A Dimensional Reduction Approach Based on Essential Constraints in Linear Programming

This paper presents a new dimension reduction strategy for medium and large-scale linear programming problems. The proposed method uses a subset of the original constraints and combines two algorithms: the weighted average and the cosine simplex algorithm. The first approach identifies binding constraints by using the weighted average of each constraint, whereas the second algorithm is based on the cosine similarity between the vector of the objective function and the constraints. These two approaches are complementary, and when used together, they locate the essential subset of initial constraints required for solving medium and large-scale linear programming problems. After reducing the dimension of the linear programming problem using the subset of the essential constraints, the solution method can be chosen from any suitable method for linear programming. The proposed approach was applied to a set of well-known benchmarks as well as more than 2000 random medium and large-scale linear programming problems. The results are promising, indicating that the new approach contributes to the reduction of both the size of the problems and the total number of iterations required. A tree-based classification model also confirmed the need for combining the two approaches. A detailed numerical example, the general numerical results, and the statistical analysis for the decision tree procedure are presented.

Associated Productions of HZZ and HHZ at Linear Colliders in Large Extra Dimension Model

In this paper we investigate the effects of the large extra dimensions on the two processes e＋ e-→＋ H^0 Z^0 Z^0 and e^＋e^-→ H^0H^0 Z^0 at linear colliders in both unpolarized and polarized collision modes. We find that the virtual Kaluza-Klein graviton exchange can significantly enhance the cross section from their standard model expectations for these two processes. The results show that the LED effect on the process e＋ e-→＋ H^0 Z^0 Z^0 allows the observation limits on the effective scale Ms to be probed up to 9. 75 TeV and 10.1 TeV in the unpolarized and ＋-（λe＋ =1/2, λe-= -1/2） polarized beam collision modes （with Pe＋ = 0.6, Pe-=0.8）, respectively. For the process e＋ e-→＋ H^0 H^0 Z^0, these limits on Ms can be probed up to 6.06 TeV and 6.38 TeV in the unpolarized and polarized collision modes separately. We find that the λe＋ = 1/2, λe-= -1/2 polarization collision mode in both processe＋ e-→＋ H^0 Z^0 Z^0 and e＋ e-→＋ H^0 H^0 Z^0 may provide a possibility to improve the sensitivity in probing the LED effects.

Equation governing the probability density evolution of multi-dimensional linear fractional differential systems subject to Gaussian white noise

Stochastic fractional differential systems are important and useful in the mathematics,physics,and engineering fields.However,the determination of their probabilistic responses is difficult due to their non-Markovian property.The recently developed globally-evolving-based generalized density evolution equation(GE-GDEE),which is a unified partial differential equation(PDE)governing the transient probability density function(PDF)of a generic path-continuous process,including non-Markovian ones,provides a feasible tool to solve this problem.In the paper,the GE-GDEE for multi-dimensional linear fractional differential systems subject to Gaussian white noise is established.In particular,it is proved that in the GE-GDEE corresponding to the state-quantities of interest,the intrinsic drift coefficient is a time-varying linear function,and can be analytically determined.In this sense,an alternative low-dimensional equivalent linear integer-order differential system with exact closed-form coefficients for the original highdimensional linear fractional differential system can be constructed such that their transient PDFs are identical.Specifically,for a multi-dimensional linear fractional differential system,if only one or two quantities are of interest,GE-GDEE is only in one or two dimensions,and the surrogate system would be a one-or two-dimensional linear integer-order system.Several examples are studied to assess the merit of the proposed method.Though presently the closed-form intrinsic drift coefficient is only available for linear stochastic fractional differential systems,the findings in the present paper provide a remarkable demonstration on the existence and eligibility of GE-GDEE for the case that the original high-dimensional system itself is non-Markovian,and provide insights for the physical-mechanism-informed determination of intrinsic drift and diffusion coefficients of GE-GDEE of more generic complex nonlinear systems.

CONNECTION BETWEEN THE ORDER OF FRACTIONAL CALCULUS AND FRACTIONAL DIMENSIONS OF A TYPE OF FRACTAL FUNCTIONS

The linear relationship between fractal dimensions of a type of generalized Weierstrass functions and the order of their fractional calculus has been proved. The graphs and numerical results given here further indicate the corresponding relationship.

Direct linear discriminant analysis based on column pivoting QR decomposition and economic SVD

A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.

A review on the coordinative structure of human walking and the application of principal component analysis

Walking is a complex task which includes hundreds of muscles, bones and joints working together to deliver smooth movements. With the complexity, walking has been widely investigated in order to identify the pattern of multi-segment movement and reveal the control mechanism. The degree of freedom and dimensional properties provide a view of the coordinative structure during walking, which has been extensively studied by using dimension reduction technique. In this paper, the studies related to the coordinative structure, dimensions detection and pattern reorganization during walking have been reviewed. Principal component analysis, as a popular technique, is widely used in the processing of human movement data. Both the principle and the outcomes of principal component analysis were introduced in this paper. This technique has been reported to successfully reduce the redundancy within the original data, identify the physical meaning represented by the extracted principal components and discriminate the different patterns. The coordinative structure during walking assessed by this technique could provide further information of the body control mechanism and correlate walking pattern with injury.

Estimation of Maximal Dimensions of Commutable Matrix Spaces

In this paper, a lower bound of maximal dimensions of commutable matrix spaces (CMS) is given. It is found that the linear dependence of a group of one to one commutable matrices is related to whether some equations in system can be eliminated. The corresponding relation is given. By introducing conceptions of eliminating set and eliminating index, we give an estimation of upper bound of maximal dimensions of CMS. For special cases n=5,6, the further estimation of maximal dimensions of CMS is presented.

Pullback attractor of 2D nonautonomous g-Navier-Stokes equations with linear dampness

The pullback attractors for the 2D nonautonomous g-Navier-Stokes equations with linear dampness axe investigated on some unbounded domains. The existence of the pullback attractors is proved by verifying the existence of pullback D-absorbing sets with cocycle and obtaining the pullback ：D-asymptotic compactness. Furthermore, the estimation of the fractal dimensions for the 2D g-Navier-Stokes equations is given.

A New Test for Large Dimensional Regression Coefficients

In the article, hypothesis test for coefficients in high dimensional regression models is considered. I develop simultaneous test statistic for the hypothesis test in both linear and partial linear models. The derived test is designed for growing p and fixed n where the conventional F-test is no longer appropriate. The asymptotic distribution of the proposed test statistic under the null hypothesis is obtained.

Review of Dimension Reduction Methods

Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR techniques were explored. The different data types that have been applied on the various DR techniques were also explored.

One-dimensional Chain Topology in [Ag(C_6H_6NCl)_2](ClO_4), Generated through Weak Interaction between Molecules

The mononuclear complex [Ag(C6H6NCl)2](ClO() has been prepared and structurally analyzed by single-crystal X-ray diffraction. The complex crystallizes in the monoclinic system, space group C2/c with unit cell parameters: a=15.5314(2), b=8.0247(8), c=15.3701(2)?.β=118.832(2)°, V=1678.2(3)?3, Z=4, Mr=462.46, Dc=1.830Mg/m3, F(000)=912, μ(MoKα) = 1.694cm-1. The final R and wR are 0.0472 and 0.1272 for 1484 observed reflections with I≥3σ(I). The Ag atom is coordinated by two nitrogen atoms of 4-chloromethyl-pyridine in a linear coordination geometry. Each molecule is further linked by the weak interaction between the Cl and Ag atoms to form a one-dimensional chain structure with Ag－Cl distance of 3.240?.

2-维Ginzburg-Landau方程的一种混合有限元方法的高精度分析

针对2-维Ginzburg-Landau方程,采用EQ1^ rot非协调元及零阶Raviart-Thomas元讨论了一种混合有限元方法.在半离散格式和线性化的Euler格式下,分别有技巧的导出了原始变量u在H^ 1能量模意义下及流量p^→在L^2模意义下的O (h^2 +τ^2 )阶的超逼近性质.给出一个数值算例验证了理论结果的正确性.

应用线性方程组理论证明矩阵秩的性质

利用矩阵秩的定义证明矩阵秩的性质时,需要使用行列式的性质,证明过程较为复杂。线性方程组解的理论与矩阵秩的内在联系,使得用线性方程组解的理论证明矩阵秩的性质成为可能。应用线性方程组解的理论,可将矩阵秩的等式证明转化为线性方程组解空间相等的证明;将矩阵秩的不等式的证明转化为解空间包含的证明。从行列式性质法的证明转化为集合间关系的证明,不仅简化了矩阵秩的性质的证明,而且证明过程便于理解。

Temporal No-Linearity: An Alternative to Dark Energy

Mathematical compatibilities and constraints of a hypothetical 5D space-time with time referenced by two coordinates (3-2) have been revisited in detail in several recent papers. It has been prescribed from the GR the compatibility constraints of the FLRW metric in each temporal brane, to be restricted to Closed Universes, Smooth Initial Singularities, and “Open CTC”. In a first view, this leads to leaving these works considering mathematical games discarded by the Standard Candles data. However, if time would be referred by two coordinates, they would not be linearly related, and it will be mathematically stated that space-time may not be flat in any case because time-like branes geometry will never be. If so, the time scale “lived” over a two-time dimension geodesic necessarily is not constant over its linear projection on one of both coordinates. Consequently, the correlations between Redshift and Distance Modulus—Distance Ladder—may be corrected by a synchronization function (if a no-linear two-time geodesic trajectory over a “warped temporal geometry” is linearly divided into constant segments, then their projections are not linear in any case). We apply an example of time-trajectory over the time slices, matching the Standard Candle data for a Closed Universe dominated by matter (>90%) in a bulk 3-2 configuration, with open temporal branes and smooth singularity. If time can be referred by two coordinates, then there is no need of Darkness to explain astrophysical data and Universe can be closed.

一种多局部线性模式保持的降维算法

为了更准确地捕捉数据的局部非线性结构,提出一种基于多局部线性模式保持的降维算法。该文通过局部区域线性重构相应的数据点,利用方向导数代替一阶泰勒展开式中的梯度,降低逼近误差;利用多重线性模式表征数据点,从而更精确地描述数据的局部非线性几何特征;进一步通过最小化嵌入数据空间中的多局部线性重构误差得到嵌入结果。在4个合成数据集和6个真实数据集上实验,结果表明提出方法能够准确捕捉数据的多个非线性结构。

大学英语口译课程思政教学的理念与实践

口译课具有政治性、文化性和形象建构性特征,口译课的独特性与课程思政具有互通性和融合性。口译课程思政教学要以教学目标统领教学内容,以核心知识与能力培养为主线,将思辨教学作为能力培育的重要依托,把握好口译教学与思政教育的契合点和着力点,落实“三全育人”的教育目标。口译课程思政教学要突出正确价值观的导向作用,有效传递课程的思想性和价值性,凝练思政育人的教学内容,创新“以人为本”的教学方法,将探究性、体验性、合作性和反思性学习衔接起来。在夯实学生口译技能的同时实现知识学习、能力发展和价值观塑造的同频共振,实现全人教育的发展理念,培养具有较强口译实践能力和讲好中国故事的自觉性的高素质人才。

化零为整的宏观社会数据生成:基于潜变量模型和动态贝叶斯方法

对因果机制和对宏观检验的探寻催生了定量社会学研究对区群层面数据的需求,然而这类高质量的追踪数据资源相对稀缺。传统研究通常通过综合多个来源的个体社会调查数据来构建面板数据集以改善宏观数据匮乏现状,但其亦受制于社会调查在时间和空间分布上的稀疏性以及不同调查间的差异性。本文引介了一种可用于生成区群层面跨时空面板数据的动态贝叶斯潜变量建模框架,并通过应用实例展示了该方法的具体应用过程,比较了动态贝叶斯方法相较于几种常用的缺失值插补方法的优势。本文的示例结果表明,动态贝叶斯潜变量模型在跨时空、多维度的信息整合和参数不确定性探索方面具有重要的优势,可以实现对调查数据缺失年份或地区的估计和插补,大大缓解了社会学研究中面板数据不足的问题。

超高维纵向数据部分线性模型的特征筛选

超高维纵向数据的特征筛选是超高维特征筛选的难点之一,其难点是在保证边际筛选快速的前提下估计工作相关系数矩阵。文章在部分线性模型的假定下,考虑到纵向数据组间独立、组内相关的特点,采用样本协方差去估计未知的工作协方差矩阵,提出了剖面有协方差阵的确定独立筛选(PMSIS)方法,并在一定正则条件下,证明了该方法具有确定筛选性质。通过蒙特卡洛数值模拟与肠道菌群实例数据验证了该方法的有限样本性质,结果表明,新提出的PMSIS方法能有效筛选弱相关的协变量。

Global Existence of Small Solutions for Cubic Quasi-linear Klein-Gordon Systems in One Space Dimension

In this paper, we consider a system of two cubic quasi-linear Klein-Gordon equations with different masses for small, smooth, compactly supported Cauchy data in one space dimension. We show that such a system has global existence when the nonlinearities satisfy a convenient null condition. Our results extend the global existence proved by Sunagawa recently under the non-resonance assumption to that under the resonance assumption.