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FAST ultra-wideband observation of abnormal emission-shift events of PSR B0919+06

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Science China(Physics,Mechanics & Astronomy) 2019年第5期62卷 32-37页

作者： Ye-Zhao Yu Bo Peng Kuo Liu ChengMin Zhang Lin Wang feifei kou JiGuang Lu Meng Yu FAST Collaboration CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Astronomy and Space Sciences University of Chinese Academy of SciencesBeijing 100049China Max-Planck-Institut fur Radioastronomie Auf dem Hugel 69Bonn 53121Gennany 不详

PSR B0919+06 is known for its abnormal emission phenomenon, where the pulse emission window occasionally shifts progressively in longitude and returns afterwards. The physical mechanism behind this phenomenon is still under investigation. In this paper, we present our ultra-wideband observation of this pulsar using the Five-hundred-meter Aperture Spherical radio Telescope(FAST), with simultaneous measurements in the frequency ranges 280-780 and 1250-1550 MHz. We have identified three abnormal events, each of which becomes less apparent as the frequency decreases. At 1400 MHz, the averaged profile slightly shifted after the first and third abnormal events, implying a relationship between abnormal event and profile variation. We also found a linear trend in the left-edge position of the averaged profiles between the first and third events as well as after the third event, suggesting the existence of a slow-drifting mode between the two major events. The second event has a comparatively small shift in phase and is thus categorized as a "small flare state". During the third event, a sequence of approximately nine pulses was seen to significantly weaken in all frequency bands, likely associated with the pseudo-nulling observed at 150 MHz.A three-component de-composition analysis of the normal averaged profiles shows that the trailing component is dominant at our observing frequencies, while the centre component has a comparatively steeper spectrum. We found the overall flux density in an abnormal event to slightly differ from that in an ordinary state, and the difference shows a frequency dependence. A comparison of the normal, abnormal and dimmed averaged profile indicates that the leading component is likely to be stable in all states.

关键词： pulsar B0919+06 radio

Study of three rotating radio transients with FAST

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Science China(Physics,Mechanics & Astronomy) 2019年第5期62卷 25-31页

作者： JiGuang Lu Bo Peng Kuo Liu Peng Jiang YouLing Yue Meng Yu Ye-Zhao Yu feifei kou Lin Wang FAST Collaboration CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Guizhou Radio Astronomy Observatory Chinese Academy of SciencesGuiyang 550025China Max-Planck-lnstitut fiir Radioastronomie Bonn D-53121Germany College of Astronomy and Space Sciences University of Chinese Academy of SciencesBeijing 100049China 不详

Rotating radio transients(RRATs) are peculiar astronomical objects whose emission mechanism remains under *** this paper, we present observations of three RRATs, J1538+2345, J1854+0306 and J1913+1330, carried out with the Fivehundred-meter Aperture Spherical radio Telescope(FAST). Specifically, we analyze the mean pulse profiles and temporal flux density evolutions of the RRATs. Owing to the high sensitivity of FAST, the derived burst rates of the three RRATs are higher than those in previous reports. RRAT J1854+0306 exhibited a time-dynamic mean pulse profile, whereas RRAT J1913+1330 showed distinct radiation and nulling segments on its pulse intensity trains. The mean pulse profile variation with frequency is also studied for RRAT J1538+2345 and RRAT J1913+1330, and the profiles at different frequencies could be well fitted with a cone-core model and a conal-beam model, respectively.

关键词： radiation mechanisms radio pulsars

Social network search based on semantic analysis and learning

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CAAI Transactions on Intelligence Technology 2016年第4期1卷 293-302页

作者： feifei kou Junping Du Yijiang He Lingfei Ye Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing 100876 China

Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.

关键词： Semantic analysis Semantic learning Cross-modal Social network search

The Study of Unusual Emission from PSR B1859+07 using FAST

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Research in Astronomy and Astrophysics 2022年第4期22卷 1-10页

作者： Lin Wang Ye-Zhao Yu feifei kou Kuo Liu Xinxin Wang Bo Peng CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing100101China Qiannan Normal University for Nationalities Duyun558000China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing100049China Max-Plank-Institut für Radioastronomie Auf dem Hügel 69BonnD-53121Germany Xinjiang Astronomical Observatories Chinese Academy of SciencesUrumqi830011China United World College of Changshu China Changshu215500China

We present simultaneous broad-band radio observations on the abnormal emission mode from PSR B1859+07using the Five-hundred-meter Aperture Spherical radio Telescope(FAST).This pulsar shows peculiar emission,which takes the form of occasional shifts of emission to an early rotational phase and mode change of emission at the normal *** confirm all these three emission modes with our data sets,including the B(burst)and Q(quiet)modes of the non-shifted pulses and the emission shift mode with a quasi-periodicity of 155 *** also identify a new type of emission shift event,which has emission at the normal phase during the *** studied polarization properties of these emission modes in detail,and found that they all have similar polarization angle curve,indicating the emissions of all these three modes are from the same emission height.

关键词： pulsars:general pulsars:individual(B1859+07) methods:observational

SIPR: Side-Information Pointwise Ranking Model for Scientific Research Project Query

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SIPR: Side-Information Pointwise Ranking Model for Scientifi...

A New Emission Mode of PSR B1859+07

2021中国智能自动化大会(CIAC2021)

作者： Benzhi Wang feifei kou Juping Du Mingying Xu Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications

Learning to rank has been applied to many web searches, but it cannot be directly applied to retrieval scientific research projects. In scientific research project query, people are not only concerned about the name of the project, but also the digital information and sideinformation, such as duration, achievements, funding amount of this project. Howerver, the existing learning to rank methods ignore the side-information of scientific research projects. Therefore, we propose a Side-information Pointwise Ranking model(SIPR) for scientific research project query based on deep language model, click model and learning to rank. First, we use the deep language model to extract the semantic information of text, and design a relevance calculation model to extract features of side-information, then we merge the above two features. After that we use the click model to eliminate position bias, and get a ranking score through pointwise DNN. Finally, we can get the query results ordered by these scores. Experiments on real scientific research projects dataset demonstrate that our model can achieve better performance.

关键词： Learning to rank Semantic matching Relevance matching Deep language model Machine learning

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Research in Astronomy and Astrophysics 2023年第10期23卷 176-183页

作者： Tao Wang P.F.Wang J.L.Han Yi Yan Ye-Zhao Yu feifei kou National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China The key Laboratory for Radio Astronomy and Technology Chinese Academy of SciencesBeijing 100101China Qiannan Normal University for Nationalities Duyun 558000China Xinjiang Astronomical Observatories Chinese Academy of SciencesUrumqi 830011China

Previous studies have identified two emission modes in PSR B1859+07: a normal mode that has three prominent components in the average profile, with the trailing one being the brightest, and an anomalous mode(i.e., the A mode) where emissions seem to be shifted to an earlier phase. Within the normal mode, further analysis has revealed the presence of two submodes, i.e., the cW mode and c B mode, where the central component can appear either weak or bright. As for the anomalous mode, a new bright component emerges in the advanced phase while the bright trailing component in the normal mode disappears. New observations of PSR B1859+07 using the Fivehundred-meter Aperture Spherical Radio Telescope(FAST) have revealed the existence of a previously unknown emission mode, dubbed the Af mode. In this mode, all emission components seen in the normal and anomalous modes are detected. Notably, the mean polarization profiles of both the A and Af modes exhibit a jump in the orthogonal polarization angle modesin the bright leading component. The polarization angles for the central component in the original normal mode follow two distinct orthogonal polarization modes in the A and Af modes respectively. The polarization angles for the trailing component show almost the same but a small systematic shift in the A and Af modes, roughly following the values for the c W and cB modes. Those polarization features of this newly detected emission mode imply that the anomalous mode A of PSR B1859+07 is not a result of “phase shift”or “swooshes” of normal components, but simply a result of the varying intensities of different profile ***, subpulse drifting has been detected in the leading component of the Af mode.

关键词： (stars:)pulsars:individual(PSR B1859+07) polarization plasmas

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基于多重注意力的金融事件大数据精准画像

计算机科学与探索 2021年第7期15卷 1237-1244页

作者：陈剑南杜军平薛哲寇菲菲北京邮电大学智能通信软件与多媒体北京市重点实验室计算机学院北京100876

随着知识图谱技术的兴起,利用金融事件大数据中的实体关系来构建金融事件的精准画像成为一个重要的研究方向。通过对金融事件大数据信息进行精准画像,人们可以详细分析金融事件大数据中的属性关系,全面了解金融事件的发展态势,从而分析... 详细信息

随着知识图谱技术的兴起,利用金融事件大数据中的实体关系来构建金融事件的精准画像成为一个重要的研究方向。通过对金融事件大数据信息进行精准画像,人们可以详细分析金融事件大数据中的属性关系,全面了解金融事件的发展态势,从而分析金融市场发展趋势与规律。然而金融事件大数据存在文本数据噪音多、中文语义复杂以及实体关系抽取不准确等研究难点,导致金融事件大数据画像不精准。针对以上问题,提出一种基于多重注意力的金融事件大数据实体关系抽取算法(REMA)来进行实体关系的抽取,然后利用抽取的实体关系信息结合知识图谱技术进行金融事件大数据的精准画像。实验结果表明:在不使用外部资源的情况下,该算法在金融事件大数据中实体关系抽取的准确率、召回率以及F1值比其他对比算法均有所提升,其中准确率提升了5.6个百分点,召回率提升了4.6个百分点,F1值提升了5个百分点。

关键词：金融事件大数据精准画像多重注意力机制实体关系抽取

面向微博短文本的社交与概念化语义扩展搜索方法

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计算机研究与发展 2018年第8期55卷 1641-1652页

作者：崔婉秋杜军平寇菲菲李志坚 Lee JangMyung 智能通信软件与多媒体北京市重点实验室(北京邮电大学) 北京100876 釜山国立大学电子工程系韩国釜山46241

充分挖掘微博短文本的语义以实现精准搜索是一项重要任务.由于微博文本内容具有稀疏性和语义局限性的特点,使得仅通过分析字面语义来进行短文本理解和相似性匹配的传统搜索方法受到了一定的限制.因此提出了一种社交与概念化语义结合的... 详细信息

充分挖掘微博短文本的语义以实现精准搜索是一项重要任务.由于微博文本内容具有稀疏性和语义局限性的特点,使得仅通过分析字面语义来进行短文本理解和相似性匹配的传统搜索方法受到了一定的限制.因此提出了一种社交与概念化语义结合的扩展搜索方法,通过挖掘社交网络独特的社交属性如#标签#、"@"和链接信息URL,对微博短文本实现进一步的社交语义扩展.该方法将文本字面分析获取的概念词语和社交关系中潜在的关联标签信息相结合,对短文本进行2种角度下的语义特征表示,实现了基于微博短文本语义充分理解的精准搜索.在微博数据集上的对比实验表明,与已有的扩展搜索方法相比所提方法能捕捉更多的语义特征,微博搜索的性能也得到了显著的提升.

关键词：微博短文本社交与概念化语义扩展搜索概念词语关联标签

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基于时空信息增强的科技论文主题趋势预测

计算机工程与应用 2023年第14期59卷 86-93页

作者：郑长伟薛哲梁美玉杜军平寇菲菲北京邮电大学计算机学院(国家示范性软件学院)、智能通信软件与多媒体北京市重点实验室北京100876

近年来,随着社会对科研投入的增大,各种领域的研究成果数量显著增加,准确有效地预测未来研究主题的趋势可以帮助科研人员发现未来研究热点,各种领域的研究成果数量显著增加,准确有效地预测未来研究主题的趋势可以帮助科研人员发现未来... 详细信息

近年来,随着社会对科研投入的增大,各种领域的研究成果数量显著增加,准确有效地预测未来研究主题的趋势可以帮助科研人员发现未来研究热点,各种领域的研究成果数量显著增加,准确有效地预测未来研究主题的趋势可以帮助科研人员发现未来研究热点。然而由于各个研究主题之间关联逐渐紧密,大量研究主题之间存在一定的依赖关系,孤立地看待单个研究主题并采用传统的序列问题处理方法,无法有效地挖掘这些研究主题之间的空间依赖。为了同时捕获研究主题之间的空间依赖以及时间变化,提出了一种基于时空信息增强的科技论文主题趋势预测模型,该模型结合了图卷积神经网络(GCN)和时间卷积网络(TCN),具体来说,GCN用于学习研究主题的空间表示,并利用空间依赖加强空间特征,TCN用于学习研究主题趋势的动态变化,并根据时间距离计算加权损失进行优化。在论文数据集以及公开数据集上与当前主流的序列预测模型以及类似的时空模型进行了对比,实验结果表明,在研究主题预测任务以及其他动态图任务中,该模型可以有效捕获时空关系并且优于当前最新的基准算法。

关键词：科技数据预测图神经网络动态图学习膨胀卷积时间序列预测