词性是自然语言处理的基本要素,词语顺序包含了所传达的语义与语法信息,它们都是自然语言中的关键信息.在word embedding模型中如何有效地将两者结合起来,是目前研究的重点.本文提出的Structured word2vec on POS联合了词语顺序与词性...词性是自然语言处理的基本要素,词语顺序包含了所传达的语义与语法信息,它们都是自然语言中的关键信息.在word embedding模型中如何有效地将两者结合起来,是目前研究的重点.本文提出的Structured word2vec on POS联合了词语顺序与词性两种信息,不仅使模型可以感知词语位置顺序,而且利用词性关联信息来建立上下文窗口内词语之间的固有句法关系.Structured word2vec on POS将词语按其位置顺序定向嵌入,对词向量和词性相关加权矩阵进行联合优化.实验通过词语类比、词相似性任务,证明了所提出的方法的有效性.展开更多
CO_(2)地质封存GIS(Geographic Information System,地理信息系统)基础数据平台建设是部署CCUS(Carbon Capture,Utilization and Storage,碳捕集、利用与封存)全链条示范工程的重要基础。通过研究国内外典型碳封存数据平台,分析了碳封...CO_(2)地质封存GIS(Geographic Information System,地理信息系统)基础数据平台建设是部署CCUS(Carbon Capture,Utilization and Storage,碳捕集、利用与封存)全链条示范工程的重要基础。通过研究国内外典型碳封存数据平台,分析了碳封存数据的特征:多源、多尺度、多类型,认为有以下几方面需要进一步加强:平台功能基本为数据存储管理展示,需进一步加强模型嵌入动态分析;需进一步加强封存与能源资源协同方面研究。在此基础上,初步设计了CO_(2)地质封存GIS基础数据管理处理平台架构:以Map GIS空间数据库和SQL Server属性数据库为数据基础,利用实现系统Microsoft.Net Frame开发框架及Web GIS等技术构建,系统核心逻辑结构分为资源数据层、技术支撑层、业务应用层、用户权限层共4层,安全保障体系、管理支撑体系、标准规范体系共3个外围支持体系。数据平台建设关键技术包括碳封存地质GIS数据多元异构数据融合,二次开发导入封存能力评价模型、源汇匹配动态模型、能源资源协同模型共3个模型。并初步设计了数据平台首页界面、专题图件及成果输出界面、盆地级成果图件界面、封存潜力—源汇匹配—能源资源协同数据分析界面共4个界面。CO_(2)地质封存GIS基础数据管理处理平台的建设,将为源头减碳去碳解决方案选择、CCUS区域性规模化部署的地质封存选址和CCUS大规模示范工程开展提供科学基础。展开更多
A scheduling scheme is proposed to reduce execution time by means of both checkpoint sharing and task duplication under a peer-to-peer(P2P) architecture. In the scheme, the checkpoint executed by each peer(i.e., a res...A scheduling scheme is proposed to reduce execution time by means of both checkpoint sharing and task duplication under a peer-to-peer(P2P) architecture. In the scheme, the checkpoint executed by each peer(i.e., a resource) is used as an intermediate result and executed in other peers via its duplication and transmission. As the checkpoint is close to a final result, the reduction of execution time for each task becomes higher, leading to reducing turnaround time. To evaluate the performance of our scheduling scheme in terms of transmission cost and execution time, an analytical model with an embedded Markov chain is presented. We also conduct simulations with a failure rate of tasks and compare the performance of our scheduling scheme with that of the existing scheme based on client-server architecture. Performance results show that our scheduling scheme is superior to the existing scheme with respect to the reduction of execution time and turnaround time.展开更多
文摘词性是自然语言处理的基本要素,词语顺序包含了所传达的语义与语法信息,它们都是自然语言中的关键信息.在word embedding模型中如何有效地将两者结合起来,是目前研究的重点.本文提出的Structured word2vec on POS联合了词语顺序与词性两种信息,不仅使模型可以感知词语位置顺序,而且利用词性关联信息来建立上下文窗口内词语之间的固有句法关系.Structured word2vec on POS将词语按其位置顺序定向嵌入,对词向量和词性相关加权矩阵进行联合优化.实验通过词语类比、词相似性任务,证明了所提出的方法的有效性.
文摘CO_(2)地质封存GIS(Geographic Information System,地理信息系统)基础数据平台建设是部署CCUS(Carbon Capture,Utilization and Storage,碳捕集、利用与封存)全链条示范工程的重要基础。通过研究国内外典型碳封存数据平台,分析了碳封存数据的特征:多源、多尺度、多类型,认为有以下几方面需要进一步加强:平台功能基本为数据存储管理展示,需进一步加强模型嵌入动态分析;需进一步加强封存与能源资源协同方面研究。在此基础上,初步设计了CO_(2)地质封存GIS基础数据管理处理平台架构:以Map GIS空间数据库和SQL Server属性数据库为数据基础,利用实现系统Microsoft.Net Frame开发框架及Web GIS等技术构建,系统核心逻辑结构分为资源数据层、技术支撑层、业务应用层、用户权限层共4层,安全保障体系、管理支撑体系、标准规范体系共3个外围支持体系。数据平台建设关键技术包括碳封存地质GIS数据多元异构数据融合,二次开发导入封存能力评价模型、源汇匹配动态模型、能源资源协同模型共3个模型。并初步设计了数据平台首页界面、专题图件及成果输出界面、盆地级成果图件界面、封存潜力—源汇匹配—能源资源协同数据分析界面共4个界面。CO_(2)地质封存GIS基础数据管理处理平台的建设,将为源头减碳去碳解决方案选择、CCUS区域性规模化部署的地质封存选址和CCUS大规模示范工程开展提供科学基础。
基金supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2012R1A1A4A0105777)supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2013-H030113-4007) supervised by the NIPA (National IT Industry Promotion Agency)
文摘A scheduling scheme is proposed to reduce execution time by means of both checkpoint sharing and task duplication under a peer-to-peer(P2P) architecture. In the scheme, the checkpoint executed by each peer(i.e., a resource) is used as an intermediate result and executed in other peers via its duplication and transmission. As the checkpoint is close to a final result, the reduction of execution time for each task becomes higher, leading to reducing turnaround time. To evaluate the performance of our scheduling scheme in terms of transmission cost and execution time, an analytical model with an embedded Markov chain is presented. We also conduct simulations with a failure rate of tasks and compare the performance of our scheduling scheme with that of the existing scheme based on client-server architecture. Performance results show that our scheduling scheme is superior to the existing scheme with respect to the reduction of execution time and turnaround time.
