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arXiv 2019年

作者： Li, Jia Fu, Kui Zhao, Shengwei Ge, Shiming State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security at University of Chinese Academy of Sciences Beijing100095 China

—The performance of video saliency estimation techniques has achieved significant advances along with the rapid development of Convolutional Neural Networks (CNNs). However, devices like cameras and drones may have limited computational capability and storage space so that the direct deployment of complex deep saliency models becomes infeasible. To address this problem, this paper proposes a dynamic saliency estimation approach for aerial videos via spatiotemporal knowledge distillation. In this approach, five components are involved, including two teachers, two students and the desired spatiotemporal model. The knowledge of spatial and temporal saliency is first separately transferred from the two complex and redundant teachers to their simple and compact students, and the input scenes are also degraded from high-resolution to low-resolution to remove the probable data redundancy so as to greatly speed up the feature extraction process. After that, the desired spatiotemporal model is further trained by distilling and encoding the spatial and temporal saliency knowledge of two students into a unified network. In this manner, the inter-model redundancy can be further removed for the effective estimation of dynamic saliency on aerial videos. Experimental results show that the proposed approach outperforms ten state-of-the-art models in estimating visual saliency on aerial videos, while its speed reaches up to 28,738 FPS on the GPU platform. Copyright © 2019, The Authors. All rights reserved.

关键词： Students

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Cylindrical vector beam-excited frequency-tunable second harmonic generation in a plasmonic octamer

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Photonics Research 2018年第3期6卷 157-161页

作者： FAJUN XIAO WUYUN SHANG WEIREN ZHU LEI HAN MALIN PREMARATNE TING MEI JIANLIN ZHAO MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of ScienceNorthwestern Polytechnical UniversityXi'an 710129China Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems Monash UniversityClaytonVlC 3800Australia

We report a method to tune the second harmonic generation(SHG) frequency of a metallic octamer by employing cylindrical vector beams as the excitation. Our method exploits the ability to spatially match the polarization state of excitations with the fundamental target plasmonic modes, enabling flexible control of the SHG resonant *** is found that SHG of the octamer is enhanced over a broad band(400 nm) by changing the excitation from the linearly polarized Gaussian beam to radially and azimuthally polarized beams. More strikingly, when subjected to an azimuthally polarized beam, the SHG intensity of the octamer becomes 30 times stronger than that for the linearly polarized beam even in the presence of Fano resonance.

关键词： SHG Cylindrical vector beam-excited frequency-tunable second harmonic generation in a plasmonic octamer

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What can a GNOME do? Search targets for the Global Network of Optical Magnetometers for Exotic physics searches

arXiv

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arXiv 2023年

作者： Afach, S. Aybas Tumturk, D. Bekker, H. Buchler, B.C. Budker, D. Cervantes, K. Derevianko, A. Eby, J. Figueroa, N.L. Folman, R. Gavilán Martín, D. Givon, M. Grujić, Z.D. Guo, H. Hamilton, P. Hedges, M.P. Jackson Kimball, D.F. Khamis, S. Kim, D. Klinger, E. Kryemadhi, A. Liu, X. Lukasiewicz, G. Masia-Roig, H. Padniuk, M. Palm, C.A. Park, S.Y. Pearson, H.R. Peng, X. Pospelov, M. Pustelny, S. Rosenzweig, Y. Ruimi, O.M. Scholtes, T. Segura, P.C. Semertzidis, Y.K. Shin, Y.C. Smiga, J.A. Stadnik, Y.V. Stalnaker, J.E. Sulai, I.A. Tandon, D. Vu, K. Weis, A. Wickenbrock, A. Wilson, T.Z. Wu, T. Xiao, W. Yang, Y. Yu, D. Yu, F. Zhang, J. Zhao, Y. Helmholtz-Institut GSI Helmholtzzentrum fur Schwerionenforschung Mainz55128 Germany Johannes Gutenberg University Mainz55128 Germany Department of Physics University of California at Berkeley BerkeleyCA94720-7300 United States Research School of Physics Australian National University CanberraACT2601 Australia Department of Physics California State University - East Bay HaywardCA94542-3084 United States Department of Physics University of Nevada RenoNV89557 United States The University of Tokyo Institutes for Advanced Study The University of Tokyo Chiba Kashiwa277-8583 Japan Department of Physics Ben-Gurion University of the Negev Be'er Sheva84105 Israel Physics Department University of Fribourg FribourgCH-1700 Switzerland Institute of Physics Belgrade Pregrecica 118 Belgrade11080 Serbia State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Center for Quantum Information Technology Peking University Beijing100871 China Department of Physics & Astronomy University of California at Los Angeles Los AngelesCA90095-1547 United States Department of Physics KAIST Daejeon34141 Korea Republic of Center for Axion and Precision Physics Research IBS Daejeon34051 Korea Republic of Dept. Computing Math & Physics Messiah University MechanicsburgPA17055 United States Marian Smoluchowski Institute of Physics Jagiellonian University Lojasiewicza 11 Kraków30-348 Poland Department of Physics & Astronomy Oberlin College OberlinOH44074 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States William I. Fine Theoretical Physics Institute School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Racah Institute of Physics Hebrew University of Jerusalem Jerusalem9190401 Israel Leibniz Institute of Photonic Technology Albert-Einstein-Strasse 9 JenaD-07745 Germany School of Physics University of Sydney SydneyNSW20

Numerous observations suggest that there exist undiscovered beyond-the-Standard-Model particles and fields. Because of their unknown nature, these exotic particles and fields could interact with Standard Model particles in many different ways and assume a variety of possible configurations. Here we present an overview of the Global Network of Optical Magnetometers for Exotic physics searches (GNOME), our ongoing experimental program designed to test a wide range of exotic physics scenarios. The GNOME experiment utilizes a worldwide network of shielded atomic magnetometers (and, more recently, comagnetometers) to search for spatially and temporally correlated signals due to torques on atomic spins from exotic fields of astrophysical origin. We survey the temporal characteristics of a variety of possible signals currently under investigation such as those from topological defect dark matter (axion-like particle domain walls), axion-like particle stars, solitons of complex-valued scalar fields (Q-balls), stochastic fluctuations of bosonic dark matter fields, a solar axion-like particle halo, and bursts of ultralight bosonic fields produced by cataclysmic astrophysical events such as binary black hole mergers. © 2023, CC BY.

关键词： Software testing

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Understanding adversarial attacks on deep learning based medical image analysis systems

arXiv

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arXiv 2019年

作者： Ma, Xingjun Niu, Yuhao Gu, Lin Wang, Yisen Zhao, Yitian Bailey, James Lu, Feng State Key Laboratory of VR Technology and Systems School of CSE Beihang University Beijing China School of Computing and Information Systems The University of Melbourne ParkvilleVIC3010 Australia Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China National Institute of Informatics Tokyo101-8430 Japan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Cixi Instuitue of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection. However, a recent study demonstrates that medical deep learning systems can be compromised by carefully-engineered adversarial examples/attacks with small imperceptible perturbations. This raises safety concerns about the deployment of these systems in clinical settings. In this paper, we provide a deeper understanding of adversarial examples in the context of medical images. We find that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints. Surprisingly, we also find that medical adversarial attacks can be easily detected, i.e., simple detectors can achieve over 98% detection AUC against state-of-the-art attacks, due to fundamental feature differences compared to normal examples. We believe these findings may be a useful basis to approach the design of more explainable and secure medical deep learning systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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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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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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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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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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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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Why is the Winner the Best?

Why is the Winner the Best?

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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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