半环上的赋值与实赋值

在有单位元的交换半环上,引入赋值和实赋值的概念,在此基础上建立有关半环上赋值和实赋值的一些基本结论。此外,引入了赋值与亚序之间的相容性,并通过赋值与亚序的相容性建立了一个赋值是实赋值的充分必要条件。     

粗糙表面接触研究进展

工程中的任何接触表面都不会是绝对光滑的,对于某些特殊接触问题必须考虑表面形貌的接触效应.粗糙表面接触研究是一门年轻的交叉学科.它主要研究粗糙表面接触力学,接触导电和导热,接触密封,接触振动和噪声,接触刚度,骨关节接触等问题,涉及固体力学,流体力学,随机过程及概率统计,计量学,电学和传热学,振动理论以及生物学等学科知识.本文综述了粗糙表面接触的研究进展.      

局部对称的Bochner-Kaehler流形的全实极小子流形

本文拓广Urbano F.关于复射影空间的全实极小子流形的定理到局部对称的Bochner-Kaehler流形的全实极小子流形。     

Boundary and angular layer behavior in singularly perturbed semilinear systems

Some authors employed the method and technique of differential inequalities to obtain fairly general results concerning the existence and asymptotic behavior, as ε→n~+, of the solutions of scalar boundary value problems In this paper, we extend these results to vector boundary value problems, under analogous stability conditions on the solution u=u(t) of the reduced equation O=h(t, u) Two types of asymptotic behavior are studied, depending on whether the reduced solution u(t) has or does not have a con tinuous first derivative in (a, b) leading to the phenomena of boundary and angular layers.     

Rigid isotopy classification of real algebraic curves of bidegree (3,3) on quadrics

A rigid isotopy of nonsingular real algebraic curves on a quadric is a path in the space of such curves of a given bidegree. We obtain the rigid isotopy classification of nonsingular real algebraic curves of bidegree (3, 3) on a hyperboloid and on an ellipsoid. We also study of the space of real algebraic curves of bidegree (3, 3) with a single node or cusp. Translated fromMatematicheskie Zametki, Vol. 66, No. 6, pp. 810–815, December, 1999.     

适合无限维实零点定理的序域之结构Ⅰ

本文的目的是建立适合无限维实零点定理的序域的结构定理．作为预备工作,文章的第一部分研究一类无秩为d的裂缝的序群,这里d是无限基数．藉助于Hahn嵌入定理,本文给出了无秩为d的裂缝的序群的结构．     

实对称矩阵在相合分解下的Moore-Penrose型广义逆的通式

1引言与符号说明对m×n矩阵A,下列矩阵方程：(1)AXA=A,(2)XAX=x,(3)(AX)~T=AX,(4)(XA)~T=XA称为Penrose方程．如果X满足上述方程(i)(j),…(k),则称X为(ij…k)逆,其全体记为A(ij…k)．(1234)逆常记为A~ ．所有这种矩阵叫广义逆(矩阵)或Moore- Penrose型逆(矩阵)．广义逆矩阵在许多数学领域有广泛应用．它在解矩阵方程中的作用     

Ratio of Real to Imaginary for pp and (p)p Elastic Scatterings in QCD Inspired Model

We use the QCD inspired model to analyze the ratio of the real to the imaginary for pp and pp elastic scatterings. A calculation for the ratio of the real to the imaginary is performed in which the contributions from gluongluon interaction, quark-quark interaction, quark-gluon interaction, and odd eikonal profile function are included. Our results show that the QCD inspired model gives a good fit to the LHC experimental data.     

Real Sample Consistency Regularization for GANs

Mode collapse has always been a fundamental problem in generative adversarial networks. The recently proposed Zero Gradient Penalty (0GP) regularization can alleviate the mode collapse, but it will exacerbate a discriminator’s misjudgment problem, that is the discriminator judges that some generated samples are more real than real samples. In actual training, the discriminator will direct the generated samples to point to samples with higher discriminator outputs. The serious misjudgment problem of the discriminator will cause the generator to generate unnatural images and reduce the quality of the generation. This paper proposes Real Sample Consistency (RSC) regularization. In the training process, we randomly divided the samples into two parts and minimized the loss of the discriminator’s outputs corresponding to these two parts, forcing the discriminator to output the same value for all real samples. We analyzed the effectiveness of our method. The experimental results showed that our method can alleviate the discriminator’s misjudgment and perform better with a more stable training process than 0GP regularization. Our real sample consistency regularization improved the FID score for the conditional generation of Fake-As-Real GAN (FARGAN) from 14.28 to 9.8 on CIFAR-10. Our RSC regularization improved the FID score from 23.42 to 17.14 on CIFAR-100 and from 53.79 to 46.92 on ImageNet2012. Our RSC regularization improved the average distance between the generated and real samples from 0.028 to 0.025 on synthetic data. The loss of the generator and discriminator in standard GAN with our regularization was close to the theoretical loss and kept stable during the training process.