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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Jianchen Zhu Shengjie Zhao Xu Ma Gonzalo R. Arce Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai CHN School of Optics and Photonics Beijing Institute of Technology Beijing CHN Department of Electrical and Computer Engineering University of Delaware Newark Delaware USA

Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. Here, we first propose the Signal space Subspace Pursuit (SSSP) algorithm, and then we derive a low bound on the number of measurements required. The algorithm has low computational complexity and provides high recovery accuracy.

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swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor

arXiv

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arXiv 2019年

作者： Li, Mingzhen Liu, Changxi Liao, Jianjin Zheng, Xuegui Yang, Hailong Sun, Rujun Xu, Jun Gan, Lin Yang, Guangwen Luan, Zhongzhi Qian, Depei School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Beijing China National University of Singapore Singapore Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM achieves 1.79× on average compared to the state-of-the-art deep learning framework on Sunway, across six representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor. Copyright © 2019, The Authors. All rights reserved.

关键词： Program compilers

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SAR Target Recognition with Discriminant Feature Extraction and Hypergraph

SAR Target Recognition with Discriminant Feature Extraction ...

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Nan Zhang Haixia Xu Liming Yuan Xianbin Wen Key Laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China

ISBN: (纸本)9781538665664;9781538665657

Due to the high resolution and multipolarization of Synthetic Aperture Radar (SAR), dimensionality reduction is often needed as a pre-processing step for SAR target recognition. For this purpose, Laplacian Eigenmaps (LE)can be naturally regarded as a candidate, but it neglects the difference between the intra-class and inter-class discriminant information. In this paper, we attempt to integrate LE with Linear Discriminant Analysis (LDA)in order to reduce features for SAR objects. Owing to the inherent imaging mechanism and speckle noise of SAR images, we also propose applying hypergraph to formalize the complex relationships between SAR objects. We have performed extensive experiments on the Moving and Stationary Target Acquisition and Recognition database. Experimental results demonstrate the effectiveness of the presented combination of LE, LDA, and hypergraph on SAR target recognition.

关键词： Synthetic aperture radar Target recognition Laplace equations Feature extraction Manifolds Matrix converters Dimensionality reduction

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Forecasting of Forex Time Series Data Based on Deep Learning

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Procedia Computer Science 2019年 147卷 647-652页

作者： Lina Ni Yujie Li Xiao Wang Jinquan Zhang Jiguo Yu Chengming Qi College of Computer Science and Engineering Shandong University of Science and Technology Qingdao 266590 China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and Technology Qingdao 266590 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong 250014 China Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan Shandong 250014 China Beijing Union University Beijing 100101 China

This paper proposes a C-RNN forecasting method for Forex time series data based on deep-Recurrent Neural Network (RNN) and deep Convolutional Neural Network (CNN), which can further improve the prediction accuracy of deep learning algorithm for the time series data of exchange rate. We fully exploit the spatio-temporal characteristics of forex time series data based on the data-driven method. On the exchange rate data of nine major foreign exchange currencies, the experimental comparison of the forecasting method shows that the C-RNN foreign exchange time series data prediction method constructed in this paper has better applicability and higher accuracy.

关键词： Deep learning Recurrent neural network Convolutional neural network Foreign Exchange Rate Time series analysis

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Snow depth and ice thickness derived from SIMBA ice mass balance buoy data using an automated algorithm

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International Journal of Digital Earth 2019年第8期12卷 962-979页

作者： Zeliang Liao Bin Cheng JieChen Zhao Timo Vihma Keith Jackson Qinghua Yang Yu Yang Lin Zhang Zhijun Li Yubao Qiu Xiao Cheng School of Data and Computer Science Sun Yat-sen UniversityGangZhouPeople’s Republic of China Finnish Meteorological Institute(FMI) HelsinkiFinland National Marine Environmental Forecasting Centre(NMEFC) BeijingPeople’s Republic of China SAMS Research Services Ltd. ObanUK Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies School of Atmospheric SciencesSun Yat-sen UniversityZhuhaiPeople’s Republic of China Shenyang Institute of Engineering(SIE) ShanangPeople’s Republic of China Dalian University of Technology(DUT) DalianPeople’s Republic of China Institute of Remote Sensing and Digital Earth(RADI) BeijingPeople’s Republic of China State Key Laboratory of Remote Sensing Science and College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingPeople’s Republic of China

An ice mass balance buoy(IMB)monitors the evolution of snow and ice cover on seas,ice caps and lakes through the measurement of various *** crucial measurement of snow and ice thickness has been achieved using acoustic sounders in early devices but a more recently developed IMB called the Snow and Ice Mass Balance Array(SIMBA)measures vertical temperature profiles through the air-snow-ice-water column using a thermistor *** determination of snow depth and ice thickness from SIMBA temperature profiles is presently a manual *** present an automated algorithm to perform this *** algorithm is based on heat flux continuation,limit ratio between thermal heat conductivity of snow and ice,and minimum resolution(±0.0625°C)of the temperature *** algorithm results are compared with manual analyses,in situ borehole measurements and numerical model *** bias and root mean square error between algorithm and other methods ranged from 1 to 9 cm for ice thickness counting 2%–7%of the mean observed *** algorithm works well in cold condition but becomes less reliable in warmer conditions where the vertical temperature gradient is reduced.

关键词： Snow sea ice ice thickness ice mass balance buoy polar oceans

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Maximum delay anonymous clustering feature tree based privacy-preserving data publishing in social networks

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Procedia Computer Science 2019年 147卷 643-646页

作者： Jinquan Zhang Bowen Zhao Guochao Song Lina Ni Jiguo Yu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao 266590 China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and Technology Qingdao 266590 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong 250014 China Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan Shandong 250014 China

Clustering analysis has been widely used in pattern recognition and image processing in recent years, which is an important research field of data mining. Data publishing in social networks is threatened by the leakage of private information nowadays. This paper proposes a privacy preservation scheme of sensitive data publishing in social networks based on Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to tackle this issue. The scheme is divided into an online process and an offline process. Specifically, we present the Maximum Delay Anonymous Clustering Feature (MDACF) tree data publishing algorithm.

关键词： Privacy protection Data publishing Social networks (α, L)-diversity K-anonymity Clustering feature (CF) tree

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A survey on edge computing systems and tools

arXiv

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arXiv 2019年

作者： Liu, Fang Tang, Guoming Li, Youhuizi Cai, Zhiping Zhang, Xingzhou Zhou, Tongqing School of Data and Computer Science Sun Yat-sen University GuangzhouGuangdong China Key Laboratory of Science and Technology on Information System Engineering National University of Defense Technology ChangshaHunan China School of Compute Science and Technology Hangzhou Dianzi University China College of Computer National University of Defense Technology ChangshaHunan China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

—Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. Nowadays the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this survey. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy efficiency

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Author Correction: Maximizing carbon sequestration potential in Chinese forests through optimal management

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Nature communications 2024年第1期15卷 4020页

作者： Zhen Yu Shirong Liu Haikui Li Jingjing Liang Weiguo Liu Shilong Piao Hanqin Tian Guoyi Zhou Chaoqun Lu Weibin You Pengsen Sun Yanli Dong Stephen Sitch Evgenios Agathokleous Key Laboratory of Ecosystem Carbon Source and Sink China Meteorological Administration (ECSS-CMA) School of Ecology and Applied Meteorology Nanjing University of Information Science and Technology Nanjing 210044 China. zyu@***. Key Laboratory of Forest Ecology and Environment China's National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry 100091 Beijing China. zyu@***. Key Laboratory of Forest Ecology and Environment China's National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry 100091 Beijing China. liusr@***. Key Laboratory of Forest Management and Growth Modelling China's National Forestry and Grassland Administration Research Institute of Forest Resource Information Techniques Chinese Academy of Forestry 100091 Beijing China. Forest Advanced Computing and Artificial Intelligence Laboratory (FACAI) Department of Forestry and Natural Resources Purdue University West Lafayette IN 47907 USA. College of Forestry Northwest agriculture and Forestry University Yangling 712100 China. Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University 100871 Beijing China. Schiller Institute for Integrated Science and Society Department of Earth and Environmental Sciences Boston College Chestnut Hill Massachusetts MA 02467 USA. Key Laboratory of Ecosystem Carbon Source and Sink China Meteorological Administration (ECSS-CMA) School of Ecology and Applied Meteorology Nanjing University of Information Science and Technology Nanjing 210044 China. Department of Ecology Evolution and Organismal Biology Iowa State University Ames IA 50011 USA. College of Forestry Fujian Agriculture and Forestry University Fuzhou 350002 China. Key Laboratory of Forest Ecology and Environment China's National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry 10

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Safety and efficacy of low-intensity versus standard monitoring following intravenous thrombolytic treatment in patients with acute ischaemic stroke (OPTIMISTmain): an international, pragmatic, stepped-wedge, cluster-randomised, controlled non-inferiority trial

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The Lancet 2025年第10493期405卷 1909-1922页

作者： Anderson, Craig S Summers, Debbie Ouyang, Menglu Sui, Yi Johnson, Brenda Billot, Laurent Malavera, Alejandra Faigle, Roland Muñoz-Venturelli, Paula Day, Diana Liu, Xiaoqiu Li, Qiang Song, Lili Robinson, Thompson G González, Francisca Urrutia-Goldsack, Francisca Iacobelli, Michael Montalbano, Michelle Pruski, April Delcourt, Candice Durham, Alice C Ebraimo, Ahtasam Van Ta, Ha Hong Ghosh, Pratiti Leonhardt-Caprio, Ann Nguyen, Huy Thang Ton, Mai Duy Jan, Stephen Liu, Hueiming Lindley, Richard I Arauz, Antonio Mercado, Andrés Zaidi, Wan Asyraf Wan Khatri, Pooja Wang, Xia Urrutia, Victor C Jala, Sheila Day, Susan Hu, Ying Lal, Aneeta Devlin, Michael Bendall, Carol Nebauer, Christianna Maddox, Duncan Brooks, Melissa Kleinig, Timothy Cranefield, Jennifer McMahon, Samantha Drew, Roy McMullan, Patrick Straha, Joschka Rowley, Donna Rodgers, Helen Pyman, Jessica Grimley, Rohan Tastula, Kylie Li, Xiaowen (Erin) Walker, Stephen Bailey, Alanah Hennessey, Julia R Butcher, Ken S Ghia, Darshan Edmonds, Gillian Casey, Erin Ting, Lydia Prosser, Jane Franklin, Krystle Ma, Henry Phan, Thanh Vuong, Jason Urbi, Berzenn Phair, Rebecca Jose, Nino Shine, Andrew Lane, Susan Mai, Juliette Fuller, Christine Urkude, Ravindra Myers, Nerida Bradford, Nicole Sui, Yi Bian, Wei Guo, Xinze Zhao, Yang Zhang, Yang Qi, Xuchen Zhang, Yanxing Tang, Huan Fang, Xuan Zheng, Min Yan, Mingzong Yin, Yanyan Shi, Penglai Li, Junmei Luo, Junming Fan, Fuqiang Guo, Xueting Zhou, Huaxiang Ba, Xueqing Nie, Yongxia Song, Chenguang Gao, Hong'an Sun, Lili Jia, Chunnan Wang, Mingyue Zhang, Lihong Yang, Jing Song, Aijie Zhang, Yanli Yu, Shuang Guan, Ying Bai, Ruyu Li, Changqing Bai, Tong Li, Xin Wang, Dongyu Yu, Haiyan Zhao, Wei Li, Nan L, Hongjun Yan, Tao Zhang, Jinnan Jiang, Shumin Shen, Yi Hu, Xiaowei Li, Runhui Cui, Li Kong, Yan Zhang, Hong Yang, Shuang Guo, Guiying Zhao, Xiaochen Zhao, Haishan Zhang, Fan Li, Siheng Yang, Yan Wang, Ying Jin, Haiyan Liu, Zijia Jiang, Ping Jiang, Hongquan Shai, Yao Tang, Ying Xue, Jing Dong, Xinyu Wang, Jinchun Guo, Jingyu Wang, Xiuyin Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education Shanghai China The George Institute for Global Health Faculty of Medicine University of New South Wales Sydney NSW Australia Neurology Department Royal Prince Alfred Hospital Sydney NSW Australia Centro de Estudios Clínicos Instituto de Ciencias e Innovación en Medicina Facultad de Medicina Clínica Alemana Universidad del Desarrollo Santiago Chile Saint Luke's Hospital of Kansas City Marion Bloch Neuroscience Institute Kansas City MO United States Department of Neurology The Fourth People's Hospital of Shenyang China Medical University Liaoning Shenyang China The Johns Hopkins Hospital Comprehensive Stroke Center Department of Neurology Johns Hopkins University School of Medicine Baltimore MD United States Department of Neurology Sarasota Memorial Hospital Sarasota FL United States Faculty of Health Education Medicine and Social Care Anglia Ruskin University Cambridge United Kingdom NIHR Leicester Biomedical Research Centre University of Leicester Leicester United Kingdom Department of Cardiovascular Sciences University of Leicester Leicester United Kingdom Facultad de Ciencias de la Salud Blanquerna Universitat Ramon Llull Barcelona Spain Physical Medicine and Rehabilitation Services Phoenix VA Health Care System Phoenix AZ United States Macquarie University Department of Clinical Medicine Faculty of Medicine Health and Human Sciences Sydney NSW Australia University of Rochester Medical School Rochester NY United States 115 Hospital Ho Chi Minh City Viet Nam Stroke Center Bach Mai hospital Hanoi Viet Nam VNU–University of Medicine and Pharmacy Hanoi Viet Nam Hanoi Medical University Hanoi Viet Nam The George Institute of Global Health UK Faculty of Medicine Imperial College London United Kingdom Westmead Applied Research C

Background: The universally accepted best practice protocol for monitoring patients who receive intravenous thrombolysis for acute ischaemic stroke was established in the 1990s. However, the protocol is burdensome for nurses, disrupts the sleep of patients, and is potentially less relevant in patients at low risk of symptomatic intracerebral haemorrhage. We aimed to assess whether implementing a low-intensity monitoring protocol would be as safe and effective as standard high-intensity monitoring for patients with acute ischaemic stroke at low risk. Methods: OPTIMISTmain was an international, pragmatic, multicentre, stepped-wedge, cluster-randomised, controlled, non-inferiority, blinded-endpoint trial conducted at hospitals (clusters) in eight countries. It was designed to test the non-inferiority of a low-intensity monitoring protocol to a standard protocol among consecutive adults with acute ischaemic stroke who were clinically stable with mild to moderate neurological impairment (score <10 on the National Institutes of Health Stroke Scale) within 2 h of initiation of intravenous thrombolysis according to local guidelines. Participating hospitals were randomly allocated to three sequences of implementation across four periods, stratified by country and projected numbers of participants, in which sites switched from standard monitoring (control) to low-intensity monitoring (intervention) in a stepped manner. The low-intensity monitoring protocol included assessments of neurological and vital signs every 15 min for 2 h, every 2 h for 8 h (vs every 30 min for 6 h for standard monitoring), and every 4 h (vs every 1 h for standard monitoring) until 24 h after thrombolysis. The primary outcome was the proportion of participants with an unfavourable functional outcome defined by a score from 2 (indicating some disability) to 6 (death) on the modified Rankin Scale at 90 days, measured by research staff masked to group allocation. The non-inferiority margin was set at 1·15

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Polygenic risk score and lung adenocarcinoma risk among never-smokers by EGFR mutation status-a brief report

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Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 2024年第4期20卷 521-530页

作者： Batel Blechter Chao Agnes Hsiung Xiaoyu Wang Haoyu Zhang Wei Jie Seow Jianxin Shi Nilanjan Chatterjee Hee Nam Kim Maria Pik Wong Yun-Chul Hong Jason Yy Wong Juncheng Dai H Dean Hosgood Zhaoming Wang I-Shou Chang Jiyeon Choi Jiucun Wang Minsun Song Wei Hu Wei Zheng Jin Hee Kim Baosen Zhou Demetrius Albanes Min-Ho Shin Lap Ping Chung She-Juan An Hong Zheng Yasushi Yatabe Xu-Chao Zhang Young Tae Kim Xiao-Ou Shu Young-Chul Kim Roel C H Vermeulen Bryan A Bassig Jiang Chang James Chung Man Ho Bu-Tian Ji Michiaki Kubo Yataro Daigo Yukihide Momozawa Yoichiro Kamatani Takayuki Honda Hideo Kunitoh Shun-Ichi Watanabe Yohei Miyagi Haruhiko Nakayama Shingo Matsumoto Masahiro Tsuboi Koichi Goto Zhihua Yin Atsushi Takahashi Akiteru Goto Yoshihiro Minamiya Kimihiro Shimizu Kazumi Tanaka Tangchun Wu Fusheng Wei Jian Su Yeul Hong Kim In-Jae Oh Victor Ho Fun Lee Wu-Chou Su Yuh-Min Chen Gee-Chen Chang Kuan-Yu Chen Ming-Shyan Huang Hsien-Chih Lin Adeline Seow Jae Yong Park Sun-Seog Kweon Chien-Jen Chen Yu-Tang Gao Chen Wu Biyun Qian Daru Lu Jianjun Liu Hyo-Sung Jeon Chin-Fu Hsiao Jae Sook Sung Ying-Huang Tsai Yoo Jin Jung Huan Guo Zhibin Hu Tzu-Yu Chen Laurie Burdett Meredith Yeager Amy Hutchinson Sonja I Berndt Wei Wu Junwen Wang Jin Eun Choi Kyong Hwa Park Sook Whan Sung Li Liu Chang Hyun Kang Chung-Hsing Chen Jun Xu Peng Guan Wen Tan Chih-Liang Wang Alan Dart Loon Sihoe Ying Chen Yi Young Choi Jun Suk Kim Ho-Il Yoon Qiuyin Cai In Kyu Park Ping Xu Qincheng He Chih-Yi Chen Junjie Wu Wei-Yen Lim Kun-Chieh Chen John K C Chan Jihua Li Hongyan Chen Chong-Jen Yu Li Jin Joseph F Fraumeni Jie Liu Maria Teresa Landi Taiki Yamaji Yang Yang Belynda Hicks Kathleen Wyatt Shengchao A Li Hongxia Ma Bao Song Zhehai Wang Sensen Cheng Xuelian Li Yangwu Ren Motoki Iwasaki Junjie Zhu Gening Jiang Ke Fei Guoping Wu Li-Hsin Chien Fang-Yu Tsai Jinming Yu Victoria L Stevens Pan-Chyr Yang Dongxin Lin Kexin Chen Yi-Long Wu Keitaro Matsuo Nathaniel Rothman Kouya Shiraishi Hongbing Shen Stephen J Chanock Takashi Kohno Qing Lan Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan. Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA Saw Swee Hock School of Public Health National University of Singapore and National University Health System Singapore Singapore Department of Medicine Yong Loo Lin School of Medicine National University of Singapore and National University Health System Singapore Singapore. Department of Oncology School of Medicine Johns Hopkins University Baltimore MD USA Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. Department of Preventive Medicine Chonnam National University Medical School Gwangju Republic of Korea. Department of Pathology Queen Mary Hospital Hong Kong Hong Kong. Department of Preventive Medicine Seoul National University College of Medicine Seoul Republic of Korea. Epidemiology and Community Health Branch National Heart Lung and Blood Institute Bethesda MD USA. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Epidemiology and Population Health Albert Einstein College of Medicine New York USA. Department of Computational Biology St. Jude Children's Research Hospital Memphis TN USA. National Institute of Cancer Research National Health Research Institutes Zhunan Taiwan. Ministry of Education Key Laboratory of Contemporary Anthropology School of Life Sciences Fudan University Shanghai China State Key Laboratory of Genetic Engineering School of Life Sciences Fudan University Shanghai China. Department of Statistics & Research Institute of Natural Sciences Sookmyung Women's University Seoul 04310 Korea. Division of Epidemiology Department of Medicine Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center Nashville TN USA. Department of Environmental Health

We assessed the association between a genome-wide polygenic risk score (PRS) developed for lung adenocarcinoma (LUAD) risk and mutation on the epidermal growth factor receptor (EGFR) gene in 998 East Asian never-smoking female LUAD cases (518 EGFR-positive; 480 EGFR-negative) and 4,544 never-smoking controls using case-case and multinomial regression analyses. We found that the PRS was more strongly associated with EGFR-positive LUAD compared to EGFR-negative LUAD, where the association between the fourth quartile of the PRS and EGFR-positive LUAD (OR=8.63, 95% CI:5.67, 13.14) was significantly higher than the association between the fourth quartile of the PRS with EGFR-negative LUAD (OR=3.50, 95% CI: 2.44, 5.00) (p-heterogeneity=3.66x10). Our findings suggest that germline genetic susceptibility may be differentially associated with LUAD in never-smoking female East Asian patients depending on the cancer's mutation status, which may have important public health and clinical implications.

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