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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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About Step-Length of ZeaD（Zhang et al Discretization） Formula 4IgSY for Future Minimization via Fan Equations

About Step-Length of ZeaD（Zhang et al Discretization） Form...

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第30届中国控制与决策会议

作者： Yunong Zhang Zhiyuan Qi Huihui Gong Jian Li Binbin Qiu School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

Future minimization,i.e.,discrete time-varying minimization,is a difficult and meaningful *** has been successfully solved by Zhang et al using zeroing dynamics(ZD) and discretization *** this paper,a type of discrete-time ZD(DT-ZD) model,which is obtained via utilizing Zea D(Zhang et al Discretization) formula 4 I2)S Y,is analyzed and investigated to ensure its ***,via theoretical guarantees,we propose the step-length domain,or say,the effective domain of the step-length,which makes the discrete-time model ***,we make further efforts to obtain the step-length optimum which provides the optimal stability of the DT-ZD ***,numerical experiments are performed to validate the step-length domain and the step-length optimum of the DT-ZD model for future minimization.

关键词： Future minimization Zeroing dynamics(ZD) Zhang et al discretization(ZeaD) formula Step-length domain Optimum

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Jerk-Level Cyclic Motion Planning and control for Constrained Redundant Robot Manipulators Using Zhang Dynamics:Theoretics

Jerk-Level Cyclic Motion Planning and Control for Constraine...

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第30届中国控制与决策会议

作者： Min Yang Yunong Zhang Huanchang Huang Dechao Chen Jian Li School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

In this paper,based on Zhang dynamics method（ZDM）,a cyclic motion planning（CMP） scheme is developed to remedy the joint drift phenomenon of redundant robot manipulators constrained by joint physical *** existing works resolved at velocity level or acceleration level,the scheme in this paper is resolved at jerk *** achieve superior performance,the jerk-level CMP scheme is improved by the feedback control,which further leads to a jerk-level cyclic motion planning and control（CMPC） *** cyclic motion criterion and the avoidance of joint physical limits（e.g.,joint-angle,joint-velocity,joint-acceleration and joint-jerk limits） are both considered into the jerklevel CMP and CMPC *** importantly,the correctness of the presented jerk-level schemes is guaranteed by the corresponding engineering-type theoretical analyses and ***,the jerk-level CMPC scheme is reformulated as a dynamical quadratic program（DQP） which can be solved by a piecewise-linear projection equation neural network（PLPENN）,and also can be solved by a suitable numerical algorithm.

关键词： Constrained robot manipulators Joint drift phenomenon Jerk level Feedback control Theoretics

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Different-Level Dynamic Quadratic Minimization Using Square Root Matrix Based Zhang Equivalency

Different-Level Dynamic Quadratic Minimization Using Square ...

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第30届中国控制与决策会议

作者： Min Yang Huanchang Huang Jian Li Haifeng Hu Yunong Zhang School of Information Science and Technology Sun Yat-sen University (SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

For quadratic minimization(QM), it is usually assumed that the solution objective is time-invariant and at the same level. However, the QM is always time-varying(i.e., naturally and implicitly dynamic) and may be at different levels(i.e., explicitly dynamic) in practice. In this paper, different from the general QM, a novel different-level dynamic QM(DLDQM) is proposed and investigated. First, the DLDQM solving is split up into two general time-varying QM(TVQM) solving. Then, a solution model is thus proposed and investigated for the DLDQM, by applying square root matrix based Zhang equivalency and Moore-Penrose(MP) generalized inverse. In addition, it is worth pointing out that solving the DLDQM is a new and important attempt of this research. Finally, illustrative numerical experiments are conducted with numerical results presented and analyzed.

关键词： Different-level dynamic quadratic minimization(DLDQM) Square root matrix Zhang equivalency MoorePenrose(MP) generalized inverse

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Output Tracking of Time-Varying Linear and Nonlinear systems Using ZN and ZG controllers with Pseudo Division-by-Zero Phenomena Shown

Output Tracking of Time-Varying Linear and Nonlinear Systems...

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第30届中国控制与决策会议

作者： Jinjin Guo Xuyun Yang Yunong Zhang Jian Li Min Yang School of Information Science and Technology Sun Yat-sen University SYSU Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

This paper introduces Zhang neurodynamics(ZN) method and Zhang-gradient(ZG) method used to design ZN and ZG controllers for the output tracking of systems, especially time-varying nonlinear system. Thereinto, ZG method is composed of the ZN method and gradient neurodynamics(GN) method. By applying the ZN method and the ZG method, respectively, to the output tracking control problems of time-varying linear(TVL) system and time-varying nonlinear(TVN) system, specific ZN controllers and ZG controllers are obtained. Particularly, TVL system and TVN system discussed in this paper may both have pseudo division-by-zero phenomena. From the numerical results, though ZN controllers and ZG controllers perform the output tracking of TVL system and TVN system well, the infinite value of the former and the finite value of the latter at singular instants verify that the ZG method is more effective in dealing with pseudo division-by-zero problem.

关键词： Time-varying linear system Time-varying nonlinear system Output tracking control problem ZN controller ZG controller Pseudo division-by-zero phenomena

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Computer Simulations and Comparisons of Jerk-Level Cyclic Motion Planning and control for CRRM

Computer Simulations and Comparisons of Jerk-Level Cyclic Mo...

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第37届中国控制会议

作者： Dechao Chen Min Yang Huanchang Huang Jian Li Yunong Zhang School of Information Science and Technology Sun Yat-sen University (SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

In this paper, a jerk-level cyclic motion planning and control（CMPC） scheme is presented to remedy the joint drift phenomenon of the constrained redundant robot manipulators（CRRM）. Furthermore, the path-tracking simulations are presented to substantiate the effectiveness and accuracy of the jerk-level CMPC scheme. Comprehensive and detailed comparisons between the presented jerk-level CMPC scheme and other existing schemes [i.e., the Jacobian pseudo-inverse based（JPIB） scheme, the minimum jerk norm（MJN） scheme and the acceleration-level cyclic motion planning（ALCMP） scheme] illustrate that the presented CMPC scheme resolved at jerk level is superior and more applicable.

关键词： Constrained redundant robot manipulators （CRRM） jerk level cyclic motion planning and control （CMPC） scheme computer simulations comparisons

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CPSS-based Signal Forwarding Method at Relays for Full-duplex Cooperative Vehicular Networks

CPSS-based Signal Forwarding Method at Relays for Full-duple...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Han, Shuangshuang Ma, Houxue Wang, Xiao Liu, Hong Cao, Dongpu Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Shandong266000 China Qingdao Huituo Intelligent Machine Company Qingdao China JIANGSU XCMG CONSTRUCTION MACHINERY RESEARCH INSTITUTE LTD Xuzhou Jiangsu221004 China Mechanical and Mechatronics Engineering University of Waterloo 200 University Avenue Wes WaterlooONN2L 3G1 Canada Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781538644522

With increasing popularity of Internet of Vehicles (IoV), concerns for reliable and low complexity communication techniques are proposed due to the requirements of signal reliability and transmission delay for vehicles. Meanwhile, the explosive and pervasive use of social network applications further adds drivers' social relationships and behavioural characteristics into it, and makes it a cyber-physical-social system (CPSS). This paper proposes and analyzes an improved forward scheme for full deplex cooperative vehicular networks in terms of its CPSS features. The proposed CPSS-based forwarding (CPSS-F) strategy forwards a soft estimate of the received signal based on social historic data at the relay node (vehicle/infrastructure) to the destination node (vehicle/infrastructure), which achieves improved reliability than the two conventional strategies in cooperative networks, i.e., amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, the proposed CPSS-F relay achieves performance gains and complexity reduction compared to the conventional AF and DF. Experimental results further confirm the advantages of the proposed CPSS-F for cooperative vehicular networks. The proposed CPSS-F approach is easily extended to other cooperative vehicular social networks, for example, multi-way, half-duplex, or large-antenna networks. © 2018 IEEE.

关键词： Complex networks

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LoRa on the Move: Performance Evaluation of LoRa in V2X Communications

LoRa on the Move: Performance Evaluation of LoRa in V2X Comm...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Li, Yuke Han, Shuangshuang Yang, Linyao Wang, Fei-Yue Zhang, Hui State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100049 China Qingdao Huituo Intelligent Machine Company Qingdao China Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China Computer Science Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Conviva Inc. Silicon ValleyCA94404 United States

ISBN: (纸本)9781538644522

Recent years have witnessed much interest in Low Power Wide Area (LPWA) technologies, which are gaining unprecedented momentum and commercial interest towards the realisation of the Internet of Things (IoT). Long Range (LoRa), as a representative LPWA technology, has the potential to satisfy the growing demand for the longer range and larger amount connectivities in vehicular communication networks. In this paper, LoRa is firstly applied into two typical vehicular networks, namely Vehicle-to-Infrastructure (V2I)and Vehicle-to-Vehicle (V2V), and performance of LoRa schemes with different parameter configurations are evaluated and compared. Further, Monte Carlo simulations indicate that the schemes equipped with higher bandwidth or lower spreading factor exhibit significant advantages in combating the fast fading caused by Doppler effect in networks. © 2018 IEEE.

关键词： Monte Carlo methods

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Discrete-Time control and Simulation of Ship Course Tracking Using ZD Method and ZFD Formula 4NgSFD 3

Discrete-Time Control and Simulation of Ship Course Tracking...

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2017 IEEE 第3届信息技术与机电一体化工程国际学术会议(ITOEC 2017)

作者： Yunong Zhang Huinan Xiao Jinjin Wang Jian Li Pei Chen School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

This paper focuses on solving the ship course tracking problem by using Zhang dynamics(ZD) method and a new Zhang finite difference(ZFD) *** ZD method,which is a powerful class of dynamics to

ISBN: (纸本)9781509053643;9781509053636

This paper focuses on solving the ship course tracking problem by using Zhang dynamics(ZD) method and a new Zhang finite difference(ZFD) *** ZD method,which is a powerful class of dynamics to

关键词： Marine vehicles Trajectory Electronic mail Process control Convergence Mathematical model

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