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On the optimality of tape merge of two lists with similar size 27

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On the optimality of tape merge of two lists with similar si...

27th International Symposium on Algorithms and Computation, ISAAC 2016

作者： Li, Qian Sun, Xiaoming Zhang, Jialin CAS Key Lab. of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783959770262

The problem of merging sorted lists in the least number of pairwise comparisons has been solved completely only for a few special cases. Graham and Karp [18] independently discovered that the tape merge algorithm is optimal in the worst case when the two lists have the same size. Stockmeyer and Yao[28], Murphy and Paull[24], and Christen[6] independently showed when the lists to be merged are of size m and n satisfying m ≤ n ≤ ⌊3/2m⌋ + 1, the tape merge algorithm is optimal in the worst case. This paper extends this result by showing that the tape merge algorithm is optimal in the worst case whenever the size of one list is no larger than 1.52 times the size of the other. The main tool we used to prove lower bounds is Knuth's adversary methods [18]. In addition, we show that the lower bound cannot be improved to 1.8 via Knuth's adversary methods. We also develop a new inequality about Knuth's adversary methods, which might be interesting in its own right. Moreover, we design a simple procedure to achieve constant improvement of the upper bounds for 2m - 2 ≤ n ≤ 3m. © Qian Li, Xiaoming Sun, and Jialin Zhang.

关键词： Mergers and acquisitions

Network slicing for service-oriented networks under resource constraints

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arXiv 2017年

作者： Zhang, Nan Liu, Ya-Feng Farmanbar, Hamid Chang, Tsung-Hui Hong, Mingyi Luo, Zhi-Quan School of Mathematical Sciences Peking University State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huawei Canada Research Center Ottawa Canada Department of Electrical and Computer Engineering University of Minnesota United States Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Network function virtualization

Optimal Estimation of Sensor Biases for Asynchronous Multi-Sensor data Fusion

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arXiv 2017年

作者： Pu, Wenqiang Liu, Ya-Feng Yan, Junkun Liu, Hongwei Luo, Zhi-Quan National Laboratory of Radar Signal Processing Collaborative Innovation Center of Information Sensing and Understanding Xidian University Xi'an710071 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen518100 China

An important step in a multi-sensor surveillance system is to estimate sensor biases from their noisy asynchronous measurements. This estimation problem is computationally challenging due to the highly nonlinear transformation between the global and local coordinate systems as well as the measurement asynchrony from different sensors. In this paper, we propose a novel nonlinear least squares (LS) formulation for the problem by assuming the existence of a reference target moving with an (unknown) constant velocity. We also propose an efficient block coordinate decent (BCD) optimization algorithm, with a judicious initialization, to solve the problem. The proposed BCD algorithm alternately updates the range and azimuth bias estimates by solving linear least squares problems and semidefinite programs (SDPs). In the absence of measurement noise, the proposed algorithm is guaranteed to find the global solution of the problem and the true biases. Simulation results show that the proposed algorithm significantly outperforms the existing approaches in terms of the root mean square error (RMSE). Copyright © 2017, The Authors. All rights reserved.

关键词： Mean square error

Active self-paced learning for cost-effective and progressive face identification

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arXiv 2017年

作者： Lin, Liang Wang, Keze Meng, Deyu Zuo, Wangmeng Zhang, Lei School of Data and Computer Science Sun Yat-sen University Guangzhou China Engineering Research Center Advanced Computing Engineering Software of Ministry of Education China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University China School of Computer Science and Technology Harbin Institute of Technology Harbin China Dept. of Computing Hong Kong Polytechnic University Hong Kong

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://***/projects/aspl/ Copyright © 201

关键词： Cost effectiveness

An interview based study of pioneering experiences in teaching and learning complex systems in higher education

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arXiv 2018年

作者： Lizier, Joseph T. Harré, Michael S. Mitchell, Melanie DeDeo, Simon Finn, Conor Lindgren, Kristian Lizier, Amanda L. Sayama, Hiroki Centre for Complex Systems Complex Systems Research Group Faculty of Engineering and IT University of Sydney NSW Australia Department of Computer Science Portland State University PortlandOR United States Santa Fe Institute Santa FeNM United States School of Informatics and Computing Indiana University BloomingtonIN United States Department of Social and Decision Sciences Carnegie Mellon University PittsburghPA United States CSIRO Data 61 MarsfieldNSW Australia Department of Space Earth and Environment Chalmers University of Technology Göteborg Sweden School of Education University of Technology Sydney NSW Australia Center for Collective Dynamics of Complex Systems Department of Systems Science and Industrial Engineering Binghamton University State University of New York NY United States

Due to the interdisciplinary nature of complex systems as a field, students studying complex systems at University level have diverse disciplinary backgrounds. This brings challenges (e.g. wide range of computer programming skills) but also opportunities (e.g. facilitating interdisciplinary interactions and projects) for the classroom. However, there is little published regarding how these challenges and opportunities are handled in teaching and learning Complex Systems as an explicit subject in higher education, and how this differs in comparison to other subject areas. We seek to explore these particular challenges and opportunities via an interview-based study of pioneering teachers and learners (conducted amongst the authors) regarding their experiences. We compare and contrast those experiences, and analyse them with respect to the educational literature. Our discussions explored: approaches to curriculum design, how theories/models/frameworks of teaching and learning informed decisions and experience, how diversity in student backgrounds was addressed, and assessment task design. We found a striking level of commonality in the issues expressed as well as the strategies to handle them, for example a significant focus on problem-based learning, and the use of major student-led creative projects for both achieving and assessing learning outcomes. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

Psychological and personality profiles of political extremists

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arXiv 2017年

作者： Alizadeh, Meysam Weber, Ingmar Cioffi-Revilla, Claudio Fortunato, Santo Macy, Michael Center for Complex Networks and Systems Research School of Informatics and Computing Indiana University BloomingtonIN47405 United States Computational Social Science Program Department of Computational and Data Sciences George Mason University FairfaxVA22030 United States Qatar Computing Research Institute Doha Qatar Social Dynamics Laboratory Cornell University IthacaNY14853 United States

Global recruitment into radical Islamic movements has spurred renewed interest in the appeal of political extremism. Is the appeal a rational response to material conditions or is it the expression of psychological and personality disorders associated with aggressive behavior, intolerance, conspiratorial imagination, and paranoia? Empirical answers using surveys have been limited by lack of access to extremist groups, while field studies have lacked psychological measures and failed to compare extremists with contrast groups. We revisit the debate over the appeal of extremism in the U.S. context by comparing publicly available Twitter messages written by over 355,000 political extremist followers with messages written by non-extremist U.S. users. Analysis of text-based psychological indicators supports the moral foundation theory which identifies emotion as a critical factor in determining political orientation of individuals. Extremist followers also differ from others in four of the Big Five personality traits. Copyright © 2017, The Authors. All rights reserved.

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Event-driven emotion cause extraction with corpus construction

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Event-driven emotion cause extraction with corpus constructi...

2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016

作者： Gui, Lin Wu, Dongyin Xu, Ruifeng Lu, Qin Zhou, Yu School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Guangdong Provincial Engineering Technology Research Center for Data Science China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781945626258

In this paper, we present our work in emotion cause extraction. Since there is no open dataset available, the lack of annotated resources has limited the research in this area. Thus, we first present a dataset we built using SINA city news. The annotation is based on the scheme of the W3C Emotion Markup Language. Second, we propose a 7-tuple definition to describe emotion cause events. Based on this general definition, we propose a new event-driven emotion cause extraction method using multi-kernel SVMs where a syntactical tree based approach is used to represent events in text. A convolution kernel based multi-kernel SVM are used to extract emotion causes. Because traditional convolution kernels do not use lexical information at the terminal nodes of syntactic trees, we modify the kernel function with a synonym based improvement. Even with very limited training data, we can still extract sufficient features for the task. Evaluations show that our approach achieves 11.6% higher F-measure compared to referenced methods. The contributions of our work include resource construction, concept definition and algorithm development. © 2016 Association for Computational Linguistics

关键词： Extraction

Direct evidence for shock-powered optical emission in a nova

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arXiv 2020年

作者： Aydi, Elias Sokolovsky, Kirill V. Chomiuk, Laura Steinberg, Elad Li, Kwan Lok Vurm, Indrek Metzger, Brian D. Strader, Jay Mukai, Koji Pejcha, Ondřej Shen, Ken J. Wade, Gregg A. Kuschnig, Rainer Moffat, Anthony F.J. Pablo, Herbert Pigulski, Andrzej Popowicz, Adam Weiss, Werner Zwintz, Konstanze Izzo, Luca Pollard, Karen R. Handler, Gerald Ryder, Stuart D. Filipović, Miroslav D. Alsaberi, Rami Z.E. Manojlović, Perica de Oliveira, Raimundo Lopes Walter, Frederick M. Vallely, Patrick J. Buckley, David A.H. Brown, Michael J.I. Harvey, Eamonn J. Kawash, Adam Kniazev, Alexei Kochanek, Christopher S. Linford, Justin Mikolajewska, Joanna Molaro, Paolo Orio, Marina Page, Kim L. Shappee, Benjamin J. Sokoloski, Jennifer L. Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Sternberg Astronomical Institute Moscow State University Universitetskii pr. 13 Moscow119992 Russia Astro Space Center of Lebedev Physical Institute Profsoyuznaya St. 84/32 Moscow117997 Russia Columbia Astrophysics Laboratory Department of Physics Columbia University New YorkNY10027 United States Racah Institute of Physics Hebrew University Jerusalem91904 Israel Department of Physics UNIST Ulsan44919 Korea Republic of Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Tartu Observatory University of Tartu Tõravere Tartumaa61602 Estonia CRESST X-ray Astrophysics Laboratory NASA GSFC GreenbeltMD20771 United States Department of Physics University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Institute of Theoretical Physics Faculty of Mathematics and Physics Charles University Prague Czech Republic Department of Astronomy Theoretical Astrophysics Center University of California BerkeleyCA94720 United States Department of Physics and Space Science Royal Military College of Canada PO Box 17000 Station Forces KingstonONK7K 7B4 Canada Institute of Communication Networks and Satellite Communications Graz University of Technology Infeldgasse 12 Graz8010 Austria Dépt. de Physique Univ. De Montréal C.P. 6128 Succ. Centre-Ville Centre de Recherche en Astrophysique du Quéebec MontréealQCH3C 3J7 Canada AAVSO 49 Bay State Rd CambridgeMA02138 United States Instytut Astronomiczny Uniwersytet Wroclawski Kopernika 11 Wroclaw51-622 Poland Silesian University of Technology Institute of Electronics Akademicka 16 Gliwice44-100 Poland Institute for Astrophysics University of Vienna Tuerkenschanzstrasse 17 ViennaA-1180 Austria Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstrasse 25 InnsbruckA-6020 Austria D

Classical novae are thermonuclear explosions that occur on the surfaces of white dwarf stars in interacting binary systems1. It has long been thought that the luminosity of classical novae is powered by continued nuclear burning on the surface of the white dwarf after the initial runaway2. However, recent observations of GeV γrays from classical novae have hinted that shocks internal to the nova ejecta may dominate the nova emission. Shocks have also been suggested to power the luminosity of events as diverse as stellar mergers3, supernovae4, and tidal disruption events5, but observational confirmation has been lacking. Here we report simultaneous space-based optical and γ-ray observations of the 2018 nova V906 Carinae (ASASSN-18fv), revealing a remarkable series of distinct correlated flares in both bands. The optical and γ-ray flares occur simultaneously, implying a common origin in shocks. During the flares, the nova luminosity doubles, implying that the bulk of the luminosity is shock-powered. Furthermore, we detect concurrent but weak X-ray emission from deeply embedded shocks, confirming that the shock power does not appear in the X-ray band and supporting its emergence at longer wavelengths. Our data, spanning the spectrum from radio to γ-ray, provide direct evidence that shocks can power substantial luminosity in classical novae and other optical transients. Copyright © 2020, The Authors. All rights reserved.

关键词： Luminance

Dictionary pair classifier driven convolutional neural networks for object detection

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Dictionary pair classifier driven convolutional neural netwo...

2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016

作者： Wang, Keze Lin, Liang Zuo, Wangmeng Gu, Shuhang Zhang, Lei School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Computer Science and Technology Harbin Institute of Technology China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781467388511

Feature representation and object category classification are two key components of most object detection methods. While significant improvements have been achieved for deep feature representation learning, traditional SVM/softmax classifiers remain the dominant methods for the final object category classification. However, SVM/softmax classifiers lack the capacity of explicitly exploiting the complex structure of deep features, as they are purely discriminative methods. The recently proposed discriminative dictionary pair learning (DPL) model involves a fidelity term to minimize the reconstruction loss and a discrimination term to enhance the discriminative capability of the learned dictionary pair, and thus is appropriate for balancing the representation and discrimination to boost object detection performance. In this paper, we propose a novel object detection system by unifying DPL with the convolutional feature learning. Specifically, we incorporate DPL as a Dictionary Pair Classifier Layer (DPCL) into the deep architecture, and develop an end-to-end learning algorithm for optimizing the dictionary pairs and the neural networks simultaneously. Moreover, we design a multi-task loss for guiding our model to accomplish the three correlated tasks: objectness estimation, categoryness computation, and bounding box regression. From the extensive experiments on PASCAL VOC 2007/2012 benchmarks, our approach demonstrates the effectiveness to substantially improve the performances over the popular existing object detection frameworks (e.g., R-CNN [13] and FRCN [12]), and achieves new state-of-the-arts.

关键词： Object detection