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Given a graph G , a perfect secret sharing scheme based on G is a method to distribute a secret data among the vertices of G , the participants , so that a subset of participants can recover the secret if they contain an edge of G , otherwise they can obtain no information regarding the key. The average information rate is the ratio of the size of the secret and the average size of the share a participant must remember. The information rate of G is the supremum of the information rates realizable by perfect secret sharing schemes.Based on the entropy-theoretical arguments due to Capocelli et al [4], and extending the results of M. van Dijk [7] and Blundo et al [2], we construct a graph G n on n vertices with average information rate below < 4/log n . We obtain this result by determining, up to a constant factor, the average information rate of the d -dimensional cube.

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The Bregman operator divergence is introduced for density matrices by differentiation of the matrix-valued function xx log x. This quantity is compared with the relative operator entropy of Fujii and Kamei. It turns out that the trace is the usual Umegaki’s relative entropy which is the only intersection of the classes of quasi-entropies and Bregman divergences.

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Using a collection of papers gathered from the Web of Science, and defining disciplines by the JCR classification, this paper compares the disciplinary structure of the G7 countries (representing high S&T level countries) and the BRIC countries (representing fast breaking countries in S&T) by using bibliometric methods. It discusses the similarity and the balance of their disciplinary structure. We found that: (1) High S&T level countries have a similar national disciplinary structure; (2) In recent years the disciplinary structure of the BRIC countries has become more and more similar to that of the G7 countries; (3) The disciplinary structure of the G7 countries is more balanced than that of the BRIC countries (4) In the G7 countries more emphasis goes to the life sciences, while BRIC countries focus on physics, chemistry, mathematics and engineering.

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In this paper, a method for characterizing the dependence between two random variables is presented with the help of information theory. There are several well-known methods that describe the stochastic dependence. Some of these methods are based on the copula approach. The copula function is capable to exhibit the type of the dependence between two or more random variables.A method is proposed to characterize the dependence that uses certain entropy coefficients, which are calculated with the copula function associated to the joint distribution function.

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Relative entropy between two quantum states, which quantifies to what extent the quantum states can be distinguished via whatever methods allowed by quantum mechanics, is a central and fundamental quantity in quantum information theory. However, in both theoretical analysis (such as selective measurements) and practical situations (such as random experiments), one is often encountered with quantum ensembles, which are families of quantum states with certain prior probability distributions. How can we quantify the quantumness and distinguishability of quantum ensembles? In this paper, by use of a probabilistic coupling technique, we propose a notion of relative entropy between quantum ensembles, which is a natural generalization of the relative entropy between quantum states. This generalization enjoys most of the basic and important properties of the original relative entropy. As an application, we use the notion of relative entropy between quantum ensembles to define a measure for quantumness of quantum ensembles. This quantity may be useful in quantum cryptography since in certain circumstances it is desirable to encode messages in quantum ensembles which are the most quantum, thus the most sensitive to eavesdropping. By use of this measure of quantumness, we demonstrate that a set consisting of two pure states is the most quantum when the states are 45° apart.

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A review of Garfield's journal impact factor and its specific implementation as the Thomson Reuters impact factor reveals several weaknesses in this commonly-used indicator of journal standing. Key limitations include the mismatch between citing and cited documents, the deceptive display of three decimals that belies the real precision, and the absence of confidence intervals. These are minor issues that are easily amended and should be corrected, but more substantive improvements are needed. There are indications that the scientific community seeks and needs better certification of journal procedures to improve the quality of published science. Comprehensive certification of editorial and review procedures could help ensure adequate procedures to detect duplicate and fraudulent submissions.

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This article presents for the first time a portrait of intramural research conducted by the U.S. Department of Agriculture (USDA). We describe the nature, characteristics, and use of USDA research based on scientometric indicators using patent analysis and three bibliometric methods: publication analysis, citation analysis, and science mapping. Our analyses are intended to be purely descriptive in nature. They demonstrate that USDA maintains several core scientific competencies and its research is much broader than and reaches well beyond traditional agricultural sciences for which it is best known. We illustrate the current status, recent trends, and clear benchmarks for planning and assessing future USDA research across an array of scientific disciplines.

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