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arXiv 2024年

作者： Singh, Avinash Teja, Rishabh Singh Moriya, Takashi J. Maeda, Keiichi Kawabata, Koji S. Tanaka, Masaomi Imazawa, Ryo Nakaoka, Tatsuya Gangopadhyay, Anjasha Yamanaka, Masayuki Swain, Vishwajeet Sahu, D.K. Anupama, G.C. Kumar, Brajesh Anche, Ramya M. Sano, Yasuo Raj, A. Agnihotri, V.K. Bhalerao, Varun Bisht, D. Bisht, M.S. Belwal, K. Chakrabarti, S.K. Fujii, Mitsugu Nagayama, Takahiro Matsumoto, Katsura Hamada, Taisei Kawabata, Miho Kumar, Amit Kumar, Ravi Malkan, Brian K. Smith, Paul Sakagami, Yuta Taguchi, Kenta Tominaga, Nozomu Watanabe, Arata Hiroshima Astrophysical Science Centre Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Department of Astronomy The Oskar Klein Center Stockholm University AlbaNova University Center StockholmSE 106 91 Sweden Indian Institute of Astrophysics II Block Koramangala Karnataka Bengaluru560034 India Pondicherry University UT of Puducherry R.V. Nagar Kalapet Pondicherry605014 India National Astronomical Observatory of Japan National Institutes of Natural Sciences 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan School of Physics and Astronomy Monash University ClaytonVIC3800 Australia Department of Astronomy Kyoto University Kitashirakawa-Oiwake-cho Sakyo-ku Kyoto606-8502 Japan Department of Physics Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Astronomical Institute Tohoku University Aoba Sendai980-8578 Japan Department of Physics Graduate School of Advanced Science and Engineering Hiroshima Astrophysical Science Centre Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Graduate School of Science and Engineering Kagoshima University 1-21-35 Korimoto Kagoshima Kagoshima890-0065 Japan Department of Physics Indian Institute of Technology Bombay Powai Mumbai400076 India South-Western Institute for Astronomy Research Yunnan University Yunnan Kunming650504 China Steward Observatory University of Arizona 933N Cherry Avenue TucsonAZ85721 United States Observation and Data Center for Cosmosciences Faculty of Science Hokkaido University Kita-ku Sapporo-shi Hokkaido060-0810 Japan Nayoro Observatory 157-1 Nisshin Nayoro-shi Hokkaido096-0066 Japan Indian Centre for Space Physics 466 Barakhola Netai Nagar West Bengal Kolkata700099 India Cepheid Observatory India Fujii Kurosaki Observatory 4500 Kurosaki

We present a detailed investigation of photometric, spectroscopic, and polarimetric observations of the Type II SN 2023ixf. Earlier studies have provided compelling evidence for a delayed shock breakout from a confined dense circumstellar matter (CSM) enveloping the progenitor star. The temporal evolution of polarization in SN 2023ixf revealed three distinct peaks in polarization evolution at 1.4 d, 6.4 d, and 79.2 d, indicating an asymmetric dense CSM, an aspherical shock front and clumpiness in the low-density extended CSM, and an aspherical inner ejecta/He-core. SN 2023ixf displayed two dominant axes, one along the CSM-outer ejecta and the other along the inner ejecta/He-core, showcasing the independent origin of asymmetry in the early and late evolution. The argument for an aspherical shock front is further strengthened by the presence of a high-velocity broad absorption feature in the blue wing of the Balmer features in addition to the P-Cygni absorption post 16 d. Hydrodynamical light curve modeling indicated a progenitor mass of 10 M☉ with a radius of 470 R☉ and explosion energy of 2 × 1051 erg, along with 0.06 M☉ of 56 Ni, though these properties are not unique due to modeling degeneracies. The modeling also indicated a two-zone CSM: a confined dense CSM extending up to 5×1014 cm, with a mass-loss rate of 10−2 M☉ yr−1 and an extended CSM spanning from 5×1014 cm to at least 1016 cm with a mass-loss rate of 10−4 M☉ yr−1, both assuming a wind-velocity of 10 km s−1. The early nebular phase observations display an axisymmetric line profile of [O I], red-ward attenuation of the emission of Hα post 125 days and flattening in the Ks-band marking the onset of dust formation. Copyright © 2024, The Authors. All rights reserved.

关键词： Supernovae

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Multi-label Recommendation of Web Services with the Combination of Deep Neural Networks 15th

Multi-label Recommendation of Web Services with the Combinat...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Gan, Yanglan Xiang, Yang Zou, Guobing Miao, Huaikou Zhang, Bofeng School of Computer Science and Technology Donghua University Shanghai201620 China School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030301453

With the increasing number of web services on the Internet, how to effectively classify and recommend service labels has become a research issue. It plays an important role in web service organization and management. However, the deficiency of current approaches is that they either recommend only a single label for a web service or a set of independent labels without order ranking that is still difficult for service providers to publish their web services. In this paper, together with label embedding techniques, we propose a novel approach for service multi-label recommendation using deep neural networks. Unlike traditional approaches, the predicted service labels of our approach not only satisfy the demands of service multi-label recommendation, but also provide the importance with an ordered label ranking. The experiments are conducted to validate the effectiveness on a large-scale dataset from ProgrammableWeb, involving 13,869 real-world Web services. The experimental results demonstrate that our approach for multi-label recommendation of web services outperforms the competing approaches in terms of multiple evaluation metrics. © 2019, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Deep neural networks

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Deep Learning Based Code Completion Models for Programming Codes 3

Deep Learning Based Code Completion Models for Programming C...

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3rd International Symposium on Computer science and Intelligent Control, ISCSIC 2019

作者： Wang, Shuai Liu, Jinyang Qiu, Ye Ma, Zhiyi Liu, Junfei Wu, Zhonghai Academy for Advanced Interdisciplinary Studies Peking University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Computer Department Key Laboratory of High Confidence Software Technologies Peking University Beijing China School of Software and Microelectronics Peking University Beijing China

ISBN: (纸本)9781450376617

With the fast development of Information Technology, program software and mobile applications have been widely used in the world, and are playing important roles in human's daily life. Thus, writing programming codes has been important work in many fields. however, it is a hard and time-cost task which presents a great amount of workload to programmers. To make programmers' work easier, intelligent code completion models have been a popular research topic in recent years. This paper designs Deep Learning based models to automatically complete programming codes, which are LSTM-based neural networks, and are combined with several techniques such as Word Embedding models in NLP (Natural Language Processing), and Multihead Attention Mechanism. Moreover, in the models, this paper raises a new algorithm of generating input sequences from partial AST (Abstract Syntax Tree) that have most relevance with nodes to be predicted which is named as RZT (Reverse Zig-zag Traverse) Algorithm, and is the first work of applying Multihead Attention Block into this task. This paper makes insight into codes of several different programming languages, and the models this paper presents show good performances in accuracy comparing with the state-of-art models. © 2019 ACM.

关键词： Application programs

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Deep Learning Based Code Completion Models for Programming Codes 2019

Deep Learning Based Code Completion Models for Programming C...

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Proceedings of the 2019 3rd International Symposium on Computer science and Intelligent Control

作者： Shuai Wang Jinyang Liu Ye Qiu Zhiyi Ma Junfei Liu Zhonghai Wu Academy for Advanced Interdisciplinary Studies Peking University Beijing China School of Electronics Engineering & Computer Science Peking University Beijing China Computer Department Key Laboratory of High Confidence Software Technologies Peking University Beijing China School of Software and Microelectronics Peking University Beijing China

ISBN: (纸本)9781450376617

关键词： Artificial Intelligence Deep Learning Programming Language software engineering

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Collusion-Proof Result Inference in Crowdsourcing

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Journal of Computer science & Technology 2018年第2期33卷 351-365页

作者： Peng-Peng Chen Hai-Long Sun Yi-Li Fang Jin-Peng Huai State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing 100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing 100191 China

In traditional crowdsourcing, workers are expected to provide independent answers to tasks so as to ensure the diversity of answers. However, recent studies show that the crowd is not a collection of independent workers, but instead that workers communicate and collaborate with each other. To pursue more rewards with little effort, some workers may collude to provide repeated answers, which will damage the quality of the aggregated results. Nonetheless, there are few efforts considering the negative impact of collusion on result inference in crowdsourcing. In this paper, we are specially concerned with the Collusion-Proof result inference problem for general crowdsourcing tasks in public platforms. To that end, we design a metric, the worker performance change rate, to identify the colluded answers by computing the difference of the mean worker performance before and after removing the repeated answers. Then we incorporate the collusion detection result into existing result inference methods to guarantee the quality of the aggregated results even with the occurrence of collusion behaviors. With real-world and synthetic datasets, we conducted an extensive set of evaluations of our approach. The experimental results demonstrate the superiority of our approach in comparison with the state-of-the-art methods.

关键词： crowdsourcing quality control collusion collaborative crowdsourcing result inference

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Current and future directions in network biology

arXiv

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arXiv 2023年

作者： Zitnik, Marinka Li, Michelle M. Wells, Aydin Glass, Kimberly Gysi, Deisy Morselli Krishnan, Arjun Murali, T.M. Radivojac, Predrag Roy, Sushmita Baudot, Anaïs Bozdag, Serdar Chen, Danny Z. Cowen, Lenore Devkota, Kapil Gitter, Anthony Gosline, Sara Gu, Pengfei Guzzi, Pietro H. Huang, Heng Jiang, Meng Kesimoglu, Ziynet Nesibe Koyuturk, Mehmet Ma, Jian Pico, Alexander R. Pržulj, Nataša Przytycka, Teresa M. Raphael, Benjamin J. Ritz, Anna Sharan, Roded Shen, Yang Singh, Mona Slonim, Donna K. Tong, Hanghang Yang, Xinan Holly Yoon, Byung-Jun Yu, Haiyuan Milenković, Tijana Department of Biomedical Informatics Harvard University United States Department of Computer Science and Engineering University of Notre Dame United States Lucy Family Institute for Data and Society University of Notre Dame United States Eck Institute for Global Health University of Notre Dame United States Channing Division of Network Medicine Brigham and Women’s Hospital Harvard Medical School United States Department of Statistics Federal University of Paraná Brazil Department of Physics Northeastern University United States Department of Biomedical Informatics University of Colorado United States Department of Computer Science Virginia Tech United States Khoury College of Computer Sciences Northeastern University United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison United States Wisconsin Institute for Discovery United States Aix Marseille Université INSERM MMG Marseille France Department of Computer Science and Engineering University of North Texas United States Department of Mathematics University of North Texas United States Department of Computer Science Tufts University United States Morgridge Institute for Research United States Pacific Northwest National Laboratory United States Department of Medical and Surgical Sciences University Magna Graecia of Catanzaro Italy Department of Computer Science University of Maryland College Park United States National Center of Biotechnology Information National Library of Medicine National Institutes of Health Department of Computer and Data Sciences Case Western Reserve University United States Computational Biology Department School of Computer Science Carnegie Mellon University United States Institute of Data Science and Biotechnology Gladstone Institutes Department of Computer Science University College London United Kingdom ICREA Catalan Institution for Research and Advanced Studies Spain Department of Computer Science Princeton University United States Departmen

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology. © 2023, CC BY.

关键词： Machine learning

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Base editors for simultaneous introduction of C-to-T and A-to-G mutations (vol 86, pg 891, 2020)

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NATURE BIOTECHNOLOGY 2020年第7期38卷 901-901页

作者： Sakata, Rina C. Ishiguro, Soh Mori, Hideto Tanaka, Mamoru Tatsuno, Kenji Ueda, Hiroki Yamamoto, Shogo Seki, Motoaki Masuyama, Nanami Nishida, Keiji Nishimasu, Hiroshi Arakawa, Kazuharu Kondo, Akihiko Nureki, Osamu Tomita, Masaru Aburatani, Hiroyuki Yachie, Nozomu Synthetic Biology Division Research Center for Advanced Science and Technology University of Tokyo Tokyo Japan College of Arts and Sciences University of Tokyo Tokyo Japan Institute for Advanced Biosciences Keio University Tsuruoka Japan Graduate School of Media and Governance Keio University Fujisawa Japan Genome Science Division Research Center for Advanced Science and Technology University of Tokyo Tokyo Japan Biological Data Science Division Research Center for Advanced Science and Technology University of Tokyo Tokyo Japan Engineering Biology Research Center Kobe University Kobe Japan Graduate School of Science Technology and Innovation Kobe University Kobe Japan Department of Biological Sciences School of Science University of Tokyo Tokyo Japan Faculty of Environment and Information Studies Keio University Fujisawa Japan Department of Chemical Science and Engineering Graduate School of Engineering Kobe University Kobe Japan PRESTO Japan Science and Technology Agency (JST) Tokyo Japan

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Biotechnology Genetic engineering

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MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results

arXiv

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arXiv 2022年

作者： Zhu, Yurui Wang, Xi Fu, Xueyang Hu, Xiaowei Hu, Jinfan Liu, Xina Chen, Xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, Xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, Xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie Xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Biao, Jiang Yuhan, Ding Zhang, Li Wan Xiaoyue, Feng Sijing, Chen Tianheng, Zhong Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, Xiang University of Science and Technology of China China Shanghai Ai Laboratory China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Macau China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology China School of Artificial Intelligence Xidian University China School of Remote Sensing and Information Engineering Wuhan University China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Harbin Institute of Technology China Changsha Research Institute of Mining and Metallurgy China Tianjin University China University of Wurzburg Computer Vision Lab Germany Couger Inc. Japan School of Statistics and Data Science Nankai University China Founding Minds Software India Department of Electronics and Communication Srm University Fudan University China Shenyang Aerospace University China Nanjing University of Science and Technology China

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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PC-RGNN: Point cloud completion and graph neural network for 3D object detection

arXiv

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arXiv 2020年

作者： Zhang, Yanan Huang, Di Wang, Yunhong Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions. On the one hand, we introduce a point cloud completion module to recover high-quality proposals of dense points and entire views with original structures preserved. On the other hand, a graph neural network module is designed, which comprehensively captures relations among points through a local-global attention mechanism as well as multi-scale graph based context aggregation, substantially strengthening encoded features. Extensive experiments on the KITTI benchmark show that the proposed approach outperforms the previous state-of-the-art baselines by remarkable margins, highlighting its effectiveness. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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A Distributed Privacy-Preserving Learning Dynamics in General Social Networks

arXiv

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arXiv 2020年

作者： Tao, Youming Chen, Shuzhen Li, Feng Yu, Dongxiao Yu, Jiguo Sheng, Hao School of Computer Science and Technology Shandong University Qingdao266237 China Big Data Institute Qilu University of Technology Jinan250353 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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