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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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5G entering into a new era [Guest Editorial]

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China Communications 2017年第12期14卷 iv-vi页

作者： Feifei Gao Changle Li Jun Fang Kaizhi Huang Zhi Quan Chengwen Xing Jianhua Zhang Zhongshan Zhang Liqiang Zhao Yulong Zou Department of Automation Tsinghua University Beijing China State Key Laboratory of Integrated Services Networks Xidian University University of Eletronic Science and Technology of China National Digital Switching System Engineering & Technological Research Center Zhengzhou China College of Information Engineering Shenzhen University School of Information and Electronics Beijing Institute of Technology Beijing University of Posts and Telecommunication School of Computer and Communication Engineering in the University of Science and Technology Beijing State Key Lab. of Integrated Service Networks Xidian University Nanjing University of Posts and Telecommunications (NUPT) Nanjing China

The ever increasing wireless data services, such as imaging, video, audio, multimedia, etc., have demands for the very high speed wireless communications and network, which are unfortunately a bottleneck when combining with the wireline core network. Users' are now expecting high quality of experience with low-cost devices, ubiquitous connectivity, energy efficiency, high reliability, or even ultra-low latency if a vehicle terminal is applied.

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A dynamic marking method for implicit information flow in dynamic taint analysis 8

A dynamic marking method for implicit information flow in dy...

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8th International Conference on Security of Information and Networks, SIN 2015

作者： Wang, Xuefei Ma, Hengtai Jing, Lisha Sci. and Tech. on Integrated Information System Lab Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450334532

Dynamic taint analysis is an important technique for tracking information flow in software and it has been widely applied in the field of software testing, debugging and vulnerability detection. However, most of the dynamic taint analysis tools only handle explicit information flow, while ignoring the implicit information flow, resulting in a large number of false negative *** this situation, we present a dynamic marking method for implicit information flow, to handle a specific type of control-dependence. The method can identify and propagate implicit data during runtime, thus increasing the coverage of the tested program. we also propose pipeline, integrating our method in the process of dynamic taint analysis. Pipeline is implemented on the base of the dynamic taint analysis framework avalanche, and is designed to detect vulnerabilities in binary programs. In the studies, we applied the tool to 5 applications from some open-source projects, and it has effectively located and propagated the specific kind of implicit information flow. © 2015 ACM.

关键词： Software testing

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Publisher Correction: Brain charts for the human lifespan

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Nature 2022年第7931期610卷 E6页

作者： R A I Bethlehem J Seidlitz S R White J W Vogel K M Anderson C Adamson S Adler G S Alexopoulos E Anagnostou A Areces-Gonzalez D E Astle B Auyeung M Ayub J Bae G Ball S Baron-Cohen R Beare S A Bedford V Benegal F Beyer J Blangero M Blesa Cábez J P Boardman M Borzage J F Bosch-Bayard N Bourke V D Calhoun M M Chakravarty C Chen C Chertavian G Chetelat Y S Chong J H Cole A Corvin M Costantino E Courchesne F Crivello V L Cropley J Crosbie N Crossley M Delarue R Delorme S Desrivieres G A Devenyi M A Di Biase R Dolan K A Donald G Donohoe K Dunlop A D Edwards J T Elison C T Ellis J A Elman L Eyler D A Fair E Feczko P C Fletcher P Fonagy C E Franz L Galan-Garcia A Gholipour J Giedd J H Gilmore D C Glahn I M Goodyer P E Grant N A Groenewold F M Gunning R E Gur R C Gur C F Hammill O Hansson T Hedden A Heinz R N Henson K Heuer J Hoare B Holla A J Holmes R Holt H Huang K Im J Ipser C R Jack A P Jackowski T Jia K A Johnson P B Jones D T Jones R S Kahn H Karlsson L Karlsson R Kawashima E A Kelley S Kern K W Kim M G Kitzbichler W S Kremen F Lalonde B Landeau S Lee J Lerch J D Lewis J Li W Liao C Liston M V Lombardo J Lv C Lynch T T Mallard M Marcelis R D Markello S R Mathias B Mazoyer P McGuire M J Meaney A Mechelli N Medic B Misic S E Morgan D Mothersill J Nigg M Q W Ong C Ortinau R Ossenkoppele M Ouyang L Palaniyappan L Paly P M Pan C Pantelis M M Park T Paus Z Pausova D Paz-Linares A Pichet Binette K Pierce X Qian J Qiu A Qiu A Raznahan T Rittman A Rodrigue C K Rollins R Romero-Garcia L Ronan M D Rosenberg D H Rowitch G A Salum T D Satterthwaite H L Schaare R J Schachar A P Schultz G Schumann M Schöll D Sharp R T Shinohara I Skoog C D Smyser R A Sperling D J Stein A Stolicyn J Suckling G Sullivan Y Taki B Thyreau R Toro N Traut K A Tsvetanov N B Turk-Browne J J Tuulari C Tzourio É Vachon-Presseau M J Valdes-Sosa P A Valdes-Sosa S L Valk T van Amelsvoort S N Vandekar L Vasung L W Victoria S Villeneuve A Villringer P E Vértes K Wagstyl Y S Wang S K Warfield V Warrier E Westman M L Westwater H C Whalley A V Witte N Yang B Yeo H Y Autism Research Centre Department of Psychiatry University of Cambridge Cambridge UK. rb643@medschl.cam.ac.uk. Brain Mapping Unit Department of Psychiatry University of Cambridge Cambridge UK. rb643@medschl.cam.ac.uk. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Department of Child and Adolescent Psychiatry and Behavioral Science The Children's Hospital of Philadelphia Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Lifespan Brain Institute The Children's Hospital of Philadelphia and Penn Medicine Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Department of Psychiatry University of Cambridge Cambridge UK. MRC Biostatistics Unit University of Cambridge Cambridge UK. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. Lifespan Informatics & Neuroimaging Center University of Pennsylvania Philadelphia PA USA. Department of Psychology Yale University New Haven CT USA. Developmental Imaging Murdoch Children's Research Institute Melbourne Victoria Australia. Department of Medicine Monash University Melbourne Victoria Australia. UCL Great Ormond Street Institute for Child Health London UK. Weill Cornell Institute of Geriatric Psychiatry Department of Psychiatry Weill Cornell Medicine New York USA. Department of Pediatrics University of Toronto Toronto Canada. Holland Bloorview Kids Rehabilitation Hospital Toronto Canada. The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroInformation University of Electronic Science and Technology of China Chengdu China. University of Pinar del Río "Hermanos Saiz Montes de Oca" Pinar del Río Cuba. MRC Cognition and Brain Sciences Unit University of Cambridge Cambridge UK. Autism Research Centre Department of Psychiatry University of Cambridge Cambridge UK. Department of Psychology School of Philosophy Psychology and Language Sciences University of Edinburgh Edinburgh UK. Queen's

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Social sensing enhanced time estimation for bus service

Social sensing enhanced time estimation for bus service

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作者： Liu, Jin Li, Juan Cui, Xiaohui Niu, Xiaoguang Sun, Xiaoping Zhou, Jing State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China International School of Software Wuhan University Wuhan China Computer School Wuhan University Wuhan China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science Communication University of China China

The precise prediction of bus routes or the arrival time of buses for a traveler can enhance the quality of bus service. However, many social factors influence people's preferences for taking buses. These social factors may include heavy traffic cost, traffic congestion, poor air quality and so forth. Existing prediction techniques rarely consider social sensing when predicting the bus arrival time. Accordingly, this paper proposes a social sensing enhanced service for predicting bus routes, which integrates sensing ability and social networks to understand and measure the influence between social events and vehicle velocity. We focus on the analysis of two different attributions: PT service quality attributions PEAs and road condition attributions PRCAs. Both of them synthesize the social sensing in their evaluation of bus routes. PEA represents individual preferences and PRCA represents physical factors that significantly influence vehicle velocity. Bus relevant social events were further categorized into PEA events or PRCA events. PEAs of buses were scored according to the tendency of bus conditions reflected in social events. Furthermore, an artificial neural network prediction model is established to estimate the bus travel time. © 2015 John Wiley & Sons, Ltd.

关键词： Buses

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Madness: A multiresolution, adaptive numerical environment for scientific simulation

Madness: A multiresolution, adaptive numerical environment f...

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作者： Harrison, Robert J. Beylkin, Gregory Bischoff, Florian A. Calvin, Justus A. Fann, George I. Fosso-Tande, Jacob Galindo, Diego Hammond, Jeff R. Hartman-Baker, Rebecca Hill, Judith C. Jia, Jun Kottmann, Jakob S. Ou, M-J. Yvonne Pei, Junchen Ratcliff, Laura E. Reuter, Matthew G. Richie-Halford, Adam C. Romero, Nichols A. Sekino, Hideo Shelton, William A. Sundahl, Bryan E. Thornton, W. Scott Valeev, Edward F. Vázquez-Mayagoitia, Álvaro Vence, Nicholas Yanai, Takeshi Yokoi, Yukina Stony Brook University Stony BrookNY11794 United States University of Colorado at Boulder BoulderCO80309 United States Institut für Chemie Humboldt-Universität Zu Berlin Unter den Linden 6 Berlin10099 Germany Department of Chemistry Virginia Tech. BlacksburgVA24061 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Chemistry and Biochemistry Florida State University TallahasseeFL32306 United States Parallel Computing Lab Intel Corporation PortlandOR97219 United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States LinkedIn Mountain ViewCA94043 United States Department of Mathematical Sciences University of Delaware NewarkDE19716 United States State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Department of Physics University of Washington SeattleWA98195 United States Computer Science and Engineering Toyohashi University of Technology ToyohashiAichi441-8580 Japan Louisiana State University Baton RougeLA70803 United States Department of Physics LaSierra University RiversideCA92505 United States Theoretical and Computational Molecular Science Institute for Molecular Science OkazakiAichi444-8585 Japan

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision that are based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics. © 2016 Society for Industrial and Applied Mathematics.

关键词： Multiresolution analysis

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Stretchable Electronics: In‐Plane Deformation Mechanics for Highly Stretchable Electronics (Adv. Mater. 8/2017)

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Advanced Materials 2017年第8期29卷

作者： Yewang Su Xuecheng Ping Ki Jun Yu Jung Woo Lee Jonathan A. Fan Bo Wang Ming Li Rui Li Daniel V. Harburg YongAn Huang Cunjiang Yu Shimin Mao Jaehoun Shim Qinglin Yang Pei‐Yin Lee Agne Armonas Ki‐Joong Choi Yichen Yang Ungyu Paik Tammy Chang Thomas J. Dawidczyk Yonggang Huang Shuodao Wang John A. Rogers State Key Laboratory of Nonlinear Mechanics Institute of Mechanics Chinese Academy of Sciences Beijing 100190 China Department of Civil and Environmental Engineering and Department of Mechanical Engineering Northwestern University Evanston IL 60208 USA School of Engineering Science University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering Mechanics Dalian University of Technology Dalian 116024 China School of Mechanical Engineering Tianjin University of Science & Technology Tianjin 300222 China Frederick Seitz Materials Research Laboratory Department of Materials Science and Engineering University of Illinois at Urbana‐Champaign Champaign IL 61820 USA Department of Material Science and Engineering Department of Energy Engineering Hanyang University Seoul 133‐791 South Korea Department of Electrical Engineering Stanford University Stanford CA 94305 USA School of Mechanical and Aerospace Engineering Oklahoma State University Stillwater OK 74078 USA State Key Lab Digital Manufacturing Equipment and Technology and Flexible Electronics Research Center Huazhong University of Science and Technology Wuhan Hubei 430074 China Department of Mechanical Engineering University of Houston Houston TX 77204 USA Department of Electrical and Computer Engineering and the Department of Chemistry Beckman Institute for Advanced Science and Technology University of Illinois at Urbana‐Champaign Champaign Urbana IL 61820 USA

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Challenging local realism with human choices

arXiv

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arXiv 2018年

作者： Abellán, C. Acín, A. Alarcón, A. Alibart, O. Andersen, C.K. Andreoli, F. Beckert, A. Beduini, F.A. Bendersky, A. Bentivegna, M. Bierhorst, P. Burchardt, D. Cabello, A. Cariñe, J. Carrasco, S. Carvacho, G. Cavalcanti, D. Chaves, R. Cortés-Vega, J. Cuevas, A. Delgado, A. de Riedmatten, H. Eichler, C. Farrera, P. Fuenzalida, J. García-Matos, M. Garthoff, R. Gasparinetti, S. Gerrits, T. GhafariJouneghani, F. Glancy, S. Gómez, E.S. González, P. Guan, J.-Y. Handsteiner, J. Heinsoo, J. Heinze, G. Hirschmann, A. Jiménez, O. Kaiser, F. Knill, E. Knoll, L.T. Krinner, S. Kurpiers, P. Larotonda, M.A. Larsson, J.-Å. Lenhard, A. Li, H. Li, M.-H. Lima, G. Liu, B. Liu, Y. López Grande, I.H. Lunghi, T. Ma, X. Magaña-Loaiza, O.S. Magnard, P. Magnoni, A. Martí-Prieto, M. Martínez, D. Mataloni, P. Mattar, A. Mazzera, M. Mirin, R.P. Mitchell, M.W. Nam, S. Oppliger, M. Pan, J.-W. Patel, R.B. Pryde, G.J. Rauch, D. Redeker, K. Rieländer, D. Ringbauer, M. Roberson, T. Rosenfeld, W. Salathé, Y. Santodonato, L. Sauder, G. Scheidl, T. Schmiegelow, C.T. Sciarrino, F. Seri, A. Shalm, L.K. Shi, S.-C. Slussarenko, S. Stevens, M.J. Tanzilli, S. Toledo, F. Tura, J. Ursin, R. Vergyris, P. Verma, V.B. Walter, T. Wallraff, A. Wang, Z. Weinfurter, H. Weston, M.M. White, A.G. Wu, C. Xavier, G.B. You, L. Yuan, X. Zeilinger, A. Zhang, Q. Zhang, W. Zhong, J. 08860 Spain ICREA Institució Catalana de Recerca i Estudis Avançats Barcelona08010 Spain Center for Optics and Photonics MSI-Nucleus for Advanced Optics Universidad de Concepción Casilla 4016 Concepción Chile Departamento de Ingeniería Eléctrica Universidad de Concepción 160-C Concepción Chile Parc Valrose Nice Cedex 206108 France Quantum Device Lab Department of Physics ETH Zurich ZurichCH-8093 Switzerland Dipartimento di Fisica Sapienza Università di Roma RomaI-00185 Italy Departamento de Computación FCEyN UBA and ICC CONICET Ciudad Universitaria Pabellón 1 Buenos Aires1428 Argentina National Institute of Standards and Technology 325 Broadway BoulderCO80305 United States Ludwig-Maximilians-Universität München80799 Germany Departamento de Física Aplicada II Universidad de Sevilla Sevilla41012 Spain Departamento de Física Universidad de Concepción 160-C Concepción Chile International Institute of Physics Federal University of Rio Grande do Norte Natal59070-405 Brazil Austrian Academy of Sciences Boltzmanngasse 3 Vienna1090 Austria Centre for Quantum Computation and Communication Technology Griffith University BrisbaneQLD4111 Australia Centre for Quantum Dynamics Griffith University BrisbaneQLD4111 Australia Shanghai Branch National Laboratory for Physical Sciences at Microscale Dept. of Modern Physics University of Science and Technology of China Shanghai230026 China DEILAP CITEDEF & CONICET J.B. de La Salle 4397 1603 Villa Martelli Buenos Aires Argentina Departamento de Física FCEyN UBA and IFIBA Ciudad Universitaria Conicet Pabellón 1 Buenos Aires1428 Argentina Department of Electrical Engineering Linköping University Linköping581 83 Sweden State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China CAS Center for Excellence in Superconducting Electronics Shanghai200050 China School of Computer NUDT

A Bell test is a randomized trial that compares experimental observations against the philosophical worldview of local realism1, in which the properties of the physical world are independent of our observation of them and no signal travels faster than light. A Bell test requires spatially distributed entanglement, fast and high-efficiency detection and unpredictable measurement settings2,3. Although technology can satisfy the first two of these requirements4–7, the use of physical devices to choose settings in a Bell test involves making assumptions about the physics that one aims to test. Bell himself noted this weakness in using physical setting choices and argued that human ‘free will’ could be used rigorously to ensure unpredictability in Bell tests8. Here we report a set of local-realism tests using human choices, which avoids assumptions about predictability in physics. We recruited about 100,000 human participants to play an online video game that incentivizes fast, sustained input of unpredictable selections and illustrates Bell-test methodology9. The participants generated 97,347,490 binary choices, which were directed via a scalable web platform to 12 laboratories on five continents, where 13 experiments tested local realism using photons5,6, single atoms7, atomic ensembles10, and superconducting devices11. Over a 12-hour period on 30 November 2016, participants worldwide provided a sustained data flow of over 1,000 bits per second to the experiments, which used different human-generated data to choose each measurement setting. The observed correlations strongly contradict local realism and other realistic positions in bipartite and tripartite12 scenarios. Project outcomes include closing the ‘freedom-of-choice loophole’ (the possibility that the setting choices are influenced by ‘hidden variables’ to correlate with the particle properties13), the utilization of video-game methods14 for rapid collection of human generated randomness, and the use of network

关键词： Testing

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Improved MILP modeling for automatic security evaluation and application to fox

Improved MILP modeling for automatic security evaluation and...

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作者： Qiao, Kexin Hu, Lei Sun, Siwei Ma, Xiaoshuang Kan, Haibin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing100093 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China

Counting the number of differentially active S-boxes is of great importance in evaluating the security of a block cipher against differential attack. Mouha et al. proposed a technique based on Mixed-Integer Linear Programming (MILP) to automatically calculate a lower bound of the number of differentially active S-boxes for word-oriented block ciphers, and applied it to symmetric ciphers AES and Enocoro-128v2. Later Sun et al. extended the method by introducing bit-level representations for Sboxes and new constraints in the MILP problem, and applied the extended method to PRESENT-80 and LBlock. This kind of methods greatly depends on the constraints in the MILP problem describing the differential propagation of the block cipher. A more accurate description of the differential propagation leads to a tighter bound on the number of differentially active S-boxes. In this paper, we refine the constraints in the MILP problem describing XOR operations, and apply the refined MILP modeling to determine a lower bound of the number of active S-boxes for the Lai-Massey type block cipher FOX in the model of single-key differential attack, and obtain a tighter bound in FOX64 than existing results. Experimental results show that 6, instead of currently known 8, rounds of FOX64 is strong enough to resist against basic single-key differential attack since the differential characteristic probability is upper bounded by 2-64, and thus the maximum differential characteristic probability of 12-round FOX64 is upper bounded by 2-128, where 128 is the key-length of FOX64. We also get the lower bound of the number of differentially active S-boxes for 5-round FOX128, and proved the security of the full-round FOX128 with respect to single-key differential attack. Copyright © 2015 The Institute of Electronics, Information and Communication Engineers.

关键词： Integer programming

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Reflection invariant local binary patterns for image texture classification

Reflection invariant local binary patterns for image texture...

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Research in Adaptive and Convergent Systems, RACS 2015

作者： Song, Enmin Pan, Ning Hung, Chih-Cheng Li, Xiang Jin, Lianghai School of Computer Science and Technology Huazhong University of Science and Technology Key Laboratory of Education Ministry for Image Processing and Intelligent Control Wuhan430074 China College of Biomedical Engineering South-Central University for Nationalities Wuhan430074 China Sino-US Intelligent Information Processing Lab Anyang Normal University Kennesaw State University MariettaGA30060 United States

ISBN: (纸本)9781450337380

In this study we modified the local binary pattern operator (LBP) to obtain the robust invariant texture patterns for image texture classification. The modified method will be able to calculate patterns which are invariant for translation, scaling, rotation and reflection. Therefore, the modified LBP is called R-LBP. Although many variation of LBP have been proposed, most of them cannot detect and recognize the patterns of reflection. Both clockwise and counter clockwise coding is used in the proposed R-LBP operator in order to derive a minimum code to representing the pattern. Experimental results show that the proposed method is effective in determining the invariant patterns for image texture classification. © 2015 ACM.

关键词： Local binary pattern

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