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Recent advances in distributed adaptive consensus control of uncertain nonlinear multi-agent systems

Journal of Control and Decision 2020年第1期7卷 44-63页

作者： Wei Wang Jiang Long Changyun Wen Jiangshuai Huang School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191People’s Republic of China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191People’s Republic of China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Automation Chongqing UniversityChongqingPeople’s Republic of China

So far,distributed adaptive consensus problems for uncertain nonlinear multi-agent systems have not yet been extensively *** with centralised adaptive control,some new challenges need to be well addressed,for examples,(i)how to reach asymptotically consensus tracking with directed topology condition,by using totally distributed adaptive control strategies;(ii)how to ensure globally uniform boundedness of closed-loop systems while achieving leaderless consensus with semi-positive definite Laplacian matrix;(iii)how to maintain system performance while effectively reducing the communication burden among connected *** paper is mainly devoted to report some recent advances in distributed adaptive consensus ***,some interesting research topics which are worthy of further investigation will also be discussed.

关键词： Distributed adaptive control consensus nonlinear multi-agent systems uncertainties

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Improving efficiency of the path optimization method for a gauge theory

arXiv

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arXiv 2022年

作者： Namekawa, Yusuke Kashiwa, Kouji Matsuda, Hidefumi Ohnishi, Akira Takase, Hayato Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima730-0053 Japan Department of Physics Faculty of Science Kyoto University Kyoto606-8502 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka811-0295 Japan Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China

We investigate efficiency of a gauge-covariant neural network and an approximation of the Jacobian in optimizing the complexified integration path toward evading the sign problem in lattice field theories. For the construction of the complexified integration path, we employ the path optimization method. The 2-dimensional U(1) gauge theory with the complex gauge coupling constant is used as a laboratory to evaluate the efficiency. It is found that the gauge-covariant neural network, which is composed of the Stout-like smearing, can enhance the average phase factor, as the gauge-invariant input does. For the approximation of the Jacobian, we test the most drastic case in which we perfectly drop the Jacobian during the learning process. It reduces the numerical cost of the Jacobian calculation from O(N3) to O(1), where N means the number of degrees of freedom of the theory. The path optimization using this Jacobian approximation still enhances the average phase factor at expense of a slight increase of the statistical error. Copyright © 2022, The Authors. All rights reserved.

关键词： Efficiency

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Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces

arXiv

引用

arXiv 2022年

作者： Sun, Li Ye, Junda Peng, Hao Wang, Feiyang Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Computer Science University of Illinois ChicagoIL United States

Continual graph learning routinely finds its role in a variety of real-world applications where the graph data with different tasks come sequentially. Despite the success of prior works, it still faces great challenges. On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence. On the other hand, continual learners in the literature rely on abundant labels, but labeling graph in practice is particularly hard especially for the continuously emerging graphs on-the-fly. To address the aforementioned challenges, we propose to explore a challenging yet practical problem, the self-supervised continual graph learning in adaptive Riemannian spaces. In this paper, we propose a novel self-supervised Riemannian Graph Continual Learner (RieGrace). In RieGrace, we first design an Adaptive Riemannian GCN (AdaRGCN), a unified GCN coupled with a neural curvature adapter, so that Riemannian space is shaped by the learnt curvature adaptive to each graph. Then, we present a Label-free Lorentz Distillation approach, in which we create teacher-student AdaRGCN for the graph sequence. The student successively performs intra-distillation from itself and inter-distillation from the teacher so as to consolidate knowledge without catastrophic forgetting. In particular, we propose a theoretically grounded Generalized Lorentz Projection for the contrastive distillation in Riemannian space. Extensive experiments on the benchmark datasets show the superiority of RieGrace, and additionally, we investigate on how curvature changes over the graph sequence. © 2022, CC BY-NC-SA.

关键词： Distillation

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Impregnation of porous TiO₂/ZnO into MCM-41 synthesized from coal fly ash for enhanced photocatalytic activity of TiO₂/ZnO@MCM-41 composites in methylene blue removal

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental engineering 2025年 11.0卷

作者： Hakim, Ardi Lukman Sari, Inzus Etika Nur Sabrina, Bunga Jasmine Nurkhalizah, Zanna Allia Ajeng Zahabiya, Ecce Kamila Hidayat, Aditya Nur Sulistiono, Dety Oktavia Sopiah, R. Nida Kusumawati, Yuly Ediati, Ratna Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya 60111 Indonesia Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya 60111 Indonesia Automotive Engineering Engineering Department Politeknik Negeri Jember Jember 68101 Indonesia Research Center for Environmental and Clean Technology National Research and Innovation Agency Technology Energy Cluster PUSPIPTEK 625 Building South Tangerang Indonesia

MCM-41 was synthesized from coal fly ash and used as a support for TiO₂/ZnO nanoparticles in methylene blue removal. The composite was prepared using alkali fusion and hydrothermal methods, yielding MCM-41 with a surface area of 1013.73 m²/g, which decreased to 457.81 m²/g upon TiO₂/ZnO impregnation. Characterization confirmed the hexagonal mesoporous structure, uniform dispersion of TiO₂/ZnO, and enhanced light absorption. Ultraviolet–Visible Diffuse Reflectance Spectroscopy (UV–Vis DRS) results revealed a band gap reduction from 3.22 eV (TZ) to 2.77 eV (TZM), improving photocatalytic activity under UV and visible light irradiation. The 35TiO₂/65ZnO@MCM-41 composite achieved 99.21 % MB degradation in 140 min. © 2025 The Authors

关键词： Coal fly ash MB degradation MCM-41 Photocatalysis TiO2/ZnO

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Influence of nuclear physics inputs and astrophysical conditions on r-process

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science China(Physics,Mechanics & Astronomy) 2019年第8期62卷 75-82页

作者： Zhu Li ZhongMing Niu BaoHua Sun School of Physics and Nuclear Energy Engineering Beihang University School of Physics and Materials Science Anhui University Institute of Physical Science and Information Technology Anhui University Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University

The rapid neutron-capture process(r-process) is one of the main mechanisms to explain the origin of heavy elements in the universe. Although the past decades have seen great progress in understanding this process, the related nuclear physics inputs to r-process models include significant uncertainty. In this study, ten nuclear mass models, including macroscopic, macroscopicmicroscopic, and microscopic models, are used to calculate the β-decay rates and neutron-capture rates of the neutron-rich isotopes for the r-process simulations occurring in three classes of astrophysical conditions. The final r-process abundances include uncertainties introduced by the nuclear mass model mainly through the variation of neutron-capture rates, whereas the uncertainties of β-decay rates make a relatively small contribution. The uncertainties in different astrophysical scenarios are also investigated,and are found to be connected to the diverse groups of nuclei produced during nucleosynthesis.

关键词： r-process nuclear masses β-decay rates neutron-capture rates

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Discriminative features matter: Multi-layer bilinear pooling for camera localization 30

Discriminative features matter: Multi-layer bilinear pooling...

引用

30th British Machine Vision Conference, BMVC 2019

作者： Wang, Xin Wang, Xiang Wang, Chen Bai, Xiao Wu, Jing Hancock, Edwin Robert School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science University of York York United Kingdom

Deep learning based camera localization from a single image has been explored recently since these methods are computationally efficient. However, existing methods only provide general global representations, from which an accurate pose estimation can not be reliably derived. We claim that effective feature representations for accurate pose estimation shall be both "informative" (focusing on geometrically meaningful regions) and "discriminative" (accounting for different poses of similar images). Therefore, we propose a novel multi-layer factorized bilinear pooling module for feature aggregation. Specifically, informative features are selected via bilinear pooling, and discriminative features are highlighted via multi-layer fusion. We develop a new network for camera localization using the proposed feature pooling module. The effectiveness of our approach is demonstrated by experiments on an outdoor Cambridge Landmarks dataset and an indoor 7 Scenes dataset. The results show that focusing on discriminative features significantly improves the network performance of camera localization in most cases. Codes will be available soon. © 2019. The copyright of this document resides with its authors.

关键词： Deep learning

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Graphdiyne as a saturable absorber for 2-µm all-solid-state Q-switched laser

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science China Materials 2021年第3期64卷 683-690页

作者： Yuqian Zu Jia Guo Qianqian Hao Feng Zhang Cong Wang Jie Liu Bing Wang Shandong Provincial Engineering and Technical Center of Light Manipulations and Shandong Provincial Key Laboratory of Optics and Photonic Device School of Physics and ElectronicsShandong Normal UniversityJinan250014China Collaborative Innovation Center of Light Manipulations and Applications Shandong Normal UniversityJinan250358China Institute of Data Science and Technology Shandong Normal UniversityJinan250014China Institute of Microscale Optoelectronics Collaborative Innovation Center for Optoelectronic Science&TechnologyKey Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong ProvinceCollege of Physics and Optoelectronic EngineeringShenzhen Key Laboratory of Micro-Nano Photonic Information TechnologyGuangdong Laboratory of Artificial Intelligence and Digital Economy(SZ)Shenzhen UniversityShenzhen518060China

As a novel one-atom-thick carbon allotrope,graphdiyne(GDY)has attracted wide interest in two-dimensional materials owing to its unique feature with a planar layer comprising sp2-and sp-hybridized carbon atoms and a direct natural bandgap.A high-quality saturable absorber(SA)based on GDY was successfully fabricated,and the nonlinear saturable absorption properties were investigated.A compact diode-pumped solid-state passively Q-switched laser based on the as-prepared GDY-SA could be operated at 2µ*** Q-switched laser delivered a maximum average output power of 1.29 W at a central wavelength of 1908.41 nm with up to 54.8%slope efficiency.A repetition rate of 91.58 kHz and single pulse energy of 23.08µJ were *** work is the first application of GDY as an SA to generate all-solid-state pulsed *** results indicate that GDY is a promising SA for solid-state pulsed lasers and has further application potential for ultrafast photonic devices in the 2-µm region.

关键词： graphdiyne saturable absorber all-solid-state laser passively Q-switched two-dimensional materials

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Nonreciprocal coherent coupling of nanomagnets by exchange spin waves

引用

Nano Research 2021年第7期14卷 2133-2138页

作者： Hanchen Wang Jilei Chen Tao Yu Chuanpu Liu Chenyang Guo Song Liu Ka Shen Hao Jia Tao Liu Jianyu Zhang Marco A.Cabero Z Qiuming Song Sa Tu Mingzhong Wu Xiufeng Han Ke Xia Dapeng Yu Gerrit E.W.Bauer Haiming Yu Fert Beijing Institute School of Integrated Circuit Science and EngineeringBeijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Max Planck Institute for the Structure and Dynamics of Matter 22761 HamburgGermany Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100190China Shenzhen Institute for Quantum Science and Engineering(SIQSE) and Department of PhysicsSouthern University of Science and Technology(SUSTech)Shenzhen 518055China Department of Physics Beijing Normal UniversityBeijing 100875China Department of Physics Colorado State UniversityFort CollinsColorado 80523USA Kavli Institute of Nanoscience Delft University of Technology2628 CJ DelftThe Netherlands Institute for Materials Research WPI-AIMR and CSNRTohoku UniversitySendai 980-8577Japan Zernike Institute for Advanced Materials University of GroningenGroningenNijenborgh 49747 AG GroningenThe Netherlands

Nanomagnets are widely used to store information in non-volatile spintronic *** waves can transfer information with low-power consumption as their propagations are independent of charge ***,to dynamically couple two distant nanomagnets via spin waves remains a major challenge for *** we experimentally demonstrate coherent coupling of two distant Co nanowires by fast propagating spin waves in an yttrium iron garnet thin film with sub-50 nm *** in two nanomagnets are unidirectionally phase-locked with phase shifts controlled by magnon spin torque and spin-wave *** coupled system is finally formulated by an analytical theory in terms of an effective non-Hermitian *** results are attractive for analog neuromorphic computing that requires unidirectional information transmission.

关键词： spintronics nanomagnets spin waves coherent coupling nonreciprocity

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A Clinical Decision Support System of Pressure Ulcers Tissue Classification 21

A Clinical Decision Support System of Pressure Ulcers Tissue...

引用

5th International Conference on Medical and Health Informatics, ICMHI 2021

作者： Chang, Dun-Hao Chu, Po-Jui Li, Yi-Jhen Ning, Chun-Kai Chien, Ting-Ying Department of Information Management Yuan Ze University Taoyuan City Taiwan Department of Plastic and Aesthetic Surgery Far Eastern Memorial Hospital Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9781450389846

Pressure ulcers (PUs) are a common problem associated with great morbidity and cost. Because of the lack of professional personnel in the institutions or home care system, the diagnosis and treatment of PUs were sometimes delayed. We aim to develop an automatic tissue classification and severity evaluation system of PUs using AI technology. All PU images were collected from patients in the Far Eastern Memorial Hospital (FEMH) in recent 3 years and labeled according to different tissue types. The labeled images were used to train the tissue segmentation model with U-net convolutional neural network (CNN). The percentage of the different tissue can be calculated automatically and would be compared with the manually labeled one. The output of tissue classification was transformed to a clinical decision support system (CDSS) with a rule-base formula. Our study showed that proposed system can classify each tissue automatically and reach an accuracy about 93.6%. However, the CDSS's accuracy rate was only 64% for the referral suggestion and 62% for the care recommendation. Hence, the system should be modified with more data and information. © 2021 ACM.

关键词： Tissue

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COVID-19 datathon based on deidentified governmental data as an approach for solving policy challenges, increasing trust, and building a community: Case study

arXiv

引用

arXiv 2021年

作者： Peleg, Mor Reichman, Amnon Shachar, Sivan Gadot, Tamir Tsadok, Meytal Avgil Azaria, Maya Dunkelman, Orr Hassid, Shiri Partem, Daniella Shmailov, Maya Yom-Tov, Elad Cohen, Roy Data Science Research Center The University of Haifa Haifa Israel The Center for Cyber Law & Policy The University of Haifa Haifa Israel Timna - Big Data Platform unit Ministry of Health Israel The Center for the 4th Industrial Revolution Israel innovation Authority Israel Shenkar College of Engineering and Design Microsoft Research Israel

Triggered by the COVID-19 crisis, Israel's Ministry of Health (MoH) held a virtual datathon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel's research community was invited to offer insights to COVID-19 policy challenges. The datathon was designed to (1) develop operationalizable data-driven models to address COVID-19 health-policy challenges and (2) build a community of researchers from academia, industry, and government and rebuild their trust in the government. Three specific challenges were defined based on their relevance (significance, data availability, and potential to anonymize the data): immunization policies, special needs of the young population, and populations whose rate of compliance with COVID-19 testing is low. The MoH team extracted diverse, reliable, up-to-date, and deidentified governmental datasets for each challenge. Secure remote-access research environments with relevant data science tools were set on Amazon Web. The MoH screened the applicants and accepted around 80 participants, teaming them to balance areas of expertise as well as represent all sectors of the community. One week following the event, anonymous surveys for participants and mentors were distributed to assess overall usefulness and points for improvement. The 48-hour datathon and pre-event sessions included 18 multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the 3 winning teams are currently considered by the MoH as potential data science methods relevant for national policies. The most positive results were increased trust in the MoH and greater readiness to work with the government on these or future projects. Detailed feedback offered concrete lessons for improving the structure and organization of future government-led datathons. © 2021, CC BY.

关键词： Health

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