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Simultaneous Millimeter-wave, Gamma-ray, and Optical Monitoring of the Blazar PKS 2326-502 During a Flaring State

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arXiv 2023年

作者： Hood, J.C. Simpson, A. McDaniel, A. Foster, A. Ade, P.A.R. Ajello, M. Anderson, A.J. Austermann, J.E. Beall, J.A. Bender, A.N. Benson, B.A. Bianchini, F. Bleem, L.E. Carlstrom, J.E. Chang, C.L. Chaubal, P. Chiang, H.C. Chou, T.-L. Citron, R. Corbett Moran, C. Crawford, T.M. Crites, A.T. De Haan, T. Dobbs, M.A. Everett, W. Gallicchio, J. George, E.M. Gupta, N. Halverson, N.W. Hilton, G.C. Holder, G.P. Holzapfel, W.L. Hrubes, J.D. Huang, N. Hubmayr, J. Irwin, K.D. Knox, L. Lee, A.T. Li, D. Lowitz, A. Madejski, G. Malkan, M. McMahon, J.J. Meyer, S.S. Montgomery, J. Natoli, T. Nibarger, J.P. Noble, G. Novosad, V. Omori, Y. Padin, S. Patil, S. Pryke, C. Reichardt, C.L. Ruhl, J.E. Saliwanchik, B.R. Schaffer, K.K. Sievers, C. Smecher, G. Stark, A.A. Tucker, C. Veach, T. Vieira, J.D. Wang, G. Whitehorn, N. Wu, W.L.K. Yefremenko, V. Zebrowski, J.A. Zhang, L. Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics and Astronomy Vanderbilt University NashvilleTN37235 United States Department of Physics Rensselaer Polytechnic Institute TroyNY12180 United States Department of Physics and Astronomy Clemson University ClemsonSC29631 United States Department of Physics Case Western Reserve University ClevelandOH44106 United States Cardiff University CardiffCF10 3XQ United Kingdom Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States Nist Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States High Energy Physics Division Argonne National Laboratory 9700 S. Cass Avenue ArgonneIL60439 United States School of Physics University of Melbourne VIC Parkville3010 Australia Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Enrico Fermi Institute University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics McGill University 3600 Rue University QC MontrealH3A 2T8 Canada School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Durban South Africa University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Tapir Walter Burke Institute for Theoretical Physics California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St George St ON TorontoM5S 3H4 Canada Department of Astronomy & Astrophysics University of Toronto 50 St George St ON TorontoM5S 3H4 Canada Ibaraki Tsukuba305-0801 Japan Ibaraki Tsukuba305-0801 Japan Canadian Institute f

Including millimeter-wave (mm-wave) data in multi-wavelength studies of the variability of active galactic nuclei (AGN) can provide insights into AGN physics that are not easily accessible at other wavelengths. We demonstrate in this work the potential of cosmic microwave background (CMB) telescopes to provide long-term, high-cadence mm-wave AGN monitoring over large fractions of sky. We report on a pilot study using data from the SPTpol instrument on the South Pole Telescope (SPT), which was designed to observe the CMB at arcminute and larger angular scales. Between 2013 and 2016, SPTpol was used primarily to observe a single 500 deg2field, covering the entire field several times per day with detectors sensitive to radiation in bands centered at 95 and 150 GHz. We use SPT 150 GHz observations to create AGN light curves, and we compare these mm-wave light curves to those at other wavelengths, in particular γ-ray and optical. In this Letter, we focus on a single source, PKS 2326-502, which has extensive, day-timescale monitoring data in gamma-ray, optical, and now mm-wave between 2013 and 2016. We find PKS 2326-502 to be in a flaring state in the first two years of this monitoring, and we present a search for evidence of correlated variability between mm-wave, optical R band, and γ-ray observations. This pilot study is paving the way for AGN monitoring with current and upcoming CMB experiments such as SPT-3G, Simons Observatory, and CMB-S4, including multi-wavelength studies with facilities such as VRO-LSST. © 2023, CC BY.

关键词： Active Galactic Nuclei

Robust frequent directions with application in online learning

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arXiv 2017年

作者： Luo, Luo Chen, Cheng Zhang, Zhihua Li, Wu-Jun Zhang, Tong Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University 5 Yiheyuan Road Beijing100871 China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University 163 Xianlin Avenue Nanjing210023 China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Heuristic methods

NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

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NTIRE 2018 Challenge on Single Image Super-Resolution: Metho...

IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Radu Timofte Shuhang Gu Jiqing Wu Luc Van Gool Lei Zhang Ming-Hsuan Yang Muhammad Haris Greg Shakhnarovich Norimichi Ukita Shijia Hu Yijie Bei Zheng Hui Xiao Jiang Yanan Gu Jie Liu Yifan Wang Federico Perazzi Brian McWilliams Alexander Sorkine-Hornung Olga Sorkine-Hornung Christopher Schroers Jiahui Yu Yuchen Fan Jianchao Yang Ning Xu Zhaowen Wang Xinchao Wang Thomas S. Huang Xintao Wang Ke Yu Tak-Wai Hui Chao Dong Liang Lin Chen Change Loy Dongwon Park Kwanyoung Kim Se Young Chun Kai Zhang Pengjv Liu Wangmeng Zuo Shi Guo Jiye Liu Jinchang Xu Yijiao Liu Fengye Xiong Yuan Dong Hongliang Bai Alexandru Damian Nikhil Ravi Sachit Menon Cynthia Rudin Junghoon Seo Taegyun Jeon Jamyoung Koo Seunghyun Jeon Soo Ye Kim Jae-Seok Choi Sehwan Ki Soomin Seo Hyeonjun Sim Saehun Kim Munchurl Kim Rong Chen Kun Zeng Jinkang Guo Yanyun Qu Cuihua Li Namhyuk Ahn Byungkon Kang Kyung-Ah Sohn Yuan Yuan Jiawei Zhang Jiahao Pang Xiangyu Xu Yan Zhao Wei Deng Sibt Ul Hussain Muneeb Aadil Rafia Rahim Xiaowang Cai Fang Huang Yueshu Xu Pablo Navarrete Michelini Dan Zhu Hanwen Liu Jun-Hyuk Kim Jong-Seok Lee Yiwen Huang Ming Qiu Liting Jing Jiehang Zeng Ying Wang Manoj Sharma Rudrabha Mukhopadhyay Avinash Upadhyay Sriharsha Koundinya Ankit Shukla Santanu Chaudhury Zhe Zhang Yu Hen Hu Lingzhi Fu ETH Zurich Zurich Switzerland Merantix Germany NTIRE Eidgenossische Technische Hochschule Zurich Zurich ZH CH ESAT KU Leuven Belgium University of California at Merced CA USA Toyota Technological Institute TTI-Chicago Chicago US Duke University US School of Electronic Engineering Xidian University China Disney Research Facebook Oculus University of Illinois at Urbana-Champaign Illinois US Snap Inc. Snap Inc. Adobe Research Stevens Institute of Technology US University of Illinois at Urbana-Champaign US The Chinese University of Hong Kong Hong Kong China SenseTime Research School of Electrical and Computer Engineering National Institute of Science and Technolog Korea School of Electrical and Computer Engineering National Institute of Science and Technology Korea School of Computer Science and Technology Harbin Institute of Technology Harbin China Beijing University of Posts and Telecommunications China Beijing Faceall Technology Co. Ltd China Satrec Initiative R&D Center Korea Advanced Institute of Science and Technology Korea Xiamen University China Xiamen Universi China Ajou University Korea Electrical and Computer Engineering University of Alberta Canada Fuzhou University China National University of Computer & Emerging Sciences Islamabad Pakistan BOE Technology Group Co. Ltd. Yonsei University Korea Wenhua College China Software School Xiamen University China CSIR-CEERI India University of Wisconsin-Madison Madison Wisconsin US Institute of Integrated Automation School of Electronic and Information Engineering Xian Jiaotong University China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661017

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

关键词： Image resolution Degradation Training Runtime computer architecture Testing Tracking

Asteroid Measurements at Millimeter Wavelengths with the South Pole Telescope

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arXiv 2022年

作者： Chichura, P.M. Foster, A. Patel, C. Ossa-Jaen, N. Ade, P.A.R. Ahmed, Z. Anderson, A.J. Archipley, M. Austermann, J.E. Avva, J.S. Balkenhol, L. Barry, P.S. Thakur, R. Basu Beall, J.A. Benabed, K. Bender, A.N. Benson, B.A. Bianchini, F. Bleem, L.E. Bouchet, F.R. Bryant, L. Byrum, K. Carlstrom, J.E. Carter, F.W. Cecil, T.W. Chang, C.L. Chaubal, P. Chen, G. Chiang, H.C. Cho, H.-M. Chou, T.-L. Citron, R. Cliche, J.-F. Crawford, T.M. Crites, A.T. Cukierman, A. Daley, C.M. Denison, E.V. Dibert, K. Ding, J. Dobbs, M.A. Dutcher, D. Everett, W. Feng, C. Ferguson, K.R. Fu, J. Galli, S. Gallicchio, J. Gambrel, A.E. Gardner, R.W. George, E.M. Goeckner-Wald, N. Gualtieri, R. Guns, S. Gupta, N. Guyser, R. de Haan, T. Halverson, N.W. Harke-Hosemann, A.H. Harrington, N.L. Henning, J.W. Hilton, G.C. Hivon, E. Holder, G.P. Holzapfel, W.L. Hood, J.C. Howe, D. Hrubes, J.D. Huang, N. Hubmayr, J. Irwin, K.D. Jeong, O.B. Jonas, M. Jones, A. Khaire, T.S. Knox, L. Kofman, A.M. Korman, M. Kubik, D.L. Kuhlmann, S. Kuo, C.-L. Lee, A.T. Leitch, E.M. Li, D. Lowitz, A. Lu, C. Marrone, D.P. McMahon, J.J. Meyer, S.S. Michalik, D. Millea, M. Mocanu, L.M. Montgomery, J. Moran, C. Corbett Nadolski, A. Natoli, T. Nguyen, H. Nibarger, J.P. Noble, G. Novosad, V. Omori, Y. Padin, S. Pan, Z. Paschos, P. Patil, S. Pearson, J. Phadke, K.A. Posada, C.M. Prabhu, K. Pryke, C. Quan, W. Rahlin, A. Reichardt, C.L. Riebel, D. Riedel, B. Rouble, M. Ruhl, J.E. Saliwanchik, B.R. Sayre, J.T. Schaffer, K.K. Schiappucci, E. Shirokoff, E. Sievers, C. Smecher, G. Sobrin, J.A. Springmann, A. Stark, A.A. Stephen, J. Story, K.T. Suzuki, A. Tandoi, C. Thompson, K.L. Thorne, B. Tucker, C. Umilta, C. Vale, L.R. Veach, T. Vieira, J.D. Wang, G. Whitehorn, N. Wu, W.L.K. Yefremenko, V. Yoon, K.W. Young, M.R. Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics Case Western Reserve University ClevelandOH44106 United States Department of Physics University of Illinois Urbana-Champaign 1110 West Green Street UrbanaIL61801 United States Department of Physics University of Redlands 1267 East Colton Ave RedlandsCA92374 United States School of Physics and Astronomy Cardiff University CardiffCF24 3YB United Kingdom Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 West Green Street UrbanaIL61801 United States Center for AstroPhysical Surveys National Center for Supercomputing Applications UrbanaIL61801 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States Department of Physics University of California BerkeleyCA94720 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue. ArgonneIL60439 United States California Institute of Technology 1200 East California Boulevard. PasadenaCA91125 United States Institut d'Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United St

We present the first measurements of asteroids in millimeter wavelength (mm) data from the South Pole Telescope (SPT), which is used primarily to study the cosmic microwave background (CMB). We analyze maps of two ∼ 270 deg2 sky regions near the ecliptic plane, each observed with the SPTpol camera ∼ 100 times over one month. We subtract the mean of all maps of a given field, removing static sky signal, and then average the mean-subtracted maps at known asteroid locations. We detect three asteroids-(324) Bamberga, (13) Egeria, and (22) Kalliope-with signal-to-noise ratios (S/N) of 11.2, 10.4, and 6.1, respectively, at 2.0 mm (150 GHz);we also detect (324) Bamberga with S/N of 4.1 at 3.2 mm (95 GHz). We place constraints on these asteroids' effective emissivities, brightness temperatures, and light curve modulation amplitude. Our flux density measurements of (324) Bamberga and (13) Egeria roughly agree with predictions, while our measurements of (22) Kalliope suggest lower flux, corresponding to effective emissivities of 0.64±0.11 at 2.0 mm and Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

SPT clusters with DES and HST weak lensing. II. Cosmological constraints from the abundance of massive halos

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Physical Review D 2024年第8期110卷 083510页

作者： S. Bocquet S. Grandis L. E. Bleem M. Klein J. J. Mohr T. Schrabback T. M. C. Abbott P. A. R. Ade M. Aguena A. Alarcon S. Allam S. W. Allen O. Alves A. Amon A. J. Anderson J. Annis B. Ansarinejad J. E. Austermann S. Avila D. Bacon M. Bayliss J. A. Beall K. Bechtol M. R. Becker A. N. Bender B. A. Benson G. M. Bernstein S. Bhargava F. Bianchini M. Brodwin D. Brooks L. Bryant A. Campos R. E. A. Canning J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero F. J. Castander R. Cawthon C. L. Chang C. Chang P. Chaubal R. Chen H. C. Chiang A. Choi T-L. Chou R. Citron C. Corbett Moran J. Cordero M. Costanzi T. M. Crawford A. T. Crites L. N. da Costa M. E. S. Pereira C. Davis T. M. Davis J. DeRose S. Desai T. de Haan H. T. Diehl M. A. Dobbs S. Dodelson C. Doux A. Drlica-Wagner K. Eckert J. Elvin-Poole S. Everett W. Everett I. Ferrero A. Ferté A. M. Flores J. Frieman J. Gallicchio J. García-Bellido M. Gatti E. M. George G. Giannini M. D. Gladders D. Gruen R. A. Gruendl N. Gupta G. Gutierrez N. W. Halverson I. Harrison W. G. Hartley K. Herner S. R. Hinton G. P. Holder D. L. Hollowood W. L. Holzapfel K. Honscheid J. D. Hrubes N. Huang J. Hubmayr E. M. Huff D. Huterer K. D. Irwin D. J. James M. Jarvis G. Khullar K. Kim L. Knox R. Kraft E. Krause K. Kuehn N. Kuropatkin F. Kéruzoré O. Lahav A. T. Lee P.-F. Leget D. Li H. Lin A. Lowitz N. MacCrann G. Mahler A. Mantz J. L. Marshall J. McCullough M. McDonald J. J. McMahon J. Mena-Fernández F. Menanteau S. S. Meyer R. Miquel J. Montgomery J. Myles T. Natoli A. Navarro-Alsina J. P. Nibarger G. I. Noble V. Novosad R. L. C. Ogando Y. Omori S. Padin S. Pandey P. Paschos S. Patil A. Pieres A. A. Plazas Malagón A. Porredon J. Prat C. Pryke M. Raveri C. L. Reichardt J. Roberson R. P. Rollins C. Romero A. Roodman J. E. Ruhl E. S. Rykoff B. R. Saliwanchik L. Salvati C. Sánchez E. Sanchez D. Sanchez Cid A. Saro K. K. Schaffer L. F. Secco I. Sevilla-Noarbe K. Sharon E. Sheldon T. Shin C. Sievers G. Smecher M. Smith T. Somboonpanyakul M. Sommer B. Stalder A. A. Stark J. Stephen V. Straz University Observatory Faculty of Physics Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstraße 25/8 6020 Innsbruck Austria High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Max Planck Institute for Extraterrestrial Physics Gießenbachstraße 1 85748 Garching Germany Argelander-Institut für Astronomie Auf dem Hügel 71 53121 Bonn Germany Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile School of Physics and Astronomy Cardiff University Cardiff CF24 3YB United Kingdom Laboratório Interinstitucional de e-Astronomia—LIneA Rua Gal. José Cristino 77 Rio de Janeiro Rio de Janeiro—20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 USA Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom School of Physics University of Melbourne Parkville Victoria 3010 Australia NIST Quantum Devices Group 325 Broadway Mailcode 817.03 Boulder Colorado 80305 USA Department of Physics University of Colorado Boulder Colorado 80309 USA Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology Campus UAB 08193 Bellaterra (Barcelona) Spain Institute of Cosmology and Gravitation University of Portsmouth Portsmouth

We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel’dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample is constructed from the combined SPT-SZ, SPTpol ECS, and SPTpol 500d surveys, and comprises 1,005 confirmed clusters in the redshift range 0.25–1.78 over a total sky area of 5200 deg2. We use DES Year 3 weak-lensing data for 688 clusters with redshifts z<0.95 and HST weak-lensing data for 39 clusters with 0.6

关键词： Cosmological parameters Dark energy Large scale structure of the Universe Galaxy clusters

Phonetic temporal neural model for language identification

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arXiv 2017年

作者： Tang, Zhiyuan Wang, Dong Chen, Yixiang Li, Lantian Abel, Andrew Chengdu Institute of Computer Applications Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing100049 China Center for Speech and Language Technologies Tsinghua University Beijing100084 China Tsinghua National Laboratory for Information Science and Technology and the Center for Speech and Language Technologies Tsinghua University Beijing100084 China Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou215123 China

Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: it is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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

Generalized Regular Quadrilateral Mesh Generation based on Surface Foliation

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Procedia engineering 2017年 203卷 336-348页

作者： Na Lei Xiaopeng Zheng Hang Si Zhongxuan Luo Xianfeng Gu DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian 116620 China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian 116620 China Beijing Advanced Innovation Center for Imaging Technology Capital Normal University Beijing 100048 China Weierstrass Institute for Applied Analysis and Stochastics Berlin 10117 Germany Computer Science Department Stony Brook University Stony Brook 11794 NY USA

This work introduces a novel algorithm for quad-mesh generation based on surface foliation theory. The algorithm is based on the equivalence among colorable quad-meshes, measure foliations and holomorphic differentials. The holomorphic differentials can be obtained by graph-valued harmonic maps. The algorithm has several merits: it can be applied for surfaces with general topologies; the resulting quad-meshes have global tensor product structure and the least number of singularities; the algorithmic pipeline is fully automatic. The experimental results demonstrate the efficiency and efficacy of the proposed method.

关键词： quadrilateral quad-mesh structured foliation harmonic map holomorphic differential

Towards Automatic Content Transfer between Web Pages 6

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Towards Automatic Content Transfer between Web Pages

6th International Conference on Digital Home, ICDH 2016

作者： Long, Yonghao Chen, Xiangping Rao, Yongsheng Shi, Xiaohong School of Data and Computer Science National Engineering Research Center of Digital Life Sun Yat-san University Guangzhou China Institute of Advanced Technology Sun Yat-san University Guangdong Province Engineering Technology Research Center for Mathematical Educational Software Guangzhou China School of Computer Science and Educational Software Guangzhou University School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Data and Computer Science Sun Yat-san University Guangzhou China Research Institute Sun Yat-sen University in Shenzhen Shenzhen China

ISBN: (纸本)9781509044009

The Internet provides numerous web pages for learning and reusing. But reusing these pages requires manually modification and integration of the source codes, which is inefficient and complex. In this paper, we propose a method to do the content transfer between web pages. This method is based on the machine-learning, and it can use any web to be the templates. With transferring the contents from source to the reference, a web page is synthesized with the source's contents and reference's styles. The result can help inspiring the designers and reducing the developing cost. © 2016 IEEE.

关键词： Websites