From text to graph: a general transition-based AMR parsing using neural network

Neural Computing and Applications - Semantic understanding is an essential research issue for many applications, such as social network analysis, collective intelligence and content computing,...     

Exploration on efficient similar sentences extraction

Measuring the semantic similarity between sentences is an essential issue for many applications, such as text summarization, Web page retrieval, question-answer model, image extraction, and so forth. A few studies have explored on this issue by several techniques, e.g., knowledge-based strategies, corpus-based strategies, hybrid strategies, etc. Most of these studies focus on how to improve the effectiveness of the problem. In this paper, we address the efficiency issue, i.e., for a given sentence collection, how to efficiently discover the top-k semantic similar sentences to a query. The previous methods cannot handle the big data efficiently, i.e., applying such strategies directly is time consuming because every candidate sentence needs to be tested. In this paper, we propose efficient strategies to tackle such problem based on a general framework. The basic idea is that for each similarity, we build a corresponding index in the preprocessing. Traversing these indices in the querying process can avoid to test many candidates, so as to improve the efficiency. Moreover, an optimal aggregation algorithm is introduced to assemble these similarities. Our framework is general enough that many similarity metrics can be incorporated, as will be discussed in the paper. We conduct extensive experimental evaluation on three real datasets to evaluate the efficiency of our proposal. In addition, we illustrate the trade-off between the effectiveness and efficiency. The experimental results demonstrate that the performance of our proposal outperforms the state-of-the-art techniques on efficiency while keeping the same high precision as them.     

基于人工神经网络的大坝变形分析与预报研究

本文在分析大坝位移影响因子的基础上,给出了基于BP-ANN(人工神经网络)分析大坝变形时的七种输入模式。探讨了在建立BP-ANN模型时几个关键问题的处理方法。结合某一混凝土拱坝的垂线观测数据,应用BP-ANN的不同输入模式,对大坝的变形值进行了预报和分析,得出了一些有益的结论。     

热应激时奶牛血液中游离氨基酸流向与乳蛋白下降的关系研究

目的:探讨热应激时奶牛血液中氨基酸与乳蛋白的相互关系。方法:选取6头相同泌乳期奶牛,正常饲喂和饮水,实验期35 d(6月29日—8月5日)。每日10:00和18:00记录气温,统计产奶量,每周取全天奶样,Foss乳成分分析仪检测乳蛋白含量。于实验开始第1周和最后1周采集颈静脉血液,反相高效液相色谱法检测血液中游离氨基酸含量。结果:随着气温的升高(由26℃升高至最高38℃),实验奶牛日均产奶量由15.2 kg/d下降至10 kg/d,乳蛋白产量由0.47 kg/d下降至0.31 kg/d。血液中Glu、Asp、Gly和Val等主要的生糖氨基酸(参与糖异生作用)以及Leu、Ile等生酮氨基酸均升高或显著升高(0.01P0.05);总支链氨基酸(Val、Ile、Leu,主要参加免疫反应)含量显著增加(P0.05),在必需氨基酸中所占的比例由54.75%增至67.89%。结论:在夏季高温高湿条件下,奶牛可发生热应激,产奶量和乳蛋白含量均降低,但血液中游离氨基酸,特别是支链氨基酸含量显著升高。提示热应激时奶牛血液中高水平的游离氨基酸并未完全用于乳蛋白的合成,还发挥了除此之外的其他作用,而后者的作用优先于参与乳蛋白的合成,即可能优先作为功能性氨基酸参与了机体的其他活动。     

上海市政府采购管理信息系统的设计与开发

本文介绍了使用B／S技术来设计和开发上海市政府采购管理信息系统的解决方案，并分析了其成功之处。     

大坝安全监测、分析与预报的发展综述

大坝安全监测、分析与预报是大坝安全管理中涉及多学科交叉和高科技发展的重大课题,本文从大地与工程测量学的角度,系统而简要地论述了大坝安全监测的有关技术、大坝变形分析与预报的各种方法,以及大坝安全监测、分析与预报综合信息系统的建立和应用,以期加强与其它学科的交流与合作。     

Domain classifier-based transfer learning for visual attention prediction

World Wide Web - Benefitting from machine learning techniques based on deep neural networks, data-driven saliency has achieved significant success over the past few decades. However, existing...     

Communication value of English-language S&T academic journals in non-native English language countries

Scientometrics - Researchers tend to publish their articles in English in order to get more citations and influence, even in the non-native English language countries. Founding S&T academic...     

The study of the oil source in Xiaoliangshan Sag,Northwestern Qaidam Basin,China

The geochemical characteristics of the crude oil in the northwestern and southwestern Qaidam Basin and the Tertiary source rocks of Xiaoliangshan Sag were compared and analyzed. The results show that the Tertiary source rocks of N₁ and E₃² are the best two sets of source rocks in Xiaoliangshan Sag. The crude oil of the northwestern Qaidam Basin is sourced from nearby Xiaoliangshan Sag rather than Mangya Sag. The crude oil of N₂² in the shallow strata of Nanyishan, Jiandingshan and Xiaoliangshan are mainly sourced from the source rocks of N₁, while the crude oil of E₃² in the deep strata is mainly sourced from the source rocks of E₃².