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Chinese Control Conference (CCC)

作者： Guowei Sun Jie Ding Xiang Li Department of Electronic Engineering Adaptive Networks and Control Lab School of Information Science and Engineering Fudan University Shanghai China Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

Graph neural networks (GNNs), with its powerful ability to explore the topological structure of graph data, have achieved great success in various downstream tasks. However, recent studies have shown that due to the lack of robustness, GNNs are easily fooled by perturbations. For node classification tasks, the current attack methods mainly focus on manipulating edges, modifying features and adding fake nodes. Although these attacks are effective, the changes to the original graph data are easy to be detected. Therefore, to misclassify the target nodes, we propose an Edge Manipulation method via Hierarchical Deep Reinforcement Learning (EMHDRL) by taking accounting to the imperceptibility of perturbation on graph data. Through modeling the topology perturbation as Finite Horizon Markov Decision Process, EMHDRL adopts a hierarchical Q-function network to manipulate edges of graph, while trying to ensure that the degree of the attacked node and the total number of edges of the whole graph remain unchanged. Our experimental results show that the proposed method can effectively reduce the performance of GNNs in classifying the target nodes and has strong transferability.

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Strongly Interacting Bose-Fermi Mixtures: Mediated Interaction, Phase Diagram, and Sound Propagation

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Physical Review Letters 2024年第3期132卷 033401-033401页

作者： Xin Shen Nir Davidson Georg M. Bruun Mingyuan Sun Zhigang Wu College of Sciences China Jiliang University Hangzhou 310018 China Department of Physics of Complex Systems Weizmann Institute of Science Rehovot 7610001 Israel Center for Complex Quantum Systems Department of Physics and Astronomy Aarhus University Ny Munkegade DK-8000 Aarhus C Denmark State Key Lab of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing 100876 China School of Science Beijing University of Posts and Telecommunications Beijing 100876 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Department of Physics The University of Hong Kong Hong Kong China

Motivated by recent surprising experimental findings, we develop a strong-coupling theory for Bose-Fermi mixtures capable of treating resonant interspecies interactions while satisfying the compressibility sum rule. We show that the mixture can be stable at large interaction strengths close to resonance, in agreement with the experiment, but at odds with the widely used perturbation theory. We also calculate the sound velocity of the Bose gas in the Cs133−Li6 mixture, again finding good agreement with the experimental observations both at weak and strong interactions. A central ingredient of our theory is the generalization of a fermion mediated interaction to strong Bose-Fermi scatterings and to finite frequencies. This further leads to a predicted hybridization of the sound modes of the Bose and Fermi gases, which can be directly observed using Bragg spectroscopy.

关键词： Bose-Fermi mixtures Cold atoms & matter waves Quasiparticles & collective excitations Scattering theory Nonperturbative methods

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A Graph Transformer-Driven Approach for Network Robustness Learning

arXiv

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arXiv 2023年

作者： Zhang, Yu Li, Jia Ding, Jie Li, Xiang The Adaptive Networks and Control Lab Department of Electronic Engineering School of Information Science and Engineering Fudan University Shanghai200433 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China

Learning and analysis of network robustness, including controllability robustness and connectivity robustness, is critical for various networked systems against attacks. Traditionally, network robustness is determined by attack simulations, which is very time-consuming and even incapable for large-scale networks. Network Robustness Learning, which is dedicated to learning network robustness with high precision and high speed, provides a powerful tool to analyze network robustness by replacing simulations. In this paper, a novel versatile and unified robustness learning approach based on the customized graph transformer (NRL-GT) is proposed, which consists of a particularly designed backbone and three branches to accomplish the tasks of robustness curve learning, overall robustness learning and synthetic network classification simultaneously. Numerous experiments show that: 1) NRL-GT is a unified learning framework for controllability robustness and connectivity robustness, demonstrating a strong generalization ability to ensure high precision when training and test sets are distributed differently;2) It is theoretically and experimentally demonstrated the proposed graph transformer layer is capable of outperforming classical graph neural networks. Compared to the cutting-edge methods in network robustness learning, NRL-GT can simultaneously perform network robustness learning from multiple aspects and obtains superior results in less time on both synthetic and real-world networks, especially circuit networks and power networks;3) It is worth mentioning that with the transferability of the proposed backbone, NRL-GT is able to deal with complex networks of different sizes for different tasks efficiently and effectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Controllability

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FediLive: A Framework for Collecting and Preprocessing Snapshots of Decentralized Online Social Networks 25

FediLive: A Framework for Collecting and Preprocessing Snaps...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Shaojie Min Shaobin Wang Yaxiao Luo Min Gao Qingyuan Gong Yu Xiao Yang Chen Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Department of Information and Communications Engineering Aalto University Espoo Finland

ISBN: (纸本)9798400713316

Decentralized online social networks such as Mastodon have emerged quickly during the past years, and offer unique opportunities to investigate user behavior, moderation strategies, and community evolution. However, their decentralized nature imposes challenges for data collection and preprocessing, particularly for obtaining a real-time snapshot in a timely manner. This paper introduces FediLive, a framework designed to rapidly collect and preprocess the live feeds from Mastodon, generate a comprehensive snapshot in real-time, including user-generated contents, interaction networks, and users' demographic attributes. Such a snapshot could further be leveraged for data analysis from different angles, leading to a deeper understanding of user activities on Mastodon. Using FediLive, we collected a 13-day snapshot of Mastodon, covering the publicly-visible activities of all Mastodon users. Our study demonstrates the usefulness of FediLive, and reveals its potential in facilitating data-driven analysis for decentralized online social networks.

关键词： decentralized online social networks

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Soft Measures for Extracting Causal Collective Intelligence

arXiv

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arXiv 2024年

作者： Berijanian, Maryam Dork, Spencer Singh, Kuldeep Millikan, Michael Riley Riggs, Ashlin Swaminathan, Aadarsh Gibbs, Sarah L. Friedman, Scott E. Brugnone, Nathan Department of Computational Mathematics Science & Engineering Michigan State University United States Department of Computer Science Michigan State University United States Complex & Social Systems Lab Two Six Technologies United States University of South Alabama United States SIFT

Understanding and modeling collective intelligence is essential for addressing complex social systems. Directed graphs called fuzzy cognitive maps (FCMs) offer a powerful tool for encoding causal mental models, but extracting high-integrity FCMs from text is challenging. This study presents an approach using large language models (LLMs) to automate FCM extraction. We introduce novel graph-based similarity measures and evaluate them by correlating their outputs with human judgments through the Elo rating system. Results show positive correlations with human evaluations, but even the best-performing measure exhibits limitations in capturing FCM nuances. Fine-tuning LLMs improves performance, but existing measures still fall short. This study highlights the need for soft similarity measures tailored to FCM extraction, advancing collective intelligence modeling with NLP. © 2024, CC BY.

关键词： Fuzzy Cognitive Maps

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Stiffness prediction of nanofiber reinforced polymer composites: A micromechanics framework integrating load transfer theory and Mori-Tanaka method

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Composites science and Technology 2025年 269卷

作者： Zhang, Xuyao Li, Weiguo Li, Ying State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China State Key Lab for Strength and Vibration of Mechanical Structures Xi'an Jiaotong University Xi'an710049 China College of Aerospace Engineering Chongqing University Chongqing400044 China School of Aeronautics Chongqing Jiaotong University Chongqing400074 China

The low modulus of the polymer matrix imposes significant constraints on load transfer, which frequently impedes the full realization of the superior mechanical properties of nanofiber, leading to a discrepancy where theoretical predictions consistently overestimate experimental results. In this study, we introduced an effective modulus of nanofibers, grounded in the stress transfer mechanism. This enables a more accurate characterization of the fiber's actual reinforcement capability. By integrating this concept with the classical Mori-Tanaka method, a micromechanics framework for stiffness prediction was constructed. The accuracy of the framework is rigorously validated against existing experimental data, affirming its reliability and practicality. Additionally, we extended the applicability of the micromechanics framework to high-temperature environments by incorporating temperature dependent material parameters. This study provides a straightforward and efficient tool for predicting the modulus of nanofiber reinforced polymer composites. Furthermore, through systematic parametric analyses, some profound insights into the complex interactions among fiber reinforcement effects, fiber aspect ratios, and matrix moduli were proposed, which lay a solid foundation for the rational design of advanced composites with optimized performance. © 2025 Elsevier Ltd

关键词： Polymer matrix composites

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Evolution of cooperation in Snowdrift game with Multi-memory mechanism

Evolution of cooperation in Snowdrift game with Multi-memory...

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第42届中国控制会议

作者： Yaojun Liu Xingwen Liu Nyima Tashi Lab.of Complex Systems & Intelligent Control Key Lab.of Electronic Information of State Ethnic Affairs CommissionCollege of Electrical EngineeringSouthwest Minzu University Engineering Research Center for Tibetan Information Processing School of Information Science and TechnologyTibet University

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural *** memory can greatly influence the emergence of cooperative behavior,memory-based research on evolutionary games has proliferated in recent *** the present paper,inspired by the well-known "Stanford marshmallow experiment"(SME),we proposed a multi-memory mechanism in Snowdrift game(SDG) on spatial lattice networks whose core lies in what follows:Players are divided into two groups according to individual self-control;different memory lengths are set by group and by time in a single *** simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary *** specifically,as memory length increases to an intermediate length,the cooperation level increases as *** mechanism with short memory length,the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative *** findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation.

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Asynchronous L1-gain Filtering of Nonlinear Positive Semi-Markov Jump systems with Time-varying Delays

Asynchronous L1-gain Filtering of Nonlinear Positive Semi-Ma...

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第41届中国控制会议

作者： Chao Ma Hang Fu Yidao Ji Wei Wu School of Mechanical Engineering University of Science and Technology State Key Lab of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

In this paper,the L-gain based filtering problem for nonlinear positive semi-Markov jump systems is investigated by proposing a novel asynchronous design *** precisely,the mode-dependent filters are designed in terms of practical observed modes instead of true system modes,such that less conservatism can be *** addition,the effect of time-varying delays is taken into account for more robustness and *** selecting suitable stochastic Lyapunov-Krasovskii functions and applying the linear programming method,sufficient conditions are established to fulfill the desired L-gain ***,the illustrative simulation is performed to verify the effectiveness of our developed control scheme.

关键词： Positive semi-Markov jump systems nonlinear semi-Markov jump systems asynchronous filtering L1-gain

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SWDPM: A Social Welfare-Optimized Data Pricing Mechanism

arXiv

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arXiv 2023年

作者： Yu, Yi Yao, Shengyue Wang, Fei-Yue Li, Juanjuan Lin, Yilun Urban Computing Lab Shanghai AI Laboratory Shanghai China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Macau Institute of Systems Engineering Macau University of Science and Technology China

Data trading has been hindered by privacy concerns associated with user-owned data and the infinite reproducibility of data, making it challenging for data owners to retain exclusive rights over their data once it has been disclosed. Traditional data pricing models relied on uniform pricing or subscription-based models. However, with the development of Privacy-Preserving Computing techniques, the market can now protect the privacy and complete transactions using progressively disclosed information, which creates a technical foundation for generating greater social welfare through data usage. In this study, we propose a novel approach to modeling multi-round data trading with progressively disclosed information using a matchmaking-based Markov Decision Process (MDP) and introduce a Social Welfare-optimized Data Pricing Mechanism (SWDPM) to find optimal pricing strategies. To the best of our knowledge, this is the first study to model multi-round data trading with progressively disclosed information. Numerical experiments demonstrate that the SWDPM can increase social welfare 3 times by up to 54% in trading feasibility, 43% in trading efficiency, and 25% in trading fairness by encouraging better matching of demand and price negotiation among traders. Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

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