Orlicz–John ellipsoids

The Orlicz–John ellipsoids, which are in the framework of the booming Orlicz Brunn–Minkowski theory, are introduced for the first time. It turns out that they are generalizations of the classical John ellipsoid and the evolved _Lp$L_p$ John ellipsoids. The analog of Ball's volume-ratio inequality is established for the new Orlicz–John ellipsoids. The connection between the isotropy of measures and the characterization of Orlicz–John ellipsoids is demonstrated.     

Orlicz mixed quermassintegrals

The notion of mixed quermassintegrals in the classical Brunn-Minkowski theory is extended to that of Orlicz mixed quermassintegrals in the Orlicz Brunn-Minkowski theory. The analogs of the classical Cauchy-Kubota formula, the Minkowski isoperimetric inequality and the Brunn-Minkowski inequality are established for this new Orlicz mixed quermassintegrals.     

Rapid solidification processing in molten salts chemistry: X-ray analysis of deeply undercooled cryolite-alumina melts

Rapid solidification processing (cooling rate from the interval 10⁵–10⁶ K s⁻¹) was used to prepare deeply undercooled cryolite-alumina melts. Such prepared samples were analyzed by the XRD method. Besides cryolite, XRD patterns belonging to ι-Al₂O₃ were recorded. The influence of annealing on the XRD patterns of deeply undercooled melts was also investigated.     

BANACH空间有界线性算子A(2)T,S的表示

In this paper we extends the results in [1],[2],[3] and [4] to bounded linear operators in Banach spaces using matrix expression of a partitioned operator. For existence of the limit lim λ→0 (λI + GA)~(-1) G and lim λ→0 G(λI + AG)~(-1) it is necessary and sufficient condition that bounded linear operators A~((2))_(T,S) exist in Banach spaces. We get the integral representation: A(2)_(T,S)=∫∞0 exp(-GAt)Gdt.     

A Decision Criterion for the Optimal Number of Clusters in Hierarchical Clustering

Clustering has been widely used to partition data into groups so that the degree of association is high among members of the same group and low among members of different groups. Though many effective and efficient clustering algorithms have been developed and deployed, most of them still suffer from the lack of automatic or online decision for optimal number of clusters. In this paper, we define clustering gain as a measure for clustering optimality, which is based on the squared error sum as a clustering algorithm proceeds. When the measure is applied to a hierarchical clustering algorithm, an optimal number of clusters can be found. Our clustering measure shows good performance producing intuitively reasonable clustering configurations in Euclidean space according to the evidence from experimental results. Furthermore, the measure can be utilized to estimate the desired number of clusters for partitional clustering methods as well. Therefore, the clustering gain measure provides a promising technique for achieving a higher level of quality for a wide range of clustering methods.     

A characterization of sets in ℝ2 with DC distance function

Czechoslovak Mathematical Journal - We give a complete characterization of closed sets F ? ?2 whose distance function dF:= dist(·, F) is DC (i.e., is the difference of two convex...