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Spectral Energy Distributions in Three Deep-Drilling Fields of the Vera C. Rubin Observatory Legacy Survey of Space and Time: Source Classification and Galaxy Properties

arXiv

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arXiv 2022年

作者： Zou, Fan Brandt, W.N. Chen, Chien-Ting Leja, Joel Ni, Qingling Yan, Wei Yang, Guang Zhu, Shifu Luo, Bin Nyland, Kristina Vito, Fabio Xue, Yongquan Department of Astronomy and Astrophysics 525 Davey Lab The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics 104 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Marshall Space Flight Center HuntsvilleAL35811 United States Science and Technology Institute Universities Space Research Association HuntsvilleAL35805 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Astronomy University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Department of Physics and Astronomy Texas A&M University College StationTX77843-4242 United States George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy Texas A&M University College StationTX77843-4242 United States School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210093 China Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education Nanjing210093 China U.S. Naval Research Laboratory 4555 Overlook Ave SW WashingtonDC20375 United States Scuola Normale Superiore Piazza dei Cavalieri 7 Pisa56126 Italy INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Gobetti 93/3 BolognaI-40129 Italy CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China School of Astronomy and Space Sciences University of Science and Technology of China Hefei230026 China

W-CDF-S, ELAIS-S1, and XMM-LSS will be three Deep-Drilling Fields (DDFs) of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), but their extensive multi-wavelength data have not been fully utilized as done in the COSMOS field, another LSST DDF. To prepare for future science, we fit source spectral energy distributions (SEDs) from X-ray to far-infrared in these three fields mainly to derive galaxy stellar masses and star-formation rates. We use CIGALE v2022.0, a code that has been regularly developed and evaluated, for the SED fitting. Our catalog includes 0.8 million sources covering 4.9 deg2 in W-CDF-S, 0.8 million sources covering 3.4 deg2 in ELAIS-S1, and 1.2 million sources covering 4.9 deg2 in XMM-LSS. Besides fitting normal galaxies, we also select candidates that may host active galactic nuclei (AGNs) or are experiencing recent star-formation variations and use models specifically designed for these sources to fit their SEDs;this increases the utility of our catalog for various projects in the future. We calibrate our measurements by comparison with those in well-studied smaller regions and briefly discuss the implications of our results. We also perform detailed tests of the completeness and purity of SED-selected AGNs. Our data can be retrieved from a public website. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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Fsh-Pc-Sce complex mediates active transcription of Cubitus interruptus （Ci）

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Journal of Molecular Cell Biology 2018年第5期10卷 437-447页

作者： Xiangdong Lv Hao Chen Shuo Zhang Zhao Zhang Chenyu Pan Yuanxin Xia Jialin Fan Wenqing Wu Yi Lu Lei Zhang Hailong Wu Yun Zhao State Key Laboratory of Cell Biology CAS Center for Excellence in Molecular Cell Science Innovation Center for Cell Signaling Network Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences University of Chinese Academy of Sciences Shanghai 200031 China School of Life Science and Technology ShanghaiTech University Shanghai 201210 China

The Hedgehog （Hh） signaling pathway plays important roles in both embryonic development and adult tissue homeostasis. Such biological functions are mediated by the transcription factor Cubitus interruptus （Ci）. Yet the transcriptional regulation of the effector Ci itself is poorly investigated. Through an RNAi-based genetic screen, we identified that female sterile （1） homeotic （Fsh）, a transcription co-activator, directly activates Ci transcription. Biochemistry assays demonstrated physical interactions among Fsh, Sex combs extra （Sce）, and Polycomb （Pc）. Functional assays further showed that both Pc and Sce are required for Ci expression, which is not likely mediated by the derepression of Engraited （En）, a repressor of Ci, in Pc or Sce mutant cells. Finally, we provide evidence showing that Pc/Sce facilitates the binding of Fsh at Ci locus and that the physical interaction between Fsh and Pc is essential for Fsh-mediated Ci transcription. Taken together, we not only uncover that Ci is transcriptionally regulated by Fsh-Pc-Sce complex but also provide evidence for the coordination between Fsh and PcG proteins in transcriptional regulation.

关键词： Ci Fsh Hedgehog polycomb complex Sce transcriptional activation

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Predictability of real temporal networks

arXiv

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arXiv 2020年

作者： Tang, Disheng Du, Wenbo Shekhtman, Louis Wang, Yijie Havlin, Shlomo Cao, Xianbin Yan, Gang School of Electronic and Information Engineering Beihang University Beijing100191 China School of Physics Science and Engineering Tongji University Shanghai200092 China National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation Beijing100191 China Network Science Institute Northeastern University BostonMA02115 United States Department of Physics Bar Ilan University Ramat Gan5290002 Israel Shanghai Institute of Intelligence Science and Technology Tongji University Shanghai200092 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evidence has shown that the temporal nature of links in many real-world networks is not random. Nonetheless, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here, we propose an entropy-rate-based framework, based on combined topological–temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological–temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly, we find that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combined topological–temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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A quantum-inspired classical algorithm for separable non-negative matrix factorization

arXiv

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arXiv 2019年

作者： Chen, Zhihuai Li, Yinan Sun, Xiaoming Yuan, Pei Zhang, Jialin CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Centrum Wiskunde & Informatica and QuSoft Science Park 123 Amsterdam1098XG Netherlands

Non-negative Matrix Factorization (NMF) asks to decompose a (entry-wise) non-negative matrix into the product of two smaller-sized nonnegative matrices, which has been shown intractable in general. In order to overcome this issue, separability assumption is introduced which assumes all data points are in a conical hull. This assumption makes NMF tractable and is widely used in text analysis and image processing, but still impractical for huge-scale datasets. In this paper, inspired by recent development on dequantizing techniques, we propose a new classical algorithm for separable NMF problem. Our new algorithm runs in polynomial time in the rank and logarithmic in the size of input matrices, which achieves an exponential speedup in the low-rank setting. Copyright © 2019, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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The Seoul National University AGN Monitoring Project IV: Hα reverberation mapping of 6 AGNs and the Hα Size-Luminosity Relation

arXiv

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arXiv 2023年

作者： Cho, Hojin Woo, Jong-Hak Wang, Shu Son, Donghoon Shin, Jaejin Rakshit, Suvendu Barth, Aaron J. Bennert, Vardha N. Gallo, Elena Hodges-Kluck, Edmund Treu, Tommaso Bae, Hyun-Jin Cho, Wanjin Foord, Adi Geum, Jaehyuk Jadhav, Yashashree Jeon, Yiseul Kabasares, Kyle M. Kang, Daeun Kang, Wonseok Kim, Changseok Kim, Donghwa Kim, Minjin Kim, Taewoo Le, Huynh Anh N. Malkan, Matthew A. Mandal, Amit Kumar Park, Daeseong Park, Songyoun Sung, Hyun-Il Vivian, U. Williams, Peter R. Department of Physics & Astronomy Seoul National University Seoul08826 Korea Republic of Major in Astronomy and Atmospheric Sciences Kyungpook National University Daegu41566 Korea Republic of Korea Astronomy and Space Science Institute Daejeon34055 Korea Republic of Aryabhatta Research Institute of Observational Sciences Nainital Uttarakhand Manora Peak263001 India Department of Physics and Astronomy 4129 Frederick Reines Hall University of California IrvineCA92697-4575 United States Physics Department California Polytechnic State University San Luis ObispoCA93407 United States Department of Astronomy University of Michigan Ann ArborMI48109 United States NASA/GSFC Code 662 GreenbeltMD20771 United States Department of Physics and Astronomy University of California Los AngelesCA90095-1547 United States Department of Astronomy Yonsei University Seoul03722 Korea Republic of Kavli Institute of Particle Astrophysics and Cosmology Stanford University StanfordCA94305 United States National Youth Space Center Goheung59567 Korea Republic of Spacebeam Inc. Cheongju28165 Korea Republic of Graduate School of Data Science Seoul National University Seoul08826 Korea Republic of Department of Astronomy and Space Science Chungbuk National University Cheongju28644 Korea Republic of CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China

The broad line region (BLR) size-luminosity relation has paramount importance for estimating the mass of black holes in active galactic nuclei (AGNs). Traditionally, the size of the Hβ BLR is often estimated from the optical continuum luminosity at 5100Å, while the size of the Hα BLR and its correlation with the luminosity is much less constrained. As a part of the Seoul National University AGN Monitoring Project (SAMP) which provides six-year photometric and spectroscopic monitoring data, we present our measurements of the Hα lags of 6 high-luminosity AGNs. Combined with the measurements for 42 AGNs from the literature, we derive the size-luminosity relations of Hα BLR against broad Hα and 5100Å continuum luminosities. We find the slope of the relations to be 0.61±0.04 and 0.59 ± 0.04, respectively, which are consistent with the Hβ size-luminosity relation. Moreover, we find a linear relation between the 5100Å continuum luminosity and the broad Hα luminosity across 7 orders of magnitude. Using these results, we propose a new virial mass estimator based on the Hα broad emission line, finding that the previous mass estimates based on the scaling relations in the literature are overestimated by up to 0.7 dex at masses lower than 107 M☉. Copyright © 2023, The Authors. All rights reserved.

关键词： Luminance

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MFPR: A Personalized Ranking Recommendation with Multiple Feedback

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ACM Transactions on Social Computing 2018年第2期1卷 1–22页

作者： Shi, Chuan Liu, Jian Zhang, Yiding Hu, Binbin Liu, Shenghua Yu, Philip S. Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications No. 10 Xi Tu Cheng Road Beijing China Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Zhongguancun Beijing China Department of Computer Science University of Illinois at Chicago 851 South Morgan Street Chicago United States

Recently, recommender systems have played an important role in improving web user experiences and increasing profits. Recommender systems exploit users' behavioral history (i.e., feedback on items) to build models. The feedback usually includes explicit feedback (e.g., ratings) and implicit feedback (e.g., browsing history, click logs), which are both useful for improving recommendations. However, as far as we are concerned, no existing works have integrated both explicit and multiple implicit feedback simultaneously. Therefore, we propose a unified and flexible model, named Multiple Feedback-based Personalized Ranking (MFPR), to make full use of multiple feedback, which uses a personalized ranking framework. To train model MFPR, we design an algorithm to generate ordered item pairs as labeled data, with consideration of both rating scores and multiple implicit feedback. Extensive experiments on two real-world datasets validate the effectiveness of the MFPR model. With the integration of multiple feedback, MFPR significantly improves recommendation performance. © 2018 ACM.

关键词： Recommender systems

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Improved Siamese network for Video-Based Person Re-identification

Improved Siamese Network for Video-Based Person Re-identific...

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International Conference on networking and network Applications (NaNA)

作者： Zhongmin Wang Lanlan Cai Na Duan Lin Fan Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

In the field of human-computer interaction and visual surveillance, person re-identification is undoubtedly a key task. In this paper, we propose improved Siamese network for video-based person re-identification, which combines the verification model and the classification model, in a way that all the supervision information of the samples is fully utilized. The key frame extraction is able to select informative frames over the sequence, which removes occluded frames according to optical flow vector and measures the similarity of two adjacent frames' contents in terms of the block mutual information entropy. A convolutional network and long short-term memory joint training is used as a feature extractor to re-identify using the improved Siamese network framework. Experimental data on the iLIDS-VID and PRID-2011 datasets show that this approach is superior to existing video-based re-identification methods.

关键词： Feature extraction Optical flow Mutual information data mining Entropy Logic gates Cameras

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Emotional Analysis Based on Dynamic Functional Brain network

Emotional Analysis Based on Dynamic Functional Brain Network

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International Conference on networking and network Applications (NaNA)

作者： Zhongmin Wang Yue Tong Xia Heng Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

The human brain dynamically changes in the second and millisecond levels, so constructing a functional brain network using static connections can result in the absence of some time-related effective features. In this paper, a dynamic network model construction method is proposed. The basic idea of the proposed dynamic emotion analysis method is to construct different network modes based on the channel correlation mode and band power distribution method, and use the sliding window method to estimate the dynamic correlation mode and dynamic power distribution changes, Then the multivariate features of network-level brain dynamics are extracted and classified. We conducted experiments on the SEED database to study network-level dynamic transformations in the process of positive, neutral, and negative emotions. The results verify the feasibility of the emotional assessment method based on dynamic functional connection, and provide a new way to establishing brain dynamic model under different emotional states. The results show that emotion analysis based on dynamic networks is more accurate than static networks.

关键词： Correlation Electroencephalography Power distribution Windows Feature extraction Time series analysis databases

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Competitive endogenous RNA is an intrinsic component of EMT regulatory circuits and modulates EMT (vol 10, 1637, 2019)

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NATURE COMMUNICATIONS 2019年第1期10卷 1-12页

作者： Liu, Yuwei Xue, Mengzhu Du, Shaowei Feng, Wanwan Zhang, Ke Zhang, Liwen Liu, Haiyue Jia, Guoyi Wu, Lingshuang Hu, Xin Chen, Luonan Wang, Peng Laboratory of Systems Biology Shanghai Advanced Research Institute Chinese Academy of Sciences 200031 Shanghai China University of Chinese Academy of Sciences 200031 Shanghai China School of Life Sciences Shanghai University 200031 Shanghai China Bio-Med Big Data Center CAS Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and Health Shanghai Institutes for Biological Sciences Chinese Academy of Sciences 200031 Shanghai China Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Institute of Biochemistry and Cell Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences 200031 Shanghai China Shanghai Institute of Materia Medica Chinese Academy of Sciences 200031 Shanghai China School of Life Science and Technology ShanghaiTech University 201210 Shanghai China Department of Breast Surgery Key Laboratory of Breast Cancer in Shanghai Fudan University Shanghai Cancer Center Fudan University 200032 Shanghai China Precision Cancer Medicine Center Fudan University Shanghai Cancer Center 200032 Shanghai China Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences 650223 Kunming China Research Center for Brain Science and Brain-Inspired Intelligence 201210 Shanghai China

The competitive endogenous RNA (ceRNA) hypothesis suggests an intrinsic mechanism to regulate biological processes. However, whether the dynamic changes of ceRNAs can modulate miRNA activities remains controversial. Here, we examine the dynamics of ceRNAs during TGF-β-induced epithelial-to-mesenchymal transition (EMT). We observe that TGFBI, a transcript highly induced during EMT in A549 cells, acts as the ceRNA for miR-21 to modulate EMT. We further identify FN1 as the ceRNA for miR-200c in the canonical SNAIL-ZEB-miR200 circuit in MCF10A cells. Experimental assays and computational simulations demonstrate that the dynamically induced ceRNAs are directly coupled with the canonical double negative feedback loops and are critical to the induction of EMT. These results help to establish the relevance of ceRNA in cancer EMT and suggest that ceRNA is an intrinsic component of the EMT regulatory circuit and may represent a potential target to disrupt EMT during tumorigenesis.

关键词： Cancer Dynamic networks Gene regulatory networks miRNAs

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A Deep Look into Neural Ranking Models for Information Retrieval

arXiv

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arXiv 2019年

作者： Guo, Jiafeng Fan, Yixing Pang, Liang Yang, Liu Ai, Qingyao Zamani, Hamed Wu, Chen Croft, W. Bruce Cheng, Xueqi University of Chinese Academy of Sciences Beijing China Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Center for Intelligent Information Retrieval University of Massachusetts Amherst AmherstMA United States

Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods. Recently, with the advance of deep learning technology, we have witnessed a growing body of work in applying shallow or deep neural networks to the ranking problem in IR, referred to as neural ranking models in this paper. The power of neural ranking models lies in the ability to learn from the raw text inputs for the ranking problem to avoid many limitations of hand-crafted features. Neural networks have sufficient capacity to model complicated tasks, which is needed to handle the complexity of relevance estimation in ranking. Since there have been a large variety of neural ranking models proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we will take a deep look into the neural ranking models from different dimensions to analyze their underlying assumptions, major design principles, and learning strategies. We compare these models through benchmark tasks to obtain a comprehensive empirical understanding of the existing techniques. We will also discuss what is missing in the current literature and what are the promising and desired future directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Information retrieval

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