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Solvability of passivity equation for nonlinear systems

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Solvability of passivity equation for nonlinear systems

American Control Conference (ACC)

作者： S.L. Shishkin A.L. Fradkov Applied Computational Intelligence Laboratory Electxical Engineering Department Texas Tech University Lubbock TX USA Complex Systems Control Laboratory Institute for Problems of Mechanical Engineering Russian Academy of Science Saint Petersburg Russia

The passivity equation is the main necessary condition of passivity and dissipativity of nonlinear control system. The paper presents necessary and sufficient conditions of its solvability with respect to some nonnegative storage function. These results can be a basis for further investigations on passivity of control systems. Furthermore, they have applications in adaptive control.

关键词： Nonlinear equations Nonlinear systems Control systems computational intelligence Nonlinear control systems Mechanical engineering Ear Differential equations

Detection of ChatGPT Fake Science with the xFakeSci Learning Algorithm

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arXiv 2023年

作者： Hamed, Ahmed Abdeen Wu, Xindong Complex Adaptive Systems and Computational Intelligence Laboratory State University of New York at Binghamton BinghamtonNY13902 United States Sano Centre for Computational Medicine Clinical Data Science Cracow30-072 Poland Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230009 China China Electronics Data Industry Group Shenzhen518057 China

Generative AI tools exemplified by ChatGPT are becoming a new reality. This study is motivated by the premise that "AI generated content may exhibit a distinctive behavior that can be separated from scientific articles". In this study, we show how articles can be generated using means of prompt engineering for various diseases and conditions. We then show how we tested this premise in two phases and prove its validity. Subsequently, we introduce xFakeSci, a novel learning algorithm, that is capable of distinguishing ChatGPT-generated articles from publications produced by scientists. The algorithm is trained using network models driven from both sources. To mitigate overfitting issues, we incorporated a calibration step that is built upon data-driven heuristics, including proximity and ratios. Specifically, from a total of a 3952 fake articles for three different medical conditions, the algorithm was trained using only 100 articles, but calibrated using folds of 100 articles. As for the classification step, it was performed using 300 articles per condition. The actual label steps took place against an equal mix of 50 generated articles and 50 authentic PubMed abstracts. The testing also spanned publication periods from 2010 to 2024 and encompassed research on three distinct diseases: cancer, depression, and Alzheimer’s. Further, we evaluated the accuracy of the xFakeSci algorithm against some of the classical data mining algorithms (e.g., Support Vector Machines, Regression, and Naive Bayes). The xFakeSci algorithm achieved F1 scores ranging from 80% to 94%, outperforming common data mining algorithms, which scored F1 values between 38% and 52%. We attribute the noticeable difference to the introduction of calibration and a proximity distance heuristic, which underscores this promising performance. Indeed, the prediction of fake science generated by ChatGPT presents a considerable challenge. Nonetheless, the introduction of the xFakeSci algorithm is a significant st

关键词： Learning algorithms

Neural stochastic control 22

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Neural stochastic control

Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Jingdong Zhang Qunxi Zhu Wei Lin School of Mathematical Sciences SCMS SCAM and CCSB Fudan University Research Institute of Intelligent Complex Systems and MOE Frontiers Center for Brain Science Fudan University School of Mathematical Sciences SCMS SCAM and CCSB Fudan University and Research Institute of Intelligent Complex Systems and MOE Frontiers Center for Brain Science Fudan University and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University and State Key Laboratory of Medical Neurobiology Institutes of Brain Science Fudan University and Shanghai Artificial Intelligence Laboratory

ISBN: (纸本)9781713871088

Control problems are always challenging since they arise from the real-world systems where stochasticity and randomness are of ubiquitous presence. This naturally and urgently calls for developing efficient neural control policies for stabilizing not only the deterministic equations but the stochastic systems as well. Here, in order to meet this paramount call, we propose two types of controllers, viz., the exponential stabilizer (ES) based on the stochastic Lyapunov theory and the asymptotic stabilizer (AS) based on the stochastic asymptotic stability theory. The ES can render the controlled systems exponentially convergent but it requires a long computational time; conversely, the AS makes the training much faster but it can only assure the asymptotic (not the exponential) attractiveness of the control targets. These two stochastic controllers thus are complementary in applications. We also investigate rigorously the linear controller and the proposed neural stochastic controllers in both convergence time and energy cost and numerically compare them in these two indexes. More significantly, we use several representative physical systems to illustrate the usefulness of the proposed controllers in stabilization of dynamical systems.

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Neural Stochastic Control

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arXiv 2022年

作者： Zhang, Jingdong Zhu, Qunxi Lin, Wei School of Mathematical Sciences Shanghai Centre for Mathematical Sciences Fudan University China Research Institute of Intelligent Complex Systems Fudan University China MOE Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence [Fudan University Ministry of Education China State Key Laboratory of Medical Neurobiology Institutes of Brain Science Fudan University China Shanghai Artificial Intelligence Laboratory China

Control problems are always challenging since they arise from the real-world systems where stochasticity and randomness are of ubiquitous presence. This naturally and urgently calls for developing efficient neural control policies for stabilizing not only the deterministic equations but the stochastic systems as well. Here, in order to meet this paramount call, we propose two types of controllers, viz., the exponential stabilizer (ES) based on the stochastic Lyapunov theory and the asymptotic stabilizer (AS) based on the stochastic asymptotic stability theory. The ES can render the controlled systems exponentially convergent but it requires a long computational time;conversely, the AS makes the training much faster but it can only assure the asymptotic (not the exponential) attractiveness of the control targets. These two stochastic controllers thus are complementary in applications. We also investigate rigorously the linear controller and the proposed neural stochastic controllers in both convergence time and energy cost and numerically compare them in these two indexes. More significantly, we use several representative physical systems to illustrate the usefulness of the proposed controllers in stabilization of dynamical *** Codes 34K50, 60H10, 68T07, 93E15, 93E35 © 2022, CC BY.

关键词： Controllers

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

Influence of Temperature and Noise on Subthreshold Signal Propagation in Feedforward Neural Network

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Research Square

Research Square 2021年

作者： Yi, Ming Dai, Shiqi Lu, Lulu Wei, Zhouchao Zhu, Yuan School of Mathematics and Physics China University of Geosciences Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Hubei Wuhan430074 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Fudan University Shanghai200433 China

Temperature is an important environmental factor that all creatures depend on. Under the appropriate temperature, the neural system shows good biological performance. Based on an improved Hodgkin-Huxley (HH) neuron model considering temperature and noise, the ten-layers pure excitatory feedforward neural network and the ten-layers excitatory-inhibitory (EI) neural network are constructed to study the subthreshold signal propagation. It’s found that increasing temperature can restrain the signal propagation, and raise the noises intensity threshold where the failed signal propagation can transform into succeed signal propagation. Under the large noise, the signal propagation in network in different temperatures exhibits different anti-noise capabilities. There exists the saturation value of interlayer connection probability, that is, the signal propagation maintains constant when interlayer connection probability beyond a certain value. Moreover, in EI network with large noise, the network’s intrinsic oscillation activity will completely cover subthreshold signal, and block the signal propagation. The jumping phenomenon in the value of fidelity, which measures the similarity between input signal and output signal, appears in both pure excitatory network and EI network. This paper provides potential value for understanding the regulation of both temperature and noise in information propagation in neural network. © 2021, CC BY.

关键词： Backpropagation

EXACT FIRST PASSAGE TIME DISTRIBUTION FOR SECOND-ORDER REACTIONS IN CHEMICAL NETWORKS

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arXiv 2024年

作者： Rao, Changqian Waxman, David Lin, Wei Song, Zhuoyi School of Mathematical Sciences Fudan University Shanghai200433 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai China

The first passage time (FPT) is a generic measure that quantifies when a random quantity reaches a specific state. We consider the FTP distribution in nonlinear stochastic biochemical networks, where obtaining exact solutions of the distribution is a challenging problem. Even simple two-particle collisions cause strong nonlinearities that hinder the theoretical determination of the full FPT distribution. Previous research has either focused on analyzing the mean FPT, which provides limited information about a system, or has considered time-consuming stochastic simulations that do not clearly expose causal relationships between parameters and the system’s dynamics. This paper presents the first exact theoretical solution of the full FPT distribution in a broad class of chemical reaction networks involving A + B → C type of second-order reactions. Our exact theoretical method outperforms stochastic simulations, in terms of computational efficiency, and deviates from approximate analytical solutions. Given the prevalence of bimolecular reactions in biochemical systems, our approach has the potential to enhance the understanding of real-world biochemical processes. © 2024, CC BY.

关键词： Stochastic systems

Embedding theory of reservoir computing and reducing reservoir network using time delays

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Physical Review Research 2023年第2期5卷 L022041-L022041页

作者： Xing-Yue Duan Xiong Ying Si-Yang Leng Jürgen Kurths Wei Lin Huan-Fei Ma School of Mathematical Sciences Soochow University Suzhou 215006 China School of Mathematical Sciences SCMS and SCAM Fudan University Shanghai 200433 China Research Institute of Intelligent Complex Systems and Centre for Computational Systems Biology Fudan University Shanghai 200433 China Academy for Engineering and Technology Fudan University Shanghai 200433 China State Key Laboratory of Medical Neurobiology LCNBI and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai 200032 China Potsdam Institute for Climate Impact Research (PIK) Potsdam 14473 Germany Shanghai Artificial Intelligence Laboratory Shanghai 200232 China

Reservoir computing (RC), a particular form of recurrent neural network, is under explosive development due to its exceptional efficacy and high performance in reconstruction and/or prediction of complex physical systems. However, the mechanism triggering such effective applications of RC is still unclear, awaiting deep and systematic exploration. Here, combining the delayed embedding theory with the generalized embedding theory, we rigorously prove that RC is essentially a high-dimensional embedding of the original input nonlinear dynamical system. Thus, using this embedding property, we unify into a universal framework the standard RC and the time-delayed RC where we introduce time delays only into the network's output layer, and we further find a trade-off relation between the time delays and the number of neurons in RC. Based on these findings, we significantly reduce the RC's network size and promote its memory capacity in completing systems reconstruction and prediction. More surprisingly, only using a single-neuron reservoir with time delays is sometimes sufficient for achieving reconstruction and prediction tasks, while the standard RC of any large size but without time delay cannot complete them yet.

关键词： Control & applications of chaos Mathematical physics Neuronal networks Patterns in complex systems

Single-trial classification of awareness state during anesthesia by measuring critical dynamics of global brain activity (vol 9, 4927, 2019)

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SCIENTIFIC REPORTS 2019年第1期9卷 1-1页

作者： Alonso, Leandro M. Solovey, Guillermo Yanagawa, Toru Proekt, Alex Cecchi, Guillermo A. Magnasco, Marcelo O. Laboratory of integrative neuroscience The Rockefeller University New York NY 10065 USA. lalonso@brandeis.edu. Volen Center for Complex Systems Department of Biology Brandeis University Waltham MA 02454 USA. lalonso@brandeis.edu. Instituto del Cálculo FCEyN Universidad de Buenos Aires (C1428EGA) Buenos Aires Argentina. gsolovey@***. Laboratory for Adaptive Intelligence Brain Science Institute RIKEN Saitama 351-0198 Japan. Anesthesiology and Critical Care University of Pennsylvania Philadelphia PA 19104 USA. IBM Thomas J. Watson Research Center Yorktown Heights NY USA. Laboratory of integrative neuroscience The Rockefeller University New York NY 10065 USA. Volen Center for Complex Systems Department of Biology Brandeis University Waltham MA 02454 USA.

In daily life, in the operating room and in the laboratory, the operational way to assess wakefulness and consciousness is through responsiveness. A number of studies suggest that the awake, conscious state is not the default behavior of an assembly of neurons, but rather a very special state of activity that has to be actively maintained and curated to support its functional properties. Thus responsiveness is a feature that requires active maintenance, such as a homeostatic mechanism to balance excitation and inhibition. In this work we developed a method for monitoring such maintenance processes, focusing on a specific signature of their behavior derived from the theory of dynamical systems: stability analysis of dynamical modes. When such mechanisms are at work, their modes of activity are at marginal stability, neither damped (stable) nor exponentially growing (unstable) but rather hovering in between. We have previously shown that, conversely, under induction of anesthesia those modes become more stable and thus less responsive, then reversed upon emergence to wakefulness. We take advantage of this effect to build a single-trial classifier which detects whether a subject is awake or unconscious achieving high performance. We show that our approach can be developed into a means for intra-operative monitoring of the depth of anesthesia, an application of fundamental importance to modern clinical practice.

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