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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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Network Embedding based on External Word Vectors

Network Embedding based on External Word Vectors

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IEEE International Performance computing and Communications Conference

作者： XiaoKun Zhang Yan Liu Jing Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781538668092;9781538668085

Network embedding, which preserves network's sophisticated features, aims to effectively learn the low-dimensional embedding of vertices to lower the computing and storage costs. Content information network like Twitter is defined as network with rich text information. Most research about content information network are based on the information of the network itself. By introducing external word vectors into the modeling process, we can make use of the external syntactic and semantic features. In this paper, we propose NE-EWV (Network Embedding based on External Word Vectors), which preserves the feature fusion representation from both semantic feature space as well as structural feature space. Experiments are conducted in three real-world content information network datasets. In link predict task, the AUC increases 7% to 19% compared with the models which only considering the structural features, increases 1% to 12% compared with the model considering structural and text features in most cases.

关键词： Network embedding Content information network Auto-encoder External word vectors

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Efficient Verification of Hypergraph states

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Physical Review Applied 2019年第5期12卷 054047-054047页

作者： Huangjun Zhu Masahito Hayashi Department of Physics and Center for Field Theory and Particle Physics Fudan University Shanghai 200433 China State Key Laboratory of Surface Physics Fudan University Shanghai 200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 China Institute for Theoretical Physics University of Cologne Cologne 50937 Germany Graduate School of Mathematics Nagoya University Nagoya 464-8602 Japan Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518000 China Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117542 Singapore

Graph states and hypergraph states are of wide interest in quantum information processing and foundational studies. Efficient verification of these states is a key to various applications. Here we propose a simple method for verifying hypergraph states, which requires only two distinct Pauli measurements for each party, yet its efficiency is comparable to the best strategy based on entangling measurements. For a given state, the overhead is bounded by the chromatic number and degree of the underlying hypergraph. Our protocol is dramatically more efficient than all previous protocols based on local measurements, including tomography and direct-fidelity estimation. It enables the verification of hypergraph states and genuine multipartite entanglement of thousands of qubits. The protocol can also be generalized to the adversarial scenario, while achieving almost the same efficiency. This merit is particularly appealing to demonstrating blind measurement-based quantum computation and quantum supremacy.

关键词： Entanglement detection Measurement-based quantum computing Quantum benchmarking Quantum information processing Quantum networks Quantum tomography Resource theories Graph theory

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VoicePop: A Pop Noise based Anti-spoofing System for Voice Authentication on Smartphones

VoicePop: A Pop Noise based Anti-spoofing System for Voice A...

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

作者： Qian Wang Xiu Lin Man Zhou Yanjiao Chen Cong Wang Qi Li Xiangyang Luo School of Cyber Science and Engineering Wuhan University P. R. China School of Computer Science Wuhan University P. R. China Department of Computer Science City University of Hong Kong Hong Kong P. R. China Institute for Network Sciences and Cyberspace Tsinghua University P. R. China The State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou P. R. China

Voice biometrics is widely adopted for identity authentication in mobile devices. However, voice authentication is vulnerable to spoofing attacks, where an adversary may deceive the voice authentication system with pre-recorded or synthesized samples from the legitimate user or by impersonating the speaking style of the targeted user. In this paper, we design and implement VoicePop, a robust software-only anti-spoofing system on smartphones. VoicePop leverages the pop noise, which is produced by the user breathing while speaking close to the microphone. The pop noise is delicate and subject to user diversity, making it hard to record by replay attacks beyond a certain distance and to imitate precisely by impersonators. We design a novel pop noise detection scheme to pinpoint pop noises at the phonemic level, based on which we establish individually unique relationship between phonemes and pop noises to identify legitimate users and defend against spoofing attacks. Our experimental results with 18 participants and three types of smartphones show that VoicePop achieves over 93.5% detection accuracy at around 5.4% equal error rate. VoicePop requires no additional hardware but only the built-in microphones in virtually all smartphones, which can be readily integrated in existing voice authentication systems for mobile devices.

关键词： Authentication Smart phones Microphones Feature extraction Tongue Mouth Law

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Threat Analysis of Natural Gas Control System Based on Physical Process Constraints

Threat Analysis of Natural Gas Control System Based on Physi...

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Chinese Automation Congress (CAC)

作者： Yangyang Geng Wenwen Liu Yang Liu Qiang Wei Ke Liu Department of Cyberspace Security State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Department of Research and Development Center Beijing sifang automation co.ltd Beijing China

Natural gas is one of the essential energy sources for daily life. The increased reliance on energy makes control systems attractive targets for attackers. This paper explains how the control network works and describes the negative influence of attacking physical process. The paper proposes denial-of-service (DoS) attacks model and integrity attacks for the control system. In addition, our work focus on analyzing triethylene glycol (TEG) dehydration process flow for discovering the influencing factors, operational constraints and safety constraints in the dehydration process. Then, we evaluate physical and economic influences for DoS attacks and integrity attacks on TEG dehydration process in our physical testbed. The consequence shows that these kinds of attacks scenarios will drive the plant into an unsafe state. Finally, we propose an effective mechanism to defense DoS attacks and integrity attacks on TEG dehydration process.

关键词： Process control Sensors Natural gas Integrated circuits Actuators Production

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Two orders of magnitude enhancement in oxygen evolution reactivity on amorphous Ba0.5Sr0.5Co0.8Fe0.2O3-δ nanofilms with tunable oxidation state

Two orders of magnitude enhancement in oxygen evolution reac...

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2018电催化与电合成国际研讨会（ISEE 2018）

作者： Gao Chen Wei Zhou Daqin Guan Jaka Sunarso Yanping Zhu Xuefeng Hu Wei Zhang Zongping Shao Jiangsu National Synergetic Innovation Center for Advanced Materials State Key Laboratory of Materials-Oriented Chemical EngineeringCollege of Chemical EngineeringNanjing Tech University Faculty of Engineering Computing and ScienceSwinburne University of Technology Jiangsu National Synergetic Innovation Center for Advanced Materials State Key Laboratory of Materials-Oriented Chemical EngineeringCollege of EnergyNanj ing Tech University Department of Chemical Engineering Curtin University

Perovskite oxides exhibit potential for use as electrocatalysts in the oxygen evolution reaction(OER). However, their low specific surface area is the main obstacle to realizing a high mass-specific activity that is r... 详细信息

关键词： Two orders of magnitude enhancement in oxygen evolution reactivity on amorphous Ba nanofilms with tunable oxidation state

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Low-power technologies in high-performance computer: trends and perspectives

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National Science Review 2016年第1期3卷 23-25页

作者： Xianghui Xie State Key Laboratory of Mathematical Engineering and Advanced Computing

INTRODUCTION With the breakdown of Dennard’s scaling rule,chip power for a given area size is not constant as transistors shrink from generation to generation[1].In particular,the power wall problem worsens with the ... 详细信息

关键词： HPC high trends and perspectives Low-power technologies in high-performance computer GPU CPU

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Improved SIMD Vectorization Method in the Presence of Control Flow

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Ruan Jian Xue Bao/Journal of Software 2017年第8期28卷 2046-2063页

作者： Gao, Wei Li, Ying-Ying Sun, Hui-Hui Li, Yan-Bing Zhao, Rong-Cai State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Information Engineering University Zhengzhou450001 China

SIMD extension is an acceleration component integrated into the general processor for developing data level parallelism in multimedia and scientific computing applications. Control dependence hinders exploiting data level parallelism in the programs. Current vectorization methods in the presence of control flow, loop-based or SLP, all need if-conversion. They do not consider the SIMD parallelism in programs, and thus result in a lower SIMD executing efficiency. Another factor that leads to low efficiency is the lack of cost model to direct SIMD code generation in the presence of control flow. To address these problems, an improved control flow SIMD vectorization method is proposed. First, loop distribution with control dependence is put up to separate the vectorizable parts and unvectorizable parts, taking data locality into account simultaneously. Second, a direct vectorization method of control flow is presented considering the reuse of data between basic blocks. Finally, cost model is used to guide the generation of select and BOSCCs to improve the efficiency of SIMD code. Experimental results show that the code performance generated by the improved methods increase by 24% compared with those of the existing control flow vectorization methods. © Copyright 2017, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Efficiency

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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IP geolocation base on local delay distribution similarity 9th

IP geolocation base on local delay distribution similarity

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9th International Symposium on Cyberspace Safety and Security, CSS 2017

作者： Zhao, Fan Luo, Xiangyang Gan, Yong Zu, Shuodi Liu, Fenlin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450001 China

ISBN: (纸本)9783319694702

IP geolocation technology can be used to obtain the real-time locations of target Internet hosts especially mobile computers, which can help law enforcement to quickly get the criminal evidence or arrest criminals. Among existing numerous geolocation methods, SLG (Street-Level Geolocation) method can achieve geolocation result with relative higher precision for a target host. However, the geolocation accuracy will be significantly reduced once the common routers that play an important role in geolocation are anonymous, which often happens in paths detection. To solve this problem, this paper proposes an IP geolocation algorithm base on local delay distribution similarity. Firstly, the target’s location at city-level granularity is obtained based on traditional SLG method. Secondly, the landmarks connected with the target by common routers are found out by topology analysis. The target’s local delay between the nearest common router and the target is gained by multi-measurement and calculation, so do the landmarks’. Thirdly, their local delay distribution is obtained by statistical analysis. Lastly, the landmark whose local delay distribution is the most similar with the target’s is selected as the estimated location of the target. Experimental results show that the proposed algorithm obviously improves the geolocation accuracy compared with traditional SLG when the common routers are anonymous. © 2017, Springer International Publishing AG.

关键词： Routers

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