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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Mengshi Qi Yunhong Wang Jie Qin Annan Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Inception Institute of Artificial Intelligence UAE

In recent years, scene parsing has captured increasing attention in computer vision. Previous works have demonstrated promising performance in this task. However, they mainly utilize holistic features, whilst neglecting the rich semantic knowledge and inter-object relationships in the scene. In addition, these methods usually require a large number of pixel-level annotations, which is too expensive in practice. In this paper, we propose a novel Knowledge Embedded Generative Adversarial Networks, dubbed as KE-GAN, to tackle the challenging problem in a semi-supervised fashion. KE-GAN captures semantic consistencies of different categories by devising a Knowledge Graph from the large-scale text corpus. In addition to readily-available unlabeled data, we generate synthetic images to unveil rich structural information underlying the images. Moreover, a pyramid architecture is incorporated into the discriminator to acquire multi-scale contextual information for better parsing results. Extensive experimental results on four standard benchmarks demonstrate that KE-GAN is capable of improving semantic consistencies and learning better representations for scene parsing, resulting in the state-of-the-art performance.

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Embedded Partitioning Real-Time Operating System Based on Microkernel

Embedded Partitioning Real-Time Operating System Based on Mi...

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IEEE International Conference on Computational science and Engineering, CSE

作者： Tanhong Chen Huiyong Li Jianwei Niu Tao Ren Guizhou Xu School of Computer Science Beihang University Hangzhou Innovation Institute Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University BEIJING ACOINFO TECHNOLOGY CO. LTD.

The embedded partitioning operating system is active in security-related fields such as avionics and automotive embedded software. The temporal and spatial isolation of the partition is the guarantee of system security. In addition, as these areas become more demanding for applications, improving the real-time performance of partitioning operating systems has become a trend. To reach this goal, we propose a solution that leverages the microkernel architecture in the embedded partitioning operating system. The microkernel ensures the determinacy of real-time scheduling policies and system calls by simplifying kernel functionality and preserving the most basic resources. Partitions built on top of the microkernel enable low-cost communication, fast context switching and mixed task scheduling. Based on the microkernel, we developed a complete real-time partitioning operating system. The system supports multi-core processors to take full advantage of the high performance of existing embedded processors. On different hardware platforms, we conducted two types of tests about interrupt response time and latency. The results show that our proposed architecture exhibits high real-time performance.

关键词： Task analysis Real-time systems Kernel Virtual machine monitors Security

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Modeling of infant mortality in west sulawesi using zero inflated poisson regression method

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Journal of Physics: Conference Series 2020年第1期1490卷

作者： R Annisa EO Permatasari A T Rumiati Department of Statistics Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology Teknik Mesin road 175 Keputih Sukolilo Surabaya East Java

The number of infant deaths, one of which can be reflected in the Infant Mortality Rate (IMR) is one indicator to determine the development of a country's development. The AKB target in the 2015-2019 National Medium-Term Development Plan is 24 deaths per 1,000 live births. Based on the SDKI 2017, the IMR value in West Sulawesi is 42 deaths per 1,000 live births. Factors suspected of affecting infant mortality in West Sulawesi are not only in the health but also in socio-economic fields. data on the number of infant mortality in West Sulawesi is a data count that contains a value of 0. Therefore, the analysis used is Zero Inflated Poisson (ZIP) Regression to determine what factors influence infant mortality in West Sulawesi. The results of the ZIP regression analysis showed that the best model with the smallest AIC and AICc is 324,3407 and 326,7407 were models with a combination of the variables of the percentage of active maternal and child health services, percentage of society with good drinking water adequate access and percentage of society with proper sanitation access. The variable that has a significant effect on the log model is the percentage of active maternal and child health services, percentage of society with good drinking water adequate access and percentage of society with proper sanitation access. In the variable logit model that is influential is the percentage of active maternal and child health services.

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Newton-type multilevel optimization method

arXiv

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arXiv 2019年

作者： Ho, Chin Pang Kočvara, Michal Parpas, Panos School of Data Science City University of Hong Kong Hong Kong School of Mathematics University of Birmingham Institute of Information Theory and Automation Academy of Sciences of the Czech Republic Prague Department of Computing Imperial College London

Inspired by multigrid methods for linear systems of equations, multilevel optimization methods have been proposed to solve structured optimization problems. Multilevel methods make more assumptions regarding the structure of the optimization model, and as a result, they outperform single-level methods, especially for large-scale models. The impressive performance of multilevel optimization methods is an empirical observation, and no theoretical explanation has so far been proposed. In order to address this issue, we study the convergence properties of a multilevel method that is motivated by second-order methods. We take the first step toward establishing how the structure of an optimization problem is related to the convergence rate of multilevel algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Newton-Raphson method

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COVID-19 mRNA Vaccine Degradation Prediction Using LR and LGBM Algorithms

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Journal of Physics: Conference Series 2021年第1期1997卷

作者： Soon Hwai Ing Azian Azamimi Abdullah Nor Hazlyna Harun Shigehiko Kanaya Faculty of Electronic Engineering Technology Universiti Malaysia Perlis (UniMAP) Perlis Malaysia. Medical Devices and Life Sciences Cluster Sport Engineering Research Centre Centre of Excellence (SERC) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia. Data Science Research Lab School of Computing School of Computing Universiti Utara Malaysia Sintok Kedah. Computational Systems Biology Lab Graduate School of Information Science Nara Institute of Science and Technology Ikoma Nara Japan.

The threatening Coronavirus which was assigned as the global pandemic concussed not only the public health but society, economy and every walks of life. Some measurements are taken to stifle the spread and one of the best ways is to carry out some precautions to prevent the contagion of SARS-CoV-2 virus to uninfected populaces. Injecting prevention vaccines is one of the precaution steps under the grandiose blueprint. Among all vaccines, it is found that mRNA vaccine which shows no side effect with marvellous effectiveness is the most preferable candidates to be considered. However, degradation had become its biggest drawback to be implemented. Hereby, this study is held with desideratum to develop prediction models specifically to predict the degradation rate of mRNA vaccine for COVID-19. Two machine learning algorithms, which are, Linear Regression (LR) and Light Gradient Boosting Machine (LGBM) are proposed for models development using Python language. dataset comprises of thousands of RNA molecules that holds degradation rates at each position from Eterna platform is extracted, pre-processed and encoded with label encoding before loaded into algorithms. The results show that LGBM (0.2447) performs better than LR (0.3957) for this study when evaluated with the RMSE metric.

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Grouping District / Cities in Papua Based Poverty Indicators

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Journal of Physics: Conference Series 2020年第1期1490卷

作者： R Y Endartyana E O Permatasari Department of Statistics Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology Teknik Mesin road 175 Keputih Sukolilo Surabaya East Java

Welfare of the people is a very important thing in a country. One measure of the welfare of society is seen from indicators of poverty. The government conducts a long-term agreement in terms of encouraging change through SDG's. One of the government's target is less poverty in all its forms by 2030. The area in Indonesia which has the highest percentage of poor people in the provinces of Papua. Hence, the groups based Indicators of Poverty in Papua province using C-Means Clustering and Fuzzy C-Means Clustering. The indicators used in this analysis is an indicator of Health, Education Indicators and Household Characteristics indicator. Based on the analysis results obtained Fuzzy C-Means formed 5 group is the best method to classify District / Cities in Papua Province by minimumicdrate value. Areas that need to be the focus of the government to reduce poverty in Papua is the Puncak District.

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Dynamic MCMC sampling

arXiv

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arXiv 2019年

作者： Feng, Weiming He, Kun Sun, Xiaoming Yin, Yitong State Key Laboratory for Novel Software Technology Nanjing University CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

The Markov chain Monte Carlo (MCMC) methods are the primary tools for sampling from Gibbs distributions arising by various graphical models, e.g. Markov random fields (MRF). Traditional MCMC sampling algorithms are focused on a classic static setting, where the input is fixed. In this paper we study the problem of sampling from a MRF when the graphical model itself is changing dynamically with time. The problem is well motivated by the growing volume and velocity of data in today's applications of the MCMC methods. For the two major MCMC approaches, respectively for the approximate and perfect sampling, namely, the Gibbs sampling and the coupling from the past (CFTP), we give dynamic versions for the respective MCMC sampling algorithms. On MRF with n variables and bounded maximum degrees, these dynamic sampling algorithms can maintain approximate samples within 1/Poly(n) total variation errors, or perfect samples, while the MRF is dynamically changing. Furthermore, the dynamic sampling algorithms are efficient with O(n) space cost, and O(log2 n) incremental time cost upon each local update to the input MRF, as long as certain decay conditions are satisfied in each step by natural couplings of the corresponding single-site chains. These decay conditions were well known in the literature of couplings for rapid mixing of Markov chains, and now for the first time, are used to imply efficient dynamic sampling algorithms. Consequently, we have efficient dynamic (approximate or perfect) sampling algorithms with O(n) space cost and O (log2 n) incremental time cost, for the following models when the maximum degree is bounded: generalMRF satisfying the Dobrushin-Shlosman condition (for approximate sampling);Ising model with temperature β where e-2|β| > 1 - 2 β+1 (for both approximate and perfect samplings);hardcore model with fugacity λ 2λ (for approximate sampling);or q > 2Δ2 + 3Δ (for perfect sampling). These results show that the coupling of single-site Markov chains that

关键词： Learning algorithms

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A data Analytic Elucidation on the Spatial Agglomeration of Singapore Maritime Industry

International Journal on Smart and Sustainable Cities

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International Journal on Smart and Sustainable Cities 2023年第1期1卷

作者： Lock Yue Chew Ning Ning Chung Wen Xuan Sia Hoai Nguyen Huynh Glenn Sim Alvin Chua Zhongwen Huang https://***/0000-0003-1366-8205 School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Data Science & Artificial Intelligence Research Centre Nanyang Technological University Singapore 637335 Singapore Centre for University Core Singapore University of Social Sciences Singapore 599494 Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way Connexis North Tower #16-16 Singapore 138632 Singapore School of Global Affairs Faculty of Social Science & Public Policy King’s College London London WC2R 2LS UK Design & Planning Lab Urban Redevelopment Authority Singapore 179369 Singapore

In this paper, we create three new statistical measures to characterize the spatial patterns and structure of firms and subsectors of industry. The new measures give a direct quantification of the tendency of subsectors to spatially agglomerate, the likelihood of cross-subsector spatial co-agglomeration, and the potential of leading and following behaviors between firms. Unlike the conventional measures which deduce possible agglomeration through deviations from the baseline distribution or distance density, our measures study agglomeration in terms of clustering and co-location behaviors to provide a clear physical picture on spatial agglomeration. With each measure quantifying one of the three different ways in which firms agglomerate spatially, their collective use provides a combined perspective on the agglomeration behavior and location choice of firms that is not available from the conventional measures. We employ this joint application of measures to show that Singapore maritime firms have a propensity to distribute themselves spatially in a manner that allows them to reap the benefits of localization externalities from the agglomeration economy.

关键词： Spatial morphology Agglomeration Co-location Co-occurrence Urban complexity Maritime industry

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Applications of Game Theory in Vehicular Networks: A Survey

arXiv

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

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基于MeerKAT和FAST探索球状星团:脉冲星偏振研究（英文）

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science Bulletin 2025年第10期 1568-1571页

作者：张蕾 Federico Abbate 李菂 Andrea Possenti Matthew Bailes Alessandro Ridolfi Paulo C.C.Freire Scott M.Ransom 张永坤郭猛尼盟萌胡佳乐冯毅王培张洁支启军 National Astronomical Observatories Chinese Academy of Sciences Centre for Astrophysics and Supercomputing Swinburne University of Technology INAF-Osservatorio Astronomico di Cagliari Max-Planck-Institut für Radioastronomie New Cornerstone Science Laboratory Department of AstronomyTsinghua University ARC Center of Excellence for Gravitational Wave Discovery (OzGrav) Swinburne University of Technology Faculty of Physics University of Bielefeld National Radio Astronomy Observatory National Supercomputing Center in Jinan Qilu University of Technology Jinan Institute of Supercomputing Technology Research Center for Intelligent Computing Platforms Zhejiang Laboratory Institute for Astronomy School of PhysicsZhejiang University College of Physics and Electronic Engineering Qilu Normal University Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University

Magnetic fields are pervasive throughout the Universe. They are integral to a wide array of astrophysical processes that span various physical scales and field strengths. The Galactic magnetic field,in particular, holds significant importance in shaping the evolution of our Galaxy. However, our understanding of its behavior on small scales remains poor, especially when considering its penetration into the Galactic halo [1].

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