Positivity and positive definiteness in generalized function algebras

The definitions of positivity and positive definiteness are extended to generalized function algebras in coherence with the corresponding notions for distributions. Versions of Bochner's theorem for a positive definite Colombeau generalized function are given.     

Univalence and starlikeness of certain transforms defined by convolution of analytic functions

Let U(λ) denote the class of all analytic functions f in the unit disk Δ of the form f(z)=z+a₂z²+? satisfying the condition

     

NaYF4∶Eu3+, Tm3+, Yb3+材料中Stokes和反Stokes发光研究

合成了Eu3+,Tm3+和Yb3+掺杂的NaYF₄材料。360 nm光激发呈蓝色发光,峰值位于452 nm,对应Tm3+的1D₂→3F₄跃迁;395 nm光激发呈橙色发光,峰值位于591 nm,对应Eu3+的5D₀→7F₁跃迁;409 nm光激发呈红色发光,峰值位于613 nm,对应Eu3+的5D₀→7F₂跃迁;980 nm光激发呈蓝色和红色发光,发光峰位于474和646 nm。蓝光来源Tm3+的1G₄ →3H₆跃迁,红光来源Tm3+的1G₄→3F₄跃迁。在双对数曲线中,蓝光474 nm和红光646 nm的斜率分别为2.1和2.4,在980 nm光激发下,蓝光和红光发射都是双光子过程。还研究了材料的吸收光谱,并利用X射线衍射,扫描电镜测试了材料的物相结构和微观形貌。结果表明：NaYF₄∶Eu3+, Tm3+, Yb3+材料具有较规则的六方相结构,结晶良好。     

The multiple-parameter discrete fractional Hadamard transform

Discrete fractional Hadamard transform (DFrHaT) is a generalization of the Hadamard transform, which has been widely used in signal processing. In this paper, we present the multiple-parameter discrete fractional Hadamard transform (MPDFrHaT), which has multiple order parameters instead of only one in DFrHaT. The proposed MPDFrHaT is shown to possess all of the desired properties of DFrHaT. In fact, it will reduce to DFrHaT when all of its order parameters are the same. We also propose a novel encryption technique, double random amplitude (DRA) encoding scheme, by cascading twofold random amplitude filtering. As a primary application, we exploit the multiple-parameter feature of MPDFrHaT and double random amplitude encoding scheme for digital image encryption in the MPDFrHaT domain. Results show that this method can enhance data security.     

Rough set based rule induction from two decision tables

We study rule induction from two decision tables as a basis of rough set analysis of more than one decision tables. We regard the rule induction process as enumerating minimal conditions satisfied with positive examples but unsatisfied with negative examples and/or with negative decision rules. From this point of view, we show that seven kinds of rule induction are conceivable for a single decision table. We point out that the set of all decision rules from two decision tables can be split in two levels: a first level decision rule is positively supported by a decision table and does not have any conflict with the other decision table and a second level decision rule is positively supported by both decision tables. To each level, we propose rule induction methods based on decision matrices. Through the discussions, we demonstrate that many kinds of rule induction are conceivable.     

Multi-kernel regularized classifiers

A family of classification algorithms generated from Tikhonov regularization schemes are considered. They involve multi-kernel spaces and general convex loss functions. Our main purpose is to provide satisfactory estimates for the excess misclassification error of these multi-kernel regularized classifiers when the loss functions achieve the zero value. The error analysis consists of two parts: regularization error and sample error. Allowing multi-kernels in the algorithm improves the regularization error and approximation error, which is one advantage of the multi-kernel setting. For a general loss function, we show how to bound the regularization error by the approximation in some weighted L^q$L^q$ spaces. For the sample error, we use a projection operator. The projection in connection with the decay of the regularization error enables us to improve convergence rates in the literature even for the one-kernel schemes and special loss functions: least-square loss and hinge loss for support vector machine soft margin classifiers. Existence of the optimization problem for the regularization scheme associated with multi-kernels is verified when the kernel functions are continuous with respect to the index set. Concrete examples, including Gaussian kernels with flexible variances and probability distributions with some noise conditions, are used to illustrate the general theory.     

Line integrals and Green's formula on time scales

In this paper we study curves parametrized by a time scale parameter, introduce line delta and nabla integrals along time scale curves, and obtain an analog of Green's formula in the time scale setting.     

高维空间中的有界凸域

证明n维空间中的有界凸域D能被拟共形映射到n维单位球B~n(0,1),即D是拟球,从而说明拟共形映射中的黎曼定理在n维空间中的有界凸域类中是成立的．     

Applicability of medium-size basis sets in calculations of molecular dynamic polarisabilities

Static and dynamic average polarisabilities and polarisability anisotropies of seven linear non-polar and polar molecules are calculated within the CCS, CC2, and CC3 approximations using a range of medium-sized basis sets: the polarised LPol-n (n = ds, dl, fs, fl), the aug-pc-n (n = 1, 2), the def2-SVPD, and -TZVPD basis sets. Reference values are obtained using a hierarchy of Dunning's (d-)aug-cc-pVXZ (X = D, T, Q, 5) basis sets. The results are discussed together with the available CCSD values in terms of basis set and correlation method errors, and their ratio. Detailed analysis shows that already the def2-SVPD basis set can be used in CCS polarisability calculations. When affordable, the slightly larger aug-pc-1 basis set is recommended, as it leads to significant reduction of basis set error. The def2-TZVPD, LPol-ds, and aug-pc-2 basis sets are optimal choice within the CC2 approximation, with the latter allowing to approach the CC2 basis set limit. The LPol-ds, -dl, and def2-TZVPD sets outperform the aug-cc-pVTZ set in average polarisability CCSD calculations, with the def2-TZVPD being competitive to other reduced-size sets also in determination of polarisability anisotropy. The aug-pc-2 basis is a particularly attractive choice for CCSD, giving the accuracy of aug-cc-pVQZ at a significantly reduced computational cost. The polarisability anisotropy is shown to be more computationally demanding than the average polarisability, in particular with respect to the accuracy of the correlation method and an accurate evaluation of this property requires at least the CCSD model.