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Learning to reconstruct and understand indoor scenes from sparse views

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arXiv 2019年

作者： Yang, Jingyu Xu, Ji Li, Kun Lai, Yu-Kun Yue, Huanjing Lu, Jianzhi Wu, Hao Liu, Yebin School of Electrical and Information Engineering Tianjin University Tianjin300072 China Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin300350 China School of Computer Science and Informatics Cardiff University Wales United Kingdom 3vjia company Department of Automation Tsinghua University Beijing10084 China

This paper proposes a new method for simultaneous 3D reconstruction and semantic segmentation of indoor scenes. Unlike existing methods that require recording a video using a color camera and/or a depth camera, our method only needs a small number of (e.g., 3-5) color images from uncalibrated sparse views as input, which greatly simplifies data acquisition and extends applicable scenarios. Since different views have limited overlaps, our method allows a single image as input to discern the depth and semantic information of the scene. The key issue is how to recover relatively accurate depth from single images and reconstruct a 3D scene by fusing very few depth maps. To address this problem, we first design an iterative deep architecture, Iter-Net, that estimates depth and semantic segmentation alternately, so that they benefit each other. To deal with the little overlap and non-rigid transformation between views, we further propose a joint global and local registration method to reconstruct a 3D scene with semantic information from sparse *** also make available a new indoor synthetic dataset simultaneously providing photorealistic high-resolution RGB images, accurate depth maps and pixel-level semantic labels for thousands of complex layouts, useful for training and evaluation. Experimental results on public datasets and our dataset demonstrate that our method achieves more accurate depth estimation, smaller semantic segmentation errors and better 3D reconstruction results, compared with stateof-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Cameras

Asymmetric GAN for unpaired image-to-image translation

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arXiv 2019年

作者： Li, Yu Tang, Sheng Zhang, Rui Zhang, Yongdong Li, Jintao Yan, Shuicheng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China AI Institute Qihoo 360 Beijing100025 China Department of Electronics and Computer Engineering National University of Singapore Singapore

Unpaired image-to-image translation problem aims to model the mapping from one domain to another with unpaired training data. Current works like the well-acknowledged Cycle GAN provide a general solution for any two domains through modeling injective mappings with a symmetric structure. While in situations where two domains are asymmetric in complexity, i.e. the amount of information between two domains is different, these approaches pose problems of poor generation quality, mapping ambiguity, and model sensitivity. To address these issues, we propose Asymmetric GAN (AsymGAN) to adapt the asymmetric domains by introducing an auxiliary variable (aux) to learn the extra information for transferring from the information-poor domain to the information-rich domain, which improves the performance of state-of-the-art approaches in the following ways. First, aux better balances the information between two domains which benefits the quality of generation. Second, the imbalance of information commonly leads to mapping ambiguity, where we are able to model one-to-many mappings by tuning aux, and furthermore, our aux is controllable. Third, the training of Cycle GAN can easily make the generator pair sensitive to small disturbances and variations while our model decouples the ill-conditioned relevance of generators by injecting aux during training. We verify the effectiveness of our proposed method both qualitatively and quantitatively on asymmetric situation, label-photo task, on Cityscapes and Helen datasets, and show many applications of asymmetric image translations. In conclusion, our AsymGAN provides a better solution for unpaired image-to-image translation in asymmetric domains. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

Dynamics of a Persistent Insulator-to-Metal Transition in Strained Manganite Films

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Physical Review Letters 2019年第26期123卷 267201-267201页

作者： Samuel W. Teitelbaum B. K. Ofori-Okai Yu-Hsiang Cheng Jingdi Zhang Feng Jin Wenbin Wu Richard D. Averitt Keith A. Nelson Department of Chemistry Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics University of California San Diego La Jolla California 92093 USA Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China Institutes of Physical Science and Information Technology Anhui University Hefei 230601 China High Magnetic Field Laboratory Chinese Academy of Sciences Hefei 230031 China Hefei National Laboratory for Physical Sciences at Microscale University of Science and Technology of China Hefei 230026 China

Transition metal oxides possess complex free-energy surfaces with competing degrees of freedom. Photoexcitation allows shaping of such rich energy landscapes. In epitaxially strained La0.67Ca0.33MnO3, optical excitation with a sub-100-fs pulse above 2 mJ/cm2 leads to a persistent metallic phase below 100 K. Using single-shot optical and terahertz spectroscopy, we show that this phase transition is a multistep process. We conclude that the phase transition is driven by partial charge-order melting, followed by growth of the persistent metallic phase on longer timescales. A time-dependent Ginzburg-Landau model can describe the fast dynamics of the reflectivity, followed by longer timescale in-growth of the metallic phase.

关键词： Colossal magnetoresistance Exotic phases of matter Jahn-Teller effect Phase transitions Complex oxides Strongly correlated systems Transition metal oxides Femtosecond laser spectroscopy Terahertz spectroscopy Terahertz time-domain spectroscopy

Online transmission policy in wireless powered networks with urgency-aware age of information 15

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Online transmission policy in wireless powered networks with...

15th IEEE International Wireless Communications and Mobile computing Conference, IWCMC 2019

作者： Lu, Yang Xiong, Ke Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781538677476

This paper investigates the age of information (AoI) for a radio frequency (RF) energy harvesting (EH) enabled network, where a sensor first scavenges energy from a wireless power station and then transmits the collected status update to a sink node. To capture the thirst for the fresh update becoming more and more urgent as time elapsing, urgency-aware AoI (U-AoI) is defined, which increases exponentially with the increment of time between two received updates. Due to EH, a waiting time is required at the sensor before transmitting the status update. An optimization problem is formulated to minimize the long-term average U-AoI under constraint of energy causality. A two-layer algorithm is presented to solve it, where the outer loop is designed based on Dinklebach's method, and the inner loop presents a semi-closed-form expression of the optimal waiting time policy based on Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical results show that our proposed optimal transmission policy outperforms the zero time waiting policy and equal time waiting policy in terms of long-term average U-AoI, especially when the networks are in slight load. It also shows that the system U-AoI first decreases and then keeps unchanged with the increments of EH circuit's saturation level. © 2019 IEEE.

关键词： Energy harvesting

Corrigendum to “Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques” [Renew Sustain Energy Rev 144 (2021) 11096]

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Renewable and Sustainable Energy Reviews 2021年 145卷

作者： Ghezlane Halhoul Merabet Mohamed Essaaidi Mohamed Ben Haddou Basheer Qolomany Junaid Qadir Muhammad Anan Ala Al-Fuqaha Mohamed Riduan Abid Driss Benhaddou Smart Systems Laboratory (SSL) ENSIAS Mohammed V University in Rabat 713 Morocco Department of Engineering Technology University of Houston TX 77204 USA MENTIS Consulting SA 13 rue de Congrès 1000 Brussels Belgium Department of Cyber Systems College of Business and Technology University of Nebraska at Kearney (UNK) Kearney NE 68849 USA Information Technology University Lahore 54000 Pakistan Software Engineering Department Alfaisal University-Riyadh Saudi Arabia Information and Computing Technology (ICT) Division College of Science and Engineering (CSE) Hamad Bin Khalifa University Doha Qatar Department of Computer Science Western Michigan University Kalamazoo MI 49008 USA School of Science and Engineering Alakhawayn University in Ifrane 1005 Morocco

Study of Ultra-high-Energy Gamma-Ray Source 1LHAASO J0056+6346u and Its Possible Origins

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arXiv 2025年

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Chu, M.C. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Ge, T.T. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hannuksela, O.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Y.Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, H.B. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Khangulyan, D. Kuleshov, D. Kurinov, K. Li, B.B. Li, C.M. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, S.D. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ng, K.C.Y. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. Sáiz, A. Saeed, M. Semikoz, D. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Hebei Shijiazhuang050024 China University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Zhuhai Guan

We report a dedicated study of the newly discovered extended UHE γ-ray source 1LHAASO J0056+6346u. Analyzing 979 days of LHAASO-WCDA data and 1389 days of LHAASO-KM2A data, we observed a significant excess of γ-ray events with both WCDA and KM2A. Assuming a point power-law source with a fixed spectral index, the significance maps reveal excesses of 12.65 σ, 22.18 σ, and 10.24 σ in the energy ranges of 1–25 TeV, 25–100 TeV, and > 100 TeV, respectively. We use a 3D likelihood algorithm to derive the morphological and spectral parameters, and the source is detected with significances of 13.72 σ by WCDA and 25.27 σ by KM2A. The best-fit positions derived from WCDA and KM2A data are (R.A. = 13.96◦ ± 0.09◦, Decl. = 63.92◦ ± 0.05◦) and (R.A. = 14.00◦ ± 0.05◦, Decl. = 63.79◦ ± 0.02◦), respectively. The angular size (r39) of 1LHAASO J0056+6346u is 0.34◦ ± 0.04◦ at 1-25 TeV and 0.24◦ ± 0.02◦ at > 25 TeV. The differential flux of this UHE γ-ray source can be described by an exponential cutoff power-law function: (2.67±0.25)×10−15 (E / 20 TeV)(−1.97±0.10) e−E / (55.1±7.2) TeV TeV−1cm−2s−1. To explore potential sources of γ-ray emission, we investigated the gas distribution around 1LHAASO J0056+6346u. 1LHAASO J0056+6346u is likely to be a TeV PWN powered by an unknown pulsar, which would naturally explain both its spatial and spectral properties. Another explanation is that this UHE γ-ray source might be associated with gas content illuminated by a nearby CR accelerator, possibly the SNR candidate G124.0+1.4. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmic rays

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

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arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

General-purpose quantum circuit simulator with Projected Entangled-Pair States and the quantum supremacy frontier

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arXiv 2019年

作者： Guo, Chu Liu, Yong Xiong, Min Xue, Shichuan Fu, Xiang Huang, Anqi Qiang, Xiaogang Xu, Ping Liu, Junhua Zheng, Shenggen Huang, He-Liang Deng, Mingtang Poletti, Dario Bao, Wan-Su Wu, Junjie Henan Key Laboratory of Quantum Information and Cryptography SSF IEU Zhengzhou450001 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Information Systems Technology and Design Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Supremacy Future Technologies Guangzhou511340 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science and Technology of China Hefei Anhui230026 China CAS Centre for Excellence Synergetic Innovation Centre in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China Science and Math Cluster and EPD Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore

Recent advances on quantum computing hardware have pushed quantum computing to the verge of quantum supremacy. Here we bring together many-body quantum physics and quantum computing by using a method for strongly interacting two-dimensional systems, the Projected Entangled-Pair States, to realize an effective general-purpose simulator of quantum algorithms. We apply our method to study random quantum circuits, which are outstanding candidates to demonstrate quantum supremacy on quantum computers that supports nearest-neighbour gate operations on a two-dimensional configuration. Our approach allows to quantify precisely the memory usage and the time requirements of random quantum circuits, thus showing the frontier of quantum supremacy. Applying this general quantum circuit simulator we measured amplitudes for a 7×7 lattice of qubits with depth (1 + 40 + 1) and double-precision numbers in 31 minutes using less than 93 TB memory on the Tianhe-2 supercomputer. Our analytic complexity bounds also show that simulating a 8 × l circuit (l > 8) with depth (1 + 40 + 1), or a 10 × l (l > 10) circuit with depth (1 + 32 + 1) is within reach of current supercomputers. Copyright © 2019, The Authors. All rights reserved.

关键词： Timing circuits

Measurement of the inclusive tt¯ production cross section in the lepton+jets channel in pp collisions at s=7 TeV with the ATLAS detector using support vector machines

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Physical Review D 2023年第3期108卷 032014-032014页

作者： M. Aaboud G. Aad B. Abbott D. C. Abbott D. K. Abhayasinghe S. H. Abidi O. S. AbouZeid N. L. Abraham H. Abramowicz H. Abreu Y. Abulaiti B. S. Acharya S. Adachi L. Adam C. Adam Bourdarios L. Adamczyk L. Adamek J. Adelman M. Adersberger A. Adiguzel T. Adye A. A. Affolder Y. Afik C. Agapopoulou M. N. Agaras A. Aggarwal C. Agheorghiesei J. A. Aguilar-Saavedra F. Ahmadov G. Aielli S. Akatsuka T. P. A. Åkesson E. Akilli A. V. Akimov K. Al Khoury G. L. Alberghi J. Albert M. J. Alconada Verzini S. Alderweireldt M. Aleksa I. N. Aleksandrov C. Alexa D. Alexandre T. Alexopoulos M. Alhroob B. Ali G. Alimonti J. Alison S. P. Alkire C. Allaire B. M. M. Allbrooke B. W. Allen P. P. Allport A. Aloisio A. Alonso F. Alonso C. Alpigiani A. A. Alshehri M. I. Alstaty M. Alvarez Estevez B. Alvarez Gonzalez D. Álvarez Piqueras M. G. Alviggi Y. Amaral Coutinho A. Ambler L. Ambroz C. Amelung D. Amidei S. P. Amor Dos Santos S. Amoroso C. S. Amrouche F. An C. Anastopoulos N. Andari T. Andeen C. F. Anders J. K. Anders A. Andreazza V. Andrei C. R. Anelli S. Angelidakis I. Angelozzi A. Angerami A. V. Anisenkov A. Annovi C. Antel M. T. Anthony M. Antonelli D. J. A. Antrim F. Anulli M. Aoki J. A. Aparisi Pozo L. Aperio Bella G. Arabidze J. P. Araque V. Araujo Ferraz R. Araujo Pereira A. T. H. Arce F. A. Arduh J-F. Arguin S. Argyropoulos J.-H. Arling A. J. Armbruster L. J. Armitage A. Armstrong O. Arnaez H. Arnold G. Artoni S. Artz S. Asai N. A. Asbah E. M. Asimakopoulou L. Asquith K. Assamagan R. Astalos R. J. Atkin M. Atkinson N. B. Atlay H. Atmani K. Augsten G. Avolio R. Avramidou M. K. Ayoub A. M. Azoulay G. Azuelos A. E. Baas M. J. Baca H. Bachacou K. Bachas M. Backes F. Backman P. Bagnaia H. Bahrasemani A. J. Bailey V. R. Bailey J. T. Baines M. Bajic C. Bakalis O. K. Baker P. J. Bakker D. Bakshi Gupta S. Balaji E. M. Baldin P. Balek F. Balli W. K. Balunas J. Balz E. Banas A. Bandyopadhyay Sw. Banerjee A. A. E. Bannoura L. Barak W. M. Barbe E. L. Barberio D. Barberis M. Barbero T. Barillari M-S. Barisits J. Barkeloo T. Barklow R. Barnea S. L. LPMR Faculté des Sciences Université Mohamed Premier Oujda Morocco CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman Oklahoma USA Department of Physics University of Massachusetts Amherst Massachusetts USA Department of Physics Royal Holloway University of London Egham United Kingdom Department of Physics University of Toronto Toronto ON Canada Niels Bohr Institute University of Copenhagen Copenhagen Denmark Department of Physics and Astronomy University of Sussex Brighton United Kingdom Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion Israel Institute of Technology Haifa Israel High Energy Physics Division Argonne National Laboratory Argonne Illinois USA INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy International Center for Elementary Particle Physics and Department of Physics University of Tokyo Tokyo Japan Institut für Physik Universität Mainz Mainz Germany IJCLab Université Paris-Saclay CNRS/IN2P3 91405 Orsay France AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Northern Illinois University DeKalb Illinois United States of America Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Department of Physics Bogazici University Istanbul Türkiye Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz California USA LPC Université Clermont Auvergne CNRS/IN2P3 Clermont-Ferrand France Institute for Mathematics Astrophysics and Particle Physics Radboud University/Nikhef Nijmegen Netherlands Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Departamento de Física Teórica y del Cosmos Universidad de Granada

A measurement of the top quark pair-production cross section in the lepton+jets decay channel is presented. It is based on 4.6 fb−1 of s=7 TeV pp collision data collected during 2011 by the ATLAS experiment at the CERN Large Hadron Collider. A three-class, multidimensional event classifier based on support vector machines is used to differentiate tt¯ events from backgrounds. The tt¯ production cross section is found to be σtt¯=168.5±0.7(stat) −5.9+6.2(syst) −3.2+3.4(lumi) pb. The result is consistent with the Standard Model prediction based on QCD calculations at next-to-next-to-leading order.

关键词： Particle production Top quark Hadron colliders