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The most cited articles on the topic of health behaviors in google Trends research: a systematic review

Advances in Generial Practice of Medicine 2018年第1期1卷 1-7页

作者： Wei Chih Kan Tsair Wei Chien Hsien Yi Wang Willy Chou Department of Nephrology Chi-Mei Medical Center Tainan Taiwan Department of Biological Science and Technology Chung Hwa University of Medical Technology Tainan Taiwan Medical research Department Chi-Mei Medical Center Tainan Taiwan Department of Sport Management College of Leisure and Recreation Management Chia Nan University of Pharmacy and Science Tainan Taiwan Department of physical medicine and rehabilitation Chi Mei medical center Tainan Taiwan Department of Recreation and Health-Care Management Institute of recreation Industry Management Chia Nan University of Pharmacy Tainan Taiwan

Background: Over the past decade, the use of Web-based data in public health issues has been proven useful in assessing various aspects of human behavior. google Trends is the most popular tool to gather such information and has been applied to several topics with the most focused subject related to health and medicine. However, the most cited articles and the popular medical subject headings (MESH terms) on health behaviors in google Trends research remain unknown. The web-based behavior requires to monitor and analyze on-line data for examining actual human behavior to predict and even prevent health-related issues that constantly arise in daily life. Objective: This systematic review aimed at reporting and further presenting the most cited articles and the popular MESH terms on health behaviors in google Trends (infodemiology) researches in health-related topics since 2009 to provide an overview of the topic burst for future research on the subject of health behavior. Methods: Following the Meta-Analyses guidelines for selecting studies, we searched for the term “google Trends [Title]” in PubMed databases since 2009, applying specific criteria for types of journal articles. A total of 86 published papers were extracted, excluding those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to MESH terms using social network analysis (SNA) and selected the most cited articles that related to the health behavior in google Trends. Results: The most cited articles are those from the US in 2009 (PMID= 19845471 cited 88 times) and the UK in 2013 (PMID= 23619126 cited 74 times). The MESH term represented by Internet earns the highest impact factor (IF) and presents significantly different among term clusters (F (3,20)=15.79, p<0.001). The most number of citing journals is from PloS One. The most number of author affiliations is from the US. Conclusion: The monitoring of online

关键词： health behavior infodemiology google Trends medical subject headings social network analysis

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Fixing, preventing, and recovering from concurrency bugs

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Science China(Information Sciences) 2015年第5期58卷 58-75页

作者： DENG DongDong JIN GuoLiang de KRUIJF Marc LI Ang LIBLIT Ben LU Shan QI ShanXiang REN JingLei SANKARALINGAM Karthikeyan SONG LinHai WU YongWei ZHANG MingXing ZHANG Wei ZHENG WeiMin Computer Science Department University of Wisconsin google Computer Science Department University of Chicago Tsinghua National Laboratory for Information Science and Technology Tsinghua University IBM research

Concurrency bugs are becoming widespread with the emerging ubiquity of multicore processors and multithreaded software. They manifest during production runs and lead to severe losses. Many effective concurrency-bug detection tools have been built. However, the dependability of multi-threaded software does not improve until these bugs are handled statically or dynamically. This article discusses our recent progresses on fixing, preventing, and recovering from concurrency bugs.

关键词： concurrency bugs bug detection production run performance counters

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Graph convolution machine for context-aware recommender system

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Frontiers of Computer Science 2022年第6期16卷 81-92页

作者： Jiancan WU Xiangnan HE Xiang WANG Qifan WANG Weijian CHEN Jianxun LIAN Xing XIE School of Information Science and Technology University of Science and Technology of ChinaHefei230026China National University of Singapore 5 Prince George’s ParkSingapore118404Singapore google research Mountain ViewCA94043USA Microsoft research Asia Beijing100190China

The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction ***,such finding is mostly restricted to the collaborative filtering(CF)scenario,where the interaction contexts are not *** this work,we extend the advantages of graph convolutions to context-aware recommender system(CARS,which represents a generic type of models that can handle various side information).We propose Graph Convolution Machine(GCM),an end-to-end framework that consists of three components:an encoder,graph convolution(GC)layers,and a *** encoder projects users,items,and contexts into embedding vectors,which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on the user-item *** decoder digests the refined embeddings to output the prediction score by considering the interactions among user,item,and context *** conduct experiments on three real-world datasets from Yelp and Amazon,validating the effectiveness of GCM and the benefits of performing graph convolutions for CARS.

关键词： context-aware recommender systems graph convolution

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An artificial neural network emulator of the rangeland hydrology and erosion model

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International Soil and Water Conservation research 2024年第2期12卷 241-257页

作者： Mahmoud Saeedimoghaddam Grey Nearing Mariano Hernandez Mark A.Nearing David C.Goodrich Loretta J.Metz Department of Land Air&Water ResourcesUniversity of CaliforniaDavisCAUSA google research Mountain ViewCAUSA USDA-Agricultural research Service Southwest Watershed Research CenterTucsonAZUSA USDA-NRCS Resource Inventory and Assessment Divisionxo CEAP-Grazing LandsTucsonAZUSA

Machine learning(ML)is becoming an ever more important tool in hydrologic *** studies have shown the higher prediction accuracy of those ML models over traditional process-based ***,there is another advantage of ML which is its lower computational *** is important for the applications such as hydraulic soil erosion estimation over a large area and at a finer spatial *** traditional models like Rangeland Hydrology and Erosion Model(RHEM)requires too much computation time and *** this study,we designed an Artificial Neural Network that is able to recreate the RHEM outputs(annual average runoff,soil loss,and sediment yield and not the daily storm event-based values)with high accuracy(Nash-Sutcliffe Efficiency≈1.0)and a very low computational time(13 billion times faster on average using a GPU).We ran the RHEM for more than a million synthetic scenarios and train the Emulator with *** also,fine-tuned the trained Emulator with the RHEM runs of the real-world scenarios(more than 32,000)so the Emulator remains comprehensive while it works specifically accurately for the real-world *** also showed that the sensitivity of the Emulator to the input variables is similar to the RHEM and it can effectively capture the changes in the RHEM outputs when an input variable ***,the dynamic prediction behavior of the Emulator is statistically similar to the RHEM.

关键词： RHEM Sediment yield Soil loss Runoff Deep learning

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TensorFlow Lite:端侧机器学习框架

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计算机研究与发展 2020年第9期57卷 1839-1853页

作者：李双峰 google TensorFlow团队北京100190

TensorFlow Lite(TFLite)是一个轻量、快速、跨平台的专门针对移动和IoT场景的开源机器学习框架,是TensorFlow的一部分,支持安卓、iOS、嵌入式Linux以及MCU等多个平台部署.它大大降低开发者使用门槛,加速端侧机器学习的发展,推动机器学... 详细信息

TensorFlow Lite(TFLite)是一个轻量、快速、跨平台的专门针对移动和IoT场景的开源机器学习框架,是TensorFlow的一部分,支持安卓、iOS、嵌入式Linux以及MCU等多个平台部署.它大大降低开发者使用门槛,加速端侧机器学习的发展,推动机器学习无处不在.介绍了端侧机器学习的浪潮、挑战和典型应用;TFLite的起源和系统架构;TFLite的最佳实践,以及适合初学者的工具链;展望了未来的发展方向.

关键词：机器学习端侧机器学习 TensorFlow TensorFlow Lite TFLite 移动物联网

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STrans:A Comprehensive Framework for Structure Transformation

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Tsinghua Science and Technology 2016年第2期21卷 231-240页

作者： Jiangzhou He Wenguang Chen Zhizhong Tang Department of Computer Science and Technology Tsinghua National Laboratory for Information Science and TechnologyTsinghua UniversityBeijing 100084China google lnc 1600 Amphitheatre PkwyMountain ViewCA 94043USA. research Institute of Tsinghua University in Shenzhen Shenzhen 518057China

Structure Data Layout Optimization （SDLO） is a prevailing compiler optimization technique to improve cache efficiency. Structure transformation is a critical step for SDLO. Diversity of transformation methods and existence of complex data types are major challenges for structure transformation. We have designed and implemented STrans, a well-defined system which provides controllable and comprehensive functionality on structure transformation. Compared to known systems, it has less limitation on data types for transformation. In this paper we give formal definition of the approach STrans transforms data types. We have also designed Transformation Specification Language, a mini language to configure how to transform structures, which can be either manually tuned or generated by compiler. STrans supports three kinds of transformation methods, i.e., splitting, peeling, and pool-splitting, and works well on different combinations of compound data types. STrans is the transformation system used in ASLOP and is well tested for all benchmarks for ASLOR

关键词： Structure Data Layout Optimization (SDLO) STrans ASLOP structure transformation

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Deep image synthesis from intuitive user input:A review and perspectives

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Computational Visual Media 2022年第1期8卷 3-31页

作者： Yuan Xue Yuan-Chen Guo Han Zhang Tao Xu Song-Hai Zhang Xiaolei Huang College of Information Sciences and Technology the Pennsylvania State UniversityUniversity ParkPAUSA Department of Computer Science and Technology Tsinghua UniversityBeijingChinaand Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijingChina google Brain Mountain ViewCAUSA Facebook Menlo ParkCAUSA.

In many applications of computer graphics,art,and design,it is desirable for a user to provide intuitive non-image input,such as text,sketch,stroke,graph,or layout,and have a computer system automatically generate photo-realistic images according to that *** classically,works that allow such automatic image content generation have followed a framework of image retrieval and composition,recent advances in deep generative models such as generative adversarial networks(GANs),variational autoencoders(VAEs),and flow-based methods have enabled more powerful and versatile image generation *** paper reviews recent works for image synthesis given intuitive user input,covering advances in input versatility,image generation methodology,benchmark datasets,and evaluation *** motivates new perspectives on input representation and interactivity,cross fertilization between major image generation paradigms,and evaluation and comparison of generation methods.

关键词： image synthesis intuitive user input deep generative models synthesized image quality evaluation

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Tiny Det: accurately detecting small objects within 1 GFLOPs

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Science China(Information Sciences) 2023年第1期66卷 318-319页

作者： Shaoyu CHEN Tianheng CHENG Jiemin FANG Qian ZHANG Yuan LI Wenyu LIU Xinggang WANG School of Electronic Information and Communications Huazhong University of Science and Technology Institute of Artificial Intelligence Huazhong University of Science and Technology Horizon Robotics Inc. google Inc.

Dear editor,Object detection plays an important role in computer vision,and has gradually become the technical foundation of many applications. However, the inference procedure of advanced detection models requires ma... 详细信息

关键词： Economic and social effects

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End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design

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npj Computational Materials 2023年第1期9卷 1072-1083页

作者： Han Liu Yuhan Liu Kevin Li Zhangji Zhao Samuel S.Schoenholz Ekin D.Cubuk Puneet Gupta Mathieu Bauchy SOlids inFormaTics AI-Laboratory(SOFT-AI-Lab) College of Polymer Science and EngineeringSichuan UniversityChengdu 610065China AIMSOLID research Wuhan 430223China Physics of AmoRphous and Inorganic Solids Laboratory(PARISlab) Department of Civil and Environmental EngineeringUniversity of CaliforniaLos AngelesCA 90095USA Department of Computer Science University of CaliforniaLos AngelesCA 90095USA google research Brain TeamMountain ViewCAUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

Numerical simulations have revolutionized material ***,although simulations excel at mapping an input material to its output property,their direct application to inverse design has traditionally been limited by their high computing cost and lack of ***,taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm,we introduce a computational inverse design framework that addresses these challenges,by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end *** to its differentiability,the simulation is used to directly train a deep generative model,which outputs an optimal porous matrix based on an arbitrary input sorption isotherm ***,this inverse design pipeline leverages the power of tensor processing units(TPU)—an emerging family of dedicated chips,which,although they are specialized in deep learning,are flexible enough for intensive scientific *** approach holds promise to accelerate inverse materials design.

关键词： enough inverse isotherm

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前言

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计算机研究与发展 2016年第2期53卷 229-230页

作者：孟小峰王建勇董欣中国人民大学北京100872 清华大学北京100084 google research

随着互联网的快速普及与发展，互联网数据以惊人的速度在全世界范围内呈现出指数级增长的态势。而数据作为客观世界在信息世界中的抽象表达，其必然带有普遍的关联性。如何从海量的异构数据中挖掘实体及其语义关联和属性，并进行知识的... 详细信息

随着互联网的快速普及与发展，互联网数据以惊人的速度在全世界范围内呈现出指数级增长的态势。而数据作为客观世界在信息世界中的抽象表达，其必然带有普遍的关联性。如何从海量的异构数据中挖掘实体及其语义关联和属性，并进行知识的融合，进而构建大规模的知识图谱，为语义搜索、深度问答、文本理解等应用提供有力支撑,已成为数据管理、数据挖掘和信息抽取等领域的一个重要研究方向。相比于传统的数据集成，在面向大规模的数据和知识融合过程中，融合算法的效率、多源数据的数据质量评估和基于语义的数据和知识融合等都给现有的数据集成和知识融合技术带来了巨大的挑战。2016年《计算机研究与发展》数据融合和知识融合专题侧重大规模数据和知识的抽取、融合及应用等诸多方面，涉及到数据管理、信息抽取和知识工程等多个交叉学科领域，研究主题包括数据与知识抽取技术、歧义性消除、数据与知识融合技术、数据与知识建模、关联知识库的应用等。本期专题经过公开征文收到43篇投稿，并最终收录了7篇论文，内容涉及实体抽取、实体链接、数据融合与溯源、短文本理解、数据查询、知识表示等主题，为相关领域的研究者探讨面向大数据的数据融合和知识融合的基础理论研究及其应用、讨论该领域内最新的突破性进展、交流新的学术思想和新方法以及展望未来的发展趋势提供了很好的沟通和交流机会。

关键词：知识融合数据融合专题信息抽取世界范围语义关联文本理解数据管理

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