Computation of (3)J(HH) coupling constants with a combination of density functional theory and semiempirical calculations. Application to complex molecules

This work evaluates the efficiency of economic levels of theory for the prediction of (3)J(HH) spin-spin coupling constants, to be used when robust electronic structure methods are prohibitive. To that purpose, DFT methods like mPW1PW91. B3LYP and PBEPBE were used to obtain coupling constants for a test set whose coupling constants are well known. Satisfactory results were obtained in most of cases, with the mPW1PW91/6-31G(d,p)//B3LYP/6-31G(d,p) leading the set. In a second step. B3LYP was replaced by the semiempirical methods PM6 and RM1 in the geometry optimizations. Coupling constants calculated with these latter structures were at least as good as the ones obtained by pure DFT methods. This is a promising result, because some of the main objectives of computational chemistry - low computational cost and time, allied to high performance and precision - were attained together. (C) 2012 Elsevier B.V. All rights reserved.

Analytic perturbation theory for bound states in the transfer matrix spectrum of weakly correlated lattice ferromagnetic spin systems

We consider general d-dimensional lattice ferromagnetic spin systems with nearest neighbor interactions in the high temperature region ('beta' << 1). Each model is characterized by a single site apriori spin distribution taken to be even. We also take the parameter 'alfa' = ('S POT.4') - 3 '(S POT.2') POT.2' > 0, i.e. in the region which we call Gaussian subjugation, where ('S POT.K') denotes the kth moment of the apriori distribution. Associated with the model is a lattice quantum field theory known to contain a particle of asymptotic mass -ln 'beta' and a bound state below the two-particle threshold. We develop a 'beta' analytic perturbation theory for the binding energy of this bound state. As a key ingredient in obtaining our result we show that the Fourier transform of the two-point function is a meromorphic function, with a simple pole, in a suitable complex spectral parameter and the coefficients of its Laurent expansion are analytic in 'beta'.

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

Revisiting the Security of the ALRED Design and Two of Its Variants: Marvin and LetterSoup

The ALRED construction is a lightweight strategy for constructing message authentication algorithms from an underlying iterated block cipher. Even though this construction's original analyses show that it is secure against some attacks, the absence of formal security proofs in a strong security model still brings uncertainty on its robustness. In this paper, aiming to give a better understanding of the security level provided by different authentication algorithms based on this design strategy, we formally analyze two ALRED variants-the MARVIN message authentication code and the LETTERSOUP authenticated-encryption scheme,-bounding their security as a function of the attacker's resources and of the underlying cipher's characteristics.

First-principles studies of complex magnetism in Mn nanostructures on the Fe(001) surface

The magnetic properties of Mn nanostructures on the Fe(001) surface have been studied using the noncollinear first-principles real space-linear muffin-tin orbital-atomic sphere approximation method within density-functional theory. We have considered a variety of nanostructures such as adsorbed wires, pyramids, and flat and intermixed clusters of sizes varying from two to nine atoms. Our calculations of interatomic exchange interactions reveal the long-range nature of exchange interactions between Mn-Mn and Mn-Fe atoms. We have found that the strong dependence of these interactions on the local environment, the magnetic frustration, and the effect of spin-orbit coupling lead to the possibility of realizing complex noncollinear magnetic structures such as helical spin spiral and half-skyrmion.

Using complex networks to quantify consistency in the use of words

In this paper we have quantified the consistency of word usage in written texts represented by complex networks, where words were taken as nodes, by measuring the degree of preservation of the node neighborhood. Words were considered highly consistent if the authors used them with the same neighborhood. When ranked according to the consistency of use, the words obeyed a log-normal distribution, in contrast to Zipf's law that applies to the frequency of use. Consistency correlated positively with the familiarity and frequency of use, and negatively with ambiguity and age of acquisition. An inspection of some highly consistent words confirmed that they are used in very limited semantic contexts. A comparison of consistency indices for eight authors indicated that these indices may be employed for author recognition. Indeed, as expected, authors of novels could be distinguished from those who wrote scientific texts. Our analysis demonstrated the suitability of the consistency indices, which can now be applied in other tasks, such as emotion recognition.

Hybrid heuristic-waterfilling game theory approach in MC-CDMA resource allocation

This paper discusses the power allocation with fixed rate constraint problem in multi-carrier code division multiple access (MC-CDMA) networks, that has been solved through game theoretic perspective by the use of an iterative water-filling algorithm (IWFA). The problem is analyzed under various interference density configurations, and its reliability is studied in terms of solution existence and uniqueness. Moreover, numerical results reveal the approach shortcoming, thus a new method combining swarm intelligence and IWFA is proposed to make practicable the use of game theoretic approaches in realistic MC-CDMA systems scenarios. The contribution of this paper is twofold: (i) provide a complete analysis for the existence and uniqueness of the game solution, from simple to more realist and complex interference scenarios; (ii) propose a hybrid power allocation optimization method combining swarm intelligence, game theory and IWFA. To corroborate the effectiveness of the proposed method, an outage probability analysis in realistic interference scenarios, and a complexity comparison with the classical IWFA are presented. (C) 2011 Elsevier B.V. All rights reserved.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Adsorption of NO on the Rh-13, Pd-13, Ir-13, and Pt-13 Clusters: A Density Functional Theory Investigation

The adsorption of NO on transition-metal (TM) surfaces has been widely studied by experimental and theoretical techniques; however, our atomistic understanding of the interaction of nitrogen monoxide (NO) with small TM clusters is far from satisfactory, which compromises a deep understanding of real catalyst devices. In this study, we report a density functional theory study of the adsorption properties of NO on the TM13 (TM = Rh, Pd, Ir, Pt) clusters employing the projected augmented wave method. We found that the interaction of NO with TM13 is much more complex than that for NO/TM(111). In particular, for low symmetry TM13 clusters, there is a strong rearrangement of the electronic charge density upon NO adsorption and, as a consequence, the adsorption energy shows a very complex dependence even for adsorption sites with the same local effective coordination. We found a strong enhancement of the binding energy of NO to the TM13 clusters compared with the TM(111) surfaces, as the antibonding NO states are not occupied for NO/TM13, and the general relationship based on the d-band model between adsorption energy and the center of gravity of the occupied d-states does not hold for the studied TM13 clusters, in particular, for clusters with low symmetry. In contrast with the adsorption energy trends, the geometric NO/TM13 parameters and the vibrational N-O frequencies for different coordination sites follow the same trend as for the respective TM(111) surfaces, while the changes in the frequencies between different surfaces and TM13 clusters reflect the strong NO-TM13 interaction.

A quantitative analysis of current security concerns and solutions for cloud computing

The development of cloud computing services is speeding up the rate in which the organizations outsource their computational services or sell their idle computational resources. Even though migrating to the cloud remains a tempting trend from a financial perspective, there are several other aspects that must be taken into account by companies before they decide to do so. One of the most important aspect refers to security: while some cloud computing security issues are inherited from the solutions adopted to create such services, many new security questions that are particular to these solutions also arise, including those related to how the services are organized and which kind of service/data can be placed in the cloud. Aiming to give a better understanding of this complex scenario, in this article we identify and classify the main security concerns and solutions in cloud computing, and propose a taxonomy of security in cloud computing, giving an overview of the current status of security in this emerging technology.