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

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

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Elastic parity logging for SSD RAID arrays 46

Elastic parity logging for SSD RAID arrays

引用

46th IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2016

作者： Li, Yongkun Chan, Helen H.W. Lee, Patrick P.C. Xu, Yinlong School of Computer Science and Technology University of Science and Technology of China China Collaborative Innovation Center of High Performance Computing National University of Defense Technology China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong AnHui Province Key Laboratory of High Performance Computing China

ISBN: (纸本)9781467388917

Parity-based RAID poses a design trade-off issue for large-scale SSD storage systems: it improves reliability against SSD failures through redundancy, yet its parity updates incur extra I/Os and garbage collection operations, thereby degrading the endurance and performance of SSDs. We propose EPLOG, a storage layer that reduces parity traffic to SSDs, so as to provide endurance, reliability, and performance guarantees for SSD RAID arrays. EPLOG mitigates parity update overhead via elastic parity logging, which redirects parity traffic to separate log devices (to improve endurance and reliability) and eliminates the need of pre-reading data in parity computations (to improve performance). We design EPLOG as a user-level implementation that is fully compatible with commodity hardware and general erasure coding schemes. We evaluate EPLOG through reliability analysis and trace-driven testbed experiments. Compared to the Linux software RAID implementation, our experimental results show that our EPLOG prototype reduces the total write traffic to SSDs, reduces the number of garbage collection operations, and increases the I/O throughput. In addition, EPLOG significantly improves the I/O performance over the original parity logging design, and incurs low metadata overhead. © 2016 IEEE.

关键词： Reliability analysis

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Overview of KAGRA: Detector design and construction history

arXiv

引用

arXiv 2020年

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M.A. Cannon, K. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chu, H. Chu, Y.-K. Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fukunaga, M. Fukushima, M. Ge, G. Hagiwara, A. Haino, S. Hasegawa, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B.H. Huang, C.-Z. Huang, P. Huang, Y. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Itoh, Y. Izumi, K. Jung, K. Jung, P. Kajita, T. Kamiizumi, M. Kanda, N. Kang, G. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J.C. Kim, W.S. Kim, Y.-M. Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A.K.H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H.-S. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H.K. Lee, H.W. Lee, R. Leonardi, M. Lin, C. Lin, C.-Y. Lin, F.-L. Liu, G.C. Luo, L. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Narikawa, T. Negishi, R. Ni, W.-T. Nishizawa, A. Obuchi, Y. Ogaki, W. Oh, J.J. Oh, S.H. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Oohara, K. Ooi, C.P. Oshino, S. Pan, K. Pang, H. Park, J. Peña Arellano, F.E. Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E.J. Sotani, H. Sugimoto, R. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takano, S. Takeda, H. Takeda, M. Tanaka, H. Tanaka, K. Tanaka, K. Tanaka, T. Tanaka, T. Tanioka, S. Tapia San Martin, E.N. Telada, S. Tomaru, T. Tomigami, Y. Tomura, T. Mitaka City Tokyo181-8588 Japan Japan Department of Physics University of Tokyo Bunkyo-ku Tokyo113-0033 Japan University of Tokyo Bunkyo-ku Tokyo113-0033 Japan KAGRA Observatory University of Tokyo Kashiwa City Chiba277-8582 Japan Tsukuba City Ibaraki305-0801 Japan Earthquake Research Institute University of Tokyo Bunkyo-ku Tokyo113-0032 Japan Kamioka-cho Hida City Gifu506-1205 Japan Mitaka City Tokyo181-8588 Japan Yuseong-gu Daejeon34141 Korea Republic of National Institute for Mathematical Sciences Daejeon34047 Korea Republic of Department of Earth and Space Science Graduate School of Science Osaka University Toyonaka City Osaka560-0043 Japan Tsukuba City Ibaraki305-0801 Japan Department of Applied Physics Fukuoka University Jonan Fukuoka City Fukuoka814-0180 Japan Department of Physics Tamkang University Danshui Dist. New Taipei City25137 Taiwan Department of Physics and Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Department of Physics Center for High Energy and High Field Physics National Central University Zhongli District Taoyuan City32001 Taiwan Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Université Savoie Mont Blanc CNRS/IN2P3 AnnecyF-74941 France Department of Astronomy University of Tokyo Mitaka City Tokyo181-8588 Japan Chinese Academy of Sciences Xiaohongshan Wuhan430071 China Tsukuba City Ibaraki305-0801 Japan KAGRA Observatory University of Tokyo Kamioka-cho Hida City Gifu506-1205 Japan College of Industrial Technology Nihon University Narashino City Chiba275-8575 Japan Graduate School of Science and Technology Niigata University Nishi-ku Niigata City Niigata950-2181 Japan Department of Physics National Taiwan Normal University sec. 4 Taipei116 Taiwan University of Tokyo Kashiwa City Chiba277-8582 Japan Kavli Institute for Astronomy and Astrophysics Peking University China Kyoto University Sakyou-ku Kyot

KAGRA is a newly built gravitational-wave telescope, a laser interferometer comprising arms with a length of 3 km, located in Kamioka, Gifu, Japan. KAGRA was constructed under the ground and it is operated using cryogenic mirrors that help in reducing the seismic and thermal noise. Both technologies are expected to provide directions for the future of gravitational-wave telescopes. In 2019, KAGRA finished all installations with the designed configuration, which we call the baseline KAGRA. In this occasion, we present an overview of the baseline KAGRA from various viewpoints in a series of of articles. In this article, we introduce the design configurations of KAGRA with its historical background. Copyright © 2020, The Authors. All rights reserved.

关键词： Laser interferometry

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Ultrafast spin-to-charge conversion at the surface of topological insulator thin films

arXiv

引用

arXiv 2018年

作者： Wang, Xinbo Cheng, Liang Zhu, Dapeng Wu, Yang Chen, Mengji Wang, Yi Zhao, Daming Boothroyd, Chris B. Lam, Yeng Ming Zhu, Jian-Xin Battiato, Marco Song, Justin C.W. Yang, Hyunsoo Chia, Elbert E.M. Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link 637371 Singapore Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Theoretical Division Center for Integrated Nanotechnologies Los Alamos National Laboratory NM87545 United States Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China Institute of Solid State Physics Vienna University of Technology Vienna1040 Austria Institute of High Performance Computing Agency for Science Technology and Research 138632 Singapore

Topological insulators (TIs) provide a fascinating example where strong spin-orbit interaction leads to the formation of spin-momentum-locked (topological) surface states,[1,2] and are anticipated to mediate superior spin-charge conversion (SCC).[3,4] As a result, employing conventional steady-state transport set-ups, TIs have been exploited to exert spin orbit torque on the adjacent ferromagnetic (FM) layers,[5-12] as well as generate charge current from spin current.[13-19] However, these transport studies involved contact fabrication, required complicated data analysis to extract the spin-related component, and their signal is in the DC regime. Direct access to the dynamics of SCC - a crucial ingredient in optimizing TI based spintronic devices - remains conspicuously missing. The difficultly of tracking SCC dynamics stems from the need to address both the ultrafast timescales involved as well as obtain spin resolution simultaneously. Copyright © 2018, The Authors. All rights reserved.

关键词： Surface states

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Neural generative question answering 25

Neural generative question answering

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Yin, Jun Xin, Jiang Lu, Zhengdong Shang, Lifeng Li, Hang Li, Xiaoming School of Electronic Engineering and Computer Science Peking University China Noah's Ark Lab Huawei Technologies China Collaborative Innovation Center of High Performance Computing NUDT Changsha China

This paper presents an end-to-end neural network model, named Neural Generative Question Answering (GENQA), that can generate answers to simple factoid questions, based on the facts in a knowledge-base. More specifically, the model is built on the encoder-decoder framework for sequence-to-sequence learning, while equipped with the ability to enquire the knowledge-base, and is trained on a corpus of question-answer pairs, with their associated triples in the knowledge-base. Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base. The experiment on question answering demonstrates that the proposed model can outperform an embedding-based QA model as well as a neural dialogue model trained on the same data.

关键词： Neural network models

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Publisher Correction: Extreme risk induced by communities in interdependent networks

引用

Communications Physics 2019年第1期2卷 1页

作者： Jiachen Sun Rui Zhang Ling Feng Christopher Monterola Xiao Ma Céline Rozenblat H. Eugene Stanley Boris Podobnik Yanqing Hu School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Institute for Molecular Engineering University of Chicago Chicago USA Institute of High Performance Computing A*STAR Singapore Singapore Department of Physics National University of Singapore Singapore Singapore Asian Institute of Management Metro Manila Philippines School of Data and Computer Science Sun Yat-sen University Guangzhou China Institute of Geography University of Lausanne Lausanne Switzerland Department of Physics Boston University Boston USA Faculty of Civil Engineering University of Rijeka Rijeka Croatia Faculty of Information Studies Novo Mesto Slovenia Zagreb School of Economics and Management Zagreb Croatia Luxembourg School of Business Luxembourg Luxembourg

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Person re-identification by camera correlation aware feature augmentation

arXiv

引用

arXiv 2017年

作者： Chen, Ying-Cong Zhu, Xiatian Zheng, Wei-Shi Lai, Jian-Huang School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha410073 China Department of Computer Science and Engineering Chinese University of Hong Kong School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Key Laboratory of Machine Intelligence and Advanced Computing Sun Yatsen University Ministry of Education China Guangdong Province Key Laboratory of Information Security China

The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coRrelation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state-of

关键词： Cameras

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GECKA3D: A 3D game engine for commonsense knowledge acquisition 29

GECKA3D: A 3D game engine for commonsense knowledge acquisit...

引用

29th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016

作者： Cambria, Erik Nguyen, Tam V. Cheng, Brian Kwok, Kenneth Sepulveda, Jose School of Computer Engineering Nanyang Technological University Singapore ARTIC Research Center Singapore Polytechnic Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore

ISBN: (纸本)9781577357568

Commonsense knowledge representation and reasoning is key tor tasks such as artificial intelligence and natural language understanding. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. In this paper, we introduce a novel 3D game engine for commonsense knowledge acquisition (GECKA3D) which aims to collect commonsense from game designers through the development of serious games. GECKA3D integrates the potential of serious games and games with a purpose. This provides a platform for the acquisition of re-usable and multi-purpose knowledge, and also enables the development of games that can provide entertainment value and teach players something meaningful about the actual world they live in. © 2016, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Serious games

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JOINT DETECTION AND CLINICAL SCORE PREDICTION IN PARKINSON'S DISEASE VIA MULTI-MODAL SPARSE LEARNING

JOINT DETECTION AND CLINICAL SCORE PREDICTION IN PARKINSON'S...

引用

International Symposium on Biomedical Imaging

作者： Haijun Lei Jian Zhang Zhang Yang Ee-leng Tan Baiying Lei Qiuming Luo College of Computer Science and Software Engineering Shenzhen University Key Laboratory of Service Computing and Applications Guangdong Province Key Laboratory of Popular High Performance Computers School of Information Engineering Shenzhen University School of Electric and Electronic Engineering Nanyang Technological University School of Biomedical Engineering Shenzhen University Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound

ISBN: (纸本)9781509011735

In this study, a novel feature selection framework is proposed to simultaneously perform classification and clinical scores prediction of Parkinson's disease (PD) via multi-modal neuroimaging data. Specifically, a new feature selection model is devised to capture discriminative features to train support vector regression model for clinical scores (e.g., sleep scores and olfactory scores) prediction and support vector classification model for class label identification. Our method is evaluated on a public dataset of 208 subjects including 56 normal controls (NC), 123 PD and 29 scans without evidence of dopamine deficit (SWEDD) via a 10-fold cross-validation method. The experimental results demonstrate that multimodal data can effectively improve the performance in disease status identification and clinical scores prediction compared to one single modality. Our proposed method also outperforms the related methods.

关键词： Parkinson's disease Feature selection Classification Prediction Multi-modal data

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Recent advances in electromagnetic compatibility of 3D-ICS - Part II

引用

IEEE Electromagnetic Compatibility Magazine 2016年第1期5卷 65-74页

作者： Sicard, Etienne Jianfei, Wu Shen, Rongjun Li, Er-Ping Liu, En-Xiao Kim, Joungho Cho, Jonghyun Swaminathan, Madhavan INSA GEI 135 Avenue de Rangueil Toulouse31077 France School of Electronic Science and Engineering NUDT Changsha Hunan410073 China Institute of High Performance Computing ASTAR 1 Fusionopolis Way Singapore138632 Singapore Daejeon Korea Republic of John Pippin Department in Electromagnetics School of Electrical and Computer Engineering Interconnect and Packaging Center Georgia Institute of Technology AtlantaGA30332-0250 United States

This second part addresses a selection of topics related to Electromagnetic interference (EMI) issues in three-dimensional integrated components. Details about Through Silicon Via (TSV) technology, modelling and parasitic effects are introduced in the first part, then key concepts such as signal integrity (SI), power integrity (PI), and Electromagnetic Interference are introduced. © 2012 IEEE.

关键词： Signal interference

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