融合主题语义特征的高分辨率遥感影像建筑物自动化识别

建筑物提取是高分辨率遥感影像解译领域的一项重要工作,由于遥感技术的宏观性、实时性和周期性特点,基于遥感影像的建筑物识别对于城市管理与国土规划等领域具有非常大的应用潜力.本文介绍了一种基于影像主题语义特征建模的高分辨率遥感影像建筑物提取方法并通过具体的影像场景加以实践应用,该方法首先对影像进行对象级的分割,然后利用LDA...     

主题化、主位推进模式与语篇连贯

指称是常用的五种衔接语篇的手段之一。一个所指往往表现为话语话题或话题主体。在汉语 (特别是科技类语篇 )英译过程中 ,译者不要照搬汉语的主题结构 ,要充分运用主题化手段 ,选择合适的主位推进模式 ,以实现最大限度的篇章连贯。语篇类型影响主位推进模式的选择。     

Quantifying the UK's incentives for low carbon investment

Abstract

The UK climate change programme has introduced a range of instruments to foster investment in low carbon technologies and markets. We estimate the total value of these interventions, in terms of the redirection of financial flows and directly foregone tax income, to be about £1.3 billion per year (c. Euro or US$ 2 billion per year), as from 2002 to 2003 when the renewable obligation certificates (ROCs) first take effect. About 20% of this consists of direct expenditure, the remaining 80% is in the form of indirect expenditures contained within sectors (ROCs, the energy efficiency commitments), and foregone tax revenues. Most of the energy-efficiency investment is estimated to recoup expenditure within normal life-cycles and may thus be considered profitable; the profitability of the supply-side interventions is predicated mostly upon expected cost reductions associated with the build up of the associated industries.     

四川省防震减灾素质教育工程设计

四川省防震减灾素质教育工程的建设内容包括1个防震减灾素质教育中心、39个防震减灾科普宣传服务站、180个防震减灾科普示范学校和25个防震减灾科普示范社区。教育工程能够实现全省防震减灾科普知识的有效宣传,以及地震信息的实时发布和地震救援信息的实时更新。     

灾害预警平台多源信息多层次精细化编码系统研究

由于我国各类灾害频繁发生, 防灾减灾迫切需要建立一个覆盖全国、 畅通有效的灾害预警信息综合发布平台。通过分析对比国内外有关灾害预警信息编码, 设计适合我国国情, 并与国际通用警报协议接轨的灾害预警平台多源信息多层次精细化编码系统, 有效地解决预警信息的地理编码、 信息编码和共享规范问题, 为我国建立覆盖全国、 畅通有效的灾害预警信息综合发布平台提供技术支撑。     

Quantifying the UK’s incentives for low carbon investment

The UK climate change programme has introduced a range of instruments to foster investment in low carbon technologies and markets. We estimate the total value of these interventions, in terms of the redirection of financial flows and directly foregone tax income, to be about £1.3 billion per year (c. Euro or US$ 2 billion per year), as from 2002 to 2003 when the renewable obligation certificates (ROCs) first take effect. About 20% of this consists of direct expenditure, the remaining 80% is in the form of indirect expenditures contained within sectors (ROCs, the energy efficiency commitments), and foregone tax revenues. Most of the energy-efficiency investment is estimated to recoup expenditure within normal life-cycles and may thus be considered profitable; the profitability of the supply-side interventions is predicated mostly upon expected cost reductions associated with the build up of the associated industries.     

Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment

Current disaster management procedures to cope with human and economic losses and to manage a disaster’s aftermath suffer from a number of shortcomings like high temporal lags or limited temporal and spatial resolution. This paper presents an approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic information extraction with spatial and temporal analysis (local spatial autocorrelation) for hot spot detection. Our results demonstrate that earthquake footprints can be reliably and accurately identified in our use case. More, a number of relevant semantic topics can be automatically identified without a priori knowledge, revealing clearly differing temporal and spatial signatures. Furthermore, we are able to generate a damage map that indicates where significant losses have occurred. The validation of our results using statistical measures, complemented by the official earthquake footprint by US Geological Survey and the results of the HAZUS loss model, shows that our approach produces valid and reliable outputs. Thus, our approach may improve current disaster management procedures through generating a new and unseen information layer in near real time.     

基于多模态的音乐推荐系统

使用传统协同过滤的方式进行推荐往往会忽视音乐底层特征.通过将音乐的音频特征与歌词信息进行多模态融合,并将融合后的特征信息作为协同过滤推荐的补充,提出了一种基于多模态的音乐推荐系统.主要探讨了音频特征与歌词信息的提取,并在提取歌词信息时利用LDA主题模型进行特征降维.针对多模态融合问题,使用一种特征级联早融合法（EFFC）融合方式,并将多模态融合后的结果与单模态结果进行了比较.对于结果的推荐,以多模态特征信息为依据建立用户兴趣模型,并将该模型通过LSTM神经网络,以过滤与优化协同推荐的用户组.结果表明,基于多模态的音乐推荐系统将推荐结果的误差项平方和（SSE）由传统的2.009降至0.388 6,验证了该方法的有效性.     

Description and characterization of place properties using topic modeling on georeferenced tags

ABSTRACT

User-Generated Content (UGC) provides a potential data source which can help us to better describe and understand how places are conceptualized, and in turn better represent the places in Geographic Information Science (GIScience). In this article, we aim at aggregating the shared meanings associated with places and linking these to a conceptual model of place. Our focus is on the metadata of Flickr images, in the form of locations and tags. We use topic modeling to identify regions associated with shared meanings. We choose a grid approach and generate topics associated with one or more cells using Latent Dirichlet Allocation. We analyze the sensitivity of our results to both grid resolution and the chosen number of topics using a range of measures including corpus distance and the coherence value. Using a resolution of 500 m and with 40 topics, we are able to generate meaningful topics which characterize places in London based on 954 unique tags associated with around 300,000 images and more than 7000 individuals.     

枞阳县拔茅山矿区拔茅山—余家园－李家湾－石庄环状成矿带地质特征及找矿潜力分析

由余家园、李家湾、石庄、拔茅山铜矿带环绕皂树咀辉石粗安玢岩体与上侏罗统砖桥组火山岩系地层内外接触带附近构成未封闭的环状矿带,具面状蚀变,铜草发育,矿化多呈细脉浸染状,地表矿化普遍较强,寻找与拔茅山铜矿类似近接触带细脉浸染型带状斑岩型铜矿具一定潜力。