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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hantao Yao Shiliang Zhang Dongming Zhang Yongdong Zhang Jintao Li Yu Wang Qi Tian University of Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Electronic Engineering and Computer Science Peking University Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Computer Science University of Texas at San Antonio San Antonio USA

ISBN: (纸本)9781509060689

This paper targets to bring together the research efforts on two fields that are growing actively in the past few years: multicamera person Re-Identification (ReID) and large-scale image retrieval. We demonstrate that the essentials of image retrieval and person ReID are the same, i.e., measuring the similarity between images. However, person ReID requires more discriminative and robust features to identify the subtle differences of different persons and overcome the large variance among images of the same person. Specifically, we propose a coarse-to-fine (C2F) framework and a Convolutional Neural network structure named as Conv-Net to tackle the large-scale person ReID as an image retrieval task. Given a query person image, the C2F firstly employ Conv-Net to extract a compact descriptor and perform the coarse-level search. A robust descriptor conveying more spatial cues is hence extracted to perform the fine-level search. Extensive experimental results show that the proposed method outperforms existing methods on two public datasets. Further, the evaluation on a large-scale Person-520K dataset demonstrates that our work is significantly more efficient than existing works, e.g., only needs 180ms to identify a query person from 520K images.

关键词： Training Feature extraction Cameras Robustness Image retrieval Linear programming

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Time-Bin-Encoded Boson Sampling with a Single-Photon Device

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Physical Review Letters 2017年第19期118卷 190501-190501页

作者： Yu He X. Ding Z.-E. Su H.-L. Huang J. Qin C. Wang S. Unsleber C. Chen H. Wang Y.-M. He X.-L. Wang W.-J. Zhang S.-J. Chen C. Schneider M. Kamp L.-X. You Z. Wang S. Höfling Chao-Yang Lu Jian-Wei Pan Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China CAS-Alibaba Quantum Computing Laboratory CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China Technische Physik Physikalisches Instität and Wilhelm Conrad Röntgen-Center for Complex Material Systems Universitat Würzburg Am Hubland D-97074 Wüzburg Germany State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology (SIMIT) Chinese Academy of Sciences 865 Changning Road Shanghai 200050 China SUPA School of Physics and Astronomy University of St Andrews St Andrews KY16 9SS United Kingdom

Boson sampling is a problem strongly believed to be intractable for classical computers, but can be naturally solved on a specialized photonic quantum simulator. Here, we implement the first time-bin-encoded boson sampling using a highly indistinguishable (∼94%) single-photon source based on a single quantum-dot-micropillar device. The protocol requires only one single-photon source, two detectors, and a loop-based interferometer for an arbitrary number of photons. The single-photon pulse train is time-bin encoded and deterministically injected into an electrically programmable multimode network. The observed three- and four-photon boson sampling rates are 18.8 and 0.2 Hz, respectively, which are more than 100 times faster than previous experiments based on parametric down-conversion.

关键词： Optical tests of quantum theory Quantum algorithms Quantum optics Computational complexity

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Deterministic realization of collective measurements via photonic quantum walks

arXiv

引用

arXiv 2017年

作者： Hou, Zhibo Tang, Jun-Feng Shang, Jiangwei Zhu, Huangjun Li, Jian Yuan, Yuan Wu, Kang-Da Xiang, Guo-Yong Li, Chuan-Feng Guo, Guang-Can Key Laboratory of Quantum Information University of Science and Technology of China CAS Hefei230026 China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Naturwissenschaftlich-Technische Fakultät Universität Siegen Siegen57068 Germany Beijing Key Laboratory of Nanophotonics Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing100081 China Institute for Theoretical Physics University of Cologne Cologne50937 Germany Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China State Key Laboratory of Surface Physics Fudan University Shanghai200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China Institute of Signal Processing Transmission 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

Collective measurements on identically prepared quantum systems can extract more information than local measurements, thereby enhancing information-processing efficiency. Although this nonclassical phenomenon has been known for two decades, it has remained a challenging task to demonstrate the advantage of collective measurements in experiments. Here we introduce a general recipe for performing deterministic collective measurements on two identically prepared qubits based on quantum walks. Using photonic quantum walks, we realize experimentally an optimized collective measurement with fidelity 0.9946 without post selection. As an application, we achieve the highest tomographic efficiency in qubit state tomography to date. Our work offers an effective recipe for beating the precision limit of local measurements in quantum state tomography and metrology. In addition, our study opens an avenue for harvesting the power of collective measurements in quantum information processing and for exploring the intriguing physics behind this power. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum optics

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Classification Based on Multilayer Extreme Learning Machine for Motor Imagery Task from EEG Signals

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Procedia Computer science 2016年 88卷 176-184页

作者： Lijuan Duan Menghu Bao Jun Miao Yanhui Xu Juncheng Chen College of Computer Science and Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing 100124 China

Classification of motor imagery electroencephalogram (EEG) is one of the most important technologies for BCI. To improve the accuracy, this paper introduces a classification system based on Multilayer Extreme Learning Machine (ML-ELM). In the system, the combination of PCA and LDA is chosen as the method of feature extraction and the ML-ELM is used to classify. The ML-ELM has not only the advantage which ELM has but also better performance than ELM. In the experiment, our method is compared with the methods based on ELM, such as kernel-ELM, Constrained-ELM and V-ELM, and some state-of–the-art methods on the same dataset. The experimental results show that ML-ELM is much more suitable for motor imagery EEG data and has better performance than the others.

关键词： Electroencephalogram Classification Motor Imagery Extreme Learning Machine Multilayer Extreme Learning Machine

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HANSpeller++: A unified framework for chinese spelling correction 8

HANSpeller++: A unified framework for chinese spelling corre...

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8th SIGHAN Workshop on Chinese Language Processing, SIGHAN 2015

作者： Zhang, Shuiyuan Xiong, Jinhua Hou, Jianpeng Zhang, Qiao Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology China Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781941643570

Increased interest in China from foreigners has led to a corresponding interest in the study of Chinese. However, the learning of Chinese by non-native speakers will encounter many difficulties, Chinese spelling check techniques for Chinese as a Foreign Language(CFL) learners is highly desirable. This paper presents our work on the SIGHAN-2015 Chinese Spelling Check task. The task focuses on spelling checking on Chinese essays written by CFL learners. We propose a unified framework called HANSpeller++ based on our previous HANSpeller for Chinese spelling correction. The framework consists of candidate generating,candidates re-ranking and final global decision making. Experiments show good performance on the test data of the task. © 2015 Proceedings of the 8th SIGHAN Workshop on Chinese Language Processing, SIGHAN 2015 - co-located with 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, ACL IJCNLP 2015. All rights reserved.

关键词： Decision making

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A simulator for swarm AUVs acoustic communication networking 11

A simulator for swarm AUVs acoustic communication networking

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11th ACM International Conference on Underwater networks and Systems, WUWNet 2016

作者： Li, Guannan Liu, Jun Wang, Xue Xu, Hongli Cui, Jun-Hong Shenyang Institute of Automation CAS University of Chinese Academy of Sciences No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Changchun China Shenyang Institute of Automation CAS Northeast University No. 114 Nanta Street Shenhe District Shenyang China Shenyang Institute of Automation CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States

ISBN: (纸本)9781450346375

This paper presents a simulator for swarm operations designed to verify algorithms for a swarm of autonomous underwater robots (AUVs), specifically for constructing an underwater communication network with AUVs carrying acoustic communication devices. This simulator consists of three nodes: a virtual vehicle node (VV), a virtual environment node (VE), and a visual showing node (VS). The modular design treats AUV models as a combination of virtual equipment. An expert acoustic communication simulator is embedded in this simulator, to simulate scenarios with dynamic acoustic communication nodes. The several simulations we have performed demonstrate that this simulator is easy to use and can be further improved.

关键词： Simulators

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Simulate Human Saccadic Scan-Paths in Target Searching

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International Journal of Intelligence science 2016年第1期6卷 1-9页

作者： Lijuan Duan Jun Miao David M. W. Powers Jili Gu Laiyun Qing Beijing Key Laboratory of Trusted Computing Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data College of Computer Science and Technology Beijing University of Technology Beijing China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing Technology CAS Beijing China School of Computer Science Engineering & Maths Flinders University of South Australia Adelaide South Australia Beijing Samsung Telecom R&D Center Beijing China University of Chinese Academy of Sciences Beijing China

Human saccade is a dynamic process of information pursuit. There are many methods using either global context or local context cues to model human saccadic scan-paths. In contrast to them, this paper introduces a model for gaze movement control using both global and local cues. To test the performance of this model, an experiment is done to collect human eye movement data by using an SMI iVIEW X Hi-Speed eye tracker with a sampling rate of 1250 Hz. The experiment used a two-by-four mixed design with the location of the targets and the four initial positions. We compare the saccadic scan-paths generated by the proposed model against human eye movement data on a face benchmark dataset. Experimental results demonstrate that the simulated scan-paths by the proposed model are similar to human saccades in term of the fixation order, Hausdorff distance, and prediction accuracy for both static fixation locations and dynamic scan-paths.

关键词： Saccadic Scan-Paths Eye Movement Fixation Locations Dynamic Scan-Paths

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The Nanograv 11-year data set: Limits on gravitational wave memory

arXiv

引用

arXiv 2019年

作者： Aggarwal, K. Arzoumanian, Z. Bakerx, P.T. Brazier, A. Brook, P.R. Burke-Spolaor, S. Chatterjee, S. Cordes, J.M. Cornish, N.J. Crawford, F. Cromartie, H.T. Crowter, K. Decesary, M. Demorest, P.B. Dolch, T. Ellisy, J.A. Ferdman, R.D. Ferrara, E.C. Fonsecn, E. Garver-Daniels, N. Gentile, P. Good, D. Hazbouny, J.S. Holgado, A.M. Huerta, E.A. Islo, K. Jennings, R. Jones, G. Jones, M.L. Kaplan, D.L. Kelley, L.Z. key, J.S. Lam, M.T. Lazio, T.J.W. Levin, L. Lorimer, D.R. Luo, J. Lynch, R.S. Madisony, D.R. McLaughlin, M.A. McWilliams, S.T. Mingarelli, C.M.F. Ng, C. Nice, D.J. Pennucciy, T.T. Poll, N.S. Ransom, S.M. Ray, P.S. Siemens, X. Simon, J. Spiewak, R. Stairs, H. Stinebring, D.R. Stovally, K. Swiggumy, J.K. Taylor, S.R. Vallisneri, M. Van Haasterenz, R. Vigeland, S.J. Witt, C.A. Zhu, W.W. Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Department of Physics and Astronomy Widener University One University Place thesterPA19013 United States Department of Astronomy Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Ithaca NY14853 United States Department of Physics Montana State University BozemanMT59717 United States Department of Physics and Astronomy Franklin & Marshall College P.O. Box 3003 LancasterPA17604 United States University of Virginia Department of Astronomy P.O. Box 400325 CharlottesvilleVA22904 United States Department of Physics and Astronomy University of British Columbia 6224 Agricultural Road VancouverBCV6T1Z1 Canada Department of Physics Lafayette College EastonPA18042 United States National Radio Astronomy Observatory 1003 Lopezville Rd. SocorroNM87801 United States Department of Physics Hillsdale College 33 E. College Street HillsdaleMI49242 United States Infinia ML 202 Rigsbee Avenue DurhamNC27701 United States Department of Physics University of East Anglia Norwich United Kingdom NASA Goddard Space Flight Center GreenbeltMD20771 United States Department of Physics McGill University 3600 University St. MontrealQCH3A 2T8 Canada University of Washington Bothell 18115 Campus Way NE BothellWA98011 United States NCSA Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Department of Physics Columbia University New YorkNY10027 United States Northwestern Uni

The mergers of supermassive black hole binaries (SMBHB) promise to be incredible sources of gravitational waves (GW). While the oscillatory part of the merger gravitational waveform will be outside the frequency sensitivity range of pulsar timing arrays (PTA), the non-oscillatory GW memory effect is detectable. Further, any burst of gravitational waves will produce GW memory, making memory a useful probe of unmodeled exotic sources and new physics. We searched the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) 11-year data set for GW memory. This dataset is sensitive to very low frequency GWs of ∼ 3 to 400 nHz (periods of ∼ 11 yr-1 mon). Finding no evidence for GWs, we placed limits on the strain amplitude of GW memory events during the observation period. We then used the strain upper limits to place limits on the rate of GW memory causing events. At a strain of 2.5×10-14, corresponding to the median upper limit as a function of source sky position, we set a limit on the rate of GW memory events at -1. That strain corresponds to a SMBHB merger with reduced mass of ηM ∼ 2×1010Mo and inclination of t = π/3 at a distance of 1 Gpc. , we analyzed the NANOGrav 9-year data set as well. This analysis found an anomolous signal, which does not appear in the 11-year data set. This signal is not a GW, and its origin remains unknown. Copyright © 2019, The Authors. All rights reserved.

关键词： Gravity waves

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The International Pulsar Timing Array: Second data release

arXiv

引用

arXiv 2019年

作者： Perera, B.B.P. DeCesar, M.E. Demorest, P.B. Kerr, M. Lentati, L. Nice, D.J. Oslowski, S. Ransom, S.M. Keith, M.J. Arzoumanian, Z. Bailes, M. Baker, P.T. Bassa, C.G. Bhat, N.D.R. Brazier, A. Burgay, M. Burke-Spolaor, S. Caballero, R.N. Champion, D.J. Chatterjee, S. Chen, S. Cognard, I. Cordes, J.M. Crowter, K. Dai, S. Desvignes, G. Dolch, T. Ferdman, R.D. Ferrara, E.C. Fonseca, E. Goldstein, J.M. Graikou, E. Guillemot, L. Hazboun, J.S. Hobbs, G. Hu, H. Islo, K. Janssen, G.H. Karuppusamy, R. Kramer, M. Lam, M.T. Lee, K.J. Liu, K. Luo, J. Lyne, A.G. Manchester, R.N. McKee, J.W. McLaughlin, M.A. Mingarelli, C.M.F. Parthasarathy, A.P. Pennucci, T.T. Perrodin, D. Possenti, A. Reardon, D.J. Russell, C.J. Sanidas, S.A. Sesana, A. Shaifullah, G. Shannon, R.M. Siemens, X. Simon, J. Spiewak, R. Stairs, I.H. Stappers, B.W. Swiggum, J.K. Taylor, S.R. Theureau, G. Tiburzi, C. Vallisneri, M. Vecchio, A. Wang, J.B. Zhang, S.B. Zhang, L. Zhu, W.W. Zhu, X.J. Jodrell Bank Centre for Astrophysics School of Physics and Astronomy University of Manchester ManchesterM13 9PL United Kingdom Arecibo Observatory HC3 Box 53995 AreciboPR 00612 United States Department of Physics Lafayette College EastonPA18042 United States National Radio Astronomy Observatory P.O. Box O SocorroNM87801 United States Space Science Division Naval Research Laboratory WashingtonDC20375-5352 United States Astrophysics Group Cavendish Laboratory JJ Thomson Avenue CambridgeCB3 0HE United Kingdom Centre for Astrophysics and Supercomputing Swinburne University of Technology Post Box 218 HawthornVIC3122 Australia National Radio Astronomy Observatory 520 Edgemont Road CharlottesvilleVA22903 United States Code 662 X-ray Astrophysics Laboratory NASA Goddard Space Flight Center GreenbeltMD20770 United States Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States ASTRON Netherlands Institute for Radio Astronomy Oude Hoogeveensedijk 4 Dwingeloo7991 PD Netherlands International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Department of Astronomy Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States INAF-Osservatorio Astronomico di Cagliari via della Scienza 5 SelargiusI-09047 Italy Canadian Institute for Advanced Research CIFAR Azrieli Global Scholar MaRS Centre West Tower 661 University Ave. Suite 505 TorontoONM5G 1M1 Canada Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Station de radioastronomie de Nançay Observatoire de Paris PSL Research University C

In this paper, we describe the International Pulsar Timing Array second data release, which includes recent pulsar timing data obtained by three regional consortia: the European Pulsar Timing Array, the North American Nanohertz Observatory for Gravitational Waves, and the Parkes Pulsar Timing Array. We analyse and where possible combine high-precision timing data for 65 millisecond pulsars which are regularly observed by these groups. A basic noise analysis, including the processes which are both correlated and uncorrelated in time, provides noise models and timing ephemerides for the pulsars. We find that the timing precisions of pulsars are generally improved compared to the previous data release, mainly due to the addition of new data in the combination. The main purpose of this work is to create the most up-to-date IPTA data release. These data are publicly available for searches for low-frequency gravitational waves and other pulsar science. Copyright © 2019, The Authors. All rights reserved.

关键词： Pulsars

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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