查询词典 vectors

This is a project to use the three-dimensional space vector/point type and related functions, they can be defined using a space vector/point, the realization of vector translation, scaling and rotation, the use of correlation function can also be the realization of the two vectors fork x, the absolute length of a vector, a vector unit, and get a three-point decision to the vertical plane of the vector, be the distance between two points in space, by the two vectors point multiplication by the two vectors angle, such as operations, for the use of user-friendly, each function are detailed notes, I hope all of you useless.

详细说明：这是一个项目时使用到的"三维空间向量/点"类及相关函数，使用它们可以定义一个空间向量/点，实现向量的平移，缩放和旋转等，使用相关函数还可以实现两向量的叉乘，得到一个向量的绝对长度，将一个向量单位化，得到一个三点决定的平面的垂直向量，得到空间中两点的距离，得到两向量的点积，得到两向量的夹角等操作，为方便用户的使用，每个函数都有详细的注释，希望对大家有所用处。

If deg f = n, then Rfc is an empty set. For all the functions of degree 2, Rf have at least 2n-1 vectors, when Rf is a linear subspace, the relationships between Rf and Lf are discussed . Boolean functions have no nonezero linear structure if and only if there are n linear independence vectors in f, the correlation of the vectors in ?

证明了，若degf=n，则R_f~c为空集，对于所有的二次布尔函数而言，均有R_f~c中的元素个数大于等于2~(n-1)，给出了R_f构成线性子空间时，R_f和L_f之间的关系，还给出布尔函数不含有非零线性结构的充分必要条件是ζ_f中含有n个线性无关的元素。

The method relies on the facts that the row vectors composed of all the image points span, the same linear subspace as the row vectors composed of 3D space points span and that a basis of the subspace can consist of two row vectors composed of all the image points and one row vector in 3D space that is orthogonal to the former. The row vector can be determined, and the 3D reconstruction is accomplished. The novelty lies in the fact that the method can treat all the image points uniformly.

利用所有图像序列构成的行向量生成的子空间之和与三维空间点构成的行向量生成的子空间是同一线性子空间、同时由所有图像点构成的2个行向量外加一个行向量就可以组成该子空间的一个基底的特性，线性地求取子空间中的行向量，最后完成三维重建。

While matching, only the principal component directions corresponding to the rows of the same serial numbers in searching area are needed to be extracted for the computation of projection value so as to form the vectors of projection values. The distances between MM-vectors and the vectors formed by projection values are computed, and whether the two kinds of vectors are matchable should be in accordance to those distances.

在匹配时，只需抽取搜索区域中相同序号的列向相应的主元方向作投影运算，从而构成投影值向量，进而计算该向量与预处理中所得的最大最小值构成向量间的距离，从而根据距离的大小判断是否匹配。

Based on the traits of dyadic wavelet decomposition of signal and that of the distribution of wavelet image coefficients, PLVQ and ZR are conjoined by making use of D\-4 lattice. Firstly, Pyramidal lattice vector quantization is adopted to quantize wavelet image coefficients. Nonzero lattice vectors and zero lattice vectors are formed. Secondly, nonzero lattice vectors are dealt with by adopting complex entropy coding. Finally, in order to fix on the position of nonzero lattice vector effectively, that is, to deal with zero lattice vectors effectively, the concept of significant map is introduced into. The significant map is scanned two times from down to up and from up to down.

根据信号的二进小波分解特点和小波图象系数的分布特点，利用D\-4格将PLVQ和零树结合起来，提出了一种基于零树和金字塔格型矢量量化的小波图象编码方法，该方法首先采用金字塔格型矢量方法来量化小波图象系数，以得到非零格点和零格点；然后采用复合熵编码来处理非零格点；最后为了有效确定非零格点的位置，也就是为了有效地处理零格点，又引进了重要图的概念。

Based on the intrinsic nullspace property of the SVM proven by the authors, which shows that the normal vector of the SVM decision plane is of the nullspace property in terms of the support vectors-based within-class scatter matrix, a support vectors-based method is presented to construct the Kernelized Decision Boundary Feature Matrix by using the SVM normal vectors.

进一步结合均值向量的差向量构建扩展决策边界特征矩阵。最后以支持向量为训练集合，结合零空间方法来计算投影空间，该投影空间被用来从原始图像中提取判别特征。

So in order to improve the convergence of Ritz vectors, firstly, refining -strategy is applied to the Davidson method in this paper. The process restarts with the refined Ritz vectors as the initial vectors, and then we get the refined block Davidson method. By analyzing the convergence of the new method, we give the theorem of it.

为了改善Ritz向量的收敛性，本文首先在块Davidson方法的执行过程中，运用精化策略，求出相应的精化向量并将其作为初始向量进行重新开始过程，由此得到了精化块Davidson方法，并对精化块Davidson方法进行了收敛性分析；其次，本文将精化块Davidson方法结合收缩技术，提出了收缩的精化块Davidson方法。

This method first gains shape vectors and texture vectors from given sample facial images of the same kind of expression. Then, based on the facial image morphable model, the linear combinations of the shape vectors and texture vectors of the unexpressive images are retrieved respectively using optimal linear fitting.

该方法利用人脸图像形变模型线性组合逼近的基本思想，确定合成表情图像的形状信息和纹理信息，其步骤简单，容易实现。

To investigate the expression, location and function, long distance PCR was used to expand the targets containing open reading frame. The expressive vectors of prokaryotic and eukaryotic cells were constructed after product purification and connecting with vectors. The prokayotic vectors were induced to express the protein by IPTG and the protein was seen by denaturalization PAG electrophoresis and dyeing. The eukayotic expressive vectors were transfected into culture cells and the protein was found in the nulceolus under the observation of the confocal microscope. The transfected cells were chosen by the geneticin (G418). The cell cyele was examined by flow cytometer and the cell numbers were reduced obviously in the stage of G〓-G〓 and G〓-M, but increased distinguishably in the S stage.

为研究磷酸化应激诱导蛋白的表达、定位和功能，采用了长距离聚合酶链反应扩增含有开放阅读框架的靶片段，经产物纯化、与载体连接，分别构建了STIP1基因的原核表达载体和真核表达载体；IPTG诱导原核表达载体，变性聚丙酰胺凝胶电泳与染色后，可见相应大小的蛋白质表达；STIP1基因的真核表达载体转染细胞系后，共聚焦显微镜下观察STIP1蛋白主要分布于细胞核内；用药物筛选STIP1基因转染的细胞后，流式细胞仪检测细胞周期，可见G〓-G〓期和G〓-M期的细胞明显降低，S期细胞明显增多。

The method is realized via the following steps: first to apply Isomap or LLE to get the embeddings of the original data set in the low dimensional space; then to obtain support vectors, which are the most significant and intrinsic data for the final classification result, by using support vector machine on these low dimensional embedding data; subsequently to get support vectors in the original high dimensional space based on the corresponding labels of the obtained low dimensional support vectors; finally to apply support vector machine again on these high dimensional support vectors to gain the final classification discriminant function.

数据挖掘是数据库知识发现中最重要的步骤之一，其目标是从获取的数据中高效准确地挖掘出我们所需要的信息。在实际应用中，数据往往呈现海量、高维、非线性等特性，这些特性给数据挖掘带来了很多问题，例如海量特性导致的计算效率低下问题、高维特性带来的维数灾难问题和非线性特性引起的线性模型失效问题等。幸运的是，实际中高维数据的属性之间往往存在一定的规律性和相关性，即实际数据经常存在着外在与内在两个维数。

We have no common name for a mime of Sophron or Xenarchus and a Socratic Conversation; and we should still be without one even if the imitation in the two instances were in trimeters or elegiacs or some other kind of verse--though it is the way with people to tack on 'poet' to the name of a metre, and talk of elegiac-poets and epic-poets, thinking that they call them poets not by reason of the imitative nature of their work, but indiscriminately by reason of the metre they write in.

索夫农 、森那库斯和苏格拉底式的对话采用的模仿没有一个公共的名称；三音步诗、挽歌体或其他类型的诗的模仿也没有——人们把&诗人&这一名词和格律名称结合到一起，称之为挽歌体诗人或者史诗诗人，他们被称为诗人，似乎只是因为遵守格律写作，而非他们作品的模仿本质。

The relationship between communicative competence and grammar teaching should be that of the ends and the means.

交际能力和语法的关系应该是目标与途径的关系。