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The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future (vol 398, pg 1619, 2021)

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LANCET 2021年第10317期398卷 2148-2148页

作者： Romanello, M. McGushin, A. Di Napoli, C. [a]Institute for Global Health University College London London UK [b]Institute for Sustainable Resources University College London London UK [c]UCL Energy Institute University College London London UK [d]Institute for Environmental Design and Engineering University College London London UK [e]Department of Geography University College London London UK [f]The Bartlett School of Sustainable Construction University College London London UK [g]Centre for Human Health and Performance University College London London UK [h]School of Agriculture Policy and Development University of Reading Reading UK [i]Department of Meteorology University of Reading Reading UK [j]The Walker Institute University of Reading Reading UK [k]Department of Health Sciences University of York York UK [l]Institute for Environment and Human Security United Nations University Bonn Germany [m]Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK [n]Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK [o]Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK [p]Department of Earth System Science Tsinghua University Beijing China [q]Department of Environment Climate Change and Health World Health Organization Geneva Switzerland [r]Institute for Environmental Sciences World Health Organization Geneva Switzerland [s]Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff UK [t]Yale Center on Climate Change and Health Yale University New Haven CT USA [u]School of Government University of Birmingham Birmingham UK [v]Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy [w]Natural Resources Institute University of Greenwich London UK [x]Department of Global Health University of Washington Seattle WA USA [y]Centre for He

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Detecting shilling profiles in collaborative recommender systems via multidimensional profile temporal features

Detecting shilling profiles in collaborative recommender sys...

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作者： Hao, Yaojun Zhang, Fuzhi School of Information Science and Engineering Yanshan University Qinhuangdao Hebei Province China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao Hebei Province China Department of Computer Science and Technology Xinzhou Teachers University Xinzhou Shanxi Province China

To defend recommender systems, various methods have been proposed to detect shilling profiles, which can be categorised as user- and item-based detection methods. Most of the user-based methods identify shilling profiles via statistical signatures of rating values and suffer from low precision when detecting different types of attacks. Most of the item-based methods use temporal information to detect the anomaly items, but they assume that the fake ratings were injected in short periods. So they are invalid for the long duration and decentralised injection attacks. To address these limitations, the authors extract the multidimensional profile temporal features and present a shilling detection method. First, from the user profile view, user rating behaviours are characterised by corrected conditional entropy and the dissimilarity with the rest-rating model. Second, from the item profile view, the user features are extracted according to item temporal popularity. Third, the features based on weighted deviation from dynamic mean are extracted according to the fact that the items mean changes with time. Finally, support vector machine is exploited to detect shilling profiles based on the proposed features. Experimental results on the Netflix dataset indicate that the performance of the proposed method is better than that of the benchmark methods. © The Institution of engineering and Technology 2018.

关键词： User profile

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A genetic algorithm enhanced automatic data flow management solution for facilitating data intensive applications in the cloud

A genetic algorithm enhanced automatic data flow management ...

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作者： Li, Siguang Huang, Zhengwen Han, Liangxiu Jiang, Changjun The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai China College of Electrical Engineering Binzhou University Binzhou China Department of Electronic and Computer Engineering Brunel University London London United Kingdom School of Computing Mathematics and Digital Technology Manchester Metropolitan University Manchester United Kingdom

The past few years have witnessed a rapid deployment of computing infrastructures in the cloud in support of data intensive applications. The effort of the existing works is mainly focused on data reusing mechanisms without considering data processing routes, which can significantly affect the computation costs when exchanging data among the computing node in the cloud. This paper presents a genetic algorithm enhanced Automatic Data Flow Management Solution (ADFMS) that facilitates automatic routing function and a self-adjustable intermediate data management mechanism to achieve an efficient data processing structure of cloud computing. Experimental results show that ADFMS optimizes costs in managing intermediate data in the cloud. © 2018 John Wiley & Sons, Ltd.

关键词： Petri nets

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Thermal Stress and Reliability Analysis of TSV-Based 3-D ICs With a Novel Adaptive Strategy Finite Element Method

Thermal Stress and Reliability Analysis of TSV-Based 3-D ICs...

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作者： Zhou, Hao Zhu, Hengliang Cui, Tao Pan, David Z. Zhou, Dian Zeng, Xuan State Key Laboratory of ASIC and Systems Department of Microelectronics Fudan University Shanghai200433 China State Key Laboratory of Scientific and Engineering Computing National Center for Mathematics and Interdisciplinary Sciences Institute of Computational Mathematics Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Department of Electrical Engineering University of Texas at Dallas RichardsonTX75080 United States

Thermomechanical stress is one of the most important issues in performance and reliability analysis of through silicon via-based 3-D integrated circuits (3-D ICs), where an accurate numerical approach is generally needed to produce stress models and identify weak points in the structure. In this paper, we propose a knowledge-oriented nonuniform (KONU) refinement strategy for 3-D IC stress simulation under the framework of a parallel adaptive finite element method (FEM), and apply it in 3-D IC stress and reliability analysis. Parallel adaptive FEM is promising for solving large-scale problems due to its high accuracy and parallel efficiency. It produces refined meshes based on the a posteriori error analysis, which has the quasi-optimal convergence rate for solving the problem. It has high parallel efficiency, which makes it suitable for handling large and complex structures in 3-D ICs. The KONU refinement strategy developed in this paper can efficiently reduce the number of refinement iterations in parallel adaptive FEM for 3-D IC thermomechanical stress simulation and improves the computational efficiency. It is demonstrated in this paper through several examples that parallel adaptive FEM for thermomechanical stress evaluation can be widely applied in 3-D IC reliability analysis, where accurate stress simulation and modeling is exceptionally important to produce accurate results. © 2018 IEEE.

关键词： Reliability analysis

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A convergence proof of projected fast iterative soft-thresholding algorithm for parallel magnetic resonance imaging

arXiv

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arXiv 2019年

作者： Zhang, Xinlin Lu, Hengfa Guo, Di Bao, Lijun Huang, Feng Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Provincial University Key Laboratory of Internet of Things Application Technology Xiamen University of Technology Xiamen361024 China Neusoft Medical System Shanghai200241 China

The boom of non-uniform sampling and compressed sensing techniques dramatically alleviates the prolonged data acquisition problem of magnetic resonance imaging. Sparse reconstruction, thanks to its fast computation and promising performance, has attracted researchers to put numerous efforts on it and has been adopted in commercial scanners. Algorithms for solving the sparse reconstruction models play an essential role in sparse reconstruction. Being a simple and efficient algorithm for sparse reconstruction, pFISTA has been successfully extended to parallel imaging, however, its convergence criterion is still an open question, confusing users on the setting of the parameter which assures the convergence of the algorithm. In this work, we prove the convergence of the parallel imaging version pFISTA. Specifically, the convergences of two well-known parallel imaging reconstruction models, SENSE and SPIRiT, solved by pFISTA are proved. Experiments on brain images demonstrate the validity of the convergence criterion. The convergence criterion proofed in this work can help users quickly obtain the satisfy parameter that admits faithful results and fast convergence speeds. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Scaling Limitations of Line TFETs at Sub-8-nm Technology Node

Scaling Limitations of Line TFETs at Sub-8-nm Technology Nod...

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International Symposium on VLSI Technology, systems and Applications

作者： Narasimhulu Thoti Yiming Li Sekhar Reddy Kola Parallel and Scientific Computing Laboratory National Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu Taiwan EECS International Graduate Program National Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu Taiwan Institute of Communications Engineering National Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu Taiwan Department of Electrical and Computer Engineering National Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu Taiwan

ISBN: (数字)9781728142326

ISBN: (纸本)9781728142333

The scope of the work is to investigate limitations in device scaling by identifying various parameters of short channel effects (SCEs) in current challenging geometries of the line tunnel field effect transistors (TFETs). Key factors of the considered device, such as the doping and the thickness (t n ) of n-epitaxial region, and source-to-drain length (L SDeff ) scaling cannot be tuned anymore to boost the device characteristics for the sub-8-nm technology node. The main results of this study indicates that the engineering acceptable performance are achieved at a low doping of 5 × 10 18 cm -3 , an optimal t n as low (about 0.5 nm), and a L SDeff greater than 12.5 nm. Hence, the line TFETs below sub-8-nm faces serious bottleneck of scaling and cannot be further scaled with the conventional scaling rule at all.

关键词： Electron devices Solids

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An Improved Non-Geometrical Stochastic Model for Non-WSSUS Vehicle-to-Vehicle Channels

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ZTE Communications 2019年第4期17卷 62-71页

作者： HUANG Ziwei CHENG Xiang ZHANG Nan State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Key Laboratory of Wireless Sensor Network&Communication Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Wireless Algorithm Department Product Research and Development SystemZTE CorporationShanghai 201203China

A novel non-geometrical stochastic model(NGSM)for non-wide sense station ary uncorrelated scattering(non-WSSUS)vehicle-to-vehicle(V2V)channels is *** model is based on a conventional NGSM and employs a more accurate method to reproduce the realistic characteristics of V2V channels,which successfully extends the existing NGSM to include the line-of-sight(LoS)***,the statistical properties of the proposed model in different scenarios,including Doppler power spectral density(PSD),power delay profile(PDP),and the tap correlation coefficient matrix are simulated and compared with those of the existing ***,the simulation results dem onstrate not only the utility of the proposed model,but also the correctness of our theoreti cal derivations.

关键词： vehicle-to-vehicle non-WSSUS channels non-geometrical stochastic model LoS component statistical properties

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A rich ranking model based on the matthew effect optimization 7th

A rich ranking model based on the matthew effect optimizatio...

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7th International Conference on Computational Data and Social Networks, CSoNet 2018

作者： Li, Jinzhong Liu, Guanjun Department of Computer Science and Technology College of Electronic and Information Engineering Jinggangshan University Ji’an343009 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610054 China Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030046477

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

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Automating semantic publishing

Data Science

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Data Science 2017年第1-2期1卷 155-173页

作者： Peroni, Silvio Digital and Semantic Publishing Laboratory Department of Computer Science and Engineering University of Bologna Bologna Italy

Semantic Publishing involves the use of Web and Semantic Web technologies and standards for the semantic enhancement of a scholarly work so as to improve its discoverability, interactivity, openness and (re-)usability for both humans and machines. Recently, people have suggested that the semantic enhancements of a scholarly work should be undertaken by the authors of that scholarly work, and should be considered as integral parts of the contribution subjected to peer review. However, this requires that the authors should spend additional time and effort adding such semantic annotations, time that they usually do not have available. Thus, the most pragmatic way to facilitate this additional task is to use automated services that create the semantic annotation of authors' scholarly articles by parsing the content that they have already written, thus reducing the additional time required of the authors to that for checking and validating these semantic annotations. In this article, I propose a generic approach called compositional and iterative semantic enhancement (CISE) that enables the automatic enhancement of scholarly papers with additional semantic annotations in a way that is independent of the markup used for storing scholarly articles and the natural language used for writing their content. © 2017 - IOS Press and the authors.

关键词： CISE compositional and iterative semantic enhancement downward causation principle of compositionality rhetorical components Semantic publishing structural patterns structural semantics syntactic containment

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Author Correction: Wireless multilateral devices for optogenetic studies of individual and social behaviors

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Nature neuroscience 2022年第10期25卷 1394页

作者： Yiyuan Yang Mingzheng Wu Abraham Vázquez-Guardado Amy J Wegener Jose G Grajales-Reyes Yujun Deng Taoyi Wang Raudel Avila Justin A Moreno Samuel Minkowicz Vasin Dumrongprechachan Jungyup Lee Shuangyang Zhang Alex A Legaria Yuhang Ma Sunita Mehta Daniel Franklin Layne Hartman Wubin Bai Mengdi Han Hangbo Zhao Wei Lu Yongjoon Yu Xing Sheng Anthony Banks Xinge Yu Zoe R Donaldson Robert W Gereau Cameron H Good Zhaoqian Xie Yonggang Huang Yevgenia Kozorovitskiy John A Rogers Department of Mechanical Engineering Northwestern University Evanston IL USA. Department of Neurobiology Northwestern University Evanston IL USA. Center for Bio-Integrated Electronics Northwestern University Evanston IL USA. US Army Research Laboratory Aberdeen Proving Ground MD USA. US Army Medical Research Institute of Chemical Defense Aberdeen Proving Ground MD USA. Washington University Pain Center and Department of Anesthesiology Washington University St. Louis MO USA. State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China. Department of Civil and Environmental Engineering Northwestern University Evanston IL USA. Department of Materials Science and Engineering Northwestern University Evanston IL USA. Department of Physics Tsinghua University Beijing China. SURVICE Engineering Belcamp MD USA. Chemistry of Life Processes Institutes Northwestern University Evanston IL USA. Neurolux Inc Evanston IL USA. School of Civil Engineering Southwest JiaoTong University Chengdu China. School of Chemical Engineering and Technology Tianjin University Tianjin People's Republic of China. CSIR-Central Scientific Instruments Organization Ministry of Science & Technology Sector 30-C Chandigarh India. Department of Biomedical Engineering Northwestern University Evanston IL USA. Department of Electronic Engineering Tsinghua University Beijing China. Simpson Querrey Institute & Feinberg Medical School Northwestern University Evanston IL USA. Department of Biomedical Engineering City University of Hong Kong Kowloong Tong Hong Kong. Psychology and Neuroscience Molecular Cellular and Developmental Biology University of Colorado Boulder Boulder CO USA. State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering Mechanics Dalian University of Technology Dalian P.R. China. zxie@***. Department of Mechanical Engineering Northwestern University Evanston IL USA. y-huang@northwest

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