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Characterizing Human Collective Behaviors During COVID-19 — Hong Kong SAR, China, 2020

China CDC weekly 2023年第4期5卷 71-75,I0001-I0003页

作者： Zhanwei Du Xiao Zhang Lin Wang Sidan Yao Yuan Bai Qi Tan Xiaoke Xu Sen Pei Jingyi Xiao Tim K.Tsang Qiuyan Liao Eric H.YLau Peng Wu Chao Gao Benjamin J.Cowling WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkNew TerritoriesHong Kong Special Administrative RegionChina Department of Genetics University of CambridgeCambridgeCB23EHUK Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)Singapore College of Information and Communication Engineering Dalian Minzu UniversityDalian CityLiaoning ProvinceChina Department of Environmental Health Sciences Mailman School of Public HealthColumbia UniversityNew York CityNYUSA School of Artificial Intelligence Opticsand Electronics(iOpen)Northwestern Polytechnical UniversityXi’an CityShaanxi ProvinceChina

Summary What is already known about this topic?People are likely to engage in collective behaviors online during extreme events,such as the coronavirus disease 2019(COVID-19)crisis,to express awareness,take action,and... 详细信息

关键词： collective likely extreme

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Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancement

arXiv

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arXiv 2023年

作者： Li, Heng Lin, Ziqin Qiu, Zhongxi Li, Zinan Niu, Ke Guo, Na Fu, Huazhu Hu, Yan Liu, Jiang The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Computer School Beijing Information Science and Technology University Beijing China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

Medical imaging provides many valuable clues involving anatomical structure and pathological characteristics. However, image degradation is a common issue in clinical practice, which can adversely impact the observation and diagnosis by physicians and algorithms. Although extensive enhancement models have been developed, these models require a well pre-training before deployment, while failing to take advantage of the potential value of inference data after deployment. In this paper, we raise an algorithm for source-free unsupervised domain adaptive medical image enhancement (SAME), which adapts and optimizes enhancement models using test data in the inference phase. A structure-preserving enhancement network is first constructed to learn a robust source model from synthesized training data. Then a teacher-student model is initialized with the source model and conducts source-free unsupervised domain adaptation (SFUDA) by knowledge distillation with the test data. Additionally, a pseudo-label picker is developed to boost the knowledge distillation of enhancement tasks. Experiments were implemented on ten datasets from three medical image modalities to validate the advantage of the proposed algorithm, and setting analysis and ablation studies were also carried out to interpret the effectiveness of SAME. The remarkable enhancement performance and benefits for downstream tasks demonstrate the potential and generalizability of SAME. The code is available at https://***/liamheng/Annotation-free-Medical-Image-Enhancement. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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The effects of point defect type, location, and density on the Schottky barrier height of Au/MoS2 heterojunction: a first-principles study (vol 12, 18001, 2022)

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SCIENTIFIC REPORTS 2022年第1期12卷 1-15页

作者： Sorkin, Viacheslav Zhou, Hangbo Yu, Zhi Gen Ang, Kah-Wee Zhang, Yong-Wei Institute of High-Performance Computing A*STAR 1 Fusionopolis Way Singapore 138632 Singapore Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Singapore Institute of Materials Research and Engineering A*STAR 2 Fusionopolis Way Singapore 138634 Singapore

Using DFT calculations, we investigate the effects of the type, location, and density of point defects in monolayer MoS2on electronic structures and Schottky barrier heights (SBH) of Au/MoS2heterojunction. Three types of point defects in monolayer MoS2, that is, S monovacancy, S divacancy and MoS(Mo substitution at S site) antisite defects, are considered. The following findings are revealed: (1) The SBH for the monolayer MoS2with these defects is universally higher than that for its defect-free counterpart. (2) S divacancy and MoSantisite defects increase the SBH to a larger extent than S monovacancy. (3) A defect located in the inner sublayer of MoS2, which is adjacent to Au substrate, increases the SBH to a larger extent than that in the outer sublayer of MoS2. (4) An increase in defect density increases the SBH. These findings indicate a large variation of SBH with the defect type, location, and concentration. We also compare our results with previously experimentally measured SBH for Au/MoS2contact and postulate possible reasons for the large differences among existing experimental measurements and between experimental measurements and theoretical predictions. The findings and insights revealed here may provide practical guidelines for modulation and optimization of SBH in Au/MoS2and similar heterojunctions via defect engineering.

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Indirect influence in social networks as an induced percolation phenomenon (vol 119, e2100151119, 2022)

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2022年第24期119卷 e2100151119-e2100151119页

作者： Xie, Jiarong Wang, Xiangrong Feng, Ling Zhao, Jin-Hua Liu, Wenyuan Moreno, Yamir Hu, Yanqing School of Computer Science and Engineering Sun Yat-sen University 510006 Guangzhou China Institute of Future Networks Southern University of Science and Technology 518055 Shenzhen China Peng Cheng Laboratory 518066 Shenzhen China Institute of High Performance Computing A*STAR 138632 Singapore Department of Physics National University of Singapore 117551 Singapore Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University 510006 Guangzhou China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University 510006 Guangzhou China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Institute for Biocomputation and Physics of Complex Systems University of Zaragoza 50018 Zaragoza Spain Department of Theoretical Physics University of Zaragoza 50018 Zaragoza Spain ISI Foundation 10126 Torino Italy Department of Statistics and Data Science College of Science Southern University of Science and Technology 518055 Shenzhen China

Percolation theory has been widely used to study phase transitions in network systems. It has also successfully explained various macroscopic spreading phenomena across different fields. Yet, the theoretical frameworks have been focusing on direct interactions among nodes, while recent empirical observations have shown that indirect interactions are common in many network systems like social and ecological networks, among others. By investigating the detailed mechanism of both direct and indirect influence on scientific collaboration networks, here we show that indirect influence can play the dominant role in behavioral influence. To address the lack of theoretical understanding of such indirect influence on the macroscopic behavior of the system, we propose a percolation mechanism of indirect interactions called induced percolation. Surprisingly, our model exhibits a unique anisotropy property. Specifically, directed networks show first-order abrupt transitions as opposed to the second-order continuous transition in the same network structure but with undirected links. A mix of directed and undirected links leads to rich hybrid phase transitions. Furthermore, a unique feature of the nonmonotonic pattern is observed in network connectivities near the critical point. We also present an analytical framework to characterize the proposed induced percolation, paving the way to further understanding network dynamics with indirect interactions.

关键词： percolation indirect interactions social network phase transition behavioral contagion

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A digital 3D TCAM accelerator for the inference phase of Random Forest

A digital 3D TCAM accelerator for the inference phase of Ran...

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Design Automation Conference

作者： Chieh-Lin Tsai Chun-Feng Wu Yuan-Hao Chang Han-Wen Hu Yung-Chun Lee Hsiang-Pang Li Tei-Wei Kuo Department of Computer Science and Information Engineering National Taiwan University Department of Computer Science National Yang Ming Chiao Tung University Institute of Information Science Academia Sinica Taiwan Macronix International Co. Ltd Department of Electrical Engineering National Tsing Hua University Mohamed Bin Zayed University of Artificial Intelligence NTU High Performance and Scientific Computing Center National Taiwan University

Random forest is a popular ensemble machine-learning algorithm for classification and regression tasks. However, the irregular tree shapes and non-deterministic memory access patterns make it hard for the current von Neumann architecture to handle random forest efficiently. This paper proposes a digital 3D TCAM-based accelerator for the random forest, adopting the idea of processing-in-memory (PIM) to reduce data movement. By utilizing this accelerator, we propose a TCAM-based approach to provide real-time inference with low energy consumption, making it suitable for edge or embedded environments. In the experiments, the proposed approach achieves an average of 3.13 times higher throughput with 22 times more energy saving than the GPU approach.

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Defining Thin Objects in computer Vision

Defining Thin Objects in Computer Vision

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ICT Conference (AICTC), Arab

作者： Rabeea Jaffari Syed Saad Ahmed Manzoor Ahmed Hashmani Syed Fahad Ahmed Software Engineering Department Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Universiti Teknologi Petronas Seri Iskander Perak Malaysia Electronics Engineering Department Universiti Tunku Abdul Rahman Kampar Perak Malaysia Computer and Information Sciences Department High Performance Cloud Computing Center (HPC3) Universiti Teknologi Petronas Perak Seri Iskander Malaysia UI/UX Designer Technoholic Advance Solutions Houston Texas USA

ISBN: (数字)9798350342086

ISBN: (纸本)9798350342093

Thin image objects detection in computer vision is a challenging task due to the compactness, slenderness, and extremely small coverage of these objects in the images. This makes these objects difficult to detect especially in the presence of background clutter. Efficient detection of thin image objects is of utmost importance in research areas including but not limited to electrical transmission and mooring fault detection systems, medical analysis, collision and obstacle detection in unmanned aerial and autonomous vehicles, plant stem detection in biological studies, road-lane-line detection and so on. Despite the significance of thin objects detection from images, there is no formal and accurate definition of what thin objects in computer vision are. In this research work, we propose and formalize the criteria for defining thin objects in computer vision. We conduct various experiments to identify the parameters and formalize the measures for describing thin objects in several computer vision research applications and other novel scenarios. The proposed research work can be utilized in the future research for the design and development of efficient thin objects detection and localization datasets, algorithms and other relevant tasks.

关键词： Location awareness computer vision Plants (biology) Fault detection Object detection Biology Object recognition Clutter Autonomous vehicles Biomedical imaging

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TOHAN: a one-step approach towards few-shot hypothesis adaptation 21

TOHAN: a one-step approach towards few-shot hypothesis adapt...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Haoang Chi Feng Liu Wenjing Yang Long Lan Tongliang Liu Bo Han William K. Cheung James T. Kwok State Key Laboratory of High Performance Computing College of CS NUDT and CS Department HKBU and Peng Cheng Laboratory Shenzhen DeSI Lab AAII Faculty of Engineering and IT UTS State Key Laboratory of High Performance Computing College of CS NUDT State Key Laboratory of High Performance Computing College of CS NUDT and Peng Cheng Laboratory Shenzhen TML Lab School of CS Faculty of Engineering USYD CS Department HKBU CSE Department HKUST

ISBN: (纸本)9781713845393

In few-shot domain adaptation (FDA), classifiers for the target domain are trained with accessible labeled data in the source domain (SD) and few labeled data in the target domain (TD). However, data usually contain private information in the current era, e.g., data distributed on personal phones. Thus, the private data will be leaked if we directly access data in SD to train a target-domain classifier (required by FDA methods). In this paper, to prevent privacy leakage in SD, we consider a very challenging problem setting, where the classifier for the TD has to be trained using few labeled target data and a well-trained SD classifier, named few-shot hypothesis adaptation (FHA). In FHA, we cannot access data in SD, as a result, the private information in SD will be protected well. To this end, we propose a target-oriented hypothesis adaptation network (TOHAN) to solve the FHA problem, where we generate highly-compatible unlabeled data (i.e., an intermediate domain) to help train a target-domain classifier. TOHAN maintains two deep networks simultaneously, in which one focuses on learning an intermediate domain and the other takes care of the intermediate-to-target distributional adaptation and the target-risk minimization. Experimental results show that TOHAN outperforms competitive baselines significantly.

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Medical Image Registration and Its Application in Retinal Images: A Review

arXiv

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arXiv 2024年

作者： Nie, Qiushi Zhang, Xiaoqing Hu, Yan Gong, Mingdao Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Verifying ReLU Neural Networks from a Model Checking Perspective

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Journal of computer Science & Technology 2020年第6期35卷 1365-1381页

作者： Wan-Wei Liu Fu Song Tang-Hao-Ran Zhang Ji Wang College of Computer Science National University of Defense TechnologyChangsha 410073China School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai 201210China State Key Laboratory for High Performance Computing Changsha 410073China

Neural networks, as an important computing model, have a wide application in artificial intelligence (AI) domain. From the perspective of computer science, such a computing model requires a formal description of its behaviors, particularly the relation between input and output. In addition, such specifications ought to be verified automatically. ReLU (rectified linear unit) neural networks are intensively used in practice. In this paper, we present ReLU Temporal Logic (ReTL), whose semantics is defined with respect to ReLU neural networks, which could specify value-related properties about the network. We show that the model checking algorithm for theΣ2∪Π2 fragment of ReTL, which can express properties such as output reachability, is decidable in EXPSPACE. We have also implemented our algorithm with a prototype tool, and experimental results demonstrate the feasibility of the presented model checking approach.

关键词： model checking rectified linear unit neural(ReLU)network temporal logic

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Removing Image Artifacts From Scratched Lens Protectors

Removing Image Artifacts From Scratched Lens Protectors

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Yufei Wang Renjie Wan Wenhan Yang Bihan Wen Lap-Pui Chau Alex C. Kot School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Hong Kong Baptist University Hong Kong Distributed High Performance Software Fundamental Research Laboratory Pengcheng National Lab China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong

A protector is placed in front of the camera lens for mobile devices to avoid damage, while the protector itself can be easily scratched accidentally, especially for plastic ones. The artifacts appear in a wide variety of patterns, making it difficult to see through them clearly. Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts. Though different methods have been proposed for some specific distortions, they seldom consider such inherent challenges. In our work, we consider the inherent challenges in a unified framework with two cooperative modules, which facilitate the performance boost of each other. We also collect a new dataset from the real world to facilitate training and evaluation purposes. The experimental results demonstrate that our method outperforms the baselines qualitatively and quantitatively. The code and datasets will be released at https://***/wyf0912/flare-removal

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