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arXiv 2023年

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Tao, Dacheng Li, Xuelong The School of Communication and Electrical Engineering East China Normal University Shanghai China The School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China The School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom The School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW Australia Northwestern Polytechnical University China Ministry of Industry and Information Technology Xi’an China

Deep generative models have demonstrated successful applications in learning non-linear data distributions through a number of latent variables and these models use a non-linear function (generator) to map latent samples into the data space. On the other hand, the non-linearity of the generator implies that the latent space shows an unsatisfactory projection of the data space, which results in poor representation learning. This weak projection, however, can be addressed by a Riemannian metric, and we show that geodesics computation and accurate interpolations between data samples on the Riemannian manifold can substantially improve the performance of deep generative models. In this paper, a Variational spatial-Transformer AutoEncoder (VTAE) is proposed to minimize geodesics on a Riemannian manifold and improve representation learning. In particular, we carefully design the variational autoencoder with an encoded spatial-Transformer to explicitly expand the latent variable model to data on a Riemannian manifold, and obtain global context modelling. Moreover, to have smooth and plausible interpolations while traversing between two different objects' latent representations, we propose a geodesic interpolation network different from the existing models that use linear interpolation with inferior performance. Experiments on benchmarks show that our proposed model can improve predictive accuracy and versatility over a range of computer vision tasks, including image interpolations, and reconstructions. © 2023, CC BY-SA.

关键词： Interpolation

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Modern Computing: Vision and Challenges

arXiv

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arXiv 2024年

作者： Gill, Sukhpal Singh Wu, Huaming Patros, Panos Ottaviani, Carlo Arora, Priyansh Pujol, Victor Casamayor Haunschild, David Parlikad, Ajith Kumar Cetinkaya, Oktay Lutfiyya, Hanan Stankovski, Vlado Li, Ruidong Ding, Yuemin Qadir, Junaid Abraham, Ajith Ghosh, Soumya K. Song, Houbing Herbert Sakellariou, Rizos Rana, Omer Rodrigues, Joel J.P.C. Kanhere, Salil S. Dustdar, Schahram Uhlig, Steve Ramamohanarao, Kotagiri Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Center for Applied Mathematics Tianjin University Tianjin China Raygun Performance Monitoring Wellington New Zealand Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Microsoft Schiphol Netherlands Distributed Systems Group Vienna University of Technology Vienna Austria Detecon International GmbH Munich Germany Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science University of Western Ontario London Canada Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Institute of Science and Engineering Kanazawa University Japan Tecnun School of Engineering University of Navarra Spain Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Bennett University Greater Noida India Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Baltimore United States Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia COPELABS Lusófona University Lisbon Portugal Sydney Australia The University of Melbourne Victoria Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. © 2024, CC BY.

关键词： Obsolescence

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Coarse WDM in Metropolitan Networks: Challenges, Standards, Applications, and Future Role

Coarse WDM in Metropolitan Networks: Challenges, Standards, ...

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2019 Imam Al-Kadhum International Conference for Modern Applications of Information and Communication technology, MAICT 2019

作者： Hamdi, Mustafa Maad Audah, Lukman Abduljabbar Rashid, Sami Al-Mashhadani, Mohammad A. Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Johor Batu Pahat86400 Malaysia Department of Computer Engineering Technology Al-Maarif University College Al-Anbar Iraq

Coarse Wavelength Division Multiplexing (CWDM) denotes a technology of diaphanous transport which aids to transmit simultaneously a large number of services across a single optical fiber. This technology relies on the usage of optical channels which are 20 nm from each other. These channels are employed in the CFO OPX products, which ranged from 1270 nm until 1610 nm, had been set in ITU-T G-694.2 recommendations. ITU-T managed to determine 18 optical channels together, though 16 only are used in reality, in the case of using the two lowest channels which are avoided because of the excessive-high attenuation of glass. However, the review study presented in this paper deals with the CWDM technique as the best choice in decreasing capital expenditure after taking into consideration the simplicity of design, the capability of expanded transmission, low cost of components and reduction in operational cost. The paper also focused on the standards, applications and future role of the CWDM technique. © Published under licence by IOP Publishing Ltd.

关键词： Dense wavelength division multiplexing

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Attention multiple instance learning with Transformer aggregation for breast cancer whole slide image classification

Attention multiple instance learning with Transformer aggreg...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jianxin Zhang Cunqiao Hou Wen Zhu Mingli Zhang Ying Zou Lizhi Zhang Qiang Zhang School of Computer Science and Engineering Dalian Minzu University Dalian China Pathology Department First Affiliated Hospital of Dalian Medical University Dalian China McGill Centre for Integrative Neuroscience McGill University Montreal Canada Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Recently, attention-based multiple instance learning (MIL) methods have received more concentration in histopathology whole slide image (WSI) applications. However, existing attention-based MIL methods rarely consider the cross-channel information interaction of pathology images when identifying discriminant patches. Additionally, they also have limitations on capturing the correlation between different discriminant instances for the bag-level classification. To address these challenges, we present a novel attention-based MIL model (AMIL-Trans) for breast cancer WSI classification. AMIL-Trans first embeds the efficient channel attention to realize the cross-channel interaction of pathology images, thus computing more robust features for instance selection without introducing too much computation cost. Then, it leverages vision Transformer encoder to directly aggregate selected instance features for better bag-level prediction, which effectively considers the correlation between different discriminant instances. Experiment results illustrate that AMIL-Trans respectively achieves its optimal AUC of 94.27% and 84.22% on the Camelyon-16 dataset and MSK external validation dataset, demonstrating the competitive performance compared with state-of-the-art MIL methods on breast cancer WSI classification task. The code will be available at https://*** CunqiaoHou/AMIL-Trans.

关键词： Correlation Costs Histopathology Supervised learning computer architecture Transformers Feature extraction

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Interfacial spintronic THz emission

arXiv

引用

arXiv 2023年

作者： Agarwal, Piyush Medwal, Rohit Dongol, Keynesh Mohan, John Rex Yang, Yingshu Asada, Hironori Fukuma, Yasuhiro Singh, Ranjan Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore637371 Singapore Center for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University Singapore639798 Singapore Department of Physics Indian Institute of Technology Uttar Pradesh Kanpur208016 India Department of Electronic Devices and Engineering Graduate School of Science and Engineering Yamaguchi University Ube755-8611 Japan Department of Physics and Information Technology Faculty of Computer Science and System Engineering Kyushu Institute of Technology Iizuka820-8502 Japan

The broken inversion symmetry at the ferromagnet (FM)/heavy-metal (HM) interface leads to spin-dependent degeneracy of the energy band, forming spin-polarized surface states. As a result, the interface serves as an effective medium for converting spin accumulation into two-dimensional charge current through the inverse Rashba-Edelstein effect. Exploring and assessing this spin-to-charge conversion (SCC) phenomenon at the FM/HM interface could offer a promising avenue to surpass the presumed limits of SCC in bulk HM layers. We utilize spintronic heterostructures as a platform to measure the spin-to-charge conversion (SCC) experienced by photoexcited spin currents. These heterostructures emit terahertz electric field when illuminated by femtosecond laser pulses, enabling us to quantitatively assess the ultrafast SCC process. Our results demonstrate a robust interfacial spin-to-charge conversion (iSCC) within a synthetic antiferromagnetic heterostructure, specifically for the NiFe/Ru/NiFe configuration, by isolating the SCC contribution originating from the interface itself, separate from the bulk heavy-metal (HM) region. Moreover, the iSCC at the NiFe/Ru interface is discovered to be approximately 27% of the strength observed in the highest spin-Hall conducting heavy-metal, Pt. Our results thus highlight the significance of interfacial engineering as a promising pathway for achieving efficient ultrafast spintronic devices. Copyright © 2023, The Authors. All rights reserved.

关键词： Electric fields

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Touchstone benchmark: are we on the right way for evaluating AI algorithms for medical segmentation? 24

Touchstone benchmark: are we on the right way for evaluating...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

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Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

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Computational Intelligence and Neuroscience 2022年 2022卷 8501738页

作者： Krishnaraj, N. Sivakumar, B. Kuppusamy, Ramya Teekaraman, Yuvaraja Thelkar, Amruth Ramesh Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Karnataka Bangalore562106 India Department of Electronic and Electrical Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Faculty of Electrical and Computer Engineering Jimma Institute of Technology Jimma University Jimma Ethiopia

Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved performance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The two outputs are combined in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine (ELM) classifier. Additionally, the ELM model's weight and bias values are optimally adjusted using the artificial fish swarm algorithm (AFSA). The networks' outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the difference between the input and target areas. Two benchmark datasets are used to validate the proposed model's performance. The experimental results established the proposed model's superiority over recently developed approaches. © 2022 N. Krishnaraj et al.

关键词： Generative adversarial networks

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Latency Minimization for Movable Antennas-Enabled Relay-aided D2D Mobile Edge Computing Communication Systems

arXiv

引用

arXiv 2024年

作者： Xiu, Yue Zhao, Yang Yang, Ran Tang, Huimin Qu, Long Khabbaz, Maurice Assi, Chadi Wei, Ning National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China Nanyang Technological University 50 Nanyang Ave Singapore639798 Singapore Cho Chun Shik Graduate School of Mobility Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China CMPS Department American University of Beirut Lebanon Concordia University MontrealQCH3G 1M8 Canada

Device-to-device (D2D)-assisted mobile edge computing (MEC) is one of the critical technologies of future sixth generation (6G) networks. The core of D2D-assisted MEC is to reduce system latency for network edge UEs by supporting cloud computing services, thereby achieving high-speed transmission. Due to the sensitivity of communication signals to obstacles, relaying is adopted to enhance the D2D-assisted MEC system’s performance and its coverage area. However, relay nodes and the base station (BS) are typically equipped with large-scale antenna arrays. This increases the cost of relay-assisted D2D MEC systems and limits their deployment. Movable antenna (MA) technology is used to work around this limitation without compromising performance. Specifically, the core of MA technology lies in optimizing the antenna positions to increase system capacity. Therefore, this paper proposes a novel resource allocation scheme for MA-enhanced relay-assisted D2D MEC systems. Specifically, the MA positions and beamforming of user equipments (UEs), relay, and BS as well as the allocation of resources and the computation task offloading rate at the MEC server, all are optimized herein with the objective of minimizing the maximum latency while satisfying computation and communication rate constraints. Since this is a multivariable non-convex problem, a parallel and distributed penalty dual decomposition (PDD) based algorithm is developed and combined with successive convex approximation (SCA) to solve this nonconvex problem. The results of extensive numerical analyses show that the proposed algorithm significantly improves the performance of the MA-enhanced relay-assisted D2D communication system compared to a counterpart where relays and the BS are equiped with traditional fixed-position antenna (FPA). Copyright © 2024, The Authors. All rights reserved.

关键词： Computation offloading

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EA-CNN: Enhanced attention-CNN with explainable AI for fruit and vegetable classification

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Heliyon 2024年第23期10卷 e40820页

作者： Khan, Zeshan Aslam Waqar, Muhammad Cheema, Khalid Mehmood Bakar Mahmood, Ali Abu Ain, Quratul Chaudhary, Naveed Ishtiaq Alshehri, Abdullah Alshamrani, Sultan S. Zahoor Raja, Muhammad Asif International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin ROC Douliou 64002 Taiwan Department of Electronic Engineering Fatima Jinnah Women University Rawalpindi Pakistan Department of Electrical and Computer Engineering International Islamic University Islamabad 44000 Pakistan Department of Business Management Global Banking School London United Kingdom Future Technology Research Center National Yunlin University of Science and Technology Section 3 123 University Road Yunlin ROC Douliu 64002 Taiwan Department of Information Technology Faculty of Computing and Information Al-Baha University Alaqiq 65779-7738 Saudi Arabia Department of Information Technology College of Computer and Information Technology Taif University Taif 21944 Saudi Arabia

The quality of vegetables and fruits are judged by their visual features. Misclassification of fruits and vegetables lead to a financial loss. To prevent the loss, superstores need to classify fruits and vegetables in terms of size, color and shape. To improve the accuracy of models for classifying fruits and vegetables, researchers have introduced various CNN architectures like VGG16, AlexNet, DenseNet-121 and different attention-based variants by compromising models' complexity leading to high computational cost and overlooking feature's contribution for interpretable predictions. Additionally, the major drawback in most of the existing works is the utilization of the limited dataset and number of classes for fruit and vegetable classification task instead of the benchmark dataset like Fruit-360 which comprises of 141 distinct fruit and vegetable classes. In this study, an explainable artificial intelligence (XAI) driven enhanced attention-CNN (EA-CNN) is proposed for accomplishing the fruit and vegetable classification task accurately and efficiently. The proposed EA-CNN model (a) detects the class of fruits and vegetable accurately through visual features by manipulating undiscover customized pooling technique and enhanced attention feature extraction mechanism, (b) classifies the fruits and vegetables efficiently through a simplified architecture, (c) provides interpretable predictions through XAI approach. By utilizing entire fruit-360 database, this research noteworthily enhances the model's capability to classify wide range of fruits and vegetables, thus providing an effective and reliable solution for practical applications. The proposed study outclasses baseline models with regard to accuracy and computational cost on fruit-360 benchmark dataset. EA-CNN achieves better accuracy of 98.1 % in smaller number of iterations as compared to benchmark models. Moreover, to further prove the efficiency, generalization ability, robustness, scalability and adaptabilit

关键词： Classification Convolutional neural networks Deep learning Explainable AI

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

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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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