检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station School of Ecology Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Study region: The Tibetan Plateau in China, the Third Pole of the *** focus: Accurate descriptions of spatial variation in evapotranspiration (ET) and its components on the Tibetan Plateau (TP) are crucial for understanding the dynamics of the regional water cycle and energy balance. However, how various environmental factors affect ET and its components remains unclear. This study addresses this gap using observational data from 14 sites and employing the Shuttleworth-Wallace-Hu (SWH) model to examine the spatial relationships between ET, its components, and underlying *** hydrological insights for the region: The SWH model parameters were optimized using the Monte Carlo method, achieving a satisfactory model performance with an average R² of 0.75. The ratio of transpiration to ET was generally higher in alpine meadows (0.55) than alpine steppes (0.39). Across the TP, precipitation and the leaf area index together shaped spatial variation in ET and transpiration. Precipitation emerged as the primary driver of interannual variation in ET, while the leaf area index strongly regulated the proportion of ET accounted for by transpiration. Furthermore, above aridity index values of 0.73, grassland ecosystems transitioned from water to energy. This finding is significant as it challenges the common application of an aridity index value of 0.65 to distinguish arid from humid regions. It suggests that TP grasslands, with an AI of 0.65-0.73, remain primarily water-limited. © 2024, The Authors. All rights reserved.

关键词： Biotic

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Motion vehicles tracking in intelligent transportation visual surveillance systems

引用

Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of science and technology 2012年第SUPPL.2期36卷 216-222页

作者： Wu, Lin-Feng Xiong, Gang Qian, Shou-Yi Xie, Li Zhejiang Provincial Key Lab. of Information Network Technology Department of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Cloud Computing Industrial Technology Innovation and Incubation Center Dongguan Research Institute of CASIA Chinese Academy of Sciences Dongguan 523808 China Wenzhou Research Institute of Electronics and Information Wenzhou 325003 China

In terms of the difficulty of vehicle tracking in complex environment of the visual surveillance system, an object tracking algorithm is proposed for the applications in practical visual surveillance systems for intelligent traffic. A block-based Gaussian mixture background modeling method for object detection is presented to reduce the computational complexity of moving vehicle object abstraction. An adaptive tracking algorithm fused with color features and texture features is described to better adapt the traffic scene variation. The experimental results show that the proposed algorithm can effectively deal with the complex urban traffic conditions and the tracking performance is better than the conventional particle filter method and single feature based non-adaptive object tracking method.

关键词： Tracking (position)

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Fermi-detection of γ-ray Emissions from the Hot Coronae of Radio-quiet Active Galactic Nuclei

arXiv

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arXiv 2025年

作者： Abdollahi, S. Ajello, M. Alves Batista, R. Baldini, L. Bartolini, C. Bastieri, D. Becerra Gonzalez, J. Bellazzini, R. Berenji, B. Bissaldi, E. Blandford, R.D. Bonino, R. Bruel, P. Buson, S. Cameron, R.A. Caraveo, P.A. Cavazzuti, E. Chiaro, G. Cibrario, N. Ciprini, S. Cristarella Orestano, P. Cutini, S. D’Ammando, F. Di Lalla, N. Dinesh, A. Di Venere, L. Domínguez, A. Fegan, S.J. Fiori, A. Franckowiak, A. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasbarra, C. Gasparrini, D. Germani, S. Giglietto, N. Giliberti, M. Giordano, F. Giroletti, M. Green, D. Grenier, I.A. Guiriec, S. Hashizume, M. Hays, E. Hewitt, J.W. Horan, D. Hou, Xian Karwin, C. Kayanoki, T. Kuss, M. Laviron, A. Lemoine-Goumard, M. Li, Jian Liodakis, I. Liu, Jun-Rong Longo, F. Loparco, F. Lorusso, L. Lubrano, P. Maldera, S. Marcotulli, L. Martí-Devesa, G. Mazziotta, M.N. Mereu, I. Michelson, P.F. Mirabal, N. Mitthumsiri, W. Mizuno, T. Monzani, M.E. Morishita, T. Morselli, A. Moskalenko, I.V. Negro, M. Niwa, R. Omodei, N. Orienti, M. Orlando, E. Ormes, J.F. Paneque, D. Panzarini, G. Persic, M. Pesce-Rollins, M. Pillera, R. Porter, T.A. Principe, G. Rainò, S. Rando, R. Rani, B. Razzano, M. Reimer, A. Reimer, O. Sánchez-Conde, M. Saz Parkinson, P.M. Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Suson, D.J. Tajima, H. Thayer, J.B. Torres, D.F. Wang, Jian-Min Zhao, Zi-Hao Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Instituto de Física Teórica UAM CSIC Universidad Autónoma de Madrid MadridE-28049 Spain Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy Università degli studi di Trento via Calepina 14 Trento38122 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova Via F. Marzolo 8 PadovaI-35131 Italy Center for Space Studies and Activities "G. Colombo" University of Padova Via Venezia 15 PadovaI-35131 Italy Instituto de Astrofísica de Canarias Universidad de La Laguna Dpto. Astrofísica Tenerife La Laguna38200 Spain Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy California State University Los Angeles Department of Physics and Astronomy Los AngelesCA90032 United States Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari via Amendola 173 BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Laboratoire Leprince-Ringuet CNRS IN2P3 École polytechnique Institut Polytechnique de Paris Palaiseau91120 France Institut für Theoretische Physik and Astrophysik Universität Würzburg WürzburgD-97074 Germany INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 MilanoI-20133 Italy Italian Space Agency Via del Politecnico snc Roma00133 Italy Istituto Nazionale di Fisica Nucleare Sezione di Roma "Tor Vergata" RomaI-00133 Italy Space Sci

Relativistic jets around supermassive black holes (SMBHs) are well-known powerful γ-ray emitters. In absence of the jets in radio-quiet active galactic nuclei (AGNs), how the SMBHs work in γ-ray bands is still unknown despite of great observational efforts made in the last 3 decades. Considering the previous efforts, we carefully select an AGN sample composed of 37 nearby Seyfert galaxies with ultra-hard X-rays for the goals of γ-ray detections by excluding all potential contamination in this band. Adopting a stacking technique, here we report the significant γ-ray detection (TS = 30.6, or 5.2 σ) from the sample using 15-year Fermi-Large Area Telescope (LAT) observation. We find an average γ-ray luminosity of the sample as (1.5 ± 1.0) × 1040 erg s−1 at energies from 1-300 GeV. Limited by the well-known pair production from the interaction of γ-rays with low energy photons, several GeV γ-rays are found to originate from an extended corona (∼ 2.7 × 106Rg), whereas the canonical much more compact X-ray corona (∼ 10Rg) is responsible for 1 to several GeV γ-rays. The finding of the compact region lends to strong supports to the long-time theoretical expectations, but the extended corona is beyond all the existing models. One promising scenario is that the electron-positron pairs produced in the compact X-ray corona would expand as fireball, similar to that in γ-ray bursts, forming the structure of extended corona. © 2025, CC BY.

关键词： Active Galactic Nuclei

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ADAM CHALLENGE: DETECTING AGE-RELATED MACULAR DEGENERATION FROM FUNDUS IMAGES

arXiv

引用

arXiv 2022年

作者： Fang, Huihui Li, Fei Fu, Huazhu Sun, Xu Cao, Xingxing Lin, Fengbin Son, Jaemin Kim, Sunho Quellec, Gwenole Matta, Sarah Shankaranarayana, Sharath M. Chen, Yi-Ting Wang, Chuen-Heng Shah, Nisarg A. Lee, Chia-Yen Hsu, Chih-Chung Xie, Hai Lei, Baiying Baid, Ujjwal Innani, Shubham Dang, Kang Shi, Wenxiu Kamble, Ravi Singhal, Nitin Wang, Ching-Wei Lo, Shih-Chang Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The ADAM Challenge State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore CONICET Yatiris lab of Pladema Institute Tandil Argentina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Inserm UMR 1101 BrestF-29200 France Univ Bretagne Occidentale BrestF-29200 France Zasti India Pvt. Ltd. India Graphen.Inc Muen Biomedical and Optoelectronics Technologies Inc Taiwan Department of Electrical Engineering Indian Institute of Technology Rajasthan Jodhpur India Department of Electrical Engineering National United University Taiwan Institute of Data Science Department of Statistics National Cheng Kung University Taiwan Shenzhen University Shenzhen China SGGS Institute of Engineering and Technology Nanded India Suzhou Tisu Information Technology Co. Ltd China AIRA Matrix Mumbai India Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taiwan

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models. © 2022, CC BY-NC-ND.

关键词： Cost effectiveness

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AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review

arXiv

引用

arXiv 2024年

作者： Jin, Rui Li, Derun Xiang, Dehui Zhang, Lei Zhou, Hailing Shi, Fei Zhu, Weifang Cai, Jing Peng, Tao Chen, Xinjian School of Future Science and Engineering Soochow University Suzhou China Department of Urology Peking University First Hospital Xicheng District Beijing100034 China MIPAV Lab The School of Electronic and Information Engineering Soochow University Jiangsu China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Jiangsu Kunshan215316 China Department of Mechanical Engineering and Product Design Engineering Swinburne University of Technology Melbourne Australia Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States The State Key Laboratory of Radiation Medicine and Protection Soochow University Jiangsu China

Prostate cancer represents a major threat to health. Early detection is vital in reducing the mortality rate among prostate cancer patients. One approach involves using multi-modality (CT, MRI, US, etc.) computer-aided diagnosis (CAD) systems for the prostate region. However, prostate segmentation is challenging due to imperfections in the images and the prostate’s complex tissue structure. The advent of precision medicine and a significant increase in clinical capacity have spurred the need for various data-driven tasks in the field of medical imaging. Recently, numerous machine learning and data mining tools have been integrated into various medical areas, including image segmentation. This article proposes a new classification method that differentiates supervision types, either in number or kind, during the training phase. Subsequently, we conducted a survey on artificial intelligence (AI)-based automatic prostate segmentation methods, examining the advantages and limitations of each. Additionally, we introduce variants of evaluation metrics for the verification and performance assessment of the segmentation method and summarize the current challenges. Finally, future research directions and development trends are discussed, reflecting the outcomes of our literature survey, suggesting high-precision detection and treatment of prostate cancer as a promising avenue. Impact Statement — Prostate cancer remains a significant health threat, necessitating early detection to reduce mortality rates. Current diagnostic approaches, particularly in medical imaging, face challenges in accurate prostate segmentation due to the organ's complex structure and imaging imperfections. Our review paper delves into AI-based automatic segmentation methods across various imaging modalities (CT, MRI, US), emphasizing their strengths, weaknesses, and the specific challenges they address. By classifying segmentation techniques based on supervision levels, we provide a comprehensive analys

关键词： Computer aided diagnosis

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Generalized word shift graphs: A method for visualizing and explaining pairwise comparisons between texts

arXiv

引用

arXiv 2020年

作者： Gallagher, Ryan J. Frank, Morgan R. Mitchell, Lewis Schwartz, Aaron J. Reagan, Andrew J. Danforth, Christopher M. Dodds, Peter Sheridan Network Science Institute Northeastern University BostonMA02115 United States Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15260 United States Connection Science Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Human-Centered Artifical Intelligence Stanford University StanfordCA94305 United States School of Mathematical Sciences The University of Adelaide SA5005 Australia Computational Story Lab Vermont Complex Systems Center Vermont Advanced Computing Core The University of Vermont BurlingtonVT05401 United States Gund Institute for Environment & Rubenstein School of Environment and Natural Resources The University of Vermont BurlingtonVT05401 United States Department of Ecology and Evolutionary Biology University of Colorado at Boulder BoulderCO80309 United States MassMutual Data Science AmherstMA01002 United States MassMutual Center of Excellence for Complex Systems and Data Science Department of Mathematics and Statistics The University of Vermont BurlingtonVT05401 United States

A common task in computational text analyses is to quantify how two corpora differ according to a measurement like word frequency, sentiment, or information content. However, collapsing the texts' rich stories into a single number is often conceptually perilous, and it is difficult to confidently interpret interesting or unexpected textual patterns without looming concerns about data artifacts or measurement validity. To better capture fine-grained differences between texts, we introduce generalized word shift graphs, visualizations which yield a meaningful and interpretable summary of how individual words contribute to the variation between two texts for any measure that can be formulated as a weighted average. We show that this framework naturally encompasses many of the most commonly used approaches for comparing texts, including relative frequencies, dictionary scores, and entropy-based measures like the Kullback-Leibler and Jensen-Shannon divergences. Through several case studies, we demonstrate how generalized word shift graphs can be flexibly applied across domains for diagnostic investigation, hypothesis generation, and substantive interpretation. By providing a detailed lens into textual shifts between corpora, generalized word shift graphs help computational social scientists, digital humanists, and other text analysis practitioners fashion more robust scientific narratives. Copyright © 2020, The Authors. All rights reserved.

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Reconfigurable Intelligent Surface Assisted High-Speed Train Communications: Coverage Performance Analysis and Placement Optimization

arXiv

引用

arXiv 2023年

作者： Liu, Changzhu He, Ruisi Niu, Yong Han, Zhu Ai, Bo Gao, Meilin Ma, Zhangfeng Wang, Gongpu Zhong, Zhangdui The State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The Tsinghua Space Center the Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100091 China The College of Information Engineering Shaoyang University Shaoyang422000 China The Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Reconfigurable intelligent surface (RIS) emerges as maximization problem is formulated to facilitate RIS discrete an efficient and promising technology for the next wireless phase design and RIS placement optimization, which is subject generation networks and has attracted a lot of attention owing to coverage probability constraint. Simulation results validate to the capability of extending wireless coverage by reflecting that better coverage performance and higher travel distance signals toward targeted receivers. In this paper, we consider can be achieved with deployment of RIS. The impacts of some a RIS-assisted high-speed train (HST) communication system key system parameters including transmission power, signal-to-to enhance wireless coverage and improve coverage probability. noise ratio threshold, number of RIS elements, number of RIS First, coverage performance of the downlink single-input-single- quantization bits, horizontal distance between base station and output system is investigated, and the closed-form expression RIS, and speed of HST on system performance are investigated. of coverage probability is derived. Moreover, travel distance In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications. Copyright © 2023, The Authors. All rights reserved.

关键词： Railroads

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Visual Tracking Algorithm Based on Color Name Histogram

引用

IOP Conference Series: Materials science and Engineering 2020年第1期790卷

作者： Chenchen Yue Wangsheng Yu Zhiqiang Hou Sugang Ma College of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China Information and Navigation Institute of Air Force Engineering University Xi'an 710077 China

Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on color name. Firstly, the method projected the original RGB image to CN space to obtain robust 11 feature layers. Then, we counted the each pixel numbers of feature layers. Finally, normalizing the amount of pixels in each layer. In addition, we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient). In order to prove validity of the proposed algorithm, we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled trial. In contrast with the reference algorithms, the success of our algorithm increases by 1.5% and the precision increases by 1.7%. The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation, but also enhances the robustness to illumination change.

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RadioGAT: A Joint Model-based and data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention networks

arXiv

引用

arXiv 2024年

作者： Li, Xiaojie Zhang, Songyang Li, Hang Li, Xiaoyang Xu, Lexi Xu, Haigao Mei, Hui Zhu, Guangxu Qi, Nan Xiao, Ming Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Guangdong China College of Physics Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70503 United States Research Institute China United Network Communications Corporation Beijing100048 China China Mobile Communications Group Jiangxi Co. Ltd 330038 China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing210016 China State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China Department of Information Science and Engineering School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention network (GAT) tailored for MBRMR within a single area, eliminating the need for multiregion datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multiband data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework's adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT's enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT's effectiveness and practicality for MBRMR in environments with limited data availability. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Dynamics and Resonance Fluorescence from a Superconducting Artificial Atom Doubly Driven by Quantized and Classical Fields

arXiv

引用

arXiv 2024年

作者： Ruan, Xinhui Wang, Jia-Heng He, Dong Song, Pengtao Li, Shengyong Zhao, Qianchuan Kuang, L.M. Tsai, Jaw-Shen Zou, Chang-Ling Zhang, Jing Zheng, Dongning Astafiev, O.V. Liu, Yu-Xi Peng, Zhihui Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center of Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Integrated Circuits Tsinghua University Beijing100084 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100190 China Center for Quantum Computing RIKEN Saitama351-0198 Japan Graduate School of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku Tokyo162-0825 Japan CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui230026 China Hefei National Laboratory Hefei230088 China School of Automation Science and Engineering Xi'an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China Skolkovo Institute of Science and Technology Nobel str. 3 Moscow143026 Russia Moscow Institute of Physics and Technology Institutskiy Pereulok 9 Dolgoprudny141701 Russia Royal Holloway University of London Surrey EghamTW20 0EX United Kingdom

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical one by controlling the photon number inside the cavity. The device consists of a transmon qubit strongly coupled to a one-dimensional transmission line and a coplanar waveguide resonator. We observe a sideband anti-crossing and asymmetry in the emission spectra of the system through a one-dimensional transmission line, which is fundamentally different from the weak coupling case. By changing the photon number inside the cavity, the emission spectrum of our doubly driven system approaches to the case when the atom is driven by two classical bichromatic fields. We also measure the dynamical evolution of the system through the transmission line and study the properties of the first-order correlation function, Rabi oscillations and energy relaxation in the system. The study of resonance fluorescence from an atom driven by two fields promotes understanding decoherence in superconducting quantum circuits and may find applications in superconducting quantum computing and quantum networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Electric lines

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