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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

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A discovery of Two Slow Pulsars with FAST: "Ronin" from the Globular Cluster M15

arXiv

引用

arXiv 2023年

作者： Zhou, Dengke Wang, Pei Li, Di Fang, Jianhua Miao, Chenchen Freire, Paulo C.C. Zhang, Lei Zhang, Dandan Chen, Huaxi Feng, Yi Xiao, Yifan Xie, Jintao Zhang, Xu Jin, Chenwu Wang, Han Ke, Yinan Guo, Xuerong Zhao, Rushuang Niu, Chenhui Zhu, Weiwei Xue, Mengyao Wang, Yabiao Wu, Jiafu Gan, Zhenye Sun, Zhongyi Wang, Chengjie Zhang, Junshuo Cao, Jinhuang Lu, Wanjin Zhang, Jie Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou311121 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China University of Chinese Academy of Sciences Beijing100049 China New Cornerstone Science Laboratory Shenzhen518054 China Max Planck Institut für Radioastronomie Bonn53121 Germany Centre for Astrophysics and Supercomputing Swinburne University of Technology Hawthorn3122 Australia School of Mathematical Sciences School of Physics and Electronic Sciences Guizhou Normal University Guiyang550001 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guiyang550001 China School of Physics and Electronic Engineering Qilu Normal University Jinan250200 China School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China College of Physical Science and Technology Central China Normal University Wuhan430079 China Tencent Youtu Lab Shanghai201103 China

Globular clusters harbor numerous millisecond pulsars, but long-period pulsars (P 100 ms) are rarely found. In this study, we employed a fast folding algorithm to analyze observational data from multiple globular clusters obtained by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), aiming to detect the existence of long-period pulsars. We estimated the impact of the median filtering algorithm in eliminating red noise on the minimum detectable flux density (Smin) of pulsars. Subsequently, we successfully discovered two isolated long-period pulsars in M15 with periods approximately equal to 1.928451 seconds and 3.960716 seconds, respectively. On the P − P diagram, both pulsars are positioned below the spin-up line, suggesting a possible history of partial recycling in X-ray binary systems disrupted by dynamical encounters later on. According to timing results, these two pulsars exhibit remarkably strong magnetic fields. If the magnetic fields were weakened during the accretion process, then a short duration of accretion might explain the strong magnetic fields of these pulsars. © 2023, CC BY.

关键词： Magnetic fields

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Bilateral asymmetry guided counterfactual generating network for mammogram classification

arXiv

引用

arXiv 2020年

作者： Wang, Chu-Ran Li, Jing Zhang, Fandong Sun, Xinwei Dong, Hao Yu, Yizhou Wang, Yizhou Center for Data Science Peking University Beijing100871 China Dept. of Computer Science Peking University Beijing100871 China Microsoft Research Asia Beijing100080 China Center on Frontiers of Computing Studies Dept. of Computer Science Peking University Beijing100871 China Deepwise AI Lab Beijing100080 China

Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations. Motivated by the symmetric prior that the lesions on one side of breasts rarely appear in the corresponding areas on the other side, given a diseased image, we can explore a counterfactual problem that how would the features have behaved if there were no lesions in the image, so as to identify the lesion areas. We derive a new theoretical result for counterfactual generation based on the symmetric prior. By building a causal model that entails such a prior for bilateral images, we obtain two optimization goals for counterfactual generation, which can be accomplished via our newly proposed counterfactual generative network. Our proposed model is mainly composed of Generator Adversarial Network and a prediction feedback mechanism, they are optimized jointly and prompt each other. Specifically, the former can further improve the classication performance by generating counterfactual features to calculate lesion areas. On the other hand, the latter helps counterfactual generation by the supervision of classification loss. The utility of our method and the effectiveness of each module in our model can be verified by state-of-the-art performance on INBreast and an in-house dataset and ablation studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Domain Knowledge

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BookChain: Library-Free Book Sharing Based on Blockchain Technology

BookChain: Library-Free Book Sharing Based on Blockchain Tec...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Jiajie Zeng Xiaohai Dai Jiang Xiao Wenhui Yang Weifeng Hao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728152127

ISBN: (纸本)9781728152134

Modern bookcrossing leverages the mobile networks to help readers share books via convenient connection, and thus expedites the dissemination of information. However, the lack of traceability has significantly hindered the wide adoption of mobile bookcrossing, leading to loss of books. Meanwhile, the managerial inefficiency of current mobile bookcrossing systems becomes another obstacle for advancing the book circulation. To overcome these problems, we propose to leverage the promising blockchain technology with the merits of its immutability and cryptographic smart contract. In this paper, we present BookChain, a traceable and efficient blockchain-based innercampus book sharing system. BookChain stores the complete sharing data of an interested book permanently on blockchain, such that every reader can trace the borrowing history, which reduces the potential for loss of book. BookChain also introduces the use of smart contract to automate the circulation of books with minimal human intervention, resulting in the improvement of efficiency. The experimental results show the effectiveness and low-cost of the proposed system under high concurrent users.

关键词： Contracts Web servers Web pages

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FLoc: Fingerprint-Based Indoor Localization System under a Federated Learning Updating Framework

FLoc: Fingerprint-Based Indoor Localization System under a F...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yuxiang Liu Huichuwu Li Jiang Xiao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728152127

ISBN: (纸本)9781728152134

Fingerprinting-based indoor localization via WiFi has achieved a great breakthrough in the past decade. However, it suffers from an inherent problem that the localization accuracy declines sharply over time due to the dynamic environment and unstable WiFi devices. researchers have designed many methods to update the localization model, e.g., crowdsourcing-based model updating, in order to maintain the localization accuracy. Unfortunately, they have not taken the privacy into consideration during the updating process. This will lead to a threat that the eavesdroppers could guess the location providers' private information according to the updating model. For the goal of maintaining the localization accuracy without the risk of privacy breaching, we proposed FLoc, a fingerprinting-based indoor localization system which updates the localization model via a federated learning framework. In FLoc, every provider maintains a local localization model in their own device. They will regularly encrypt the updating parameters and share them to a common model server. At the model server, it aggregates the encrypted information of the local models to maintain a general model, which will be sent back to the local devices for next updating iteration. We evaluate FLoc in an APs unknown laboratory corridor. The experiment results show that FLoc has a comparable localization performance. Moreover, it can successfully protect the providers' privacy, since the information transferred is all encrypted.

关键词： Mobile handsets Privacy Fingerprint recognition Servers Wireless fidelity databases Computational modeling

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Reasoning Based Workload Performance Prediction in Cloud data Centers

Reasoning Based Workload Performance Prediction in Cloud Dat...

引用

IEEE International Conference on Cloud computing Technology and science

作者： Adeel Aslam Hanhua Chen Jiang Xiao Hai Jin National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Cloud computing provides utility-based and scalable services to end-users. In the past decade, the demands for resource management in cloud computing have increased substantially which lead to certain challenges such as optimal resource utilization, power consumption, and service level agreement violations. Workload performance prediction serves as an assistance to address these issues. In this paper, we propose a prediction model based on clustered Case-Based Reasoning (CBR). The proposed model determines the performance metrics for workload prior to the co-operation of autonomic computing characteristics. Thus, CBR provides optimal scheduling of resources and workload monitoring for cloud data centers. In order to validate the proposed CBR-based prediction model, we perform a series of experiments and evaluate the effectiveness in terms of precision, recall, f-measure, and mean square error rate. We generate the cases for CBR using traces from the Google cluster data center. Moreover, we also validate our proposed prediction model against Support Vector Machine (SVM) prediction scheme. Experimental results show that the proposed CBR outperforms the SVM-based approach and yields 10% improvement in terms of precision.

关键词：

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HiNUMA: NUMA-Aware data Placement and Migration in Hybrid Memory Systems

HiNUMA: NUMA-Aware Data Placement and Migration in Hybrid Me...

引用

IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Zhuohui Duan Haikun Liu Xiaofei Liao Hai Jin Wenbin Jiang Yu Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Non-uniform memory access (NUMA) architectures feature asymmetrical memory access latencies on different CPU nodes. Hybrid memory systems composed of non-volatile memory (NVM) and DRAM further diversify memory access latencies due to the relatively large performance gap between NVM and DRAM. Traditional NUMA memory management policies fail to manage hybrid memories effectively and may even hurt application performance. In this paper, we present HiNUMA, a new NUMA abstraction for memory allocation and migration in hybrid memory systems. HiNUMA advocates NUMA topologyaware hybrid memory allocation policies for the initial data placement. HiNUMA also proposes a new NUMA balancing mechanism called HANB for memory migration at runtime. HANB considers both data access frequency and memory bandwidth utilization to reduce the cost of memory accesses in hybrid memory systems. We evaluate the performance of HiNUMA with several typical workloads. Experimental results show that HiNUMA can effectively utilize hybrid memories, and deliver much higher application performance than conventional NUMA memory management policies and other state-of-the-art work.

关键词： Nonvolatile memory Random access memory Bandwidth Memory management Resource management Task analysis Memory architecture

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Deciphering the Spatial Stickiness, Co-occurrence, and Co-change of Industries at the Scale of City

International Journal on Smart and Sustainable Cities

引用

International Journal on Smart and Sustainable Cities 2023年第2期01卷

作者： Lock Yue Chew Ning Ning Chung Hoai Nguyen Huynh Wen Xuan Sia Alvin Chua School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Data Science & Artificial Intelligence Research Centre Nanyang Technological University Singapore 639798 Singapore Centre for University Core Singapore University of Social Sciences Singapore 599494 Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way Connexis North Tower #16-16 Singapore 138632 Singapore Design & Planning Lab Urban Redevelopment Authority Singapore 179369 Singapore

This paper analyzes the location of industries in two metropolitan cities to investigate how firms are spatially distributed, and identify the prevailing trends in firm location choice. By defining a “Region Index” metric, we show how industries have concentrated in or diffused from the regions-of-interest over time. We apply this metric to the Central Area (CA) of Singapore and the London Region to decipher the relative tendency of industries to locate at CA as well as London. To analyze the spatial co-location of industries, we constructed a community network, which reveals subtle spatial relationships between firms. Extending this approach temporally to create a co-change network, we deduce the occurrences of industries in Singapore and London that move into or out of a spatial region synchronously. Based on this approach, we elucidate firm types that not only co-change but also co-occur in space and remain within a region-of-interest. Overall, we observe certain common and contrasting patterns of industry movements in the context of Singapore and London through the analysis. “Other Activities Auxiliary to Financial Services” is found to have the consistent tendency of diffusing out of CA and London throughout the period of study. Conversely, opposite movement patterns are observed where we see the proclivity of IT and digital service firms to locate in London but relocate out of CA, while the Media, Financial Services, and Life Insurance subsectors tend to diffuse out of London but concentrate in the CA of Singapore.

关键词： Spatial morphology cities spatial indices land use urban complexity spatial analysis networks

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Learning to Address Intra-segment Misclassification in Retinal Imaging

arXiv

引用

arXiv 2021年

作者： Zhou, Yukun Xu, Moucheng Hu, Yipeng Lin, Hongxiang Jacob, Joseph Keane, Pearse A. Alexander, Daniel C. Centre for Medical Image Computing University College London London United Kingdom Department of Medical Physics and Biomedical Engineering UCL London United Kingdom NIHR Biomedical Research Centre Moorfields Eye Hospital London United Kingdom Department of Computer Science University College London London United Kingdom Wellcome/EPSRC Centre for Interventional and Surgical Sciences London United Kingdom Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China UCL Respiratory University College London London United Kingdom UCL Institute of Ophthalmology University College London London United Kingdom

Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels in retinal photographs is one such scenario, in which arteries and veins need to be identified and differentiated from each other and from the background. Intra-segment misclassification, i.e. veins classified as arteries or vice versa, frequently occurs when arteries and veins intersect, whereas in binary retinal vessel segmentation, error rates are much lower. We thus propose a new approach that decomposes multi-class segmentation into multiple binary, followed by a binary-to-multi-class fusion network. The network merges representations of artery, vein, and multi-class feature maps, each of which are supervised by expert vessel annotation in adversarial training. A skip-connection based merging process explicitly maintains class-specific gradients to avoid gradient vanishing in deep layers, to favor the discriminative features. The results show that, our model respectively improves F1-score by 4.4%, 5.1%, and 4.2% compared with three state-of-the-art deep learning based methods on DRIVE-AV, LES-AV, and HRF-AV data sets. Code: https://***/rmaphoh/LearningAVSegmentation. Copyright © 2021, The Authors. All rights reserved.

关键词： Ophthalmology

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$\mathsf{NCF}$NCF: A Neural Context Fusion Approach to Raw Mobility Annotation

引用

IEEE Transactions on Mobile computing 2020年第1期21卷 226-238页

作者： Renjun Hu Jingbo Zhou Xinjiang Lu Hengshu Zhu Shuai Ma Hui Xiong SKLSDE Lab Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Talent Intelligence Center Baidu Inc. Beijing China Management Science and Information Systems Department Rutgers Business School Rutgers University Newark NJ USA

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e., distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct our experimental study and find that our approach consistently outperforms the state-of-the-art baselines by at least 32 percent in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.

关键词： Annotations Trajectory Mobile computing Computer architecture Business intelligence Neural networks Semantics

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