Defective venation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Results of crossing I and L material. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Low defective crosses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...

This article presents the Two Variable Per Inequality abstract domain (TVPI domain for short). This so-called weakly-relational domain is able to express systems of linear inequalities where each inequality has at most two variables. The domain represents a sweet-point in the performance-cost tradeoff between the faster Octagon domain and the more expressive domain of general convex polyhedra. ...

Clustering is used to identify the relationship among different objects from large volume of data. The clustering analysis is feasible only when the groups are formed with important features. The existing K-Means clustering processing time and the computation cost is high. The proposed two level variable weighting algorithm calculates weights for both views and variables to identify the importa...

Consider the bounded variable logics L k 1! (with k variable symbols), and C k 1! (with k variables in the presence of counting quantiiers 9 >m). These fragments of innnitary logic L 1! are well known to provide an adequate logical framework for some important issues in nite model theory. This paper deals with a translation that associates equivalence of structures in the k-variable fragments w...

We prove that the satisfiability problem for the two-variable, universal fragment of first-order logic with constants (or, alternatively phrased, for the Bernays-Schönfinkel class with two universally quantified variables) remains decidable after augmenting the fragment by the transitive closure of a single binary relation. We give a 2-NExpTime-upper bound and a 2-ExpTime-lower bound for the co...

A valued constraint satisfaction problem (VCSP) instance (V,Π, w) is a set of variables V with a set of constraints Π weighted by w. Given a VCSP instance, we are interested in a re-weighted sub-instance (V,Π′ ⊂ Π, w′) such that preserves the value of the given instance (under every assignment to the variables) within factor 1± . A well-studied special case is cut sparsification in graphs, whic...