检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany ETH Zürich Switzerland The Shenzhen Campus of Sun Yat-Sen University Sun Yat-Sen University China Bigo Technology Pte. Ltd Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023, CC BY-SA.

关键词： Image enhancement

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Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery

arXiv

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arXiv 2023年

作者： Qian, Zhen Chen, Min Sun, Zhuo Zhang, Fan Xu, Qingsong Guo, Jinzhao Xie, Zhiwei Zhang, Zhixin Nanjing Normal University Nanjing210023 China State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing210023 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing210023 China Institute of Remote Sensing and Geographical Information System School of Earth and Space Sciences Peking University China Department of Aerospace and Geodesy Data Science in Earth Observation Technical University of Munich Arcisstraße 21 Munich Bavaria80333 Germany School of Transportation and Geomatics Engineering Shenyang Jianzhu University Shenyang China College of Geography & Marine Nanjing University PO Box Nanjing2100913 China

Understanding urban dynamics and promoting sustainable development requires comprehensive insights about buildings, including their spatial locations, rooftop configurations, physical forms, and urban functions. While geospatial artificial intelligence has advanced the extraction of such details from Earth observational data, existing methods often suffer from computational inefficiencies and inconsistencies when compiling unified building-related datasets for practical applications. To bridge this gap, we introduce the Multi-task Building Refiner (MT-BR), an adaptable neural network tailored for simultaneous extraction of spatial and attributional building details from high-resolution satellite imagery, exemplified by building rooftops, urban functional types, and roof architectural types. Notably, MT-BR can be fine-tuned to incorporate additional building details, extending its applicability. For large-scale applications, we devise a novel spatial sampling scheme that strategically selects limited but representative image samples. This process optimizes both the spatial distribution of samples and the urban environmental characteristics they contain, thus enhancing extraction effectiveness while curtailing data preparation expenditures. We further enhance MT-BR’s predictive performance and generalization capabilities through the integration of advanced augmentation techniques. Our quantitative results highlight the efficacy of the proposed methods. Specifically, networks trained with datasets curated via our sampling method demonstrate improved predictive accuracy relative to those using alternative sampling approaches, with no alterations to network architecture. Moreover, MT-BR consistently outperforms other state-of-the-art methods in extracting building details across various metrics, achieving gains between 2% and 20%, with a further 5% improvement when augmentation strategies were adopted. The real-world practicality of our approaches is also demonstrated in

关键词： Buildings

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PANX2 and brain lower grade glioma genesis: A bioinformatic analysis

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science Progress 2021年第2期104卷 368504211011836-368504211011836页

作者： Xu, XiaoXue Hao, YueHan Xiong, Shuang He, ZhiYi Department of Neurology The First Hospital of China Medical University Shenyang China Key Laboratory of Neurological Disease Big Data of Liaoning Province Shenyang China Liaoning Academy of Analytic Science Construction Engineering Center of Important Technology Innovation and Research and Development Base in Liaoning Province Shenyang China

PANX2 forms large-pore channels mediating ATP release in response to physiological and pathological stimuli. Although PANX2 shows involvements in glioma genesis, the underlying mechanism remains unclear. PANX2 mRNA expression was analyzed via Oncomine and was confirmed via Gene Expression Profiling Interactive Analysis (GEPIA). The influence of PANX2 on overall survival (OS) of glioma was evaluated using LinkedOmics and further assessed through Cox regression analysis. The correlated genes with PANX2 acquired from LinkedOmics were validated through GEPIA and cBioPortal. Protein-protein interaction (PPI) of these genes was then obtained using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape with MCODE plug-in. All the PANX2-related genes underwent Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The correlation between PANX2 and cancer immune infiltrates was evaluated via Tumor Immune Estimation Resource (TIMER). A higher expression of PANX2 only revealed a better OS in brain low grade glioma (LGG). PANX2-related genes in LGG functionally enriched in neuroactive ligand-receptor interaction, synaptic vesicle cycle, and calcium signaling. The hub genes from highest module of PPI were mainly linked to chemical synaptic transmission, plasma membrane, neuropeptide, and the pathway of neuroactive ligand-receptor interaction. Besides, PANX2 expression was negatively associated with infiltrating levels of macrophage, dendritic cells, and CD4+ T cells. This study demonstrated that PANX2 likely participated in LGG pathogenesis by affecting multiple molecular pathways and immune-related processes. PANX2 was associated with LGG prognosis and might become a promising therapeutic target of LGG. © The Author(s) 2021.

关键词： Brain Lower Grade Gliomas Immune Infiltrates Microenvironment PANX2 Tumorigenesis

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Neural mechanisms of top-down divided and selective spatial attention in visual and auditory perception

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Brain science Advances 2023年第2期9卷 95-113页

作者： Zhongtian Guan Meng Lin Qiong Wu Jinglong Wu Kewei Chen Hongbin Han Dehua Chui Xu Zhang Chunlin Li School of Biomedical Engineering Capital Medical UniversityBeijing 100069China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Capital Medical UniversityBeijing 100069China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical UniversityBeijing 100069China Peking University First Hospital Beijing 100034China Biomedical Engineering Laboratory Graduate School of Natural Science and TechnologyOkayama University3-1-1 Tsushima-nakaOkayamaJapan Key Laboratory of Biomimetic Robots and Systems Ministry of EducationBeijing Institute of TechnologyBeijing 100081China Computational Image Analysis Banner Alzheimer’s Institute and Banner Good Samaritan Medical CentrePET CentrePhoenixAZ 85006USA Radiology Peking University Third HospitalBeijing 100191China Neuroscience Research Institute/Peking University Third Hospital Beijing 100191China

Top-down attention mechanisms require the selection of specificobjects or locations;however,the brain mechanism involved when attention is allocated across different modalities is not well *** aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlyingdivided and selective spatial attention.A concurrent audiovisual stimulus was used,and subjects were prompted to focus on a visual,auditory and audiovisual stimulus in a Posner *** results confirmed the better performance of selectiveattention compared to devided *** found differences in the activation level of the frontoparietal network,visual/auditorycortex,the putamen and the salience network under differentattention *** further used Granger causality(GC)toexplore effective connectivity differences between *** in GC connectivity between visual and auditory selective tasksreflected the visual dominance effect under spatial *** addition,our results supported the role of the putamen inredistributing attention and the functional separation of the *** summary,we explored the audiovisual top-down allocation of attention and observed the differences in neuralmechanisms under endogenous attention modes,which revealedthe differences in cross-modal expression in visual and auditory attention under attentional modulation.

关键词： top-down mechanism divided spatial attention selective spatial attention fMRI Granger causality

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Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation

arXiv

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arXiv 2023年

作者： Huang, Ziyi Liu, Hongshan Zhang, Haofeng Li, Xueshen Liu, Haozhe Xing, Fuyong Laine, Andrew F. Angelini, Elsa D. Hendon, Christine P. Gan, Yu Department of Electrical Engineering Columbia University New YorkNY10027 United States Department of Biomedical Engineering Stevens Institute of Technology HobokenNJ07030 United States Center for Health Innovation Stevens Institute of Technology HobokenNJ07030 United States Department of Industrial Engineering and Operations Research Columbia University New YorkNY10027 United States Department of Biomedical Engineering Columbia University New YorkNY10027 United States Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO80045 United States NIHR Imperial Biomedical Research Centre ITMAT Data Science Group Imperial College London LondonSW7 2BX United Kingdom Telecom Paris LTCI Institut Polytechnique de Paris Palaiseau91120 France

Segment anything model (SAM) has emerged as the leading approach for zero-shot learning in segmentation tasks, offering the advantage of avoiding pixel-wise annotations. It is particularly appealing in medical image segmentation, where the annotation process is laborious and expertise-demanding. However, the direct application of SAM often yields inferior results compared to conventional fully supervised segmentation networks. An alternative approach is to use SAM as the initial stage to generate pseudo labels for further network training. However, the performance is limited by the quality of pseudo labels. In this paper, we propose a novel label correction framework to push the boundary of SAM-based segmentation. Our model utilizes a label quality evaluation module to distinguish between noisy labels and clean labels. This enables the correction of the noisy labels using an uncertainty-based self-correction module, thereby enriching the clean training set. Finally, we retrain the segmentation network with updated labels to optimize its weights for future predictions. One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a set of expert-level annotations while attaining good segmentation performance. We demonstrate the effectiveness of our proposed model on three public datasets, indicating its ability to improve segmentation accuracy and outperform baseline methods in label correction. © 2023, CC BY-NC-ND.

关键词： Uncertainty analysis

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Collaboration between government and research community to respond to covid-19: Israel’s case

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Journal of Open innovation: Technology, Market, and Complexity 2021年第4期7卷

作者： Peleg, Mor Reichman, Amnon Shachar, Sivan Gadot, Tamir Tsadok, Meytal Avgil Azaria, Maya Dunkelman, Orr Hassid, Shiri Partem, Daniella Shmailov, Maya Yom-Tov, Elad Cohen, Roy Data Science Research Center The University of Haifa Haifa 3498838 Israel Faculty of Law and The Center for Cyber Law & Policy The University of Haifa Haifa 3498838 Israel Timna-Big Data Platform Unit Ministry of Health Jerusalem 9446724 Israel The Center for the 4th Industrial Revolution Israel innovation Authority Jerusalem 9695102 Israel Master in Design Department Shenkar College of Engineering and Design Ramat Gan 5252626 Israel Microsoft Research Herzeliya 4672415 Israel

Triggered by the COVID-19 crisis, Israel’s Ministry of Health (MoH) held a virtual data-thon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel’s research community was invited to offer insights to help solve COVID-19 policy challenges. The datathon was designed to develop operationalizable data-driven models to address COVID-19 health policy challenges. Specific relevant challenges were defined and diverse, reliable, up-to-date, deidentified governmental datasets were extracted and tested. Secure remote-access research envi-ronments were established. Registration was open to all citizens. Around a third of the applicants were accepted, and they were teamed to balance areas of expertise and represent all sectors of the community. Anonymous surveys for participants and mentors were distributed to assess usefulness and points for improvement and retention for future datathons. The datathon included 18 multi-disciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the three winning teams are currently considered by the MoH as potential data science methods relevant for national policies. Based on participants’ feedback, the process for future data-driven regulatory responses for health crises was improved. Participants expressed increased trust in the MoH and readiness to work with the government on these or future projects. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： COVID-19 data datathon Emergency management Evidence-based regulation Hackathon Health policy Innovative regulation, privacy Open innovation Public confidence Public engagement Public-private interface Trust of experts

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UniFault: A Fault Diagnosis Foundation Model from Bearing data

arXiv

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arXiv 2025年

作者： Eldele, Emadeldeen Ragab, Mohamed Qing, Xu Edward Chen, Zhenghua Wu, Min Li, Xiaoli Lee, Jay Institute for Infocomm Research A*STAR Singapore138632 Singapore Centre for Frontier AI Research A*STAR Singapore138632 Singapore Propulsion and Space Research Center Technology Innovation Institute Abu Dhabi9639 United Arab Emirates College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Center for Industrial Artificial Intelligence Department of Mechanical Engineering A. James Clark School of Engineering University of Maryland MD20742 United States

Machine fault diagnosis (FD) is a critical task for predictive maintenance, enabling early fault detection and preventing unexpected failures. Despite its importance, existing FD models are operation-specific with limited generalization across diverse datasets. Foundation models (FM) have demonstrated remarkable potential in both visual and language domains, achieving impressive generalization capabilities even with minimal data through few-shot or zero-shot learning. However, translating these advances to FD presents unique hurdles. Unlike the large-scale, cohesive datasets available for images and text, FD datasets are typically smaller and more heterogeneous, with significant variations in sampling frequencies and the number of channels across different systems and applications. This heterogeneity complicates the design of a universal architecture capable of effectively processing such diverse data while maintaining robust feature extraction and learning capabilities. In this paper, we introduce UniFault, a foundation model for fault diagnosis that systematically addresses these issues. Specifically, the model incorporates a comprehensive data harmonization pipeline featuring two key innovations. First, a unification scheme transforms multivariate inputs into standardized univariate sequences while retaining local inter-channel relationships. Second, a novel cross-domain temporal fusion strategy mitigates distribution shifts and enriches sample diversity and count, improving the model generalization across varying conditions. UniFault is pretrained on over 9 billion data points spanning diverse FD datasets, enabling superior few-shot performance. Extensive experiments on real-world FD datasets demonstrate that UniFault achieves state-of-the-art performance, setting a new benchmark for fault diagnosis models and paving the way for more scalable and robust predictive maintenance solutions. The code and pretrained models are available on https://***/emadeldee

关键词： Predictive maintenance

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Two Efficient Beamforming Methods for Hybrid IRS-aided AF Relay Wireless Networks

arXiv

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arXiv 2023年

作者： Wang, Xuehui Shu, Feng Huang, Mengxing Zhou, Fuhui Chen, Riqing Pan, Cunhua Wu, Yongpeng Wang, Jiangzhou The School of Information and Communication Engineering Hainan University Haikou570228 China The School of Information and Communication Engineering and Collaborative Innovation Center of Information Technology Hainan University Haikou570228 China The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China The College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210000 China The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing210000 China The Ministry of Industry and Information Technology Nanjing211106 China The Digital Fujian Institute of Big Data for Agriculture Fujian Agriculture and Forestry University Fuzhou350002 China National Mobile Communications Research Laboratory Southeast University Nanjing211111 China The Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University Minhang200240 China The School of Engineering University of Kent CanterburyCT2 7NT United Kingdom

Due to the "double fading" effect caused by conventional passive intelligent reflecting surface (IRS), the signal via the reflection link is weak. To enhance the received signal, active elements with the ability to amplify the reflected signal are introduced to the passive IRS forming hybrid IRS. In this paper, we propose a hybrid IRS-aided amplify-and-forward (AF) relay wireless network, where an optimization problem is formulated, which is subject to the constraints of transmit power budgets at the source/AF relay/hybrid IRS and that of unit modulus for passive IRS elements. By alternately designing the beamforming matrix at AF relay and the reflecting coefficient matrices at IRS, signal-to-noise ratio can be maximized. To achieve high rate performance and extend the coverage range, a high-performance method based on semidefinite relaxation and fractional programming (HP-SDR-FP) algorithm is presented. Due to its extremely high complexity, a low-complexity method based on whitening filter, general power iterative and generalized Rayleigh-Ritz (WF-GPI-GRR) is proposed, which is different from HP-SDR-FP method. It is assumed that the amplifying coefficient of each active IRS element is equal, and the corresponding analytical solution of the amplifying coefficient can be obtained according to the transmit powers at AF relay and hybrid IRS. Simulation results show that the proposed two methods can greatly improve the rate performance compared to the existing networks, such as the passive IRS-aided AF relay and only AF relay network. In particular, a 50.0% rate gain over the existing networks is approximately achieved in the high power budget region of hybrid IRS. Moreover, it is verified that the proposed HP-SDR-FP method perform better than WF-GPI-GRR method in terms of rate performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Wireless networks

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An artificial intelligence method for vessel detection in images of cardiac catheterization

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Informatics in Medicine Unlocked 2023年 39卷

作者： Chien, Ting-Ying Ting, Hsien-Wei Li, Hao-Wei Chang, Hsiao-Huang Department of Computer Science and Engineering Yuan Ze University 320 Taoyuan City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University 320 Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University 320 Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare 242 New Taipei City Taiwan Division of Cardiovascular Surgery Department of Surgery Taipei Veterans General Hospital Taipei 11217 Taiwan Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei 11031 Taiwan

Coronary artery disease (CAD) is one of the top ten leading causes of death in countries worldwide. There are several methods used by physicians to diagnose CAD. One common method is the use of X-ray angiogram (XRA) images. When employing a computer-aided automatic analysis method to locate blood vessels in XRA images, problems such as complex background noise, low contrast, and difficulty in separating blood vessels from the background are encountered, especially for blood vessels with a low contrast to the background noise. In this study, we used various image preprocessing methods to highlight blood vessels from complex backgrounds and different types of blood vessel, followed by the employment of four superpixel segmentation methods to partition an image into multiple segments, and finally the utilization of Weighted Voting to determine the best superpixel algorithm. A genetic algorithm was used to identify the best weights by which to determine the locations of blood vessels in images of cardiac catheterization. Our proposed method achieved an average F1-score of 73.4% in the analysis of five patients, which was better than the other superpixel algorithms tested. The results of this research will be helpful for future SYNTAX score analysis. © 2023

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Crowd Counting for Static Images: A Survey of Methodology

Crowd Counting for Static Images: A Survey of Methodology

引用

第三十九届中国控制会议

作者： Ying Luo Jinhu Lu Baochang Zhang School of Automation Science and Electrical Engineering Beihang University Shenyuan Honors College Beihang University School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environmentand Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang University

Crowd counting for static images is one of the typical application fields of image processing. it is essential to direct crowd analysis for public security purposes under the background of a rapidly growing social environment. Crowd counting for static images involves crucial challenges such as occlusions, scale variations, scene perspective distortions and diverse crowd distributions. It has now become an independent research hotspot. This paper investigates the literature of crowd counting tasks, summarizes the evolution of counting approaches, compares and analyzes traditional methods and current trends of CNN estimation-based counting, and then evaluates common datasets for crowd counting tasks. Conclusions concerning summarizations and prospects are made.

关键词： Crowd counting crowd analysis pattern recognition computer vision

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