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arXiv 2020年

作者： Kerr, M. Reardon, D.J. Hobbs, G. Shannon, R.M. Manchester, R.N. Dai, S. Russell, C.J. Zhang, S.-B. van Straten, W. Oslowski, S. Parthasarathy, A. Spiewak, R. Bailes, M. Bhat, N.D.R. Cameron, A.D. Coles, W.A. Dempsey, J. Deng, X. Goncharov, B. Kaczmarek, J.F. Keith, M.J. Lasky, P.D. Lower, M.E. Preisig, B. Sarkissian, J.M. Toomey, L. Wang, H. Wang, J. Zhang, L. Zhu, X. Space Science Division Naval Research Laboratory WashingtonDC20375-5352 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia CSIRO Astronomy and Space Science Australia Telescope National Facility PO Box 76 EppingNSW1710 Australia CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 AlexandriaNSW1435 Australia University of Chinese Academy of Sciences Beijing100049 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210008 China International Centre for Radio Astronomy Research University of Western Australia CrawleyWA6009 Australia Institute for Radio Astronomy & Space Research Auckland University of Technology Private Bag 92006 Auckland1142 New Zealand International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Department of Electrical and Computer Engineering University of California San DiegoCA92093 United States CSIRO Information Management & Technology GPO Box 1700 CanberraACT2601 Australia School of Physics and Astronomy Monash University VIC3800 Australia CSIRO Astronomy and Space Science Parkes Observatory 473 Telescope road ParkesNSW2870 Australia Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy University of Manchester Alan Turing Building Oxford Road ManchesterM13 9PL United Kingdom School of Physics and Electronic Engineering Guangzhou University Guangzhou510006 China Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science 1-Street Urumqi Xinjiang830011 China National Astronomical Observatories Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China

We describe 14 years of public data from the Parkes Pulsar Timing Array (PPTA), an ongoing project that is producing precise measurements of pulse times of arrival from 26 millisecond pulsars using the 64-m Parkes radio telescope with a cadence of approximately three weeks in three observing bands. A comprehensive description of the pulsar observing systems employed at the telescope since 2004 is provided, including the calibration methodology and an analysis of the stability of system components. We attempt to provide full accounting of the reduction from the raw measured Stokes parameters to pulse times of arrival to aid third parties in reproducing our results. This conversion is encapsulated in a processing pipeline designed to track provenance. Our data products include pulse times of arrival for each of the pulsars along with an initial set of pulsar parameters and noise models. The calibrated pulse profiles and timing template profiles are also available. These data represent almost 21,000 hrs of recorded data spanning over 14 years. After accounting for processes that induce time-correlated noise, 22 of the pulsars have weighted root-mean-square timing residuals of Copyright © 2020, The Authors. All rights reserved.

关键词： Pulsars

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VFlash: Virtualized Flash for Optimizing the I/O Performance in Mobile Devices

VFlash: Virtualized Flash for Optimizing the I/O Performance...

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作者： Chen, Renhai Wang, Yi Hu, Jingtong Liu, Duo Shao, Zili Guan, Yong Beijing Advanced Innovation Center for Imaging Technology Beijing100048 China College of Information Engineering Capital Normal University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74074 United States College of Computer Science Chongqing University Chongqing400044 China Embedded Systems and CPS Laboratory Department of Computing Hong Kong Polytechnic University Hong Kong

I/O is becoming one of major performance bottlenecks in NAND-flash-based mobile devices. Novel nonvolatile memories (NVMs), such as phase change memory and spin-transfer torque random access memory, can provide fast read/write operations. In this paper, we propose a unified NVM/flash architecture to improve the I/O performance. A transparent scheme, virtualized flash (vFlash), is also proposed to manage the unified architecture. Within vFlash, interapp and intra-app techniques are proposed to optimize the application performance by exploiting the historical locality and I/O access patterns of applications. Since vFlash is on the bottom of the I/O stack, the application features will be lost. Therefore, we also propose a cross-layer technique to transfer the application information from the application layer to the vFlash layer. The proposed scheme is evaluated based on an Android platform, and the experimental results show that the proposed scheme can effectively improve the I/O performance of mobile devices. © 1982-2012 IEEE.

关键词： Phase change memory

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Synergistic interactions promote behavior spreading and alter phase transition on multiplex Networks

arXiv

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arXiv 2018年

作者： Liu, Quan-Hui Wang, Wei Cai, Shi-Min Tang, Ming Lai, Ying-Cheng Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Laboratory for Modeling of Biological and Socio-technical Systems Northeastern University BostonMA02115 United States College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Center for Polymer Studies and Department of Physics Boston University BostonMA02215 United States School of Information Science Technology East China Normal University Shanghai200241 China School of Electrical Arizona State University Computer and Energy Engineering TempeAZ85287 United States

Synergistic interactions are ubiquitous in the real world. Recent studies have revealed that, for a single-layer network, synergy can enhance spreading and even induce an explosive contagion. There is at the present a growing interest in behavior spreading dynamics on multiplex networks. What is the role of synergistic interactions in behavior spreading in such networked systems? To address this question, we articulate a synergistic behavior spreading model on a double layer network, where the key manifestation of the synergistic interactions is that the adoption of one behavior by a node in one layer enhances its probability of adopting the behavior in the other layer. A general result is that synergistic interactions can greatly enhance the spreading of the behaviors in both layers. A remarkable phenomenon is that the interactions can alter the nature of the phase transition associated with behavior adoption or spreading dynamics. In particular, depending on the transmission rate of one behavior in a network layer, synergistic interactions can lead to a discontinuous (first-order) or a continuous (second-order) transition in the adoption scope of the other behavior with respect to its transmission rate. A surprising two-stage spreading process can arise: Due to synergy, nodes having adopted one behavior in one layer adopt the other behavior in the other layer and then prompt the remaining nodes in this layer to quickly adopt the behavior. Analytically, we develop an edge-based compartmental theory and perform a bifurcation analysis to fully understand, in the weak synergistic interaction regime where the dynamical correlation between the network layers is negligible, the role of the interactions in promoting the social behavioral spreading dynamics in the whole system. Copyright © 2018, The Authors. All rights reserved.

关键词： Network layers

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Using convolutional neural network with asymmetrical kernels to predict speed of elevated highway 2nd

Using convolutional neural network with asymmetrical kernels...

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2nd IFIP TC 12 International Conference on Intelligence science, ICIS 2017

作者： Zang, Di Ling, Jiawei Cheng, Jiujun Tang, Keshuang Li, Xin Department of Computer Science and Technology Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Transportation Information and Control Engineering Tongji University Shanghai China Shanghai Lujie Electronic Technology Co. Ltd. Pudong Shanghai China

ISBN: (纸本)9783319681207

In this paper, we present a deep learning based approach to performing the whole-day prediction of the traffic speed for the elevated highway. In order to learn the temporal features of traffic speed data in a hierarchical way, an improved convolutional neural network (CNN) with asymmetric kernels is proposed. Speed data are collected from loop detectors of Yan’an elevated highway of Shanghai. To test the performance of the presented method, we compare it with some conventional approaches of traffic speed estimation. Experimental results demonstrate that our method outperforms all of them. © IFIP International Federation for information Processing 2017.

关键词： Speed

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Traffic parameters prediction using a three-channel convolutional neural network 2nd

Traffic parameters prediction using a three-channel convolut...

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2nd IFIP TC 12 International Conference on Intelligence science, ICIS 2017

作者： Zang, Di Wang, Dehai Cheng, Jiujun Tang, Keshuang Li, Xin Department of Computer Science and Technology Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Transportation Information and Control Engineering Tongji University Shanghai China Shanghai Lujie Electronic Technology Co. Ltd. PudongShanghai China

ISBN: (纸本)9783319681207

Traffic three elements consisting of flow, speed and occupancy are very important parameters representing the traffic information. Prediction of them is a fundamental problem of Intelligent Transportation Systems (ITS). Convolutional Neural Network (CNN) has been proved to be an effective deep learning method for extracting hierarchical features from data with local correlations such as image, video. In this paper, in consideration of the spatiotemporal correlations of traffic data, we propose a CNN-based method to forecast flow, speed and occupancy simultaneously by converting raw flow, speed and occupancy (FSO) data to FSO color images. We evaluate the performance of this method and compare it with other prevailing methods for traffic prediction. Experimental results show that our method has superior performance. © IFIP International Federation for information Processing 2017.

关键词： Forecasting

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Quantum key distribution with distinguishable decoy states

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Physical Review A 2018年第1期98卷 012330-012330页

作者： Anqi Huang Shi-Hai Sun Zhihong Liu Vadim Makarov Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 People's Republic of China Institute for Quantum Computing University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario N2L 3G1 Canada College of Science National University of Defense Technology Changsha 410073 People's Republic of China College of Mechatronic Engineering and Automation National University of Defense Technology Changsha 410073 People's Republic of China Russian Quantum Center and MISIS University Moscow Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada

The decoy-state protocol has been considered to be one of the most important methods to protect the security of quantum key distribution (QKD) with a weak coherent source. Here we test two experimental approaches to generating the decoy states with different intensities: modulation of the pump current of a semiconductor laser diode, and external modulation by an optical intensity modulator. The former approach shows a side channel in the time domain that allows an attacker to distinguish s signal state from a decoy state, breaking a basic assumption in the protocol. We model a photon-number-splitting attack based on our experimental data, and show that it compromises the system's security. Then, based on the work of Tamaki et al. [New J. Phys. 18, 065008 (2016)], we obtain two analytical formulas to estimate the yield and error rate of single-photon pulses when the signal and decoy states are distinguishable. The distinguishability reduces the secure key rate below that of a perfect decoy-state protocol. To mitigate this reduction, we propose to calibrate the transmittance of the receiver (Bob's) unit. We apply our method to three QKD systems and estimate their secure key rates.

关键词： Quantum communication Quantum cryptography

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A community-powered search of machine learning strategy space to find NMR property prediction models

arXiv

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arXiv 2020年

作者： Bratholm, Lars A. Gerrard, Will Anderson, Brandon Bai, Shaojie Choi, Sunghwan Dang, Lam Hanchar, Pavel Howard, Addison Huard, Guillaume Kim, Sanghoon Kolter, Zico Kondor, Risi Kornbluth, Mordechai Lee, Youhan Lee, Youngsoo Mailoa, Jonathan P. Nguyen, Thanh Tu Popovic, Milos Rakocevic, Goran Reade, Walter Song, Wonho Stojanovic, Luka Thiede, Erik H. Tijanic, Nebojsa Torrubia, Andres Willmott, Devin Butts, Craig P. Glowacki, David R. Participants, Kaggle School of Chemistry University of Bristol Cantock’s Close BristolBS8 1TS United Kingdom School of Mathematics University of Bristol Fry Building Woodland Road BristolBS1 1UG United Kingdom Dept. of Computer Science University of Bristol Merchant Venturer’s Building BristolBS8 1UB United Kingdom Intangible Realities Laboratory University of Bristol Cantock’s Close BristolBS8 1TS United Kingdom Bosch Center for Artificial Intelligence PittsburghPA15222 United States Carnegie Mellon University PittsburghPA15213 United States National Institute of Supercomputing and Network Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of BNP Paribas Cardif Fyusion Inc. Kaggle Google Inc. Mountain View United States Ebay Korea 34F Gangnam Finance Center 152 Teheran Ro Gangnam Gu Seoul Korea Republic of Department of Computer Science University of Chicago 5730 S Ellis Ave ChicagoIL60637 United States Department of Statistics University of Chicago 5747 S Ellis Ave ChicagoIL60637 United States Center for Computational Mathematics Flatiron Institute 162 5th Ave. New YorkNY10010 United States Bosch Research and Technology Center CambridgeMA02139 United States Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of 111 Daedeok-daero 989beon-gil Yuseong-gu Daejeon Korea Republic of MINDS AND COMPANY 2621 Nambusunhwan-ro Gangnam-gu Seoul06267 Korea Republic of Totient Inc Sindjeliceva 9 Belgrade11000 Serbia KAIST Web Security & Privacy Lab 291 Daehak-ro Yuseong-guDaejeon34141 Korea Republic of *** Alicante Spain

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published ‘in-house’ efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties. Copyright © 2020, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance

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Coherence-based Automated Essay Scoring using Self-Attention

Coherence-based Automated Essay Scoring using Self-Attention

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第十七届全国计算语言学学术会议暨第六届基于自然标注大数据的自然语言处理国际学术研讨会(CCL 2018)

作者： Xia Li Minping Chen Jianyun Nie Zhenxing Liu Ziheng Feng Yingdan Cai Key Laboratory of Language Engineering and Computing Guangdong university of foreign studies Guangzh School of Information Science and Technology/School of Cyber Security Guangdong university of foreig Department of Computer Science and Operations Research University of montreal MontrealCanada

Automated essay scoring aims to score an essay automatically without any human *** methods heavily rely on manual feature engi-neering,making it expensive to extract the *** recent studies used neural-network-based scoring models to avoid feature *** of them used CNN or RNN to learn the representation of the *** these mod-els can cope with relationships between words within a short distance,they are limited in capturing long-distance relationships across *** particular,it is difficult to assess the coherence of the essay,which is an essential criterion in essay *** this paper,we use self-attention to capture useful long-distance relationships between words so as to estimate a coherence *** tested our model on two datasets(ASAP and a new non-native speaker dataset).In both cases,our model outperforms the existing state-of-the-art models.

关键词： Self-Attention Automated Essay Scoring Neural Networks

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Joint product numerical range and geometry of reduced density matrices

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science China(Physics,Mechanics & Astronomy) 2017年第2期60卷 9-17页

作者： Jianxin Chen Cheng Guo Zhengfeng Ji Yiu-Tung Poon Nengkun YU Bei Zeng Jie Zhou Joint Center for Quantum Information and Computer Science University of Maryland College Park 20740 USA Institute for Advanced Study Tsinghua University Beijing 100084 China Centre for Quantum Computation ＆ Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 Australia State Key Laboratory of Computer Science.Institute of Software Chinese Academy of Sciences Beijing 100084 China Department of Mathematics Iowa State University Ames 50011-2140 USA Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Canada Department of Mathematics ＆ Statistics University of Guelph Guelph N1G 2 W1 Canada Perimeter Institute for Theoretical Physics Waterloo N2L 2Y5 Canada

The reduced density matrices of a many-body quantum system form a convex set, whose three-dimensional projection is convex in R3. The boundary of may exhibit nontrivial geometry, in particular ruled surfaces. Two physical mechanisms are known for the origins of ruled surfaces： symmetry breaking and gapless. In this work, we study the emergence of ruled surfaces for systems with local Hamiltonians in infinite spatial dimension, where the reduced density matrices are known to be separable as a consequence of the quantum de Finetti＇s theorem. This allows us to identify the reduced density matrix geometry with joint product numerical range II of the Hamiltonian interaction terms. We focus on the case where the interaction terms have certain structures, such that a ruled surface emerges naturally when taking a convex hull of ∏. We show that, a ruled surface on sitting in ∏ has a gapless origin, otherwise it has a symmetry breaking origin. As an example, we demonstrate that a famous ruled surface, known as the oloid, is a possible shape of , with two boundary pieces of symmetry breaking origin separated by two gapless lines.

关键词： reduced density matrices quantum tomography numerical range quantum information oloid ruled surface

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