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Was Wuhan the early epicenter of the COVID-19 pandemic?——a critique

National science Review 2023年第4期10卷 19-21页

作者： Yanan Cao Lingling Chen Hua Chen Yupeng Cun Xiaofeng Dai Hongli Du Feng Gao Fengbiao Guo Yalong Guo Pei Hao Shunmin He Shunping He XiongLei He Zheng Hu Boon-Peng Hoh Xin Jin Qian Jiang Qinghua Jiang Asifullah Khan Hong-Zhi Kong Jinchen Li Shuai Cheng Li Ying Li Qiang Lin Jianquan Liu Qi Liu Jian Lu Xuemei Lu Shujin Luo Qinghua Nie Zilong Qiu Tieliu Shi Xiaofeng Song Jianzhong Su Sheng-ce Tao Chaolong Wang Chuan-Chao Wang Guo-Dong Wang Jiguang Wang Qi Wu Shaoyuan Wu Shuhua Xu Yu Xue Wenjun Yang Zhaohui Yang Kai Ye Yuan-Nong Ye Li Yu Fangqing Zhao Yiqiang Zhao Weiwei Zhai Dandan Zhang Liye Zhang Houfeng Zheng Qi Zhou Tianqi Zhu Ya-ping Zhang Ruijin Hospital Shanghai Jiao Tong University College of Life Science and Technology Guangxi University Beijing Institute of Genomics Chinese Academy of Sciences Children's Hospital of Chongqing Medical University Wuxi School of Medicine Jiangnan University School of Biology and Biological Engineering South China University of Technology Department of Physics School of ScienceTianjin University School of Pharmaceutical Sciences Wuhan University Institute of Botany Chinese Academy of Sciences Institut Pasteur of Shanghai Chinese Academy of Sciences Institute of Biophysics Chinese Academy of Sciences Institute of Hydrobiology Chinese Academy of Sciences School of Life Sciences Sun Yat-sen University Institute of Synthetic Biology Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Faculty of Medicine and Health Sciences University College Sedaya International School of Medicine South China University of Technology Department of Medical Genetics Capital Institute of Pediatrics School of Life Science and Technology Harbin Institute of Technology Department of Biochemistry Abdul Wali Khan University Xiangya Hospital Central South University Department of Computer Science City University of Hong Kong College of Life Science and Technology Foshan University South China Sea Institute of Oceanology Chinese Academy of Sciences College of Ecology Lanzhou University School of Life Sciences and Technology Tongji University School of Life Sciences Peking University Kunming Institute of Zoology Chinese Academy of Sciences College of Animal Science South China Agricultural University Institute of Neuroscience Chinese Academy of Sciences School of Life Sciences East China Normal University Nanjing University of Aeronautics and Astronautics Wenzhou Institute University of Chinese Academy of Sciences Institute of Systems Biomedicine Shanghai Jiao Tong University Tongji Medical College Huazhong University of Science and Technology School of Life Sciences Xiamen University Division of Life Science and Department of

Recently, Worobey et al.(2022) published a report entitled ‘The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic' that succinctly summarizes their study [1]. A pre-print version of this study had earlier elicited a series of highprofile media coverages [2,3]. All these reports deliver a social-political message that the Huanan market is the epicenter of COVID-19.

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Towards certifying ∞ robustness using neural networks with ∞-dist neurons

arXiv

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arXiv 2021年

作者： Zhang, Bohang Cai, Tianle Lu, Zhou He, Di Wang, Liwei Key Laboratory of Machine Perception MOE School of EECS Peking University Department of Electrical and Computer Engineering Princeton University Zhongguancun Haihua Institute for Frontier Information Technology Department of Computer Science Princeton University Microsoft Research Center for Data Science Peking University

It is well-known that standard neural networks, even with a high classification accuracy, are vulnerable to small ∞-norm bounded adversarial perturbations. Although many attempts have been made, most previous works either can only provide empirical verification of the defense to a particular attack method, or can only develop a certified guarantee of the model robustness in limited scenarios. In this paper, we seek for a new approach to develop a theoretically principled neural network that inherently resists ∞ perturbations. In particular, we design a novel neuron that uses ∞-distance as its basic operation (which we call ∞-dist neuron), and show that any neural network constructed with ∞-dist neurons (called ∞-dist net) is naturally a 1-Lipschitz function with respect to ∞-norm. This directly provides a rigorous guarantee of the certified robustness based on the margin of prediction outputs. We then prove that such networks have enough expressive power to approximate any 1-Lipschitz function with robust generalization guarantee. We further provide a holistic training strategy that can greatly alleviate optimization difficulties. Experimental results show that using ∞-dist nets as basic building blocks, we consistently achieve state-of-the-art performance on commonly used datasets: 93.09% certified accuracy on MNIST (∊ = 0.3), 35.42% on CIFAR-10 (∊ = 8/255) and 16.31% on TinyImageNet (∊ = 1/255). Copyright © 2021, The Authors. All rights reserved.

关键词： Neurons

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Fetal Brain MRI Segmentation via Boundary-Aware Voxel-Level Contrastive Learning

Fetal Brain MRI Segmentation via Boundary-Aware Voxel-Level ...

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Software engineering, Artificial Intelligence, networking and Parallel/Distributed Computing (SNPD-Winter), ACIS International Winter Conference on

作者： Wenying Lu Xin Zhang Qin Xu Wenjun Chen Chaoxiang Yang Xiaofen Xing Xiangmin Xu AHU-IAI AI Joint Laboratory Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center School of Electronic and Information Engineering South China University of Technology School of Computer Science and Technology Anhui University Hefei China Department of Radiology Guangdong Women and Children Hospital Guangzhou China School of Future Technology South China University of Technology

Fetal brain MRI segmentation provides quantitative analysis of the fetal brain development for timely diagnosis. However, low contrast among neighboring tissues limits the precise region segmentation. This paper proposes a boundary-aware voxel-level contrastive learning mechanism for fetal brain tissue segmentation in MRI data. We design a novel sam-pling strategy for the contrastive learning which takes the prior boundary information into consideration. The proposed method can be inserted into Transformer based backbones, which clearly improves their capabilities to learn detailed features. Extensive experiments demonstrate the effective-ness of the proposed method. Best dice score of our method reaches to 89.48 and boundary accuracy is improved by 1.53%.

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Shape-aware contrastive deep supervision for esophageal tumor segmentation from CT scans

Shape-aware contrastive deep supervision for esophageal tumo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiangguo Jin Hui Cui Changming Sun Jiapeng Huang Ping Xuan Yiyue Xu Linlin Wang Leilei Cao Leyi Wei Ran Su School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China Department of Computer Science and Information Technology La Trobe University Melbourne Australia CSIRO Data61 Sydney Australia Department of Computer Science School of Engineering Shantou University Guangdong China Department of Radiation Oncology Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Shandong China Innovation Center of Yangtze River Delta Zhejiang University Zhejiang China School of Software Shandong University Shandong China School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China

Accurate tumor segmentation is crucial for esophageal cancer radiotherapy treatment planning. The low contrast among the esophagus, tumors, and surrounding tissues, and irregular tumor shapes limit the performance of automatic segmentation methods. In this paper, we aim to exploit the irregular shapes of tumors to facilitate accurate segmentation. We propose a simple and pluggable shape-aware contrastive deep supervision network (SCDSNet) with shape-aware regularization and voxel-to-voxel contrastive deep supervision. Specifically, the shape-aware regularization with an uncertainty minimization strategy encourages the precise predictions of an additional shape-aware head. The voxel-to-voxel contrastive deep supervision enhances the multi-scale shape-tumor contrast for better voxel-to-voxel prediction of shapes. The proposed method is simple and highly pluggable, which can easily be extended to other frameworks. Further, we establish a large in-house dataset on esophageal cancer to validate the effectiveness of our proposed method. The quantitative and qualitative experimental results demonstrate the effectiveness of SCDSNet on the esophageal cancer dataset.

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A PCA-Based Internet Delay Space Dividing Algorithm

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China Communications 2019年第4期16卷 172-185页

作者： Zhanfeng Wang Guang Cheng Huaping Cao Ming Chen Chao Hu Li Han School of Computer Science and Engineering Southeast University Nanjing Lake Bell Information Technology Co. Ltd. school of cyber science ang engineering Southeast University National Computer Network Emergency Response Technique Team/Coordination Center of China Army Egineering University of PLA College of Comannd Control Engineering

It's a hot issue to improve the accuracy of network coordinate systems(NCS). By analyzing the error source of NCSes, it's emphasized that guaranteeing the low dimensionality of embedding datasets is the principle on the designing of NCSes. Based on this observation, a PCA based Internet delay space dividing algorithm(IDSD-PCA) is proposed. Beginning at choosing an optimized sub dataset randomly, IDSD-PCA partitions the Internet delay dataset into several sub datasets with low dimensionality by iterations. After the partition, the whole delay dataset and sub datasets are embedded into a hierarchical NCS. The experimental results show that with this framework both the metric space based models and the non-metric space based models can predict the end-to-end delays more accurately.

关键词： Internet delay space end-to-end delay low dimensionality network coordinate system

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Short-term train arrival delay prediction:a data-driven approach

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Railway sciences 2024年第4期3卷 514-529页

作者： Qingyun Fu Shuxin Ding Tao Zhang Rongsheng Wang Ping Hu Cunlai Pu Postgraduate Department China Academy of Railway SciencesBeijingChina Signal and Communication Research Institute China Academy of Railway Sciences Corporation LimitedBeijingChina Traffic Management Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail TransitBeijingChina Scientific and Technological Information Research Institute China Academy of Railway Sciences Corporation LimitedBeijingChina Yibin Track Signal and Communication DepotChina Railway Chengdu Group Co.LtdChengduChina School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingChina

Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays ***,delay predictions often involve imprecise shifts based on known delay ***-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment *** current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen ***/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival *** combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information *** on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding ***-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late *** show precise and rapid *** with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late *** enhanced adaptability to real-world train operations,training with full data is ***/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay *** innovatively compares and analyzes the model’s performance using both full data and delay data ***,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstra

关键词： Train delay prediction Intelligent dispatching command Deep learning Convolutional neural network Long short-term memory Attention mechanism

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Joint noise-tolerant learning and meta camera shift adaptation for unsupervised person re-identification

arXiv

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arXiv 2021年

作者： Yang, Fengxiang Zhong, Zhun Luo, Zhiming Cai, Yuanzheng Lin, Yaojin Li, Shaozi Sebe, Nicu Artificial Intelligence Department Xiamen University China Department of Information Engineering and Computer Science University of Trento Italy Postdoc Center of Information and Communication Engineering Xiamen University China Minjiang University China Minnan Normal University China

This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data. One popular method is to obtain pseudo-label by clustering and use them to optimize the model. Although this kind of approach has shown promising accuracy, it is hampered by 1) noisy labels produced by clustering and 2) feature variations caused by camera shift. The former will lead to incorrect optimization and thus hinders the model accuracy. The latter will result in assigning the intra-class samples of different cameras to different pseudo-label, making the model sensitive to camera variations. In this paper, we propose a unified framework to solve both problems. Concretely, we propose a Dynamic and Symmetric Cross-Entropy loss (DSCE) to deal with noisy samples and a camera-aware meta-learning algorithm (MetaCam) to adapt camera shift. DSCE can alleviate the negative effects of noisy samples and accommodate to the change of clusters after each clustering step. MetaCam simulates cross-camera constraint by splitting the training data into meta-train and meta-test based on camera IDs. With the interacted gradient from meta-train and meta-test, the model is enforced to learn camera-invariant features. Extensive experiments on three re-ID benchmarks show the effectiveness and the complementary of the proposed DSCE and MetaCam. Our method outperforms the state-of-the-art methods on both fully unsupervised re-ID and unsupervised domain adaptive re-ID. Copyright © 2021, The Authors. All rights reserved.

关键词： Cameras

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LocalViT: Analyzing Locality in Vision Transformers

LocalViT: Analyzing Locality in Vision Transformers

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yawei Li Kai Zhang Jiezhang Cao Radu Timofte Michele Magno Luca Benini Luc Van Goo Computer Vision Lab D-ITET ETH Zurich Switzerland Center for Artificial Intelligence and Data Science (CAIDAS) University of Wurzburg Germany Center for Project-Based Learning D-ITET ETH Zurich Switzerland Integrated Systems Laboratory D-ITET ETH Zurich Switzerland Department of Electrical Electronic and Information Engineering University of Bologna Italy Processing Speech and Images (PSI) KU Leuven Belgium

The aim of this paper is to study the influence of locality mechanisms in vision transformers. Transformers originated from machine translation and are particularly good at modelling long-range dependencies within a long sequence. Although the global interaction between the token embeddings could be well modelled by the self-attention mechanism of transformers, what is lacking is a locality mechanism for infor-mation exchange within a local region. In this paper, locality mechanism is systematically investigated by carefully designed controlled experiments. We add locality to vision transformers into the feed-forward network. This seemingly simple solution is inspired by the comparison between feed-forward networks and inverted residual blocks. The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to vision transformers with different architecture designs, which shows the generalization of the locality concept. For ImageNet2012 classification, the locality-enhanced transformers outperform the baselines Swin-T [1], DeiT-T [2] and PVT-T [3] by 1.0%, 2.6 % and 3.1 % with a negligible increase in the number of parameters and computational effort. Code is available at https://***/ofsoundof/LocalViT.

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InSAR phase unwrapping with the constrained nonlinear least squares method 13th

InSAR phase unwrapping with the constrained nonlinear least ...

引用

13th International Conference on Genetic and Evolutionary Computing, ICGEC 2019

作者： Zhang, Xiaoqing Liu, Weike Zheng, Yongguo Wang, Zhiyong College of Computer Science and Engineering Shandong University of Science and Technology 579 Qianwangang Road Qingdao China Center of Information and Network Shandong University of Science and Technology 579 Qianwangang Road Qingdao China College of Geomatics Shandong University of Science and Technology 579 Qianwangang Road Qingdao China

ISBN: (纸本)9789811533075

Interferogram phase unwrapping is one of the important data processing step in InSAR applications, however, it is affected by the high slope of terrain factors. In this paper, an improved constrained nonlinear least squares InSAR phase unwrapping method is proposed. First, we estimate the phase instantaneous frequency of the interferogram by the least squares method. Then, through the transformation between the phase instantaneous frequency and the phase gradient, we pre-estimate a phase gradient model considering terrain factors. Finally, using phase instantaneous frequency estimation (PIFE) model as the constraints of the nonlinear least squares phase, we propose the improved constrained nonlinear least squares (CNLS) phase unwrapping method. When compares with the other algorithms in the interferometric phase unwrapping experiments, the improved method is shown to be the most robust to noise caused by the terrain factors and to be the most suitable for phase unwrapping of the InSAR data in rugged and varied terrain regions. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Landforms

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Target Defense against Sequentially Arriving Intruders

Target Defense against Sequentially Arriving Intruders

引用

IEEE Conference on Decision and Control

作者： Arman Pourghorban Michael Dorothy Daigo Shishika Alexander Von Moll Dipankar Maity Department of Electrical and Computer Engineering The University of North Carolina at Charlotte NC USA Computational and Information Sciences Directorate Army Research Laboratory APG MD USA Mechanical Engineering Department George Mason University Fairfax VA USA Air Force Research Laboratory WPAFB Control Science Center OH USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We consider a variant of the target defense problem where a single defender is tasked to capture a sequence of incoming intruders. The intruders’ objective is to breach the target boundary without being captured by the defender. As soon as the current intruder breaches the target or gets captured by the defender, the next intruder appears at a random location on a fixed circle surrounding the target. Therefore, the defender’s final location at the end of the current game becomes its initial location for the next game. Thus, the players pick strategies that are advantageous for the current as well as for the future games. Depending on the information available to the players, each game is divided into two phases: partial information and full information phase. Under some assumptions on the sensing and speed capabilities, we analyze the agents’ strategies in both phases. We derive equilibrium strategies for both the players to optimize the capture percentage using the notions of engagement surface and capture circle. We quantify the percentage of capture for both finite and infinite sequences of incoming intruders.

关键词： Level set Games Sensors Task analysis IEEE Regions

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