Reconocimiento mineralógico de cerámicas árabes

Las muestras de cerámicas árabes estudiadas, presentan una composición mineralógica derivadas de haber sido cocidas a temperaturas comprendidas entre 800 y 1000ºC. Todas ellas tienen en común la utilización de cuarzo y feldespatos como desgrasantes. El yacimiento contiene piezas muy diversas, hecho que guarda relación con su emplazamiento.A study of arab ceramics fragments, shows a mineralogical composition derived from the fact that they probably were fired at a temperature between 800-1000ºC. Every samples have in common the temper composition in quartz and feldspars. The deposit includes ceramics from several procedences, according to its location

Estructura cristalina de la succinodiamidoxima (c4h10n4o2)

Se ha determinado la estructura cristalina de la succinodiamidoxima. Pertenece al sistema monoclínico; P21/n; a=4.883(1), b=5.197(2), c=13.283(2) A, β=100.75(2)º; V=331.2(2 ) A3; Z=2; M=146.149; Dcal.=1.465 Mg.m-3; λ=1.5405 A; μ=9.625 cm-1;F(000) =156; valor final de R=0.040, para 2207 reflexiones observadas, medidas a temperatura ambiente; Rw=0.036.Monoclinic P21/n; a=4.883(1), b=5.197(2), c=13.283(2) A, β=100.75(2)º; V=331.2(2 ) A3; Z=2; M=146.149; Dcal.=1.465 Mg.m-3; λ=1.5405 A; μ=9.625 cm-1;F(000) =156; R=0.040, four 2207 observed reflexions, room temperature; Rw=0.036

Reconocimiento mineralógico de cerámicas árabes

Las muestras de cerámicas árabes estudiadas, presentan una composición mineralógica derivadas de haber sido cocidas a temperaturas comprendidas entre 800 y 1000ºC. Todas ellas tienen en común la utilización de cuarzo y feldespatos como desgrasantes. El yacimiento contiene piezas muy diversas, hecho que guarda relación con su emplazamiento.A study of arab ceramics fragments, shows a mineralogical composition derived from the fact that they probably were fired at a temperature between 800-1000ºC. Every samples have in common the temper composition in quartz and feldspars. The deposit includes ceramics from several procedences, according to its location

Redundancy, Deduction Schemes, and Minimum-Size Bases for Association Rules

Association rules are among the most widely employed data analysis methods in the field of Data Mining. An association rule is a form of partial implication between two sets of binary variables. In the most common approach, association rules are parameterized by a lower bound on their confidence, which is the empirical conditional probability of their consequent given the antecedent, and/or by some other parameter bounds such as "support" or deviation from independence. We study here notions of redundancy among association rules from a fundamental perspective. We see each transaction in a dataset as an interpretation (or model) in the propositional logic sense, and consider existing notions of redundancy, that is, of logical entailment, among association rules, of the form "any dataset in which this first rule holds must obey also that second rule, therefore the second is redundant". We discuss several existing alternative definitions of redundancy between association rules and provide new characterizations and relationships among them. We show that the main alternatives we discuss correspond actually to just two variants, which differ in the treatment of full-confidence implications. For each of these two notions of redundancy, we provide a sound and complete deduction calculus, and we show how to construct complete bases (that is, axiomatizations) of absolutely minimum size in terms of the number of rules. We explore finally an approach to redundancy with respect to several association rules, and fully characterize its simplest case of two partial premises.Comment: LMCS accepted pape

Estructura cristalina de la succinodiamidoxima (c4h10n4o2)

Se ha determinado la estructura cristalina de la succinodiamidoxima. Pertenece al sistema monoclínico; P21/n; a=4.883(1), b=5.197(2), c=13.283(2) A, β=100.75(2)º; V=331.2(2 ) A3; Z=2; M=146.149; Dcal.=1.465 Mg.m-3; λ=1.5405 A; μ=9.625 cm-1;F(000) =156; valor final de R=0.040, para 2207 reflexiones observadas, medidas a temperatura ambiente; Rw=0.036.Monoclinic P21/n; a=4.883(1), b=5.197(2), c=13.283(2) A, β=100.75(2)º; V=331.2(2 ) A3; Z=2; M=146.149; Dcal.=1.465 Mg.m-3; λ=1.5405 A; μ=9.625 cm-1;F(000) =156; R=0.040, four 2207 observed reflexions, room temperature; Rw=0.036

A Hierarchy of Polynomial Kernels

In parameterized algorithmics, the process of kernelization is defined as a polynomial time algorithm that transforms the instance of a given problem to an equivalent instance of a size that is limited by a function of the parameter. As, afterwards, this smaller instance can then be solved to find an answer to the original question, kernelization is often presented as a form of preprocessing. A natural generalization of kernelization is the process that allows for a number of smaller instances to be produced to provide an answer to the original problem, possibly also using negation. This generalization is called Turing kernelization. Immediately, questions of equivalence occur or, when is one form possible and not the other. These have been long standing open problems in parameterized complexity. In the present paper, we answer many of these. In particular, we show that Turing kernelizations differ not only from regular kernelization, but also from intermediate forms as truth-table kernelizations. We achieve absolute results by diagonalizations and also results on natural problems depending on widely accepted complexity theoretic assumptions. In particular, we improve on known lower bounds for the kernel size of compositional problems using these assumptions