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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yangtao Zheng Di Huang Songtao Liu Yunhong Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach.

关键词： Feature extraction Subspace constraints Object detection Detectors Task analysis Semantics Prototypes

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Quantitative gadolinium chelate-enhanced magnetic resonance imaging of normal endothelial barrier disruption from nanoparticle biophilicity interactions

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Materials Today: Proceedings 2021年 45卷 3795-3799页

作者： Hemant Sarin Steve H. Fung Ariel S Kanevsky Haitao Wu Colin M. Wilson Howard Vo Sungyoung Auh Daniel Glen Richard Reynolds Charleston WV USA Houston Methodist Hospital and Research Institute Houston TXUSA Jacobi Medical Center Bronx NY USA Imaging Probe Development Center Bethesda MD USA Department of Radiology University of New Mexico USA Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign USA National Institute of Neurologic Disorders and Stroke National Institutes of Health USA Scientific and Statistical Computing Core National Institute of Mental Helath National Institutes of Health USA

The biocompatibility of macromolecular therapeutics for enhanced passive permeation and retention remains a concern due to release kinetics or surface area properties. Dendritic conjugates functionalized with paramagnetic metal chelate with diameters less than (<) 12 nm and within the 8 to 10 nm size range can be utilized for high-resolution Gadolinium-based r 1 −1 -adjusted dynamic magnetic resonance concentration mapping of solid tumor barrier hyper-permeability and study of contrast agent pharmacodynamics. Cationic dye- or doxorubicin-linked dendrimer nanoparticle surface-to-membrane channel or receptor biophilicity interaction results in abnormal contrast enhancement of endothelial barriers identifiable by transvenous dual flip angle T 1 -weighted blood–brain barrier imaging with higher-generation polyamidoamine interior core gadopentetic acid-chelated dendrimers with 1 + IS 1 + functionalized exteriors. The neutralization of nanoparticle exterior surface charge would afford biocompatibility in clinical transability across abnormal barrier endothelium pores for effective transvascular delivery.

关键词： Theranostic agent Cationotoxicity Molecular diameter Chemoxenobiotic Vasomodulation General kinetic model Endocytic effective pressure

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Cross-domain object detection through coarse-to-fine feature adaptation

arXiv

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arXiv 2020年

作者： Zheng, Yangtao Huang, Di Liu, Songtao Wang, Yunhong Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing100191 China

Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarseto-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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Behavior variations and their implications for popularity promotions: From elites to mass in Weibo

arXiv

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arXiv 2020年

作者： Shi, Bowen Xu, Ke Zhao, Jichang State Key Lab of Software Development Environment Beihang University Beijing China School of Economics and Management Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China

The boom in social media with regard to producing and consuming information simultaneously implies the crucial role of online user influence in determining content popularity. In particular, understanding behavior variations between the influential elites and the mass grassroots is an important issue in communication. However, how their behavior varies across user categories and content domains, and how these differences influence content popularity are rarely addressed. From a novel view of seven content-domains, a detailed picture of behavior variations among five user groups, from both views of elites and mass, is drawn in Weibo, one of the most popular Twitter-like services in China. Interestingly, elites post more diverse contents with video links while the mass possess retweeters of higher loyalty. According to these variations, user-oriented actions of enhancing content popularity are discussed and testified. The most surprising finding is that the diversity of contents do not always bring more retweets, and the mass and elites should promote content popularity by increasing their retweeter counts and loyalty, respectively. Our results for the first time demonstrate the possibility of highly individualized strategies of popularity promotions in social media, instead of a universal principle. Keywords: Social media, User behavior, Elites, Mass, User influence, Popularity promotion Copyright © 2020, The Authors. All rights reserved.

关键词： Social networking (online)

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Matrix Classifier On Dynamic Functional Connectivity For Mci Identification

Matrix Classifier On Dynamic Functional Connectivity For Mci...

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IEEE International Conference on Image Processing

作者： Lei Zhou Liang Zhang Xiao Bai Jun Zhou School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Software Development Environment Beihang University Beijing School of Information and Communication Technology Griffith University Nathan

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

One of the most popular method for Alzheimer's disease (AD) diagnosis is exploring the Brain functional connectivity (FC) from resting-state functional magnetic resonance imaging (RS-fMRI). To early prevent AD, it is crucial to distinguish AD and and its preclinical stage, mild cognitive impairment (MCI) and early MCI (eMCI). In many existing works, dynamic functional connectivity (dFC) which contains rich spatiotemporal information has been exploited for the MCI and eMCI identification. However, most of these dFC based methods only consider the correlation between discrete brain status while ignore the valuable spatiotemporal information contained in dFC. To overcome this limitation, we propose a matrix classifier based method on the dFC signal for MCI and eMCI identification. Specifically, we first represent the dFC correlations by matrix features which contain rich spatiotemporal information and then learn the support matrix machines (SMM) to classify AD and its preclinical stage. Experiments on 600 real people data provide by the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed matrix classifier based method outperforms other FC and dFC based methods for both normal controls (NC)/MCI identification and NC/eMCI identification.

关键词： Feature extraction Correlation Dementia Microsoft Windows Spatiotemporal phenomena Training

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A Theoretical Review: Risk Mitigation Through Trusted Human Framework for Insider Threats

A Theoretical Review: Risk Mitigation Through Trusted Human ...

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2019 International Conference on Cybersecurity, ICoCSec 2019

作者： Apau, Mohd Nazer Sedek, Muliati Ahmad, Rabiah Information Security and Network Research Group Center for Advanced Computing Technology Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Department of Computer Science Department of Language and Human Development Durian Tunggal Melaka76109 Malaysia

ISBN: (纸本)9781728156576

This paper discusses the possible effort to mitigate insider threats risk and aim to inspire organizations to consider identifying insider threats as one of the risks in the company's enterprise risk management activities. The paper suggests Trusted Human Framework (THF) as the on-going and cyclic process to detect and deter potential employees who bound to become the fraudster or perpetrator violating the access and trust given. The mitigation's control statements were derived from the recommended practices in the 'Common Sense Guide to Mitigating Insider Threats' produced by the Software Engineering Institute, Carnegie Mellon University (SEI-CMU). The statements validated via a survey which was responded by fifty respondents who work in Malaysia. © 2019 IEEE.

关键词： Surveys

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Vehicle re-identification with location and time stamps 32

Vehicle re-identification with location and time stamps

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32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019

作者： Lv, Kai Du, Heming Hou, Yunzhong Deng, Weijian Sheng, Hao Jiao, Jianbin Zheng, Liang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University University of Chinese Academy of Sciences Australian National University

ISBN: (纸本)9781728125060

This paper focuses on the problem of vehicle re-identification (Re-ID). In our attempt, we propose a re-identification framework by exploiting vehicle location and time stamps. The location and time information have the potential to cover the shortage of appearance-based feature representations. First, we introduce an ensemble technique to combine the informative cues of multiple Re-ID models effectively. To further improve the accuracy, we then build up a system to acquire the vehicle location and time stamps. Specifically, we utilize the detected results to obtain the needed information. With the help of the proposed system, we can remove irrelevant images from a given ranking list. Our system finished 3rd place in the 2019 AI-City challenge for city-scale multi-camera vehicle re-identification. © 2019 IEEE Computer Society. All rights reserved.

关键词： Vehicles

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Signal approximation with Pascal’s triangle and sampling

Signal approximation with Pascal’s triangle and sampling

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Chinese Control and Decision Conference, CCDC

作者： Lei Chen Xinghuo Yu Jinhu Lü School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Engineering RMIT University Melbourne VIC Australia

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

This brief explores the approximation properties of a unique basis expansion based on Pascal's triangle, which realizes a sampled-data driven approach between a continuous-time signal and its discrete-time representation. The roles of certain parameters, such as sampling time interval or model order, and signal characteristics, i.e., its curvature, on the approximation are investigated. Approximate errors in one and multiple-step predictions are analyzed. Furthermore, time-variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.

关键词： Shape Electronic mail Analytical models Mathematical analysis Data models Upper bound

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Comprehending the risk of foodborne and waterborne disease outbreaks: Current situation and control measures with Special reference to the Indian Scenario

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Heliyon 2024年第16期10卷 e36344页

作者： Albert, Venencia Ramamurthy, Thandavarayan Das, Samaresh G Dolma, Karma Majumdar, Tapan Baruah, Pranjal Jyoti Chaliha Hazarika, Suranjana Apum, Basumoti Das, Madhuchhanda Indian Council of Medical Research Ansari Nagar East New Delhi 110029 India ICMR-National Institute for Research in Bacterial Infections (NIRBI) West Bengal Kolkata 700010 India Center for Development of Advanced Computing (CDAC) West Bengal Kolkata 700 091 India Department of Microbiology Sikkim Manipal Institute of Medical Sciences (SMIMS) Sikkim Manipal University Sikkim Gangtok 737102 India Department of Microbiology Agartala Government Medical College Tripura Agartala 799006 India ICMR Regional Medical Research Centre (RMRC) Assam Dibrugarh 786001 India Department of Microbiology Gauhati Medical College and Hospital Assam Guwahati 781032 India Department of Microbiology Bankin Pertin General Hospital & Research Institute Arunachal Pradesh Pasighat 791102 India Division of Development Research Indian Council of Medical Research Department of Health Research (Ministry of Health & Family Welfare) P.O. Box No. 4911 Ansari Nagar East New Delhi 110029 India

Background: Foodborne and waterborne diseases and outbreaks are a neglected public health issue worldwide. In developing countries, diarrheal disease caused by foodborne and waterborne infections is a major cause of ill health. There is a lack of information on foodborne pathogens, their transmission routes, outbreaks, and related mortalities, due to the absence of a robust disease surveillance system and adequately equipped laboratories. Although hygiene practices are much better in Western countries, the widespread use of preserved and raw food items is a cause of concern. Consequently, the occurrence of foodborne diseases is not rare in these countries either. WHO has recently released the ‘Global Strategy for Food Safety 2022–2030’, addressing the emerging challenges, new technologies, and innovative approaches to strengthen food safety systems and enhance laboratory capacity for foodborne disease surveillance. Foodborne outbreaks are a huge challenge in India. Malnutrition, anemia, hookworm and enteric infections, are the predominant cryptic health conditions among children in rural and tribal areas, leading to severe consequences, including death, and posing a substantial threat to public health. Combating such events with adequate food safety and hygiene practices is achievable. Systematic collection of data can help to develop food safety policies that could reduce the burden of foodborne diseases. Objective: This review aims to examine the current situation of foodborne and waterborne diseases, identification of the factors contributing to their occurrence and outbreaks, and defining the gaps in control measures, challenges, and potential solutions in improving the public health system. Methods: Strengths, weaknesses, opportunities, and threats (SWOT) analysis was made based on the literature review of foodborne and waterborne infections to assess the current situation and to identify knowledge gaps. Finding: SWOT analysis showed the strength and gaps in th

关键词： Foodborne diseases Hygiene practices Outbreaks Public health Surveillance

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Deep Learning-Based Automatic Multi-Level Airway Collapse Monitoring on Obstructive Sleep Apnea Patients

arXiv

引用

arXiv 2024年

作者： Hsu, Ying-Chieh Liu, Stanley Yung-Chuan Huang, Chao-Jung Wu, Chi-Wei Cheng, Ren-Kai Hsu, Jane Yung-Jen Huang, Shang-Ran Cheng, Yuan-Ren Hsu, Fu-Shun Department of Biomedical Engineering National Taiwan University Taipei Taiwan Department of Otolaryngology Head and Neck Surgery Taipei Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation Taipei Taiwan Tzu Chi University School of Medicine Hualien Taiwan Department of Oral & Maxillofacial Surgery Nova Southeastern University College of Dental Medicine College of Allopathic Medicine Florida United States All Vista Healthcare Center Center for AI and Advanced Robotics National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Artificial Intelligence Chang Gung University College of Intelligent Computing Taoyuan Taiwan Heroic Faith Medical Science Co. Ltd. New Taipei Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department of Internal Medicine Chung Shan Medical University Taichung Taiwan Hospital Development Affairs Office Chung Shan Medical University Hospital Taichung Taiwan

This study investigated the use of deep learning to identify multi-level upper airway collapses in obstructive sleep apnea (OSA) patients based on snoring sounds. We fi-ne-tuned ResNet-50 and Audio Spectrogram Transformer (AST) models using snoring recordings from 37 subjects undergoing drug-induced sleep endoscopy (DISE) between 2020 and 2021. Snoring sounds were labeled according to the VOTE (Velum, Orophar-ynx, Tongue Base, Epiglottis) classification, resulting in 259 V, 403 O, 77 T, 13 E, 1016 VO, 46 VT, 140 OT, 39 OE, 30 VOT, and 3150 non-snoring (N) 0.5-second clips. The models were trained for two multi-label classification tasks: identifying obstructions at V, O, T, and E levels, and identifying retropalatal (RP) and retroglossal (RG) obstruc-tions. Results showed AST slightly outperformed ResNet-50, demonstrating good abil-ity to identify V (F1-score: 0.71, MCC: 0.61, AUC: 0.89), O (F1-score: 0.80, MCC: 0.72, AUC: 0.94), and RP obstructions (F1-score: 0.86, MCC: 0.77, AUC: 0.97). However, both models struggled with T, E, and RG classifications due to limited data. Retrospective analysis of a full-night recording showed the potential to profile airway obstruction dynamics. We expect this information, combined with polysomnography and other clinical parameters, can aid clinical triage and treatment planning for OSA patients. © 2024, CC BY-SA.

关键词： Endoscopy

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