Spatiotemporal Input Control: Leveraging Temporal Variation in Network Dynamics

Yihan Lin; Jiawei Sun; Guoqi Li; Gaoxi Xiao; Changyun Wen; Lei Deng; H. Eugene Stanley

doi:10.1109/JAS.2022.105455

Volume 9 Issue 4

Apr. 2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(4): 635-651

Y. H. Lin, J. W. Sun, G. Q. Li, G. X. Xiao, C. Y. Wen, L. Deng, and H. E. Stanley, “Spatiotemporal input control: Leveraging temporal variation in network dynamics,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 635–651, Apr. 2022. doi: 10.1109/JAS.2022.105455

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Y. H. Lin, J. W. Sun, G. Q. Li, G. X. Xiao, C. Y. Wen, L. Deng, and H. E. Stanley, “Spatiotemporal input control: Leveraging temporal variation in network dynamics,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 635–651, Apr. 2022. doi: 10.1109/JAS.2022.105455

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Spatiotemporal Input Control: Leveraging Temporal Variation in Network Dynamics

doi: 10.1109/JAS.2022.105455

Yihan Lin^1
,,
Jiawei Sun^2
,,
Guoqi Li^{1, 3
,
,},
Gaoxi Xiao^4
,,
Changyun Wen^4
,,
Lei Deng^1
,,
H. Eugene Stanley^5
,

1.
Department of Precision Instrument, Tsinghua University, Beijing 100084, China
2.
Department of Bio-engineering, Stanford University, CA 94305 USA
3.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
4.
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
5.
Department of Physics and Center for Polymer Studies, Boston University, MA 02215 USA

Funds: This work was partially supported by the National Key RD Program of China (2020AAA0105200, 2018AAA01012600), National Natural Science Foundation of China (61876215), Beijing Academy of Artificial Intelligence (BAAI), in part by the Science and Technology Major Project of Guangzhou (202007030006), and Pengcheng laboratory. It is also partially funded by the Ministry of Education, Singapore, under contract RG19/20, and partly supported by the Future Resilient Systems Project (FRS-II) at the Singapore-ETH Centre (SEC), funded by the National Research Foundation of Singapore (NRF)

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Author Bio:
Yihan Lin received the B.E. degree from Tsinghua University in 2020. He is a Ph.D. candidate at the Department of Precision Instrument, Tsinghua University. His research interests include brain-inspired artificial intelligence, neuromorphic computing, and complex systems

Jiawei Sun received the B.Eng. degree in mechanics from Tsinghua University in 2019. He is currently a Ph.D. candidate at the Department of Bio-engineering at Stanford University in California, USA. His current research interests include the biophysics of bacterial interactions and modeling of complex systems

Guoqi Li (Member, IEEE) received the B.Eng. degree from the Xi’an University of Technology in 2004, the M.Eng. degree from Xi’an Jiaotong University in 2007, and the Ph.D. degree from Nanyang Technological University, Singapore, in 2011. From 2011 to 2014, he was a Scientist with the Data Storage Institute and the Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore. Since 2014, he has been with the Center for Brain Inspired Computing Research, Department of Precision Instrument, Tsinghua University, where he is currently an Associate Professor. He has authored or co-authored more than 140 journal and conference papers. His current research interests include brain-inspired computing, machine learning, neuromorphic computing and complex systems. Dr. Li has been actively involved in professional services such as serving as a Tutorial Chair, an International Technical Program Committee Member, a PC Member, a Publication Chair and a Track Chair for several international conferences. He is an Editorial-Board Member for Control and Decision, and served as Associate Editors for Journal of Control and Decision and Frontiers in Neuroscience: Neuromorphic Engineering. He is a Reviewer for Mathematical Reviews published by the American Mathematical Society and serves as a Reviewer for a number of prestigious international journals and top AI conferences including ICLR, NeurIPS, ICML, AAAI, and so on. He received the outstanding Young Talent Award of the Beijing Natural Science Foundation in 2021

Gaoxi Xiao (Senior Member, IEEE) received the B.S. and M.S. degrees in applied mathematics from Xidian University in 1991 and 1994, respectively. He was an Assistant Lecturer in Xidian University in 1994–1995. In 1998, he received the Ph.D. degree in computing from the Hong Kong Polytechnic University, China. He was a Postdoctoral Research Fellow in Polytechnic University, USA, in 1999; and a Visiting Scientist in the University of Texas at Dallas in 1999–2001. He joined the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, in 2001, where he is now an Associate Professor. His research interests include complex systems and complex networks, smart grids, system resilience, cybersecurity and communication networks. Dr. Xiao serves/served as an Associate Editor or Guest Editor for IEEE Transactions on Network Science and Engineering, PLOS ONE and Advances in Complex Systems etc., and a TPC Member for numerous conferences including IEEE ICC and IEEE GLOBECOM etc

Changyun Wen (Fellow, IEEE) received the B.Eng. degree from Xi’an University in 1983, and the Ph.D. degree from the University of Newcastle, Australia, in 1990. From August 1989 to August 1991, he was a Postdoctoral Fellow at University of Adelaide, Australia. Since August 1991, he has been with Nanyang Technological University, Singapore, where he is currently a Full Professor. His main research activities are in the areas of control systems and applications, cyber-physical systems, smart grids, complex systems and networks. Prof Wen is a Fellow of IEEE, was a Member of IEEE Fellow Committee from January 2011 to December 2013 and a Distinguished Lecturer of IEEE Control Systems Society from 2010 to 2013. He is currently the Co-Editor-in-Chief of IEEE Transactions on Industrial Electronics, Associate Editor of Automatica (from Feb 2006) and Executive Editor-in-Chief of Journal of Control and Decision. He also served as an Associate Editor of IEEE Transactions on Automatic Control from 2000 to 2002, IEEE Transactions on Industrial Electronics from 2013 to 2020 and IEEE Control Systems Magazine from 2009 to 2019. He has been actively involved in organizing international conferences playing the roles of General Chair (including the General Chair of IECON 2020 and IECON 2023), TPC Chair (e.g., the TPC Chair of Chinese Control and Decision Conference since 2008) etc. He was the Recipient of a number of awards, including the Prestigious Engineering Achievement Award from the Institution of Engineers, Singapore in 2005, and the Best Paper Award of IEEE Transactions on Industrial Electronics in 2017

Lei Deng (Member, IEEE) received the B.E. degree from University of Science and Technology of China in 2012, and the Ph.D. degree from Tsinghua University in 2017. He was a Postdoctoral Fellow at the Department of Electrical and Computer Engineering, University of California, USA, from 2017 to 2021. He is currently an Assistant Professor at Center for Brain Inspired Computing Research (CBICR), Tsinghua University. His research interests span the areas of brain-inspired computing, machine learning, neuromorphic chip, and computer architecture. Dr. Deng has authored or co-authored over 80 refereed publications. He was a PC Member for International Joint Conference on Neural Networks (IJCNN) 2021, IEEE International Conference on Application-Specific Systems, Architectures and Processors (ASAP) 2021, and International Symposium on Neural Networks (ISNN) 2019, and served as a Guest Associate Editor for Frontiers in Neuroscience and Frontiers in Computational Neuroscience. He was a Recipient of 2021 Young Scholar of Chinese Institute for Brain Research, Beijing, and 2019 MIT Technology Review Innovators Under 35 China

H. Eugene Stanley received the B.A. degree from Wesleyan University, in 1962, and the Ph.D. degree from Harvard University, in 1967, both in physics. He is currently an American Physicist, and a University Professor and a William Fairfield Warren Distinguished Professor with Boston University, USA. He has made fundamental contributions to complex systems, urban science, and is one of the founding fathers of econo-physics. His current research interests include econo-physics, complexity science, and urban science. He was elected to the U.S. National Academy of Sciences, in 2004
Corresponding author: Guoqi Li, e-mail: liguoqi@mail.tsinghua.edu.cn
Yihan Lin and Jiawei Sun contributed equally to this work.
Received Date: 2021-07-23
Revised Date: 2021-10-12
Accepted Date: 2021-12-26

Available Online: 2022-02-15

Abstract

Abstract

The number of available control sources is a limiting factor to many network control tasks. A lack of input sources can result in compromised controllability and/or sub-optimal network performance, as noted in engineering applications such as the smart grids. The mechanism can be explained by a linear time-invariant model, where structural controllability sets a lower bound on the number of required sources. Inspired by the ubiquity of time-varying topologies in the real world, we propose the strategy of spatiotemporal input control to overcome the source-related limit by exploiting temporal variation of the network topology. We theoretically prove that under this regime, the required number of sources can always be reduced to 2. It is further shown that the cost of control depends on two hyperparameters, the numbers of sources and intervals, in a trade-off fashion. As a demonstration, we achieve controllability over a complex network resembling the nervous system of Caenorhabditis elegans using as few as 6% of the sources predicted by a static control model. This example underlines the potential of utilizing topological variation in complex network control problems.
- Complex network,
- complex system control theory,
- complex system optimization,
- temporal network,
- time-varying system

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Yihan Lin and Jiawei Sun contributed equally to this work.

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We show that through designing the spatiotemporal inputs, the lower bound of required sources in Controllability of complex networks [1] can be reduced. In this work, we subvert the idea by proving that via temporally switching the node-source connections, the required number of sources can be reduced to a minimum of 2, or even 1 in some special cases, and the control energy can be significantly lowered as well. At the same time, we put forward the corresponding control method.
We uncover that the trade-off between the minimized energy for controlling networks with spatiotemporal inputs and the two important hyper-parameters: the number of sources and the number of intervals, which is a significant and important finding regarding the previous work [2](Li et al, Science, 2017).
The presented methods for constructing and optimizing the input given a temporal control structure can serve as a pioneering method for designing spatiotemporal inputs and a basis for further improvement.

Spatiotemporal Input Control: Leveraging Temporal Variation in Network Dynamics

doi: 10.1109/JAS.2022.105455

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