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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous methods assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using previous methods. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view clustering problem;2) it designs the low-rank representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods. We provide codes for all of our experiments in https://***/ZeusDavide/TETCI_UIMC. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National Science Review 2025年第5期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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Rational Manipulation of Stacking Arrangements in Three-Dimensional Zeolites Built from Two-Dimensional Zeolitic Nanosheets

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Angewandte Chemie 2020年第45期132卷

作者： Xu L. Ma T. Shen Y. Wang Y. Han L. Chaittisilp W. Yokoi T. Sun J. Wakihara T. Okubo T. Department of Chemical System Engineering The University of Tokyo Tokyo 113-8656 Japan College of Chemistry and Molecular Engineering Peking University Beijing 100871 China Chemical Resources Laboratory Tokyo Institute of Technology Yokohama 226-8503 Japan School of Chemical Science and Engineering Tongji University Shanghai 200092 China Research and Services Division of Materials Data and Integrated System National Institute for Materials Sciences (NIMS) Ibaraki 305-0044 Japan

Unit-cell-thin zeolitic nanosheets have emerged as fascinating materials for catalysis and separation. The controllability of nanosheet stacking is extremely challenging in the chemistry of two-dimensional zeolitic materials. To date, the organization of zeolitic nanosheets in hydrothermal synthesis has been limited by the lack of tunable control over the guest–host interactions between organic structure-directing agents (OSDAs) and zeolitic nanosheets. A direct synthetic methodology is reported that enables systematic manipulation of the aluminosilicate MWW-type nanosheet stacking. Variable control of guest–host interactions is rationally achieved by synergistically altering the charge density of OSDAs and synthetic silica-to-alumina composition. These finely controlled interactions allow successful preparation of a series of three-dimensional (3D) zeolites, with MWW-layer stacking in wide ranges from variably disorder to fully ordered, leading to tunable catalytic activity in the cracking reaction. These results highlight unprecedented opportunities to modulate zeolitic nanosheets arrangement in 3D zeolites whose structure can be tailored for catalysis and separation. © 2020 Wiley-VCH GmbH

关键词： catalysis crystal growth layered compounds structure transformations

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Waterline mapping of inland great lake with subpixel accuracy from GF-3 SAR images 6

Waterline mapping of inland great lake with subpixel accurac...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture Radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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REMOVED: Machine learning in health condition check-up: An approach using Breiman's random forest algorithm

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Measurement: Sensors 2022年 23卷

作者： Yousef Methkal Abd Algani Mahyudin Ritonga B. Kiran Bala Mohammed Saleh Al Ansari Malek Badr Ahmed I. Taloba Department of Mathematics Sakhnin College Israel Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel Muhammadiyah University of West Sumatra Indonesia Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering (Autonomous) Samayapuram 621112 Trichy India College of Engineering Department of Chemical Engineering University of Bahrain Bahrain The University of Mashreq Research Center Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad 10021 Iraq Department of Computer Science College of Science and Arts in Qurayyat Jouf University Saudi Arabia Information System Department Faculty of Computers and Information Assiut University Assiut Egypt

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Persistent eutrophication and hypoxia in the coastal ocean

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Cambridge Prisms: Coastal Futures 2023年 1卷 e19-e19页

作者： Dai, Minhan Zhao, Yangyang Chai, Fei Chen, Mingru Chen, Nengwang Chen, Yimin Cheng, Danyang Gan, Jianping Guan, Dabo Hong, Yuanyuan Huang, Jialu Lee, Yanting Leung, Kenneth Mei Yee Lim, Phaik Eem Lin, Senjie Lin, Xin Liu, Xin Liu, Zhiqiang Luo, Ya-Wei Meng, Feifei Sangmanee, Chalermrat Shen, Yuan Uthaipan, Khanittha Wan Talaat, Wan Izatul Asma Wan, Xianhui Sean Wang, Cong Wang, Dazhi Wang, Guizhi Wang, Shanlin Wang, Yanmin Wang, Yuntao Wang, Zhe Wang, Zhixuan Xu, Yanping Yang, Jin-Yu Terence Yang, Yan Yasuhara, Moriaki Yu, Dan Yu, Jianmin Yu, Liuqian Zhang, Zengkai Zhang, Zhouling State Key Laboratory of Marine Environmental Science Xiamen University Xiamen China College of Ocean and Earth Sciences Xiamen University Xiamen China Fujian Institute for Sustainable Oceans Xiamen University Xiamen China China-ASEAN College of Marine Sciences Xiamen University Malaysia Sepang Malaysia College of the Environment and Ecology Xiamen University Xiamen China Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies Xiamen University Xiamen China Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China Thrust of Earth Ocean and Atmospheric Sciences Department of Ocean Science Department of Mathematics The Hong Kong University of Science and Technology Kowloon Hong Kong Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai China School of Biological Sciences Area of Ecology and Biodiversity Swire Institute of Marine Science Institute for Climate and Carbon Neutrality Musketeers Foundation Institute of Data Science State Key Laboratory of Marine Pollution The University of Hong Kong Hong Kong Hong Kong State Key Laboratory of Marine Pollution and Department of Chemistry City University of Hong Kong Hong Kong Hong Kong Institute of Ocean and Earth Sciences University of Malaya Kuala Lumpur Malaysia Department of Marine Sciences University of Connecticut Groton CT United States Department of Ocean Science and Engineering Southern University of Science and Technology Shenzhen China Marine and Coastal Resources Research and Development Institute Department of Marine and Coastal Resources Bangkok Thailand Institute of Oceanography and Environment Universiti Malaysia Terengganu Kuala Terengganu Malaysia Department of Geosciences Princeton University Princeton NJ United States State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography Ministry of Natural Resour

Coastal eutrophication and hypoxia remain a persistent environmental crisis despite the great efforts to reduce nutrient loading and mitigate associated environmental damages. Symptoms of this crisis have appeared to spread rapidly, reaching developing countries in Asia with emergences in Southern America and Africa. The pace of changes and the underlying drivers remain not so clear. To address the gap, we review the up-To-date status and mechanisms of eutrophication and hypoxia in global coastal oceans, upon which we examine the trajectories of changes over the 40 years or longer in six model coastal systems with varying socio-economic development statuses and different levels and histories of eutrophication. Although these coastal systems share common features of eutrophication, site-specific characteristics are also substantial, depending on the regional environmental setting and level of social-economic development along with policy implementation and management. Nevertheless, ecosystem recovery generally needs greater reduction in pressures compared to that initiated degradation and becomes less feasible to achieve past norms with a longer time anthropogenic pressures on the ecosystems. While the qualitative causality between drivers and consequences is well established, quantitative attribution of these drivers to eutrophication and hypoxia remains difficult especially when we consider the social economic drivers because the changes in coastal ecosystems are subject to multiple influences and the cause-effect relationship is often non-linear. Such relationships are further complicated by climate changes that have been accelerating over the past few decades. The knowledge gaps that limit our quantitative and mechanistic understanding of the human-coastal ocean nexus are identified, which is essential for science-based policy making. Recognizing lessons from past management practices, we advocate for a better, more efficient indexing system of coastal eutrophica

关键词： eutrophication harmful algal bloom hypoxia regime shift socioeconomic-ecological system

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Towards a widespread usage of computational fluid dynamics simulations for automated virtual nasal surgery planning

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Future Generation Computer systems 2026年 174卷

作者： Mario Rüttgers Moritz Waldmann Fabian Hübenthal Klaus Vogt Makoto Tsubokura Sangseung Lee Andreas Lintermann Data-Driven Fluid Engineering (DDFE) Laboratory Inha University Incheon South Korea Institute of Aerodynamics and Chair of Fluid Mechanics (AIA) RWTH Aachen University Germany Faculty of Medicine Center of Experimental Surgery University of Latvia 1586 Riga Latvia Department of Computational Science Graduate School of System Informatics Kobe University Japan Complex Phenomena Unified Simulation Research Team RIKEN Center for Computational Science Kobe Japan Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich GmbH Germany

Efficient computational approaches are crucial for advancing computational fluid dynamics (CFD)-based automated planning in nasal surgeries, such as septoplasties and turbinectomies. This study introduces a hybrid lattice-Boltzmann and level-set method to address the trade-off between computational cost and automation. By interpolating geometry variations in discrete steps between pre-surgical and target states, the approach achieves computational efficiency with only 21 surface variations per intervention. Previous methods rely on more costly coupling strategies, such as reinforcement learning or thermal modeling, which may still be appropriate for complex planning scenarios involving multiple intervention sites or thermal flow analysis. In contrast, the presented method reduces complexity while retaining key predictive capabilities, making it particularly suitable for widespread, time-sensitive clinical use focused on a single surgical intervention. Fluid mechanical metrics, including pressure loss and volume flow rate balance, are evaluated alongside tissue removal volume to recommend optimized surgical plans. Case studies on three patients demonstrate tissue savings of 12–25% without compromising key flow parameters. Additionally, a non-linear regression model trained on as few as 11 CFD simulations predicts pressure loss and flow rates with errors below 4%, and reduces computational costs by 50%. The proposed framework represents a significant step toward making CFD-based virtual nasal surgery planning more accessible and practical.

关键词： Septoplasty Turbinectomy Non-linear regression

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Author Correction: The influence of natural variability on extreme monsoons in Pakistan

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npj Climate and Atmospheric Science 2023年第1期6卷 1页

作者： Moetasim Ashfaq Matthew F. Horan Salil Mahajan Katherine J. Evans Nathaniel Johnson Fred Kucharski Muhammad Adnan Abid Noah S. Diffenbaugh Deepti Singh Subimal Ghosh Auroop R. Ganguly Shafiqul Islam Computational Sciences and Engineering Division (CSED) Oak Ridge National Laboratory Oak Ridge TN USA Bredesen Center University of Tennessee Knoxville TN USA National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory Princeton NJ USA Earth System Physics Abdus Salam International Centre for Theoretical Physics Trieste Italy Doerr School of Sustainability Stanford University Stanford CA USA School of the Environment Washington State University Vancouver WA USA Department of Civil Engineering Indian Institute of Technology Bombay Mumbai India Sustainability and Data Sciences Laboratory Civil and Environmental Engineering Northeastern University Boston MA USA Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory Richland WA USA Indian Institute of Technology Gandhinagar Gandhinagar GJ India Department of Civil and Environmental Engineering The Fletcher School of Law and Diplomacy Tufts University Medford MA USA

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Phonon structure of titanium under shear deformation along {101¯2} twinning mode

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Physical Review B 2020年第2期102卷 024106-024106页

作者： Atsushi Togo Yuta Inoue Isao Tanaka Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba Ibaraki 305-0047 Japan Center for Elements Strategy Initiative for Structural Materials Kyoto University Sakyo Kyoto 606-8501 Japan Department of Materials Science and Engineering Kyoto University Sakyo Kyoto 606-8501 Japan Nanostructures Research Laboratory Japan Fine Ceramics Center Atsuta Nagoya 456-8587 Japan

We investigated phonon behavior of hexagonal close-packed titanium under homogeneous shear deformation corresponding to the {101¯2} twinning mode using first-principles calculations and phonon calculations. By this deformation, we found that a phonon mode located at a point on the Brillouin zone boundary is drastically softened, increasing the shear, and finally it triggers a spontaneous structural transition by breaking the crystal symmetry toward twin from parent.

关键词： Phonons Plasticity

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A structural haplotype in the 17q21.31 MAPT region is associated with increased risk for chronic traumatic encephalopathy endophenotypes

Cell Reports Medicine

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Cell Reports Medicine 2025年第5期6卷 102084-102084页

作者： Han, Xudong Zhang, Yichi Petrosky, Jillian N. Bald, Sarah Sherva, Richard M. Labadorf, Adam Cherry, Jonathan D. Chung, Jaeyoon Farrell, Kurt Abdolmohammadi, Bobak Durape, Shruti Martin, Brett M. Palmisano, Joseph N. Farrell, John J. Alvarez, Victor E. Huber, Bertrand R. Dwyer, Brigid Daneshvar, Daniel H. Jun, Gyungah R. Lunetta, Kathryn L. Goldstein, Lee E. Katz, Douglas I. Cantu, Robert C. Shenton, Martha E. Cummings, Jeffrey L. Reiman, Eric M. Stern, Robert A. Alosco, Michael L. Tripodis, Yorghos Farrer, Lindsay A. Stein, Thor D. Crary, John F. McKee, Ann C. Mez, Jesse Bioinformatics Graduate Program Boston University Boston MA United States Section of Biomedical Genetics Department of Medicine Boston University Chobanian & Avedisian School of Medicine Boston MA United States Department of Neurology Boston University Chobanian & Avedisian School of Medicine Boston MA United States Boston University Alzheimer's Disease Research Center Boston University Chobanian & Avedisian School of Medicine Boston MA United States Boston VA Healthcare System Boston MA United States Boston University Chronic Traumatic Encephalopathy Center Boston University Boston MA United States Department of Pathology and Laboratory Medicine Boston University Chobanian & Avedisian School of Medicine Boston MA United States Departments of Pathology Neuroscience and Artificial Intelligence & Human Health Neuropathology Brain Bank & Research CoRE Friedman Brain Institute Ronald M. Loeb Center for Alzheimer's Disease Icahn School of Medicine at Mount Sinai New York NY United States Biostatistics and Epidemiology Data Analytics Center Boston University School of Public Health Boston MA United States Bedford VA Healthcare System Bedford MA United States Braintree Rehabilitation Hospital Braintree MA United States Department of Physical Medicine and Rehabilitation Harvard Medical School Boston MA United States Department of Rehabilitation and Human Performance Brain Injury Research Center Icahn School of Medicine at Mount Sinai New York NY United States Department of Neurology Icahn School of Medicine at Mount Sinai New York NY United States Department of Biostatistics Boston University School of Public Health Boston MA United States Department of Ophthalmology Boston University Chobanian & Avedisian School of Medicine Boston MA United States Departments of Biomedical Electrical & Computer Engineering Boston University College of Engineering Boston MA United States Departments of Radiology and Psychiatry Boston University Chobanian & Avedisia

Chronic traumatic encephalopathy (CTE) is a neurodegenerative tauopathy associated with repetitive head impact (RHI) exposure. Genetic variation in the 17q21.31 region, containing microtubule-associated protein tau (MAPT), has been implicated in tauopathies but has not been investigated in CTE. The region includes a megabase-long inversion (H1/H2) and copy-number variations, including α, β, and γ segments, which can be characterized as nine segregating structural haplotypes. We leveraged array SNP data and a reference panel across the 17q21.31 region to impute structural haplotypes and test their association with CTE endophenotypes in 447 European ancestry brain donors with RHI exposure. The H1β1γ1 haplotype was significantly associated with dementia and semi-quantitative tau burden in multiple cortical and medial temporal regions commonly affected in CTE. H1β1γ1 differential expression analyses in dorsolateral frontal cortex implicated cis-acting genes and inflammatory pathways. Taken together, the H1β1γ1 haplotype may help explain CTE heterogeneity among those with similar RHI exposure. © 2025 The Authors

关键词： 17q21.31 chronic traumatic encephalopathy dementia differential gene expression immune system MAPT repetitive head impacts structural haplotypes tauopathy traumatic brain injury

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