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Research Square 2021年

作者： Zhu, Ping Li, Li Fang, Yu He, Yuling Wang, Shuo Han, Rui Liu, Yue Wang, Xiaojing Zhang, Yang Zhang, Xiaodong Yu, Qingquan Hu, Liqun Wang, Huihui Sun, Youwen Wei, Lai Tang, Weikang Liu, Tong Wang, Zhengxiong Yan, Xingting Huang, Wenlong Hou, Yawei Ji, Xiaoquan Zeng, Shiyong Abdullah, Zafar Chen, Zhongyong Zeng, Long Li, Haolong Chen, Zhipeng Wang, Zhijiang Rao, Bo Zhang, Ming Ding, Yonghua Pan, Yuan International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Department of Engineering Physics University of Wisconsin-Madison MadisonWI53706 United States College of Science Donghua University Shanghai201620 China School of Physics Dalian University of Technology Dalian116024 China Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials School of Physics and Telecommunication Engineering South China Normal University Guangzhou510006 China Southwestern Institute of Physics P.O. Box 432 Chengdu610041 China CAS Key Laboratory of Geospace Environment Department of Engineering and Applied Physics University of Science and Technology of China Hefei230026 China CAS Institute of Plasma Physics Hefei Anhui230031 China Max-Planck-Institute for Plasma Physics Garching Germany School of Computer Science and Technology Anhui Engineering Laboratory for Industrial Internet Intelligent Applications and Security Anhui University of Technology Ma’anshan Anhui243002 China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China

The China Fusion engineering Test Reactor (CFETR) and the Huazhong Field Reversed Configuration (HFRC), currently both under intensive physical and engineering designs in China, are the two major projects representative of the low-density steady-state and high-density pulsed pathways to fusion. One of the primary tasks of the physics designs for both CFETR and HFRC is the assessment and analysis of the magnetohydrodynamic (MHD) stability of the proposed design schemes. Comprehensive efforts on the assessment of MHD stability of CFETR and HFRC baseline scenarios have led to preliminary progresses that may further benefit engineering designs. For CFETR, the ECCD power and current for full stabilization on NTM have been predicted in this work, as well as the corresponding controlled magnetic island width. A thorough investigation on RWM stability for CFETR is performed. For 80% of the steady state operation scenarios, active control methods may be required for RWM stabilization. The process of disruption mitigation with massive neon injection on CFETR is simulated. The time scale of and consequences of plasma disruption on CFETR are estimated, which are found equivalent to ITER. Major MHD instabilities such as NTM and RWM remain challenge to steady state tokamak operation. On this basis, next steps on CFETR MHD study are planned on NTM, RWM, and SPI disruption mitigation. For HFRC, plasma heating due to 2D adiabatic compression has been demonstrated in NIMROD simulations. The tilt and rotational instabilities grow on ideal MHD time scale in single fluid MHD model as shown from NIMROD calculations. Two-fluid MHD calculations using NIMROD find FLR stabilizing effects on both tilt and rotational modes. Energetic-particle stabilization of tilt mode was previously demonstrated in C-2 experiments and NIMROD simulations. With stabilization on major MHD instabilities from two-fluid and energetic particle effects, FRC may promise to be an alternative route to compact magnetic f

关键词： Magnetohydrodynamics

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MHD analysis on the physical designs of CFETR and HFRC

arXiv

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arXiv 2021年

作者： Zhu, Ping Li, Li Fang, Yu He, Yuling Wang, Shuo Han, Rui Liu, Yue Wang, Xiaojing Zhang, Yang Zhang, Xiaodong Yu, Qingquan Hu, Liqun Wang, Huihui Sun, Youwen Wei, Lai Tang, Weikang Liu, Tong Wang, Zhengxiong Yan, Xingting Huang, Wenlong Hou, Yawei Ji, Xiaoquan Zeng, Shiyong Abdullah, Zafar Chen, Zhongyong Zeng, Long Li, Haolong Chen, Zhipeng Wang, Zhijiang Rao, Bo Zhang, Ming Ding, Yonghua Pan, Yuan International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Department of Engineering Physics University of Wisconsin-Madison MadisonWI53706 United States College of Science Donghua University Shanghai201620 China Key Laboratory of Materials Modification by Laser Ion and Electron Beams Ministry of Education School of Physics Dalian University of Technology Dalian116024 China Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials School of Physics and Telecommunication Engineering South China Normal University Guangzhou510006 China Southwestern Institute of Physics P.O. Box 432 Chengdu610041 China CAS Key Laboratory of Geospace Environment Department of Engineering and Applied Physics University of Science and Technology of China Hefei230026 China CAS Institute of Plasma Physics Anhui Hefei230031 China Max-Planck-Institute for Plasma Physics Garching Germany School of Computer Science and Technology Anhui Engineering Laboratory for Industrial Internet Intelligent Applications and Security Anhui University of Technology Anhui Ma'anshan243002 China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China

关键词： Magnetoplasma

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Improved impossible differential cryptanalysis of large-block Rijndael

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Science China(Information Sciences) 2019年第3期62卷 5-18页

作者： Ya LIU Yifan SHI Dawu GU Bo DAI Fengyu ZHAO Wei LI Zhiqiang LIU Zhiqiang ZENG Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University School of Computer Science and Technology Donghua University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Science and Technology on Information Assurance Laboratory

Rijndael is a substitution-permutation network（SPN） block cipher for the AES development process. Its block and key sizes range from 128 to 256 bits in steps of 32 bits, which can be denoted by Rijndael-b-k, where b and k are the block and key sizes, respectively. Among them, Rijndael-128-128/192/256,that is, AES, has been studied by many researchers, and the security of other large-block versions of Rijndael has been exploited less frequently. However, more attention has been paid to large-block versions of block ciphers with the fast development of quantum computers. In this paper, we propose improved impossible differential attacks on 10-round Rijndael-256-256, 10-round Rijndael-224-256, and 9-round Rijndael-224-224 using precomputation tables, redundancies of key schedules, and multiple impossible differentials. For 10-round Rijndael-256-256, the data, time, and memory complexities of our attack were approximately 2244.4chosen plaintexts, 2240.1encryptions, and 2181.4blocks, respectively. For 10-round Rijndael-224-256, the data, time, and memory complexities of our attack were approximately 2214.4chosen plaintexts, 2241.3encryptions, and 2183.4blocks, respectively. For 9-round Rijndael-224-224, the data, time, and memory complexities of our attack are approximately 2214.4chosen plaintexts, 2113.4encryptions, and 287.4blocks,respectively, or 2206.6chosen plaintexts, 2153.6encryptions, and 2111.6blocks, respectively. To the best of our knowledge, our results are currently the best on Rijndael-256-256 and Rijndael-224-224/256.

关键词： block cipher Rijndael precomputation tables impossible di?erentials multiple impossible differential attacks

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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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Prototype Framework of Low-Latency Collection of Multi-Point Video Stream over Multi-Hop Multi-Channel Wireless Network

Prototype Framework of Low-Latency Collection of Multi-Point...

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作者： Aung Myo Htut Juseong Kim JongWon Kim Chaodit Aswakul Wireless Network and Future Internet Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Networked Computing Systems (NetCS) Laboratory School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology

Big cities are facing heavy road traffic congestion. The main reason is the increasing number of vehicles beyond the road network capacity, which forces traffic police at times to be incapable of controlling the inevitable traffic jams. To resolve those traffic jams, traffic police need the near real-time information of the road network e.g. from traffic cameras. In this paper, a prototype framework of low-latency collection of multi-point video streams over a multi-hop multi-channel wireless network is proposed. Video frames are captured by Raspberry Pi cameras and sent by the wireless link to the herein-called IoT Concentrators. IoTCloud Hub then pulls and stores those video frames from the IoT Concentrators. Users（traffic police） can watch traffic videos from the display screen of the IoT-Cloud Hub. As a proof-of-concept implementation, two Pi Cameras have been set up with Apache Kafka Producers and two additional Raspberry Pi's have been set up as the IoT Concentrators, which run the leader and follower Apache Kafka Brokers. To complete our system, an Intel NUC as IoT-Cloud Hub and another Intel NUC as Orchestration-Hub have been also set up. We assume the available wired communication linkage between the Orchestration-Hub and the other devices. However, the links between the Pi Cameras, IoT Concentrators and IoT-Cloud Hub are wireless with Wi-Fi. To achieve a high performance, multiple Wi-Fi channels can be dynamically configured by the Orchestration-Hub to steer the VDO traffic（based on TCP） away from the congested channels. Based on the implementation prototype, the average TCP throughputs over all the wireless links have been improved by 56.046% on average, compared with the case without the dynamic Wi-Fi channel selection.

关键词： Apache Kafka Raspberry Pi video streaming wireless

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Towards Pattern-aware Privacy-preserving Real-time Data Collection

Towards Pattern-aware Privacy-preserving Real-time Data Coll...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Zhibo Wang Wenxin Liu Xiaoyi Pang Ju Ren Zhe Liu Yongle Chen Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University P. R. China Department of Computer Science and Technology Tsinghua University P. R. China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics P. R. China College of Information and Computer Taiyuan University of Technology P. R. China

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Although time-series data collected from users can be utilized to provide services for various applications, they could reveal sensitive information about users. Recently, local differential privacy (LDP) has emerged as the state-of-art approach to protect data privacy by perturbing data locally before outsourcing. However, existing works based on LDP perturb each data point separately without considering the correlations between consecutive data points in time-series. Thus, the important patterns of each time-series might be distorted by existing LDP-based approaches, leading to severe degradation of data utility. In this paper, we focus on real-time data collection under a honest-but-curious server, and propose a novel pattern-aware privacy-preserving approach, called PatternLDP, to protect data privacy while the pattern of time-series can still be preserved. To this end, instead of providing the same level of privacy protection at each data point, each user only samples remarkable points in time-series and adaptively perturbs them according to their impacts on local patterns. In particular, we propose a pattern-aware sampling method based on Piecewise Linear Approximation (PLA) to determine whether to sample and perturb current data point. To reduce the utility loss caused by pattern change after perturbation, we propose an importance-aware randomization mechanism to adaptively perturb sampled data locally while achieving better trade-off between privacy and utility. A novel metric-based w-event privacy is introduced to measure the privacy protection degree for pattern-rich time-series. We prove that PatternLDP can provide the above privacy guarantee, and extensive experiments on real-world datasets demonstrate that PatternLDP outperforms existing mechanisms and can effectively preserve the important patterns.

关键词： Privacy Data collection Real-time systems Servers Task analysis

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A Hole Repairing Method Based on Edge-Preserving Projection 12th

A Hole Repairing Method Based on Edge-Preserving Projection

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Wang, Yinghui Zhao, Yanni Wang, Ningna Ning, Xiaojuan Shi, Zhenghao Zhao, Minghua Lv, Ke Huang, Liangyi Institute of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an China Department of Computer Science Shannxi Vocational and Technical College Xi’an China Booking B.V. Herengracht 597 Amsterdam1017 CE Netherlands Institute of Automation Chinese Academy of Sciences Beijing China School of Engineering and Applied Science George Washington University Washington D.C. United States

ISBN: (纸本)9783030237110

A point cloud hole repairing method based on edge-preserving projection is proposed in order to maintain sharp features of holes. First, the hole boundary points are acquired by quadrants and angles, then connections among 3D hole boundary points are projected onto the two-dimensional plane using the edge-preserving projection. Secondly, the two-dimensional region obtained from projection is subjected to point collectivization for obtaining filling points, at the same time, the radial-basis interpolation mapping technique is used to construct the hole surface according to the 3D hole boundary point. Finally, the two-dimensional filling points are reflected onto the surface of the constructed hole to complete hole repairing. Experimental results show that our method can effectively repair the hole of the point cloud and restore the sharp features of the hole. © 2019, Springer Nature Switzerland AG.

关键词： Repair

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A Hole Repairing Method Based on Slicing 12th

A Hole Repairing Method Based on Slicing

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Zhao, Yanni Wang, Yinghui Wang, Ningna Ning, Xiaojuan Shi, Zhenghao Zhao, Minghua Lv, Ke Huang, Liangyi Institute of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an China Department of Computer Science Shannxi Vocational and Technical College Xi’an China Booking B.V. Herengracht 597 Amsterdam1017 CE Netherlands Institute of Automation Chinese Academy of Sciences Beijing China School of Engineering and Applied Science George Washington University Washington D.C. United States

ISBN: (纸本)9783030237110

The repairing of 3D point cloud holes has an important meaning to ensure the integrity of cloud data. We present a slice-based repairing method for 3D point cloud in this paper. Firstly, the model is horizontal sliced and each slice is projected on a two-dimensional plane, then the band-shaped points obtained during projection are clustered to select boundary points of the hole. Combined with optimal fitting points, the hole repairing point sets in the projection layer are re-sampled based on the cubic B-spline curve to fit boundary points. Finally, all hole repairing point sets in the projection layer are combined in 3D space to finish the entire hole recovery. The experimental results show that the proposed method can effectively repair complex holes for various point cloud models. © 2019, Springer Nature Switzerland AG.

关键词： Curve fitting

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Correlation-Aware Virtual Machine Placement in Data Center Networks 7th

Correlation-Aware Virtual Machine Placement in Data Center N...

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7th EAI International Conference on Cloud computing, CloudComp 2016 and 1st EAI International Conference on security and Privacy in New computing Environments, SPNCE 2016

作者： Chen, Tao Zhu, Yaoming Gao, Xiaofeng Kong, Linghe Chen, Guihai Wang, Yongjian Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Information Network Security of Ministry of Public Security The Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9783319696041

The resource utilization (CPU, memory) is a key performance metric in data center networks. The goal of the cloud platform supported by data center networks is achieving high average resource utilization while guaranteeing the quality of cloud services. Previous work focus on increasing the time-average resource utilization and decreasing the overload ratio of servers by designing various efficient virtual machine placement schemes. Unfortunately, most of virtual machine placement schemes did not involve the service level agreements and statistical methods. In this paper, we propose a correlation-aware virtual machine placement scheme that effectively places virtual machines on physical machines. First, we employ Neural Networks model to forecast the resource utilization trend according to the historical resource utilization data. Second, we design correlation-aware placement algorithms to enhance resource utilization while meeting the user-defined service level agreements. The results show that the efficiency of our virtual machine placement algorithms outperform the previous work by about 15%. © 2018, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Virtual machine

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Cryptanalysis of an elgamal-like cryptosystem based on matrices over group rings 1

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12th Chinese Conference on Trusted computing and Information security, CTCIS 2018

作者： Jia, Jianwei Wang, Houzhen Zhang, Huanguo Wang, Shijia Liu, Jinhui Huawei Technologies Co. Ltd. Xi’an710075 China School of Cyber Science and Engineering WuhanHubei430072 China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education WuhanHubei430072 China Department of Statistics and Actuarial Science Simon Fraser University Burnaby Canada School of Computer Science Shaanxi Normal University Xi’an710062 China

ISBN: (数字)9789811359132

ISBN: (纸本)9789811359125

ElGamal cryptography is one of the most important Public Key Cryptography (PKC) since Diffie-Hellman exchanges was proposed, however these PKCs which are based on the hard problems that discrete logarithm problem and integer factorization problem are weak with advances in quantum computers. So some alternatives should be proposed. Majid Khan et al. proposed two ElGamal-like public-key encryption schemes based on large abelian subgroup of general linear group over a residue ring, however the two schemes were not long before it was proved unsafe by us. Then, Saba Inam and Rashid (2016) proposed an improved cryptosystem which can resist my attack on "NEURAL computing & APPLICATIONS". By analyzing the security of the public key cryptography, we propose an improved method of algebraic key-recovery attack in the polynomial computational complexity despiteing the designers’ claim the cryptosystem is optimal security. Besides, we provide corresponding practical attack example to illustrate the attack method in our cryptanalysis, which breaks instances claiming 80 bits of security less than one minute on a single desktop computer. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Computational complexity

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