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233 results on '"Mao, Yiming"'

1. A Shock Flash Breaking Out of a Dusty Red Supergiant

Author

Li, Gaici, Hu, Maokai, Li, Wenxiong, Yang, Yi, Wang, Xiaofeng, Yan, Shengyu, Hu, Lei, Zhang, Jujia, Mao, Yiming, Riise, Henrik, Gao, Xing, Sun, Tianrui, Liu, Jialian, Xiong, Dingrong, Wang, Lifan, Mo, Jun, Iskandar, Abdusamatjan, Xi, Gaobo, Xiang, Danfeng, Wang, Lingzhi, Sun, Guoyou, Zhang, Keming, Chen, Jian, Lin, Weili, Guo, Fangzhou, Liu, Qichun, Cai, Guangyao, Zhou, Wenjie, Zhao, Jingyuan, Chen, Jin, Zheng, Xin, Li, Keying, Zhang, Mi, Xu, Shijun, Lyu, Xiaodong, Castro-Tirado, A. J., Chufarin, Vasilii, Potapov, Nikolay, Ionov, Ivan, Korotkiy, Stanislav, Nazarov, Sergey, Sokolovsky, Kirill, Hamann, Norman, and Herman, Eliot

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract

Shock breakout emission is light that arises when a shockwave, generated by core-collapse explosion of a massive star, passes through its outer envelope. Hitherto, the earliest detection of such a signal was at several hours after the explosion, though a few others had been reported. The temporal evolution of early light curves should reveal insights into the shock propagation, including explosion asymmetry and environment in the vicinity, but this has been hampered by the lack of multiwavelength observations. Here we report the instant multiband observations of a type II supernova (SN 2023ixf) in the galaxy M101 (at a distance of 6.85+/-0.15 Mpc), beginning at about 1.4 hours after the explosion. The exploding star was a red supergiant with a radius of about 440 solar radii. The light curves evolved rapidly, on timescales of 1-2 hours, and appeared unusually fainter and redder than predicted by models within the first few hours, which we attribute to an optically thick dust shell before it was disrupted by the shockwave. We infer that the breakout and perhaps the distribution of the surrounding dust were not spherically symmetric., Comment: 32 pages, 9 figures, and 3 tables. Online publication in Nature (Dec. 13th 2023)

Published
2023
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3. A shock flash breaking out of a dusty red supergiant

Author

Li, Gaici, Hu, Maokai, Li, Wenxiong, Yang, Yi, Wang, Xiaofeng, Yan, Shengyu, Hu, Lei, Zhang, Jujia, Mao, Yiming, Riise, Henrik, Gao, Xing, Sun, Tianrui, Liu, Jialian, Xiong, Dingrong, Wang, Lifan, Mo, Jun, Iskandar, Abdusamatjan, Xi, Gaobo, Xiang, Danfeng, Wang, Lingzhi, Sun, Guoyou, Zhang, Keming, Chen, Jian, Lin, Weili, Guo, Fangzhou, Liu, Qichun, Cai, Guangyao, Zhou, Wenjie, Zhao, Jingyuan, Chen, Jin, Zheng, Xin, Li, Keying, Zhang, Mi, Xu, Shijun, Lyu, Xiaodong, Castro-Tirado, Alberto J., Chufarin, Vasilii, Potapov, Nikolay, Ionov, Ivan, Korotkiy, Stanislav, Nazarov, Sergey, Sokolovsky, Kirill, Hamann, Norman, and Herman, Eliot

Published
2024
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5. Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection

Author

Fan, Lue, Yang, Yuxue, Mao, Yiming, Wang, Feng, Chen, Yuntao, Wang, Naiyan, and Zhang, Zhaoxiang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract

This paper aims for high-performance offline LiDAR-based 3D object detection. We first observe that experienced human annotators annotate objects from a track-centric perspective. They first label the objects with clear shapes in a track, and then leverage the temporal coherence to infer the annotations of obscure objects. Drawing inspiration from this, we propose a high-performance offline detector in a track-centric perspective instead of the conventional object-centric perspective. Our method features a bidirectional tracking module and a track-centric learning module. Such a design allows our detector to infer and refine a complete track once the object is detected at a certain moment. We refer to this characteristic as "onCe detecTed, neveR Lost" and name the proposed system CTRL. Extensive experiments demonstrate the remarkable performance of our method, surpassing the human-level annotating accuracy and the previous state-of-the-art methods in the highly competitive Waymo Open Dataset without model ensemble. The code will be made publicly available at https://github.com/tusen-ai/SST.

Published
2023

6. Coalition Formation Game for Reconnaissance and Attack Task Allocation in Heterogeneous UAV Swarm

Author

Chen, Jiaxin, Fang, Tao, Li, Qingwei, He, Jiafan, Xia, Zhijie, Mao, Yiming, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, and Chinese Institute of Command and Control, editor

Published
2024
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7. Air Combat Agent Construction Based on Hybrid Self-play Deep Reinforcement Learning

Author

Xia, Zhijie, Mao, Yiming, He, Jiafan, Chen, Jiaxin, Li, Qingwei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, and Chinese Institute of Command and Control, editor

Published
2024
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8. Towards a General Pre-training Framework for Adaptive Learning in MOOCs

Author

Zhong, Qingyang, Yu, Jifan, Zhang, Zheyuan, Mao, Yiming, Wang, Yuquan, Lin, Yankai, Hou, Lei, Li, Juanzi, and Tang, Jie

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Adaptive learning aims to stimulate and meet the needs of individual learners, which requires sophisticated system-level coordination of diverse tasks, including modeling learning resources, estimating student states, and making personalized recommendations. Existing deep learning methods have achieved great success over statistical models; however, they still lack generalization for diverse tasks and suffer from insufficient capacity since they are composed of highly-coupled task-specific architectures and rely on small-scale, coarse-grained recommendation scenarios. To realize the idea of general adaptive systems proposed in pedagogical theory, with the emerging pre-training techniques in NLP, we try to conduct a practical exploration on applying pre-training to adaptive learning, to propose a unified framework based on data observation and learning style analysis, properly leveraging heterogeneous learning elements. Through a series of downstream tasks of Learning Recommendation, Learning Resource Evaluation, Knowledge Tracing, and Dropout Prediction, we find that course structures, text, and knowledge are helpful for modeling and inherently coherent to student non-sequential learning behaviors and that indirectly relevant information included in the pre-training foundation can be shared across downstream tasks to facilitate effectiveness. We finally build a simplified systematic application of adaptive learning and reflect on the insights brought back to pedagogy. The source code and dataset will be released., Comment: 13 pages, 8 figures

Published
2022

12. Air-to-Air Missile Launch Envelope Computation Based on Neural Network

Author

Fang, Feng, Li, Qingwei, He, Jiafan, Mao, Yiming, Ding, Feng, Fei, Aiguo, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Fu, Wenxing, editor, Gu, Mancang, editor, and Niu, Yifeng, editor

Published
2023
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13. A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data

Author

Jia, Shen, Zhang, Yulin, Mao, Yiming, Gao, Jiawei, Chen, Yixuan, Jiang, Yuxuan, Luo, Haochen, Lv, Kebo, and Su, Jionglong

Subjects
Quantitative Biology - Tissues and Organs
Abstract

DNA methylation is a well-studied genetic modification that regulates gene transcription of Eukaryotes. Its alternations have been recognized as a significant component of cancer development. In this study, we use the DNA methylation 450k data from The Cancer Genome Atlas to evaluate the efficacy of DNA methylation data on cancer classification for 30 cancer types. We propose a new method for gene selection in high dimensional data(over 450 thousand). Variance filtering is first introduced for dimension reduction and Recursive feature elimination (RFE) is then used for feature selection. We address the problem of selecting a small subsets of genes from large number of methylated sites, and our parsimonious model is demonstrated to be efficient, achieving an accuracy over 91%, outperforming other studies which use DNA micro-arrays and RNA-seq Data . The performance of 20 models, which are based on 4 estimators (Random Forest, Decision Tree, Extra Tree and Support Vector Machine) and 5 classifiers (k-Nearest Neighbours, Support Vector Machine, XGboost, Light GBM and Multi-Layer Perceptron), is compared and robustness of the RFE algorithm is examined. Results suggest that the combined model of extra tree plus catboost classifier offers the best performance in cancer identification, with an overall validation accuracy of 91% , 92.3%, 93.3% and 93.5% for 20, 30, 40 and 50 features respectively. The biological functions in cancer development of 50 selected genes is also explored through enrichment analysis and the results show that 12 out of 16 of our top features have already been identified to be specific with cancer and we also propose some more genes to be tested for future studies. Therefore, our method may be utilzed as an auxiliary diagnostic method to determine the actual clinicopathological status of a specific cancer., Comment: 39 pages

Published
2021

17. Controllable Person Image Synthesis with Attribute-Decomposed GAN

Author

Men, Yifang, Mao, Yiming, Jiang, Yuning, Ma, Wei-Ying, and Lian, Zhouhui

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper introduces the Attribute-Decomposed GAN, a novel generative model for controllable person image synthesis, which can produce realistic person images with desired human attributes (e.g., pose, head, upper clothes and pants) provided in various source inputs. The core idea of the proposed model is to embed human attributes into the latent space as independent codes and thus achieve flexible and continuous control of attributes via mixing and interpolation operations in explicit style representations. Specifically, a new architecture consisting of two encoding pathways with style block connections is proposed to decompose the original hard mapping into multiple more accessible subtasks. In source pathway, we further extract component layouts with an off-the-shelf human parser and feed them into a shared global texture encoder for decomposed latent codes. This strategy allows for the synthesis of more realistic output images and automatic separation of un-annotated attributes. Experimental results demonstrate the proposed method's superiority over the state of the art in pose transfer and its effectiveness in the brand-new task of component attribute transfer., Comment: Accepted by CVPR 2020 (Oral). Project Page: https://menyifang.github.io/projects/ADGAN/ADGAN.html

Published
2020

18. Single‐Cell Spatial Transcriptomics Unveils Platelet‐Fueled Cycling Macrophages for Kidney Fibrosis

Author

Liu, Jun, primary, Zheng, Bo, additional, Cui, Qingya, additional, Zhu, Yu, additional, Chu, Likai, additional, Geng, Zhi, additional, Mao, Yiming, additional, Wan, Lin, additional, Cao, Xu, additional, Xiong, Qianwei, additional, Guo, Fujia, additional, Yang, David C, additional, Hsu, Ssu‐Wei, additional, Chen, Ching‐Hsien, additional, and Yan, Xiangming, additional

Published
2024
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22. Construction Method of Air Combat Agent Based on Reinforcement Learning

Author

Mao, Yiming, Li, Qingwei, He, Jiafan, Xia, Zhijie, Fei, Aiguo, Chinese Institute of Command and Control, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, and Zhang, Junjie James, Series Editor

Published
2022
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25. Expertise-Aware Crowdsourcing Taxonomy Enrichment

Author

Wang, Yuquan, Wang, Yanpeng, Mao, Yiming, Yu, Jifan, Zeng, Kaisheng, Hou, Lei, Li, Juanzi, Tang, Jie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhang, Wenjie, editor, Zou, Lei, editor, Maamar, Zakaria, editor, and Chen, Lu, editor

Published
2021
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26. Learning Behavior-Aware Cognitive Diagnosis for Online Education Systems

Author

Mao, Yiming, Xu, Bin, Yu, Jifan, Fang, Yifan, Yuan, Jie, Li, Juanzi, Hou, Lei, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zeng, Jianchao, editor, Qin, Pinle, editor, Jing, Weipeng, editor, Song, Xianhua, editor, and Lu, Zeguang, editor

Published
2021
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31. A shock flash breaking out of a dusty red supergiant

Author

Li, Gaici, primary, Hu, Maokai, additional, Li, Wenxiong, additional, Yang, Yi, additional, Wang, Xiaofeng, additional, Yan, Shengyu, additional, Hu, Lei, additional, Zhang, Jujia, additional, Mao, Yiming, additional, Riise, Henrik, additional, Gao, Xing, additional, Sun, Tianrui, additional, Liu, Jialian, additional, Xiong, Dingrong, additional, Wang, Lifan, additional, Mo, Jun, additional, Iskandar, Abdusamatjan, additional, Xi, Gaobo, additional, Xiang, Danfeng, additional, Wang, Lingzhi, additional, Sun, Guoyou, additional, Zhang, Keming, additional, Chen, Jian, additional, Lin, Weili, additional, Guo, Fangzhou, additional, Liu, Qichun, additional, Cai, Guangyao, additional, Zhou, Wenjie, additional, Zhao, Jingyuan, additional, Chen, Jin, additional, Zheng, Xin, additional, Li, Keying, additional, Zhang, Mi, additional, Xu, Shijun, additional, Lyu, Xiaodong, additional, Castro-Tirado, Alberto J., additional, Chufarin, Vasilii, additional, Potapov, Nikolay, additional, Ionov, Ivan, additional, Korotkiy, Stanislav, additional, Nazarov, Sergey, additional, Sokolovsky, Kirill, additional, Hamann, Norman, additional, and Herman, Eliot, additional

Published
2023
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43. Heterogeneous Face Recognition Based on Super Resolution Reconstruction by Adaptive Multi-dictionary Learning

Author

Zhao, Jingjing, Mao, Yiming, Fang, Qi, Liang, Zhicheng, Yang, Fumeng, Zhan, Shu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Yang, Jinfeng, editor, Yang, Jucheng, editor, Sun, Zhenan, editor, Shan, Shiguang, editor, Zheng, Weishi, editor, and Feng, Jianjiang, editor

Published
2015
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44. Robust Face Detection Based on Enhanced Local Sensitive Support Vector Machine

Author

Li, Xiaohong, Tao, Qinqin, Zhao, Jingjing, Mao, Yiming, Zhan, Shu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Yang, Jinfeng, editor, Yang, Jucheng, editor, Sun, Zhenan, editor, Shan, Shiguang, editor, Zheng, Weishi, editor, and Feng, Jianjiang, editor

Published
2015
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49. Preparation of Mesoporous Structure Electrode Materials ZnMn2O4 by Co-precipitation Method

Author

Song Jiaxing, Guo Tao, Wang Qiong, Yao Miao, and Mao Yiming

Subjects
Environmental sciences, GE1-350
Abstract

In order to study the fast and efficient method of preparing electrode material ZnMn2O4, in this paper, oxalic acid was selected as the precipitator, oxalic acid dihydrate (C2H2O4•2H2O) as the chemical precipitator, manganese sulfate monohydrate (MnSO4•H2O) as the manganese source and zinc acetate dihydrate (C4H6O4Zn•2H2O) as the zinc source. The precursor was prepared by co-precipitation method. Then, ZnMn2O4 powders with mesoporous structure were obtained at different calcination temperatures. The phase of ZnMn2O4 powders at different calcination temperatures was characterized by XRD analysis. After considering the crystallinity and high temperature agglomeration, samples calcinated at 600oC were selected as the cathode material of the battery, and were characterized by SEM and TEM. The experimental results show that the electrode material ZnMn2O4 prepared by this method has high crystallinity, high preparation efficiency, energy saving, environmental protection and good dispersion.

Published
2020
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