arXiv - PHYS - Adaptation and Self-Organizing Systems最新文献_第9页

Confinement-driven state transition and bistability in schooling fish 洄游鱼类的封闭驱动状态转换和双稳态性

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-01-03 DOI: arxiv-2401.01850

Baptiste Lafoux, Paul Bernard, Benjamin Thiria, Ramiro Godoy-Diana

引用次数: 0

An internet reviews topic hierarchy mining method based on modified continuous renormalization procedure 基于修正的连续重正化程序的网络评论主题层次挖掘方法

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-01-02 DOI: arxiv-2401.01118

Lin Qi, Feiyan Guo, Jian Zhang, Yuwei Wang

{"title":"An internet reviews topic hierarchy mining method based on modified continuous renormalization procedure","authors":"Lin Qi, Feiyan Guo, Jian Zhang, Yuwei Wang","doi":"arxiv-2401.01118","DOIUrl":"https://doi.org/arxiv-2401.01118","url":null,"abstract":"Mining the hierarchical structure of Internet review topics and realizing a\u0000fine classification of review texts can help alleviate users' information\u0000overload. However, existing hierarchical topic classification methods primarily\u0000rely on external corpora and human intervention. This study proposes a Modified\u0000Continuous Renormalization (MCR) procedure that acts on the keyword\u0000co-occurrence network with fractal characteristics to achieve the topic\u0000hierarchy mining. First, the fractal characteristics in the keyword\u0000co-occurrence network of Internet review text are identified using a\u0000box-covering algorithm for the first time. Then, the MCR algorithm established\u0000on the edge adjacency entropy and the box distance is proposed to obtain the\u0000topic hierarchy in the keyword co-occurrence network. Verification data from\u0000the Dangdang.com book reviews shows that the MCR constructs topic hierarchies\u0000with greater coherence and independence than the HLDA and the Louvain\u0000algorithms. Finally, reliable review text classification is achieved using the\u0000MCR extended bottom level topic categories. The accuracy rate (P), recall rate\u0000(R) and F1 value of Internet review text classification obtained from the\u0000MCR-based topic hierarchy are significantly improved compared to four target\u0000text classification algorithms.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"407 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139083716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How Network Topology Affects the Strength of Dangerous Power Grid Perturbations 网络拓扑如何影响危险电网扰动的强度

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2023-12-31 DOI: arxiv-2401.00552

Calvin Alvares, Soumitro Banerjee

引用次数: 0

Dynamics of oscillator populations with disorder in the coupling phase shifts 耦合相移无序的振荡器群体动力学

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2023-12-30 DOI: arxiv-2401.00281

Arkady Pikovsky, Franco Bagnoli

引用次数: 0

Universality of critical dynamics on a complex network 复杂网络上临界动力学的普遍性

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2023-12-29 DOI: arxiv-2401.00092

Mrinal Sarkar, Tilman Enss, Nicolò Defenu

{"title":"Universality of critical dynamics on a complex network","authors":"Mrinal Sarkar, Tilman Enss, Nicolò Defenu","doi":"arxiv-2401.00092","DOIUrl":"https://doi.org/arxiv-2401.00092","url":null,"abstract":"We investigate the role of the spectral dimension $d_s$ in determining the\u0000universality of phase transitions on a complex network. Due to its structural\u0000heterogeneity, a complex network generally acts as a disordered system.\u0000Specifically, we study the synchronization and entrainment transitions in the\u0000nonequilibrium dynamics of the Kuramoto model and the phase transition of the\u0000equilibrium dynamics of the classical $XY$ model, thereby covering a broad\u0000spectrum from nonlinear dynamics to statistical and condensed matter physics.\u0000Using linear theory, we obtain a general relationship between the dynamics\u0000occurring on the network and the underlying network properties. This yields the\u0000lower critical spectral dimension of the phase synchronization and entrainment\u0000transitions in the Kuramoto model as $d_s=4$ and $d_s=2$ respectively, whereas\u0000for the phase transition in the $XY$ model it is $d_s=2$. To test our\u0000theoretical hypotheses, we employ a network where any two nodes on the network\u0000are connected with a probability proportional to a power law of the distance\u0000between the nodes; this realizes any desired $d_sin [1, infty)$. Our detailed\u0000numerical study agrees well with the prediction of linear theory for the phase\u0000synchronization transition in the Kuramoto model. However, it shows a clear\u0000entrainment transition in the Kuramoto model and phase transition in the $XY$\u0000model at $d_s gtrsim 3$, not $d_s=2$ as predicted by linear theory. Our study\u0000indicates that network disorder in the region $2 leq d_s lesssim 3$ seems to\u0000be relevant and have a profound effect on the dynamics.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"103 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139079889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High Fidelity Human Trajectory Tracking Based on Surveillance Camera Data 基于监控摄像头数据的高保真人体轨迹跟踪

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2023-12-26 DOI: arxiv-2312.16328

Zexu Li, Lei Fang

{"title":"High Fidelity Human Trajectory Tracking Based on Surveillance Camera Data","authors":"Zexu Li, Lei Fang","doi":"arxiv-2312.16328","DOIUrl":"https://doi.org/arxiv-2312.16328","url":null,"abstract":"Human crowds exhibit a wide range of interesting patterns, and measuring them\u0000is of great interest in areas ranging from psychology and social science to\u0000civil engineering. While textit{in situ} measurements of human crowd patterns\u0000require large amounts of time and labor to obtain, human crowd experiments may\u0000result in statistics different from those that would emerge with a naturally\u0000emerging crowd. Here we present a simple, broadly applicable, highly accurate\u0000human crowd tracking technique to extract high-fidelity kinematic information\u0000from widely available surveillance camera videos. With the proposed technique,\u0000researchers can access scientific crowd data on a scale that is orders of\u0000magnitude larger than before. In addition to being able to measure an\u0000individual's time-resolved position and velocity, our technique also offers\u0000high validity time-resolved acceleration and step frequency, and step length.\u0000We demonstrate the applicability of our technique by applying it to\u0000surveillance camera videos in Tokyo Shinjuku streamed on YouTube and exploiting\u0000its high fidelity to expose the hidden contribution of walking speed variance\u0000at the crossroad. The high fidelity and simplicity of this powerful technique\u0000open up the way to utilize the large volume of existing surveillance camera\u0000data around the world for scientific studies.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139064847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Search for crucial events in physiological processes 寻找生理过程中的关键事件

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2023-12-26 DOI: arxiv-2312.15875

Yawer Hussain Shah, Paolo Grigolini

{"title":"Search for crucial events in physiological processes","authors":"Yawer Hussain Shah, Paolo Grigolini","doi":"arxiv-2312.15875","DOIUrl":"https://doi.org/arxiv-2312.15875","url":null,"abstract":"The main purpose of this paper is to attract the attention of researchers\u0000working in the field of physiological processes, towards crucial events.\u0000Crucial events are often confused with extreme events thereby generating the\u0000misleading impression that their treatment should be based on quantum\u0000mechanical formalism. We show that crucial events are invisible and should not\u0000be confused with catastrophes. Crucial events are generated by\u0000self-organization processes yielding a form of swarm intelligence, and signal\u0000their action with fluctuations characterized by anomalous scaling and 1/f\u0000spectrum. The existence or the lack of crucial events can be revealed with an\u0000entropic method of analysis called the Diffusion Entropy Analysis (DEA).\u0000However, anomalous scaling and 1/f spectrum are not a compelling signature of\u0000efficient self-organization, and physiological processes with anomalous scaling\u0000and 1/f noise spectrum without crucial events are a signature of collapsing\u0000physiological organizations. In the case of physiological processes like cancer\u0000dynamics, the existence of crucial events is a signal of intelligence that must\u0000be destroyed rather than reinforced.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139055866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forecasting Fold Bifurcations through Physics-Informed Convolutional Neural Networks 通过物理信息卷积神经网络预测褶皱分岔

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2023-12-21 DOI: arxiv-2312.14210

Giuseppe Habib, Ádám Horváth

{"title":"Forecasting Fold Bifurcations through Physics-Informed Convolutional Neural Networks","authors":"Giuseppe Habib, Ádám Horváth","doi":"arxiv-2312.14210","DOIUrl":"https://doi.org/arxiv-2312.14210","url":null,"abstract":"This study proposes a physics-informed convolutional neural network (CNN) for\u0000identifying dynamical systems' time series near a fold bifurcation. The\u0000peculiarity of this work is that the CNN is trained with a relatively small\u0000amount of data and on a single, very simple system. In contrast, the CNN is\u0000validated on much more complicated systems. A similar task requires significant\u0000extrapolation capabilities, which are obtained by exploiting physics-based\u0000information. Physics-based information is provided through a specific\u0000pre-processing of the input data, consisting mostly of a transformation into\u0000polar coordinates, normalization, transformation into the logarithmic scale,\u0000and filtering through a moving mean. The results illustrate that such data\u0000pre-processing enables the CNN to grasp the important features related to\u0000approaching a fold bifurcation, namely, the trend of the oscillation amplitude,\u0000and neglect other characteristics that are not particularly relevant, such as\u0000the vibration frequency. The developed CNN was able to correctly classify\u0000trajectories near a fold for a mass-on-moving-belt system, a van der\u0000Pol-Duffing oscillator with an attached tuned mass damper, and a\u0000pitch-and-plunge wing profile. The results obtained pave the way for the\u0000development of similar CNNs effective in real-life applications.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139035759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collective dynamics and long-range order in thermal neuristor networks 热神经元网络中的集体动力学和长程秩序

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2023-12-20 DOI: arxiv-2312.12899

Yuan-Hang Zhang, Chesson Sipling, Erbin Qiu, Ivan K. Schuller, Massimiliano Di Ventra

{"title":"Collective dynamics and long-range order in thermal neuristor networks","authors":"Yuan-Hang Zhang, Chesson Sipling, Erbin Qiu, Ivan K. Schuller, Massimiliano Di Ventra","doi":"arxiv-2312.12899","DOIUrl":"https://doi.org/arxiv-2312.12899","url":null,"abstract":"In the pursuit of scalable and energy-efficient neuromorphic devices, recent\u0000research has unveiled a novel category of spiking oscillators, termed ``thermal\u0000neuristors.\" These devices function via thermal interactions among neighboring\u0000vanadium dioxide resistive memories, closely mimicking the behavior of\u0000biological neurons. Here, we show that the collective dynamical behavior of\u0000networks of these neurons showcases a rich phase structure, tunable by\u0000adjusting the thermal coupling and input voltage. Notably, we have identified\u0000phases exhibiting long-range order that, however, does not arise from\u0000criticality, but rather from the time non-local response of the system. In\u0000addition, we show that these thermal neuristor arrays achieve high accuracy in\u0000image recognition tasks through reservoir computing, without taking advantage\u0000of this long-range order. Our findings highlight a crucial aspect of\u0000neuromorphic computing with possible implications on the functioning of the\u0000brain: criticality may not be necessary for the efficient performance of\u0000neuromorphic systems in certain computational tasks.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138823996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Symbolic Regression of Dynamic Network Models 动态网络模型的符号回归

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2023-12-15 DOI: arxiv-2401.05369

Govind Gandhi

{"title":"Symbolic Regression of Dynamic Network Models","authors":"Govind Gandhi","doi":"arxiv-2401.05369","DOIUrl":"https://doi.org/arxiv-2401.05369","url":null,"abstract":"Growing interest in modelling complex systems from brains to societies to\u0000cities using networks has led to increased efforts to describe generative\u0000processes that explain those networks. Recent successes in machine learning\u0000have prompted the usage of evolutionary computation, especially genetic\u0000programming to evolve computer programs that effectively forage a\u0000multidimensional search space to iteratively find better solutions that explain\u0000network structure. Symbolic regression contributes to these approaches by\u0000replicating network morphologies using both structure and processes, all while\u0000not relying on the scientists intuition or expertise. It distinguishes itself\u0000by introducing a novel formulation of a network generator and a parameter-free\u0000fitness function to evaluate the generated network and is found to consistently\u0000retrieve synthetically generated growth processes as well as simple,\u0000interpretable rules for a range of empirical networks. We extend this approach\u0000by modifying generator semantics to create and retrieve rules for time-varying\u0000networks. Lexicon to study networks created dynamically in multiple stages is\u0000introduced. The framework was improved using methods from the genetic\u0000programming toolkit (recombination) and computational improvements (using\u0000heuristic distance measures) and used to test the consistency and robustness of\u0000the upgrades to the semantics using synthetically generated networks. Using\u0000recombination was found to improve retrieval rate and fitness of the solutions.\u0000The framework was then used on three empirical datasets - subway networks of\u0000major cities, regions of street networks and semantic co-occurrence networks of\u0000literature in Artificial Intelligence to illustrate the possibility of\u0000obtaining interpretable, decentralised growth processes from complex networks.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139462913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0