to maintain cooperation, even when individuals face the prisoner's dilemma. This suggests that lattice structures help individuals resist the temptation to defect, promoting social harmony and sustainable development.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 6","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144336373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inertia-induced mechanism for giant enhancement of transport generated by active fluctuations. 由主动波动产生的输运巨大增强的惯性诱导机制。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0264855

K Białas, J Spiechowicz

引用次数: 0

Learning topological horseshoes in time series via deep neural networks. 通过深度神经网络在时间序列中学习拓扑马蹄铁。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0270132

Xiao-Song Yang, Junfeng Cheng

引用次数: 0

Chaos最新文献