Article
Mathematics, Interdisciplinary Applications
Ziling Jiang, Fan Huang, Haijian Shao, Shuiming Cai, Xiaobo Lu, Shengqin Jiang
Summary: In this study, we propose a novel time-varying finite-time control scheme to achieve synchronization of attack-induced uncertain neural networks. Our method takes into consideration various factors that can induce uncertainties in neural networks, and we establish sufficient criteria for ensuring synchronization within a desired settling time. Numerical simulations validate the efficacy of our proposed method.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Computer Science, Artificial Intelligence
Zhilei Xu, Wei Huang, Jinsong Wang
Summary: This study introduces a time-varying neural network (TVNN) for solving the time-varying minimum spanning tree problem with constraints (CTMST), which outperforms traditional algorithms in terms of parallel computing, response speed, and solution accuracy. Time-varying neurons play a key role in achieving these improvements, making the proposed algorithm more efficient on large-scale time-varying networks.
Article
Computer Science, Artificial Intelligence
Changxin Mo, Dimitrios Gerontitis, Predrag S. Stanimirovie
Summary: This paper investigates the time-varying tensor square root problem and proposes a new finite-time convergent Zhang neural network model. The existence and uniqueness of the solution are discussed, and numerical examples confirm the reliability and superiority of the proposed model.
Article
Mathematics, Applied
Dimitrios Gerontitis, Ratikanta Behera, Panagiotis Tzekis, Predrag Stanimirovic
Summary: A family of varying-parameter finite-time zeroing neural networks (VPFTZNN) is proposed for solving the time-varying Sylvester equation (TVSE), with analysis of convergence speed, stability, and noise resistance, and experimental verification of theoretical results.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2022)
Article
Computer Science, Information Systems
Yixuan Zhang, Jiaqi Liu, Bin Guo, Zhu Wang, Yunji Liang, Zhiwen Yu
Summary: App popularity prediction is a significant task in mobile service development. This paper proposes DeePOP, a popularity prediction model that leverages time-varying hierarchical interactions. By integrating internal factors and time-varying hierarchical interactions, DeePOP achieves higher prediction accuracy compared to existing methods.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Physics, Fluids & Plasmas
William Qian, Lia Papadopoulos, Zhixin Lu, Keith A. Kroma-Wiley, Fabio Pasqualetti, Dani S. Bassett
Summary: This study investigates the influence of interaction topology on synchronization in networks of coupled oscillators. It shows that changes in connection topology alone can drive hysteresis synchronization behavior in networks of coupled inertial oscillators. Certain fixed-density rewiring schemes induce significant changes to the level of global synchrony, which remain robust to network perturbations.
Article
Computer Science, Artificial Intelligence
Zhilei Xu, Wei Huang, Jinsong Wang
Summary: This paper introduces a novel neural network framework WTNN that can solve the time-varying shortest path problem and achieve global optimal solutions for three different waiting policies, outperforming traditional algorithms.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Yinyan Zhang
Summary: This paper proposes an improved gradient neural network (IGNN) method by introducing additional nonlinearity to address the issue of inaccurate solution in traditional GNN models when dealing with time-varying Lyapunov equations (LEs). Simulation results demonstrate that the IGNN method achieves finite-time convergence even in the presence of bounded additive time-varying noises.
INFORMATION SCIENCES
(2022)
Article
Engineering, Biomedical
Xiabing Zhang, Shu Zhang, Bin Lu, Yifeng Wang, Ning Li, Yueheng Peng, Jingming Hou, Jing Qiu, Fali Li, Dezhong Yao, Peng Xu
Summary: This study investigates the interaction patterns between the central and peripheral systems during different stages of movement using time-varying corticomuscular networks. Results show that muscles transmit bottom-up movement information in the preparation stage, while the brain issues top-down control commands and dominates in the execution stage. Classifying different movement stages based on time-varying corticomuscular network indicators achieves an average accuracy above 74%.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Automation & Control Systems
Lin Xiao, Xiaopeng Li, Wenqian Huang, Lei Jia
Summary: This article introduces the background and challenges of the time-varying tensor inversion problem, and proposes a novel DP-ZNN model to solve this problem. Through theoretical analysis and experimental verification, it is proved that the model has superior convergence performance.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Weihua Jiang, Feng Tong
Summary: This article evaluates a UWA sensor network adopting the sparsity exploitation physical layer and compares the network behavior under different media access control protocols. The evaluation results verify the effectiveness of sparsity exploitation in improving UWA sensor network performance.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Automation & Control Systems
Lin Xiao, Juan Tao, Weibing Li
Summary: This article proposes an arctan-type varying-parameter neural network (ATVP-ZNN) with finite-time convergence for solving time-varying complex Sylvester equations. Theoretical analysis and numerical studies demonstrate the superior convergence of ATVP-ZNN.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Neurosciences
Yuhao Jiang, Rui Qiao, Yupan Shi, Yi Tang, Zhengjun Hou, Yin Tian
Summary: In this study, the relationship between attention and auditory-visual integration was explored using time-varying network analysis. The results showed that auditory-visual integration occurred before attention, supporting the early integration framework.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Caicai Zheng, Cheng Hu, Juan Yu, Haijun Jiang
Summary: This article investigates the fixed-time (FXT) synchronization of discontinuous competitive neural networks (CNNs) with time-varying delays. Two types of discontinuous FXT control schemes are proposed, and two forms of Lyapunov function based on p-norm and 1-norm are constructed to analyze the FXT synchronization of CNNs. By employing nonsmooth analysis and inequality techniques, simple criteria for achieving FXT synchronization and an upper bound of the settling time with less conservativeness are derived. The effect of time scale on FXT synchronization of CNNs is also considered, and numerical results for an example are provided to validate the theoretical findings.
Article
Economics
Ye Li, Reza Mohajerpoor, Mohsen Ramezani
Summary: This study introduces a new perimeter control method that adjusts region boundaries in real-time to tackle the propagation of local congestion, significantly reducing total travel time for vehicles in the network.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Physics, Multidisciplinary
Julien Petit, Malbor Asllani, Duccio Fanelli, Ben Lauwens, Timoteo Carletti
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2016)
Article
Physics, Multidisciplinary
Julien Petit, Ben Lauwens, Duccio Fanelli, Timoteo Carletti
PHYSICAL REVIEW LETTERS
(2017)
Review
Physics, Multidisciplinary
Federico Battiston, Giulia Cencetti, Iacopo Iacopini, Vito Latora, Maxime Lucas, Alice Patania, Jean-Gabriel Young, Giovanni Petri
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
(2020)
Article
Mathematics, Applied
J. Newman, M. Lucas, A. Stefanovska
Summary: This study introduces a new mathematical framework for analyzing the qualitative stability of finite-time processes under slow external influences, through a slow-fast formalism and consideration of stability in the singular limit. It provides stability definitions for one-dimensional phase dynamics analogous to classical infinite-time definitions, formalizing and generalizing a phase-stabilization phenomenon previously described in physics literature.
Article
Multidisciplinary Sciences
Yuanzhao Zhang, Maxime Lucas, Federico Battiston
Summary: Higher-order networks are a powerful framework for modeling complex systems and their collective behavior. The choice between simplicial complexes and hypergraphs has a significant impact on the dynamics of the system.
NATURE COMMUNICATIONS
(2023)
Article
Physics, Multidisciplinary
Maxime Lucas, Iacopo Iacopini, Thomas Robiglio, Alain Barrat, Giovanni Petri
Summary: Single contagion processes can transition continuously from an epidemic-free state to an epidemic state above a critical threshold. However, when two simple contagion processes are coupled bidirectionally in an asymmetric manner, this transition can become discontinuous. By considering a simplicial contagion that unidirectionally drives a simple contagion, researchers have found that the driven simple contagion can exhibit both discontinuous transitions and bistability above a critical driving strength, which is not observed otherwise. This demonstrates how a simple contagion process can display higher-order contagion phenomena through a hidden driving mechanism.
PHYSICAL REVIEW RESEARCH
(2023)
Article
Biochemical Research Methods
Maxime Lucas, Arthur Morris, Alex Townsend-Teague, Laurent Tichit, Bianca Habermann, Alain Barrat
Summary: Phasik is an automatic method that identifies the temporal organization of biological systems by combining time series data and interaction data. It builds a (partially) temporal network and uses clustering to infer temporal phases. We demonstrate its effectiveness by studying different phases of the cell cycle and phase arrests of mutants, as well as temporal gene expression data from circadian rhythms in mouse models. This method will be valuable for studying temporal regulation in lesser-known biological contexts as high-resolution multiomics datasets become more common.
CELL REPORTS METHODS
(2023)
Article
Mathematics, Applied
Julien Petit, Renaud Lambiotte, Timoteo Carletti
Summary: Graph-limit theory focuses on the convergence of sequences of increasingly large graphs, providing a framework for studying dynamical systems on massive graphs where traditional methods would become computationally intractable. By approximating standard ordinary differential equations with nonlocal evolution equations on the unit interval, this methodology is employed to prove the validity of the continuum limit of random walks. The theory is applied to two classes of processes on dense weighted graphs, in discrete and continuous time, with dynamics encoded in transition matrices or random-walk Laplacians.
SIAM JOURNAL ON APPLIED MATHEMATICS
(2021)
Article
Physics, Multidisciplinary
Maxime Lucas, Giulia Cencetti, Federico Battiston
PHYSICAL REVIEW RESEARCH
(2020)
Article
Computer Science, Theory & Methods
Julien Petit, Renaud Lambiotte, Timoteo Carletti
APPLIED NETWORK SCIENCE
(2019)
Article
Physics, Fluids & Plasmas
Maxime Lucas, Duccio Fanelli, Aneta Stefanovska
Article
Physics, Fluids & Plasmas
Julien Petit, Martin Gueuning, Timoteo Carletti, Ben Lauwens, Renaud Lambiotte
Article
Physics, Fluids & Plasmas
Maxime Lucas, Julian Newman, Aneta Stefanovska
