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Evidence of jet activity from the secondary black hole in the OJ287 binary system

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arXiv 2024年

作者： Valtonen, Mauri J. Zola, Staszek Gupta, Alok C. Kishore, Shubham Gopakumar, Achamveedu Jorstad, Svetlana G. Wiita, Paul J. Gu, Minfeng Nilsson, Kari Marscher, Alan P. Zhang, Zhongli Hudec, Rene Matsumoto, Katsura Drozdz, Marek Ogloza, Waldemar Berdyugin, Andrei V. Reichart, Daniel E. Mugrauer, Markus Dey, Lankeswar Pursimo, Tapio Lehto, Harry J. Ciprini, Stefano Nakaoka, T. Uemura, M. Imazawa, Ryo Zejmo, Michal Kouprianov, Vladimir V. Davidson, James W. Sadun, Alberto Štrobl, Jan Weaver, Z.R. Jelínek, Martin FINCA University of Turku TurkuFI-20014 Finland Tuorla Observatory Department of Physics and Astronomy University of Turku TurkuFI-20014 Finland Astronomical Observatory Jagiellonian University ul. Orla 171 Krakow30-244 Poland Manora Peak Nainital263001 India Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences 80 Nandan Road Shanghai200030 China Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Mumbai400005 India Institute for Astrophysical Research Boston University 725 Commonwealth Avenue BostonMA02215 United States Department of Physics The College of New Jersey 2000 Pennington Rd. EwingNJ08628-0718 United States Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Radio Astronomy and Technology Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China Faculty of Electrical Engineering Czech Technical University Prague166 36 Czech Republic Astronomical Institute ASU CAS Ondřejov251 65 Czech Republic Astronomical Institute Osaka Kyoiku University 4-698 Asahigaoka Kashiwara Osaka582-8582 Japan Mt. Suhora Astronomical Observatory University of the National Education Commission ul.Podchorazych 2 Krakow30-084 Poland Department of Physics and Astronomy University of North Carolina at Chapel Hill Chapel HillNC27599 United States Astrophysical Institute University Observatory Schillergässchen 2 JenaD-07745 Germany Department of Physics and Astronomy West Virginia University PO Box 135 Willey Street MorgantownWV26506 United States Nordic Optical Telescope Apartado 474 Santa Cruz de La PalmaE-38700 Spain Istituto Nazionale di Fisica Nucleare Sezione di Roma "Tor Vergata" RomaI-00133 Italy Space Science Data Center - Agenzia Spaziale Italiana Via del Politecnico snc RomaI-00133 Italy Hiroshima Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyam

We report the study of a huge optical intraday flare on November 12, 2021, at 2 am UT, in the blazar OJ 287. In the binary black hole model it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact, based on a prediction made eight years earlier. The first I-band results of the flare have already been reported by Kishore et al. (2024). Here we combine these data with our monitoring in the R-band. There is a big change in the R-I spectral index by 1.0 ± 0.1 between the normal background and the flare, suggesting a new component of radiation. The polarization variation during the rise of the flare suggests the same. The limits on the source size place it most reasonably in the jet of the secondary black hole. We then ask why we have not seen this phenomenon before. We show that OJ 287 was never before observed with sufficient sensitivity on the night when the flare should have happened according to the binary model. We also study the probability that this flare is just an oversized example of intraday variability, using the Krakow-dataset of intense monitoring between 2015 and 2023. We find that the occurrence of a flare of this size and rapidity is unlikely. In the Appendix, we give the full orbit-linked historical light curve of OJ 287 as well as the dense monitoring sample of Krakow. Copyright © 2024, The Authors. All rights reserved.

关键词： Active Galactic Nuclei

Sending-or-not-sending with independent lasers: Secure twin-field quantum key distribution over 509 km

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arXiv 2019年

作者： Chen, Jiu-Peng Zhang, Chi Liu, Yang Jiang, Cong Zhang, Weijun Hu, Xiao-Long Guan, Jian-Yu Yu, Zong-Wen Xu, Hai Lin, Jin Li, Ming-Jun Chen, Hao Li, Hao You, Lixing Wang, Zhen Wang, Xiang-Bin Zhang, Qiang Pan, Jian-Wei Shanghai Branch National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science and Technology of China Shanghai201315 China Shanghai Branch CAS Center for Excellence Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Jinan Institute of Quantum Technology Jinan Shandong250101 China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Data Communication Science and Technology Research Institute Beijing100191 China Corning Incorporated CorningNY14831 United States

Twin field quantum key distribution promises high key rates at long distance to beat the rate distance limit. Here, applying the sending or not sending TF QKD protocol, we experimentally demonstrate a secure key distribution breaking the absolute key rate limit of repeaterless QKD over 509 km, 408 km ultra-low loss optical fibre and 350 km standard optical fibre. Two independent lasers are used as the source with remote frequency locking technique over 500 km fiber distance;Practical optical fibers are used as the optical path with appropriate noise filtering;And finite key effects are considered in the key rate analysis. The secure key rates obtained at different distances are more than 5 times higher than the conditional limit of repeaterless QKD, a bound value assuming the same detection loss in the comparison. The achieved secure key rate is also higher than that a traditional QKD protocol running with a perfect repeaterless QKD device and even if an infinite number of sent pulses. Our result shows that the protocol and technologies applied in this experiment enable TF QKD to achieve high secure key rate at long distribution distance, and hence practically useful for field implementation of intercity QKD. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum cryptography

Learning knowledge representation across knowledge graphs 31

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Learning knowledge representation across knowledge graphs

31st AAAI Conference on Artificial Intelligence, AAAI 2017

作者： Cai, Pengshan Li, Wei Feng, Yansong Wang, Yuanzhuo Jia, Yantao CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Science Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing China Institute of Computer Science and Technology Peking University Beijing China

ISBN: (纸本)9781577357865

Distributed knowledge representation learning (KRL) methods encode both entities and relations in knowledge graphs (KG) in a lower-dimensional semantic space, which model relatively dense knowledge graphs well and greatly improve the performance of knowledge graph completion and knowledge reasoning. However, existing KRL methods including Trans(E, H, R, D and Sparse) hardly obtain comparative performances on sparse KGs where most of entities and relations have very low frequencies. Furthermore, all existing methods target at KRL on one knowledge graph independently. The embeddings of different KGs are independent with each other. In this paper, we propose a novel cross-knowledge- graph (cross-KG) KRL method which learns embeddings for two different KGs simultaneously. Through projecting semantic related entities and relations in two KGs to a uniform semantic space, our method could learn better embeddings for sparse KGs by incorporating information from another relatively larger and denser KG. The learned embeddings are also helpful for downstream cross-KGs or cross-linguals tasks like ontology alignment. The experiment results show that our method could significandy outperform corresponding baseline methods on knowledge graph completion on single KG and cross-KG entity prediction and mapping tasks. © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge graph

A bimodal burst energy distribution of a repeating fast radio burst source

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arXiv 2021年

作者： Li, D. Wang, Pei Zhu, W.W. Zhang, B. Zhang, X.X. Duan, R. Zhang, Y.K. Feng, Y. Tang, N.Y. Chatterjee, S. Cordes, J.M. Cruces, M. Dai, S. Gajjar, V. Hobbs, G. Jin, C. Kramer, M. Lorimer, D.R. Miao, C.C. Niu, C.H. Niu, J.R. Pan, Z.C. Qian, L. Spitler, L. Werthimer, D. Zhang, G.Q. Wang, F.Y. Xie, X.Y. Yue, Y.L. Zhang, L. Zhi, Q.J. Zhu, Y. CAS Key Laboratory of FAST NAOC Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Department of Physics and Astronomy University of Nevada Las Vegas Las VegasNV89154 United States CSIRO Astronomy and Space Science PO Box 76 EppingNSW1710 Australia Department of Physics Anhui Normal University Anhui Wuhu241002 China Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Astronomy University of California Berkeley BerkeleyCA94720 United States Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV United States School of Astronomy and Space Science Nanjing University Nanjing210093 China Ministry of Education Nanjing210093 China Guizhou Normal University Guiyang550001 China School of Physics and Technology Wuhan University Wuhan430072 China Western Sydney University Locked Bag 1797 PenrithNSW2751 Australia Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China

The event rate, energy distribution, and time-domain behaviour of repeating fast radio bursts (FRBs) contains essential information regarding their physical nature and central engine, which are as yet unknown. As the first precisely-localized source, FRB 121102 has been extensively observed and shows non-Poisson clustering of bursts over time and a power-law energy distribution. However, the extent of the energy distribution towards the fainter end was not known. Here we report the detection of 1652 independent bursts with a peak burst rate of 122 hr-1, in 59.5 hours spanning 47 days. A peak in the isotropic equivalent energy distribution is found to be ~ 4.8 × 1037erg at 1.25 GHz, below which the detection of bursts is suppressed. The burst energy distribution is bimodal, and well characterized by a combination of a log-normal function and a generalized Cauchy function. The large number of bursts in hour-long spans allow sensitive periodicity searches between 1 ms and 1000 s. The non-detection of any periodicity or quasi-periodicity poses challenges for models involving a single rotating compact object. The high burst rate also implies that FRBs must be generated with a high radiative efficiency, disfavoring emission mechanisms with large energy requirements or contrived triggering conditions. Copyright © 2021, The Authors. All rights reserved.

关键词： Poisson distribution

Balanced distribution adaptation for transfer learning

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arXiv 2018年

作者： Wang, Jindong Chen, Yiqiang Hao, Shuji Feng, Wenjie Shenk, Zhiqi Beijing Key Laboratory of Mobile Computing and Pervasive Device CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Institute of High Performance Computing A*STAR School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite common in real problems. To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution Adaptation (BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA. Based on BDA, we also propose a novel Weighted Balanced Distribution Adaptation (W-BDA) algorithm to tackle the class imbalance issue in transfer learning. W-BDA not only considers the distribution adaptation between domains but also adaptively changes the weight of each class. To evaluate the proposed methods, we conduct extensive experiments on several transfer learning tasks, which demonstrate the effectiveness of our proposed algorithms over several state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

A Ship Draft Line Detection Method Based on Image Processing and Deep Learning

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Zhong Wang Peibei Shi Chao Wu School of Computer Science and Technology Hefei Normal University No. 1688 Lianhua Road Hefei Anhui China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China No. 96 Jinzhai Road Hefei Anhui China Network and Information Center University of Science and Technology of China No. 96 Jinzhai Road Hefei Anhui China

The traditional ship draft detection method mainly adopts the method of the human eye observation, which has the problems of large precision error and slow detection speed. Aiming at this problem, this paper proposes a ship draft line detection method based on image processing and deep learning. The color image is first transformed into a grayscale image, and the corner detection is carried out by Harris operator. Contour map is obtained using Canny operator for edge detection, and line segment detection is performed using Hough algorithm. The water surface is determined by calculating the lowest position of the corner point. Then the grayscale image is binarized to increase the contrast of the image, the digital area is determined by the corner positions of the highest and lowest points, and finally the convolutional neural network is used to perform character recognition and calculate the draft of the ship. Tested on videos of various scenes, the error of the proposed method is less than 1cm.

关键词：

Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity

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Genomics, Proteomics & Bioinformatics 2019年第4期17卷 441-452页

作者： Zhenyu Liu Jiangang Liu Huijuan Yuan Taiyuan Liu Xingwei Cui Zhenchao Tang Yang Du Meiyun Wang Yusong Lin Jie Tian CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Department of Endocrinology and Metabolism Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou UniversityZhengzhou 450003China Department of Radiology Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou UniversityZhengzhou 450003China Cooperative Innovation Center for Internet Healthcare&School of Software Zhengzhou UniversityZhengzhou 450003China School of Mechanical Electrical&Information EngineeringShandong University(Weihai)Weihai 264209China University of Chinese Academy of Sciences Beijing 100080China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’an 710126China

Majority of type 2 diabetes mellitus(T2DM)patients are highly susceptible to several forms of cognitive impairments,particularly ***,the underlying neural mechanism of these cognitive impairments remains *** aimed to investigate the correlation between whole brain resting state functional connections(RSFCs)and the cognitive status in 95 patients with *** constructed an elastic net model to estimate the Montreal Cognitive Assessment(MoCA)scores,which served as an index of the cognitive status of the patients,and to select the RSFCs for further ***,we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected *** estimated and chronological MoCA scores were significantly correlated with R=0.81 and the mean absolute error(MAE)=***,cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54%and the area under the receiver operating characteristic(ROC)curve(AUC)of *** connectivity pattern not only included the connections between regions within the default mode network(DMN),but also the functional connectivity between the task-positive networks and the DMN,as well as those within the task-positive *** results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.

关键词： Type 2 diabetes mellitus Resting state functional connectivity Elastic net Support vector machines MoCA

A Performance Comparison of Big data Processing Platform Based on Parallel Clustering Algorithms

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Procedia Computer science 2018年 139卷 127-135页

作者： Mo Hai Yuejing Zhang Haifeng Li School of Information Central University of Finance and Economics Beijing 100081 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu 610054 China

The performance of three typical big data processing platform: Hadoop, Spark and dataMPI are compared based on different parallel clustering algorithms: parallel K-means, parallel fuzzy K-means and parallel Canopy. Experiments are performed on different text as well as numeric dataset and clusters of different scale. The results show that: (1) for the same data set, when the memory of each node is 4GB, dataMPI can achieve about 60% performance improvement compared with Hadoop, and can achieve about 32% performance improvement compared with Spark; (2) in order to obtain a high clustering performance, a cluster with 6 nodes and 6GB memory of each node should be selected.

关键词： Hadoop Spark dataMPI K-means fuzzy K-means Canopy

Building Efficient CNN Architecture for Offline Handwritten Chinese Character Recognition

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arXiv 2018年

作者： Li, Zhiyuan Teng, Nanjun Jin, Min Lu, Huaxiang CAS Center for Excellence in Brain Science and Intelligence Technology Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Lab of Artificial Networks Institute of Semiconductors CAS University of Chinese Academy of Sciences

Deep convolutional neural networks based methods have brought great breakthrough in images classification, which provides an end-to-end solution for handwritten Chinese character recognition(HCCR) problem through learning discriminative features automatically. Nevertheless, state-of-the-art CNNs appear to incur huge computation cost, and require the storage of a large number of parameters especially in fully connected layers, which is difficult to deploy such networks into alternative hardware devices with limited computation amount. To solve the storage problem, we propose a novel technique called Global Weighted Arverage Pooling for reducing the parameters in fully connected layer without loss in accuracy. Besides, we implement a cascaded model in single CNN by adding mid output layer to complete recognition as early as possible, which reduces average inference time significantly. Experiments were performed on the ICDAR-2013 offline HCCR dataset, and it is found that our proposed approach only needs 6.9ms for classfying a chracter image on average, and achieves the state-of-the-art accuracy of 97.1% while requires only 3.3MB for storage. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks