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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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A Performance Evaluation of Classification Algorithms for Big data

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Procedia Computer science 2017年 122卷 1100-1107页

作者： Mo Hai You Zhang Yuejin Zhang School of Information Central University of Finance and Economics Beijing China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China School of Information Systems &Management Heinz College Carnegie Mellon University Pittsburgh PA 1521 USA

The performance of two typical classification algorithms in Spark: random forest and naïve bayes are evaluated by using four metrics: classification accuracy, speedup, scaleup and sizeup. Experiments are performed on dataset and clusters of different scale. The results show that: (1) the accuracy of the two algorithms is high; (2) the increase of speedup is not linear. For the dataset with different size, the numbers of nodes is different when the speedup is the maximal; (3) the scaleup of random forest reaches its peak when the number of nodes is 2, and after that the scaleup decreases with the increase of the number of nodes;(4) for random forest, when the number of nodes is 2, the sizeup increases sharply with the increase of the size of dataset, and when the number of nodes is greater than 2, the sizeup increases more slowly with the increase of the size of dataset; for naïve bayes, when the number of nodes is smaller than 6, the sizeup increases with the increase of the size of dataset, when number of nodes is 6 and the size of dataset is larger than that of Sogou_5, the change of the sizeup is not obvious with the increase of the size of dataset.

关键词： Spark Random forest Naïve bayes Classification accuracy Speedup Scaleup Sizeup

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A constructive approach to high-dimensional regression

arXiv

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arXiv 2017年

作者： Huang, Jian Jiao, Yuling Liu, Yanyan Lu, Xiliang Department of Statistics and Actuarial Science 241 SH University of Iowa Iowa CityIA52242 United States School of Mathematics and Statistics Wuhan University Whuhan China School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan China Institute of Big Data Zhongnan University of Economics and Law Wuhan China School of Mathematics and Statistics Wuhan University Whuhan China Hubei Key Laboratory of Computational Science Wuhan University Whuhan China

We develop a constructive approach to estimating sparse, highdimensional linear regression models. The approach is a computational algorithm motivated from the KKT conditions for the 0-penalized least squares solutions. It generates a sequence of solutions iteratively, based on support detection using primal and dual information and root finding. We refer to the algorithm as SDAR for brevity. Under a sparse Rieze condition on the design matrix and certain other conditions, we show that with high probability, the 2 estimation error of the solution sequence decays exponentially to the minimax error bound in O(√J log(R)) steps;and under a mutual coherence condition and certain other conditions, the ∞ estimation error decays to the optimal error bound in O(log(R)) steps, where J is the number of important predictors, R is the relative magnitude of the nonzero target coefficients. Computational complexity analysis shows that the cost of SDAR is O(np) per iteration. Moreover the oracle least squares estimator can be exactly recovered with high probability at the same cost if we know the sparsity level. We also consider an adaptive version of SDAR to make it more practical in applications. Numerical comparisons with Lasso, MCP and greedy methods demonstrate that SDAR is competitive with or outperforms them in accuracy and efficiency. Copyright © 2017, The Authors. All rights reserved.

关键词： Regression analysis

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Study on prediction model of grain post-harvest loss

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Procedia Computer science 2017年 122卷 122-129页

作者： Hanxiao Yu Bingchan Li Dongqin Shen Jie Cao Bo Mao College of Information Engineering Collaborative Innovation Center for Modern Grain Circulation and Safety Jiangsu Key Laboratory of Grain Big-data Mining Nanjing University of Finance & Economics Nanjing 210023 China School of Electrical and Automation Engineering Jiangsu Maritime Institute Nanjing 211170 China Computer Science and Technology Computer Science and Engineering College Nanjing University of Science and Technology Nanjing 210094 China

With the arrival of the information age, a great deal of data has been produced in a series of process of grain post-harvest. The rational use of these data allows us to obtain more intelligent, in-depth and valuable information. In this paper, we set up a variety of prediction models for the consumption of grain post-harvest loss, and select the appropriate classifier through comparison. On this basis, the dimension reduction is processed and the confusion matrix is used as the evaluation index to evaluate the prediction effect. Make correlation analysis of the data, obtained the main influencing factors of post-harvest consumption loss link. Finally, visualize the results of the process.

关键词： Prediction model Evaluating indicator Visualization

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Analysis of Grain Storage Loss Based on Decision Tree Algorithm

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Procedia Computer science 2017年 122卷 130-137页

作者： Xueli Liu Bingchan Li Dongqin Shen Jie Cao Bo Mao College of Information Engineering Collaborative Innovation Center for Modern Grain Circulation and Safety Jiangsu Key Laboratory of Grain Big-data Mining Nanjing University of Finance & Economics Nanjing 210023 China School of Electrical and Automation Engineering Jiangsu Maritime Institute Nanjing 211170 China Computer Science and Technology Computer Science and Engineering College Nanjing University of Science and Technology Nanjing 210094 China

Different grain storage factors will cause different degrees of grain loss. In this paper, the data mining method is used to study the loss of grain storage, and the grain loss analysis and forecasting model based on decision tree algorithm is proposed. The paper analyzes and predicts the grain loss caused by different grain storage factors. And the influence of model parameters on model fitting and accuracy is verified by the verification curve. Then the decision tree model is optimized by the method of grid search and cross validation, which improves the prediction accuracy of the decision tree model to analyze the grain loss.

关键词： Decision tree Grid search Cross validation Validation curve Grain storage loss

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Analysis of the grain loss in harvest based on logistic regression

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Procedia Computer science 2017年 122卷 698-705页

作者： Tingkai Huang Bingchan Li Dongqin Shen Jie Cao Bo Mao College of Information Engineering Collaborative Innovation Center for Modern Grain Circulation and Safety Jiangsu Key Laboratory of Grain Big-data Mining Nanjing University of Finance & Economics Nanjing 210023 China School of Electrical and Automation Engineering Jiangsu Maritime Institute Nanjing 211170 China Computer Science and Technology Computer Science and Engineering College Nanjing University of Science and Technology Nanjing 210094 China

In this paper, the grain loss assessment was studied based on logistic regression, and 5400 samples of 31 provinces in our country in the year 2012-2014 were selected, and the 7 typical provinces among them were respectively tested. Using the logistic regression model to predict the loss rate of grain harvesting step, the prediction result is 86.25%. The stochastic gradient descent algorithm is used to optimize the parameters of the model, when the learning_rate is 0.1, the prediction results of grain harvest losses of up to 92.53%, which further improves the prediction accuracy of grain harvest step loss.

关键词： Logistic regression stochastic gradient descent algorithm grain harvest step loss

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A constructive approach to L0 penalized regression

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2018年第1期19卷

作者： Jian Huang Yuling Jiao Yanyan Liu Xiliang Lu Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong China School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan China and Institute of Big Data of Zhongnan University of Economics and Law School of Mathematics and Statistics Wuhan University Wuhan China School of Mathematics and Statistics Wuhan University Wuhan China and Hubei Key Laboratory of Computational Science

We propose a constructive approach to estimating sparse, high-dimensional linear regression models. The approach is a computational algorithm motivated from the KKT conditions for the l0-penalized least squares solutions. It generates a sequence of solutions iteratively, based on support detection using primal and dual information and root finding. We refer to the algorithm as SDAR for brevity. Under a sparse Riesz condition on the design matrix and certain other conditions, we show that with high probability, the l2 estimation error of the solution sequence decays exponentially to the minimax error bound in O(log(Rp√J)) iterations, where J is the number of important predictors and R is the relative magnitude of the nonzero target coefficients; and under a mutual coherence condition and certain other conditions, the l∞ estimation error decays to the optimal error bound in O(log(R)) iterations. Moreover the SDAR solution recovers the oracle least squares estimator within a finite number of iterations with high probability if the sparsity level is known. Computational complexity analysis shows that the cost of SDAR is O(np) per iteration. We also consider an adaptive version of SDAR for use in practical applications where the true sparsity level is unknown. Simulation studies demonstrate that SDAR outperforms Lasso, MCP and two greedy methods in accuracy and efficiency.

关键词： KKT conditions geometrical convergence nonasymptotic error bounds oracle property root finding support detection

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Robust decoding from 1-Bit compressive sampling with least squares

arXiv

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arXiv 2017年

作者： Huang, Jian Jiao, Yuling Lu, Xiliang Zhu, Liping Department of Applied Mathematics Hong Kong Polytechnic University 999077 Hong Kong School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan430063 China School of Mathematics and Statistics Hubei Key Laboratory of Computational Science Wuhan University Wuhan430072 China Institute of Statistics and Big Data Center for Applied Statistics Renmin University of China Beijing100872 China

In 1-bit compressive sensing (1-bit CS) where target signal is coded into a binary measurement, one goal is to recover the signal from noisy and quantized samples. Mathematically, the 1-bit CS model reads: y = η o sign(Ψx∗ + ∈), where x∗ ∈ Rn, y ∈ Rm, Ψ ∈ Rm×n, and ∈ is the random error before quantization and η ∈ Rnis a random vector modeling the sign flips. Due to the presence of nonlinearity, noise and sign flips, it is quite challenging to decode from the 1-bit CS. In this paper, we consider least squares approach under the over-determined and under-determined settings. For m > n, we show that, up to a constant c, with high probability, the least squares solution xlsapproximates x∗ with precision δ as long as m ≥ Õ(n/δ2). For m 1-regularized least-squares solution x1lies in the ball with center x∗ and radius δ provided that m ≥ O(s log n/δ2) and ||x∗||0:= s Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

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A fast second-order implicit difference method for time-space fractional advection-diffusion equation

arXiv

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arXiv 2017年

作者： Zhao, Yong-Liang Huang, Ting-Zhu Gu, Xian-Ming Luo, Wei-Hua School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu Sichuan611731 China School of Economic Mathematics Southwestern University of Finance and Economics Chengdu Sichuan611130 China Data Recovery Key Laboratory of Sichuan Province Neijiang Normal University Neijiang Sichuan641100

In this paper, we consider a fast and second-order implicit difference method to approximate a class of linear time-space fractional variable coefficients advection-diffusion equation. To begin with, we construct an implicit difference scheme based on L2-1 formula [Alikhanov AA. 2015;280:424-38.] for the temporal discretization and weighted and shifted Grünwald method for the spatial discretization. Then, unconditional stability of the implicit difference scheme is proved, and we theoretically and numerically show that it converges in the L2-norm with the optimal order O(τ2+ h2) with time step τ and mesh size h. Moreover, we utilize the same measure to solve the nonlinear case of this problem. For purpose of effectively solving the discretized systems with the Toeplitz matrix, two fast Krylov subspace solvers with suitable circulant preconditioners are designed. In each iterative step, these methods reduce the storage requirements of the resulting equations from O(N2) to O(N) and the computational complexity from O(N3) to O(N logN), where N is the number of grid nodes. Numerical experiments are carried out to demonstrate that these methods are more practical than the traditional direct solvers of the implicit difference methods, in terms of memory requirement and calculation *** Codes 65F15, 65H18, 15A51 Copyright © 2017, The Authors. All rights reserved.

关键词： Mathematical transformations

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