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Research Square 2020年

作者： Zhang, Xiao-Yong Yu, Ziqi Han, Xiaoyang Zhao, Botao Zhuo, Yaoyao Ren, Yan Xue, Xiangyang Lamm, Lorenz Feng, Jianfeng Marr, Carsten Shan, Fei Peng, Tingying Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai200433 China Department of Radiology Shanghai Public Health Clinical Center Fudan University Shanghai201508 China Department of Radiology Huashan Hospital Fudan University Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Institute of Computational Biology Helmholtz Zentrum München Ingolstädter Landstraße 1 D-85764 Germany Helmholtz AI Helmholtz Zentrum München Ingolstädter Landstraße 1 NeuherbergD-85764 Germany

Currently, reliable, robust and ready-to-use CT-based tools for prediction of COVID-19 progression are still lacking. To address this problem, we present DABC-Net, a novel deep learning (DL) tool that combines a 2D U-net for intra-slice spatial information processing, and a recurrent LSTM network to leverage inter-slice context, for automatic volumetric segmentation of lung and pneumonia lesions. We evaluate DABC-Net on more than 10,000 radiologists-labeled CT slices from four different cohorts. Compared to state-of-the-art segmentation tools, DABC-Net is much faster, more robust, and able to estimate segmentation uncertainty. Based only on the first two CT scans within 3 days after admission from 656 longitudinal CT scans, the AUC of our DBAC-Net for disease progression prediction reaches 93%. We release our tool as a GUI for patient-specific prediction of pneumonia progression, to provide clinicians with additional assistance to triage patients at early days after the diagnosis and to optimize the assignment of limited medical resources, which is of particular importance in current critical COVID-19 pandemic. © 2020, CC BY.

关键词： COVID-19

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Multifractal analysis of weighted networks by a modified sandbox algorithm

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Scientific reports 2015年第1期5卷 17628页

作者： Yu-Qin Song Jin-Long Liu Zu-Guo Yu Bao-Gen Li Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China. College of Science Hunan University of technology Zhuzhou Hunan 412007 China. School of Mathematical Sciences Queensland University of Technology Brisbane Q4001 Australia.

Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. Some algorithms for MFA of unweighted complex networks have been proposed in the past a few years, including the sandbox (SB) algorithm recently employed by our group. In this paper, a modified SB algorithm (we call it SBw algorithm) is proposed for MFA of weighted networks. First, we use the SBw algorithm to study the multifractal property of two families of weighted fractal networks (WFNs): "Sierpinski" WFNs and "Cantor dust" WFNs. We also discuss how the fractal dimension and generalized fractal dimensions change with the edge-weights of the WFN. From the comparison between the theoretical and numerical fractal dimensions of these networks, we can find that the proposed SBw algorithm is efficient and feasible for MFA of weighted networks. Then, we apply the SBw algorithm to study multifractal properties of some real weighted networks - collaboration networks. It is found that the multifractality exists in these weighted networks, and is affected by their edge-weights.

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NTIRE 2020 Challenge on Image and Video Deblurring

arXiv

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arXiv 2020年

作者： Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

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GAMMA Challenge: Glaucoma grAding from Multi-Modality imAges

arXiv

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arXiv 2022年

作者： Wu, Junde Fang, Huihui Li, Fei Fu, Huazhu Lin, Fengbin Li, Jiongcheng Huang, Yue Yu, Qinji Song, Sifan Xu, Xinxing Xu, Yanyu Wang, Wensai Wang, Lingxiao Lu, Shuai Li, Huiqi Huang, Shihua Lu, Zhichao Ou, Chubin Wei, Xifei Liu, Bingyuan Kobbi, Riadh Tang, Xiaoying Lin, Li Zhou, Qiang Hu, Qiang Bogunović, Hrvoje Orlando, José Ignacio Zhang, Xiulan Xu, Yanwu State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China Singapore School of Informatics Xiamen University Xiamen China Shanghai Jiao Tong University Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Institute of High Performance Computing A*STAR Singapore Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin China School of Medical Technology Beijing Institute of Technology Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Weizhi Medical Technology Company Suzhou China École de Technologie Supérieure Montreal Montreal Canada DIAGNOS Inc. Quebec Canada Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Technology Co. Ltd. Shanghai China School of Information and Electronics Beijing Institute of Technology Beijing China Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology Medical University of Vienna Austria Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfill the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyze their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniqu

关键词： Grading

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Discovery of Four Pulsars in a Pilot Survey at Intermediate Galactic Latitudes with FAST

arXiv

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arXiv 2023年

作者： Zhi, Q.J. Bai, J.T. Dai, S. Xu, X. Dang, S.J. Shang, L.H. Zhao, R.S. Li, D. Zhu, W.W. Wang, N. Yuan, J.P. Wang, P. Zhang, L. Feng, Y. Wang, J.B. Wang, S.Q. Wu, Q.D. Dong, A.J. Yang, H. Tian, J. Zhong, W.Q. Luo, X.H. Filipović, Miroslav D. Qiao, G.J. School of Physics and Electronic Science Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Xinjiang Astronomical Observatory Chinese Academy of Sciences Xinjiang Urumqi830011 China School of Science Western Sydney University Locked Bag 1797 Penrith South DCNSW2751 Australia School of Mathematical Sciences Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Key Laboratory of Radio Astronomy Chinese Academy of Sciences Xinjiang Urumqi830011 China Xinjiang Key Laboratory of Radio Astrophysics 150 Science1-Street Xinjiang Urumqi830011 China Zhejiang Lab Zhejiang Hangzhou311121 China Institute of Optoelectronic Technology Lishui University Lishui323000 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing100049 China School of Physics Peking University Beijing100871 China

We present the discovery and timing results of four pulsars discovered in a pilot survey at intermediate Galactic latitudes with the Five-hundred Aperture Spherical Telescope (FAST). Among these pulsars, two belong to the category of millisecond pulsars (MSPs) with spin periods of less than 20 ms. The other two fall under the classification of "mildly recycled" pulsars, with massive white dwarfs as companions. Remarkably, this small survey, covering an area of 4.7 deg2, led to the discovery of four recycled pulsars. Such success underscores the immense potential of future surveys at intermediate Galactic latitudes. In order to assess the potential yield of MSPs, we conducted population simulations and found that both FAST and Parkes new phased array feed surveys, focusing on intermediate Galactic latitudes, have the capacity to uncover several hundred new MSPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Pulsars

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Deep Stable Learning for Cross-lingual Dependency Parsing

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ACM Transactions on Asian and Low-Resource Language information processing 1000年

作者： Haijiang Liu Chen Qiu Qiyuan Li Maofu Liu Li Wang Jinguang Gu Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Institute of Scientific and Technical Information of China Beijing China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing China

The Cross-lingual Dependency Parsing (XDP) task poses a significant challenge due to the differences in dependency structures between training and testing languages, known as the out-of-distribution (OOD) problem. Our research delved into this issue in the XDP dataset by selecting 43 languages from 22 language families. We found that the primary factor of the OOD problem is the unbalanced length distribution among languages. To address the impact of the OOD problem, we propose deep stable learning for Cross-lingual Dependency Parsing (SL-XDP), which utilizes deep stable learning with a feature fusion module. In detail, we implemented five feature fusion operations for generating comprehensive representations with dependency relations and the deep stable learning algorithm to decorrelate dependency structures with sequence length. Our experiments on Universal Dependencies have demonstrated that SL-XDP can lessen the impact of the OOD problem and improve the model generalization among 21 languages, with a maximum improvement of 18%.

关键词： Cross-lingual Dependency Parsing Deep Stable Learning Out-of-Distribution Problem

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Study on the degree distribution properties of soil crack

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IOP Conference Series: Earth and Environmental Science 2019年第1期289卷

作者： Zuhan Liu Jiangxi Province Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang Jiangxi China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education Jiangxi Normal University Nanchang Jiangxi China Department of Geography and Geographic Information Science University of Cincinnati Cincinnati OH 45221-0131 America

The Dry-hot Valley Region (DVR) is one of the typical ecologically fragile areas, but also it is a key and difficult region of vegetation rehabilitation in southwestern mountain area of China. Due to the extremely hot dry climate and strong expansion/shrinkage characteristic of soil, the cracking is a kind of extremely universal phenomenon. Soil cracking accelerates the water loss by means of evaporation, intensifying the damage of soil drought to plants, so it has become is one of the important reasons for vegetation recovery. In this paper, according to complex network theory, we analyse the degree distribution properties of two typical soil-crack network in Yuanmou Dry-hot Valley Region, which includes not only the topological characteristics of the fracture network itself, but also more important study the reflected characteristics of the soil crack under different network topologies.

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Path Planning Algorithm for Unmanned Surface Vessel Based on Multiobjective Reinforcement Learning

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Computational intelligence and neuroscience 2023年第1期2023卷 2146314页

作者： Caipei Yang Yingqi Zhao Xuan Cai Wei Wei Xingxing Feng Kaibo Zhou MOE Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China. Wuhan Second Ship Design and Research Institute Wuhan 430205 China.

It is challenging to perform path planning tasks in complex marine environments as the unmanned surface vessel approaches the goal while avoiding obstacles. However, the conflict between the two subtarget tasks of obstacle avoidance and goal approaching makes the path planning difficult. Thus, a path planning method for unmanned surface vessel based on multiobjective reinforcement learning is proposed under the complex environment with high randomness and multiple dynamic obstacles. Firstly, the path planning scene is set as the main scene, and the two subtarget scenes including obstacle avoidance and goal approaching are divided from it. The action selection strategy in each subtarget scene is trained through the double deep -network with prioritized experience replay. A multiobjective reinforcement learning framework based on ensemble learning is further designed for policy integration in the main scene. Finally, by selecting the strategy from subtarget scenes in the designed framework, an optimized action selection strategy is trained and used for the action decision of the agent in the main scene. Compared with traditional value-based reinforcement learning methods, the proposed method achieves a 93% success rate in path planning in simulation scenes. Furthermore, the average length of the paths planned by the proposed method is 3.28% and 1.97% shorter than that of PER-DDQN and dueling DQN, respectively.

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Regularly Truncated M-estimators for Learning with Noisy Labels

arXiv

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arXiv 2023年

作者： Xia, Xiaobo Lu, Pengqian Gong, Chen Han, Bo Yu, Jun Yu, Jun Liu, Tongliang The Sydney AI Center School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia The Australian AI Institute Faculty of Engineering and IT The University of Technology Sydney BroadwayNSW2007 Australia The PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing Hong Kong Polytechnic University Hong Kong The Department of Computer Science Hong Kong Baptist University Hong Kong The School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China The Department of Automation University of Science and Technology of China Hefei230026 China

The sample selection approach is very popular in learning with noisy labels. As deep networks "learn pattern first", prior methods built on sample selection share a similar training procedure: the small-loss examples can be regarded as clean examples and used for helping generalization, while the large-loss examples are treated as mislabeled ones and excluded from network parameter updates. However, such a procedure is arguably debatable from two folds: (a) it does not consider the bad influence of noisy labels in selected small-loss examples;(b) it does not make good use of the discarded large-loss examples, which may be clean or have meaningful information for generalization. In this paper, we propose regularly truncated M-estimators (RTME) to address the above two issues simultaneously. Specifically, RTME can alternately switch modes between truncated M-estimators and original M-estimators. The former can adaptively select small-losses examples without knowing the noise rate and reduce the side-effects of noisy labels in them. The latter makes the possibly clean examples but with large losses involved to help generalization. Theoretically, we demonstrate that our strategies are label-noise-tolerant. Empirically, comprehensive experimental results show that our method can outperform multiple baselines and is robust to broad noise types and levels. The implementation is available at https://***/xiaoboxia/RTM_LNL. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Interpretable Saliency Map for Deep Reinforcement Learning

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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Hong Zheng Yinglong Dai Fumin Yu Yuezhen Hu School of Physics and Electronics Hunan Normal University Changsha 410081 China College of Liberal Arts and Sciences National University of Defense Technology Changsha 410073 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha 410081 China

Deep reinforcement learning (deep RL) achieved big successes with the advantage of deep learning techniques, while it also introduces the disadvantage of the model interpretability. Bad interpretability is a great obstacle for deep RL to be applied in real situations or human-machine interaction situations. Borrowed from the deep learning field, the techniques of saliency maps recently become popular to improve the interpretability of deep RL. However, the saliency maps still cannot provide specific and clear enough model interpretations for the behavior of deep RL agents. In this paper, we propose to use hierarchical conceptual embedding techniques to introduce prior-knowledge in the deep neural network (DNN) based models of deep RL agents and then generate the saliency maps for all the embedded factors. As a result, we can track and discover the important factors that influence the decisions of deep RL agents.

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