在体裁分析理论框架下,从交际意图和修辞目的的角度,探索理工科院校研究生英语学术写作能力构建。以理工科国际学术期刊论文为研究对象,利用Swales的CRS(create a research space)语轮一语步研究方法,对理工科英语学术论文引言部分进行...在体裁分析理论框架下,从交际意图和修辞目的的角度,探索理工科院校研究生英语学术写作能力构建。以理工科国际学术期刊论文为研究对象,利用Swales的CRS(create a research space)语轮一语步研究方法,对理工科英语学术论文引言部分进行分析。建立语篇图式结构,揭示语篇图示结构的学科特征,归纳微观语体特征,确立学科语篇写作范式和写作步骤,为英语学术写作能力构建提供理论依据和实践借鉴。展开更多
A two-level Bregmanized method with graph regularized sparse coding （TBGSC） is presented for image interpolation. The outer-level Bregman iterative procedure enforces the observation data constraints, while the inne...A two-level Bregmanized method with graph regularized sparse coding （TBGSC） is presented for image interpolation. The outer-level Bregman iterative procedure enforces the observation data constraints, while the inner-level Bregmanized method devotes to dictionary updating and sparse represention of small overlapping image patches. The introduced constraint of graph regularized sparse coding can capture local image features effectively, and consequently enables accurate reconstruction from highly undersampled partial data. Furthermore, modified sparse coding and simple dictionary updating applied in the inner minimization make the proposed algorithm converge within a relatively small number of iterations. Experimental results demonstrate that the proposed algorithm can effectively reconstruct images and it outperforms the current state-of-the-art approaches in terms of visual comparisons and quantitative measures.展开更多
文摘在体裁分析理论框架下,从交际意图和修辞目的的角度,探索理工科院校研究生英语学术写作能力构建。以理工科国际学术期刊论文为研究对象,利用Swales的CRS(create a research space)语轮一语步研究方法,对理工科英语学术论文引言部分进行分析。建立语篇图式结构,揭示语篇图示结构的学科特征,归纳微观语体特征,确立学科语篇写作范式和写作步骤,为英语学术写作能力构建提供理论依据和实践借鉴。
基金The National Natural Science Foundation of China (No.61362001,61102043,61262084,20132BAB211030,20122BAB211015)the Basic Research Program of Shenzhen(No.JC201104220219A)
文摘A two-level Bregmanized method with graph regularized sparse coding （TBGSC） is presented for image interpolation. The outer-level Bregman iterative procedure enforces the observation data constraints, while the inner-level Bregmanized method devotes to dictionary updating and sparse represention of small overlapping image patches. The introduced constraint of graph regularized sparse coding can capture local image features effectively, and consequently enables accurate reconstruction from highly undersampled partial data. Furthermore, modified sparse coding and simple dictionary updating applied in the inner minimization make the proposed algorithm converge within a relatively small number of iterations. Experimental results demonstrate that the proposed algorithm can effectively reconstruct images and it outperforms the current state-of-the-art approaches in terms of visual comparisons and quantitative measures.
