基于自标定的两视图三维重构研究

相机标定是三维重构中的关键步骤,本文对在自标定下的两视图三维几何模型恢复进行研究。先采用SURF算法得到图像匹配点对,在此基础上,利用RANSAC鲁棒估计基础矩阵;然后根据Kruppa方程所推导出的二次方程自标定出相机内参数,进一步地,通过分解本质矩阵求解出相机外参数;最后,根据三角化法求得空间三维坐标值。实验结果证实,该方法具有效性和正确性,并且能够真实地再现物体的三维模型。     

金属基复合材料液态浸渗制备工艺

液态浸渗制备工艺是目前金属基复合材料的主要制备方法之一。通过对挤压浸渗技术、无压渗透法、真空浸渗技术、气压浸渗技术、熔模铸造浸渗技术等各种不同液态浸渗制备工艺的应用及优缺点的分析,最后认为采用液相浸渗制备工艺能够实现制备组织致密、性能良好的复合材料,是一种比较经济的复合方法。     

细分图的零维数

设A(G)为简单图G的邻接矩阵。图G的零维数定义为A(G)中0特征值的重数,记为η(G)。S(G)表示非平凡图G的细分图。本文讨论细分图的零维数并分别给出树、单圈图和双圈图的细分图的零维数。     

求解大型对称特征值问题的改进块Jaeobi—Davidson方法

块Jacobi—Davidson方法是求解对称矩阵重或密集特征值问题的一种有效方法．为了提高其整体收敛速度,应用动态压缩技术,提出了动态压缩的块Jacobi—Davidson方法;为了计算大型对称矩阵的内部特征对,本文将调和Rayleigh—Ritz方法与块Jacobi—Davidson方法结合,提出了调和块Jacobi—Davidson方法,并将动态压缩技术应用于调和块Jacobi—Davidson方法,给出了动态压缩的调和块Jacobi—Davidson方法．数值结果表明,动态压缩的块Jacobi—Davidson方法优于块Jacobi—David—son方法,动态压缩的调和块Jacobi—Davidson方法能有效计算大型对称矩阵的内部重或密集特征值．     

Web document summarization by exploiting social context with matrix co-factorization

In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods.     

Coupled matrix factorization and topic modeling for aspect mining

Aspect mining, which aims to extract ad hoc aspects from online reviews and predict rating or opinion on each aspect, can satisfy the personalized needs for evaluation of specific aspect on product quality. Recently, with the increase of related research, how to effectively integrate rating and review information has become the key issue for addressing this problem. Considering that matrix factorization is an effective tool for rating prediction and topic modeling is widely used for review processing, it is a natural idea to combine matrix factorization and topic modeling for aspect mining (or called aspect rating prediction). However, this idea faces several challenges on how to address suitable sharing factors, scale mismatch, and dependency relation of rating and review information. In this paper, we propose a novel model to effectively integrate Matrix factorization and Topic modeling for Aspect rating prediction (MaToAsp). To overcome the above challenges and ensure the performance, MaToAsp employs items as the sharing factors to combine matrix factorization and topic modeling, and introduces an interpretive preference probability to eliminate scale mismatch. In the hybrid model, we establish a dependency relation from ratings to sentiment terms in phrases. The experiments on two real datasets including Chinese Dianping and English Tripadvisor prove that MaToAsp not only obtains reasonable aspect identification but also achieves the best aspect rating prediction performance, compared to recent representative baselines.     

“数值分析”中实践教学的探讨

本文阐述了在数值分析教学中加入实践环节的越要性,并通过具体实例介绍了作者在数值分析中进行实践教学的尝试,旨在对今后的教学起到一定的促进作用。     

基于波士顿咨询集团矩阵的大学学科规划研究

波士顿咨询集团矩阵是对企业经营的全部产品或者业务组合进行整体分析的一种重要的战略工具。本文分析大学利用波士顿咨询集团矩阵进行学科规划的情况,并且对大学利用波士顿咨询集团矩阵进行学科和专业规划的情况进行评价。     

基质金属蛋白酶与冠心病

冠心病的病理学基础是动脉粥样硬化.目前认为动脉粥样硬化的形成和斑块的破裂与细胞外基质的破坏和重构有密切关系,而基质金属蛋白酶(MMPs)是降解细胞外基质的重要酶类.对近年来关于MMPs的结构、功能、基因表达以及在冠心病中的作用的研究取得的成果进行了综述.     

一类Jacobi矩阵特征值反问题的新算法

In this paper, an inverse problem on Jacobi matrices presented by Shieh in 2004 is studied. Shieh’s result is improved and a new and stable algorithm to reconstruct its solution is given. The numerical examples is also given.