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Measurement-Device-Independent Quantum Secure Direct Communication

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arXiv 2018年

作者： Zhou, Zeng-Rong Sheng, Yu-Bo Niu, Peng-Hao Yin, Liu-Guo Long, Gui-Lu State Key Laboratory of Low-dimensional Quantum Physics Beijing100084 China Department of Physics Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China School of Information and Technology Tsinghua University Beijing100084 China Collaborative Innovation Center of Quantum Matter Beijing100084 China Beijing Academy of Quantum Information Beijing100084 China Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Nanjing210003 China College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing210003 China

Quantum secure direct communication (QSDC) is the technology to transmit secret information directly through a quantum channel without neither key nor ciphertext. It provides us with a secure communication structure that is fundamentally different from the one that we use today. In this Letter, we report the first measurement-device-independent(MDI) QSDC protocol with sequences of entangled photon pairs and single photons. It eliminates security loopholes associated with the measurement device. In addition, the MDI technique doubles the communication distance compared to those without using the technique. We also give a protocol with linear optical Bell-basis measurement, where only two of the four Bell-basis states could be measured. When the number of qubit in a sequence reduces to 1, the MDI-QSDC protocol reduces to a deterministic MDI quantum key distribution protocol, which is also presented in the Letter. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum communication

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arXiv 2017年

作者： Dong, Jianfeng Li, Xirong Xu, Duanqing College of Computer Science and Technology Zhejiang University Hangzhou310027 China Key Lab of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing100872 China

—In order to retrieve unlabeled images by textual queries, cross-media similarity computation is a key ingredient. Although novel methods are continuously introduced, little has been done to evaluate these methods together with large-scale query log analysis. Consequently, how far have these methods brought us in answering real-user queries is unclear. Given baseline methods that use relatively simple text/image matching, how much progress have advanced models made is also unclear. This paper takes a pragmatic approach to answering the two questions. Queries are automatically categorized according to the proposed query visualness measure, and later connected to the evaluation of multiple cross-media similarity models on three test sets. Such a connection reveals that the success of the state-of-the-art is mainly attributed to their good performance on visual-oriented queries, which account for only a small part of real-user queries. To quantify the current progress, we propose a simple text2image method, representing a novel query by a set of images selected from large-scale query log. Consequently, computing cross-media similarity between the query and a given image boils down to comparing the visual similarity between the given image and the selected images. Image retrieval experiments on the challenging Clickture dataset show that the proposed text2image is a strong baseline, comparing favorably to recent deep learning alternatives. Copyright © 2017, The Authors. All rights reserved.

关键词： Image retrieval

A novel RNN-GBRBM based feature decoder for anomaly detection technology in industrial control network

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A novel RNN-GBRBM based feature decoder for anomaly detectio...

作者： Zhang, Hua Zhu, Shixiang Ma, Xiao Zhao, Jun Shou, Zeng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications 100876 China Data Network of Power Dispatch Safety Protection of Electric Power Supervisory Control System State Gird Jiangsu Electric Power Company Shanghai Road No 215 Nanjing Jiangsu210024 China Safety Protection of Electric Power Supervisory Control System NARI Group Corporation No. 19 Chengxin Rd Jiangning Nanjing Jiangsu211000 China Dispatching Data Network and Secondary Safty Protection Electric Power Science Research Institute State Gird Jiangsu Electric Power Company Jiangning Paweier Road No 1 Nanjing Jiangsu211103 China

As advances in networking technology help to connect industrial control networks with the Internet, the threat from spammers, attackers and criminal enterprises has also grown accordingly. However, traditional network Intrusion Detection System makes significant use of pattern matching to identify malicious behaviors and have bad performance on detecting zero-day exploits in which a new attack is employed. In this paper, a novel method of anomaly detection in industrial control network is proposed based on RNN-GBRBM feature decoder. The method employ network packets and extract high-quality features from raw features which is selected manually. A modified RNN-RBM is trained using the normal traffic in order to learn feature patterns of the normal network behaviors. Then the test traffic is analyzed against the learned normal feature pattern by using osPCA to measure the extent to which the test traffic resembles the learned feature pattern. Moreover, we design a semi-supervised incremental updating algorithm in order to improve the performance of the model continuously. Experiments show that our method is more efficient in anomaly detection than other traditional approaches for industrial control network. Copyright © 2017 The Institute of Electronics, Information and Communication Engineers.

关键词： Pattern matching

Measuring the popularity of job skills in recruitment market: A multi-criteria approach

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arXiv 2017年

作者： Xu, Tong Zhu, Hengshu Zhu, Chen Li, Pan Xiong, Hui Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Rutgers Business School Rutgers University

To cope with the accelerating pace of technological changes, talents are urged to add and refresh their skills for staying in active and gainful employment. This raises a natural question: what are the right skills to learn? Indeed, it is a nontrivial task to measure the popularity of job skills due to the diversified criteria of jobs and the complicated connections within job skills. To that end, in this paper, we propose a data driven approach for modeling the popularity of job skills based on the analysis of large-scale recruitment data. Specifically, we first build a job skill network by exploring a large corpus of job postings. Then, we develop a novel Skill Popularity based Topic Model (SPTM) for modeling the generation of the skill network. In particular, SPTM can integrate different criteria of jobs (e.g., salary levels, company size) as well as the latent connections within skills, thus we can effectively rank the job skills based on their multi-faceted popularity. Extensive experiments on real-world recruitment data validate the effectiveness of SPTM for measuring the popularity of job skills, and also reveal some interesting rules, such as the popular job skills which lead to high-paid *** Codes 68T30 Copyright © 2017, The Authors. All rights reserved.

关键词： Compensation (personnel)

The XMM-SERVS survey: XMM-Newton point-source catalogs for the W-CDF-S and ELAIS-S1 fields

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arXiv 2021年

作者： Ni, Qingling Brandt, W.N. Chen, Chien-Ting Luo, Bin Nyland, Kristina Yang, Guang Zou, Fan Aird, James Alexander, David M. Bauer, Franz Erik Lacy, Mark Lehmer, Bret D. Mallick, labani Salvato, Mara Schneider, Donald P. Tozzi, Paolo Traulsen, Iris Vaccari, Mattia Vignali, Cristian Vito, Fabio Xue, Yongquan Banerji, Manda Chow, Kate Comastri, Andrea Moro, Agnese Del Gilli, Roberto Mullaney, James Paolillo, Maurizio Schwope, Axel Shemmer, Ohad Sun, Mouyuan Timlin, John D. Trump, Jonathan R. Department of Astronomy and Astrophysics 525 Davey Lab The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics 104 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Marshall Space Flight Center HuntsvilleAL35811 United States School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210093 China Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education Jiangsu Nanjing210093 China National Research Council The U.S. Naval Research Laboratory 4555 Overlook Ave. SW WashingtonDC20375 United States Department of Physics and Astronomy Texas A&M University College StationTX77843-4242 United States George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy Texas A&M University College StationTX77843-4242 United States Institute for Astronomy University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Centre for Extragalactic Astronomy Department of Physics Durham University DurhamDH1 3LE United Kingdom Instituto de Astrofísica and Centro de Astroingeniería Facultad de Física Pontificia Universidad Católica de Chile Casilla 306 Santiago 22 Chile Millennium Institute of Astrophysics Nuncio Monseñor Sótero Sanz 100 of 104 Providencia Santiago Chile Space Science Institute 4750 Walnut Street Suite 205 BoulderCO80301 United States National Radio Astronomy Observatory 520 Edgemont Road CharlottesvilleVA22903 United States Department of Physics University of Arkansas 226 Physics Building 825 West Dickson Street FayettevilleAR72701 United States Indian Institute of Astrophysics Block II Koramangala Bangalore560034 India MPE Giessenbachstrasse 1 Garching85748 Germany INAF Osservatorio Astrofisico di Firenze Largo Enrico Fermi 5 FirenzeI-50125 Italy An der Ster

We present the X-ray point-source catalogs in two of the XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS) fields, W-CDF-S (4.6 deg2) and ELAIS-S1 (3.2 deg2), aiming to fill the gap between deep pencil-beam X-ray surveys and shallow X-ray surveys over large areas. The W-CDF-S and ELAIS-S1 regions were targeted with 2.3 Ms and 1.0 Ms of XMM-Newton observations, respectively;1.8 Ms and 0.9 Ms exposures remain after flare filtering. The survey in W-CDF-S has a flux limit of 1.0 × 10−14 erg cm−2 s−1 over 90% of its area in the 0.5-10 keV band;4053 sources are detected in total. The survey in ELAIS-S1 has a flux limit of 1.3 × 10−14 erg cm−2 s−1 over 90% of its area in the 0.5-10 keV band;2630 sources are detected in total. Reliable optical-to-IR multiwavelength counterpart candidates are identified for ≈ 89% of the sources in W-CDF-S and ≈ 87% of the sources in ELAIS-S1. 3186 sources in W-CDF-S and 1985 sources in ELAIS-S1 are classified as AGNs. We also provide photometric redshifts for X-ray sources;≈ 84% of the 3319/2001 sources in W-CDF-S/ELAIS-S1 with optical-to-NIR forced photometry available have either spectroscopic redshifts or high-quality photometric redshifts. The completion of the XMM-Newton observations in the W-CDF-S and ELAIS-S1 fields marks the end of the XMM-SERVS survey data gathering. The ≈ 12,000 point-like X-ray sources detected in the whole ≈ 13 deg2 XMM-SERVS survey will benefit future large-sample AGN studies. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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REGIONAL CLIMATES

Bulletin of the American Meteorological Society 2022年第8期103卷 S341-S454页

作者： Abida, A. Agyakwah, W. Aldeco, Laura S. Alfaro, Eric J. Allen, Teddy Alves, Lincoln M. Amador, Jorge A. Andrade, B. Asgarzadeh, P. Avalos, Grinia Baez, Julian Bardin, M.Yu. Bekele, E. Bertalanic, Renato Bissolli, Peter Bochníček, Oliver Bukunt, Brandon Calderón, Blanca Campbell, Jayaka D. Chandler, Elise Charlton, Candice S. Cheng, Vincent Y.S. Clarke, Leonardo A. Correa, Kris Cortés Salazar, Catalina R. Costa, Felipe Crhová, Lenka Cunha, Ana Paula Demircan, Mesut Dhurmea, K.R. Domínguez, Diana A. Dulamsuren, Dashkhuu ElKharrim, M. Espinoza, Jhan-Carlo Fazl-Kezemi, A. Fedaeff, Nava Fenimore, Chris Fuhrman, Steven Ganter, Catherine Gleason, Karin Guard, Charles P. Hagos, Samson Hanafusa, Mizuki Heim, Richard R. Kennedy, John Hellström, Sverker Hidalgo, Hugo G. Ijampy, I.A. Im, Gyo Soon Jumaux, G. Kabidi, K. Kerr, Kenneth Khalatyan, Yelena Khan, Valentina Khiem, Mai Van Koch, Tobias Koren, Gerbrand Korshunova, Natalia N. Kruger, A.C. Lakatos, Mónika Mamen, Jostein Lam, Hoang Phuc Lander, Mark A. Lavado-Casimiro, Waldo Lee, Tsz-Cheung Leung, Kinson H.Y. Liu, Xuefeng Lu, Rui Marengo, José A. Marjan, Mohammadi Martínez, Ana E. McBride, Charlotte Mekonnen, Ademe Mietus, Mirek Misevicius, Noelia Moise, Aurel Molina-Carpio, Jorge Mora, Natali Mostafa, Awatif E. Ndiaye, O. Nieto, Juan J. Olafsdottir, Kristin Pascual Ramírez, Reynaldo Phillips, David Porat, Amos Rodriguez Guisado, Esteban Rajeevan, Madhavan Ramos, Andrea M. Recalde Coronel, Cristina Reyes Kohler, Alejandra J. Robjhon, M. Ronchail, Josyane Salinas, Roberto Sánchez-Lugo, Ahira Sato, Hirotaka Sato, Hitoshi Sayouri, Amal Sensoy, Serhat Setiawan, Amsari Mudzakir Sima, F. Smith, Adam Sorel, Matthieu Spillane, Sandra Spence, Jacqueline M. Sreejith, O.P. Srivastava, A.K. Stephenson, Tannecia S. Takahashi, Kiyotoshi Taylor, Michael A. Thiaw, Wassila M. Tobin, Skie Trescilo, Lidia Trotman, Adrian R. Van Meerbeeck, Cedric J. Vazifeh, A. Wakamatsu, Shunya Zaheer, M.F. Zeng, F. Zhang, Peiqun Zhu, Zhiwei Agence Nationale de l’Aviation Civile et de la Météorologie Senegal Climate Prediction Center National Centers for Environmental Prediction NOAA College ParkMD United States Servicio Meteorológico Nacional Buenos Aires Argentina Center for Geophysical Research School of Physics University of Costa Rica San José Costa Rica Caribbean Institute for Meteorology and Hydrology Bridgetown Barbados Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Seychelles Meteorological Authority Mahe Seychelles National Center for Climate and Drought Crisis Management Islamic Republic of Iranian Meteorological Organization Iran Servicio Nacional de Meteorología e Hidrología del Perú Lima Peru Universidad Católica Nuestra Señora de la Asunción Asunción Paraguay Yu. A. Izrael Institute of Global Climate and Ecology Institute of Geography Russian Academy of Sciences Russia Slovenian Environment Agency Slovenia Deutscher Wetterdienst WMO RA VI Regional Climate Centre Network Offenbach Germany Slovak Hydrometeorological Institute Slovakia NOAA/NWS Weather Forecast Office Guam Center for Geophysical Research University of Costa Rica San José Costa Rica Department of Physics The University of the West Indies Jamaica Bureau of Meteorology MelbourneVIC Australia Environment and Climate Change Canada TorontoON Canada Dirección de Meteorológica de Chile Santiago de Chile Chile Guayaquil Ecuador Czech Hydrometeorologival Institute Czech Republic Turkish State Meteorological Service Ankara Turkey Mauritius Meteorological Service Vacoas Mauritius Institute of Meteorology Hydrology and Environment National Agency for Meteorology Ulaanbaatar Mongolia Direction de la Météorologie Nationale Maroc Rabat Morocco Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Auckland New Zealand NOAA/NESDIS National Centers for Environmental Information AshevilleNC United States NOAA/NWS National Cen

Orderness predicts academic performance: Behavioral analysis on campus lifestyle

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arXiv 2017年

作者： Cao, Yi Gao, Jian Lian, Defu Rong, Zhihai Shi, Jiatu Wang, Qing Wu, Yifan Yao, Huaxiu Zhou, Tao CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory for NeuroInformation of Ministry of Education School of Life Science and Technology Center for Information in Medicine University of Electronic Science and Technology of China Chengdu611731 China

Quantitative understanding of relationships between students' behavioral patterns and academic performances is a significant step towards personalized education. In contrast to previous studies that mainly based on questionnaire surveys, in this paper, we collect behavioral records from 18,960 undergraduate students' smart cards and propose a novel metric, called orderness, which measures the regularity of campus daily life (e.g., meals and showers) of each student. Empirical analysis demonstrates that academic performance (GPA) is strongly correlated with orderness. Furthermore, we show that orderness is an important feature to predict academic performance, which remarkably improves the prediction accuracy even at the presence of students' diligence. Based on these analyses, education administrators could better guide students' campus lives and implement effective interventions in an early stage when necessary. Copyright © 2017, The Authors. All rights reserved.

关键词： Students

C-band real-time 400/300 Gb/s OOK bidirectional interconnection over 20 km multicore fibers

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Chinese Optics Letters 2017年第8期15卷 12-16页

作者：冯振华计红林唐明义理林甘霖薛雷吴琼李博睿赵佳佳童维军付松年刘德明胡卫生 Wuhan National Lab for Optoelectronics (WNLO) & National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Advanced Optical Communication Systems and Network Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Optical fiber and Cable Manufacture Technology Yangtze Optical fiber and Cable Joint Stock Limited Company (YOFC) Wuhan 430073 China

With the increasing capacity demands fueled by the emerging heterogeneous and bandwidth-intensive applica- tions, such as cloud computing, 4K/8K videos, and virtual reality （VR）, the need for low-cost, power-efficient, and high-density short-reach optical interconnects operating at 100 Gb/s and beyond has drawn significant research efforts.

关键词： OOK ODI C-band real-time 400/300 Gb/s OOK bidirectional interconnection over 20 km multicore fibers over time real

Creating Knowledge and Wisdom via Big data Analytics: Preface for ITQM 2017

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Procedia Computer science 2017年 122卷 1-9页

作者： Ahuja, Vandana Shi, Yong Khazanchi, Deepak Abidi, Naseem Tian, Yingjie Berg, Daniel Tien, James M. Jaypee Business School A-10 Sector-62 Noida Uttar Pradesh201 307 India School of Economics and Management University of Chinese Academy of Sciences Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha Chinese Academy of Sciences OmahaNE68182 United States College of Engineering University of Miami Coral GablesFL33124 United States