Alternative fidelity measure between quantum states

We propose an alternative fidelity measure (namely, a measure of the degree of similarity) between quantum states and benchmark it against a number of properties of the standard Uhlmann-Jozsa fidelity. This measure is a simple function of the linear entropy and the Hilbert-Schmidt inner product between the given states and is thus, in comparison, not as computationally demanding. It also features several remarkable properties such as being jointly concave and satisfying all of Jozsa's axioms. The trade-off, however, is that it is supermultiplicative and does not behave monotonically under quantum operations. In addition, metrics for the space of density matrices are identified and the joint concavity of the Uhlmann-Jozsa fidelity for qubit states is established.

TESTING STATISTICAL HYPOTHESIS ON RANDOM TREES AND APPLICATIONS TO THE PROTEIN CLASSIFICATION PROBLEM

Efficient automatic protein classification is of central importance in genomic annotation. As an independent way to check the reliability of the classification, we propose a statistical approach to test if two sets of protein domain sequences coming from two families of the Pfam database are significantly different. We model protein sequences as realizations of Variable Length Markov Chains (VLMC) and we use the context trees as a signature of each protein family. Our approach is based on a Kolmogorov-Smirnov-type goodness-of-fit test proposed by Balding et at. [Limit theorems for sequences of random trees (2008), DOI: 10.1007/s11749-008-0092-z]. The test statistic is a supremum over the space of trees of a function of the two samples; its computation grows, in principle, exponentially fast with the maximal number of nodes of the potential trees. We show how to transform this problem into a max-flow over a related graph which can be solved using a Ford-Fulkerson algorithm in polynomial time on that number. We apply the test to 10 randomly chosen protein domain families from the seed of Pfam-A database (high quality, manually curated families). The test shows that the distributions of context trees coming from different families are significantly different. We emphasize that this is a novel mathematical approach to validate the automatic clustering of sequences in any context. We also study the performance of the test via simulations on Galton-Watson related processes.

Pseudodifferential operators with C*-algebra-valued symbols: Abstract characterizations

Given a separable unital C*-algebra C with norm parallel to center dot parallel to, let E-n denote the Banach-space completion of the C-valued Schwartz space on R-n with norm parallel to f parallel to(2)=parallel to < f, f >parallel to(1/2), < f, g >=integral f(x)* g(x)dx. The assignment of the pseudodifferential operator A=a(x,D) with C-valued symbol a(x,xi) to each smooth function with bounded derivatives a is an element of B-C(R-2n) defines an injective mapping O, from B-C(R-2n) to the set H of all operators with smooth orbit under the canonical action of the Heisenberg group on the algebra of all adjointable operators on the Hilbert module E-n. In this paper, we construct a left-inverse S for O and prove that S is injective if C is commutative. This generalizes Cordes' description of H in the scalar case. Combined with previous results of the second author, our main theorem implies that, given a skew-symmetric n x n matrix J and if C is commutative, then any A is an element of H which commutes with every pseudodifferential operator with symbol F(x+J xi), F is an element of B-C(R-n), is a pseudodifferential operator with symbol G(x - J xi), for some G is an element of B-C(R-n). That was conjectured by Rieffel.

The role of the steps in the cleavage of the C-C bond during ethanol oxidation on platinum electrodes

Ethanol oxidation has been studied on stepped platinum single crystal electrodes in acid media using electrochemical and Fourier transform infrared (FTIR) techniques. The electrodes used belong to two different series of stepped surfaces: those having (111) terraces with (100) monoatomic steps and those with (111) terraces with (110) monoatomic steps. The behaviors of the two series of stepped surfaces for the oxidation of ethanol are very different. On the one hand, the presence of (100) steps on the (111) terraces provides no significant enhancement of the activity of the surfaces. On the other hand, (110) steps have a double effect on the ethanol oxidation reaction. At potentials below 0.7 V, the step catalyzes the C-C bond cleavage and also the oxidation of the adsorbed CO species formed. At higher potentials, the step is not only able to break the C-C bond, but also to catalyze the oxidation of ethanol to acetic acid and acetaldehyde. The highest catalytic activity from voltammetry for ethanol oxidation was obtained with the Pt(554) electrode.

CHEMICAL AND PHYSICO-CHEMICAL CHARACTERIZATION OF LIGNINS OBTAINED FROM ETHANOL-WATER FRACTIONATION OF BAGASSE

Organosolv lignins can replace petroleum chemicals such as phenol either partially or totally in various applications. Eight lignins, seven of which corresponded to the ethanol-water fractionation of bagasse and the other to a reference lignin (Alcell (R)) were analyzed with the aim to evaluate their chemical and physicochemical characteristics. The purity of the lignin fractions was determined by high pressure liquid chromatography (HPLC) and by ash content. Fourier Transform-Infrared Spectroscopy (FTIR) techniques and differential UV spectroscopy were applied to identify the chemical groups in the lignin samples. The molecular weight distribution was determined by size exclusion chromatography (HPSEC). Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) techniques were used to determine the mass loss due to the high temperature treatment. The lignins studied showed the presence of p-hydroxyphenyl (H unit) and a greater proportion of guaiacyl (G unit) moieties, lower purity, similar or greater amount of phenolic hydroxyl groups, and higher degradation temperatures, than the Alcell (R) lignin.

Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil

Since 2000, the southwestern Brazilian Amazon has undergone a rapid transformation from natural vegetation and pastures to row-crop agricultural with the potential to affect regional biogeochemistry. The goals of this research are to assess wavelet algorithms applied to MODIS time series to determine expansion of row-crops and intensification of the number of crops grown. MODIS provides data from February 2000 to present, a period of agricultural expansion and intensification in the southwestern Brazilian Amazon. We have selected a study area near Comodoro, Mato Grosso because of the rapid growth of row-crop agriculture and availability of ground truth data of agricultural land-use history. We used a 90% power wavelet transform to create a wavelet-smoothed time series for five years of MODIS EVI data. From this wavelet-smoothed time series we determine characteristic phenology of single and double crops. We estimate that over 3200 km(2) were converted from native vegetation and pasture to row-crop agriculture from 2000 to 2005 in our study area encompassing 40,000 km(2). We observe an increase of 2000 km(2) of agricultural intensification, where areas of single crops were converted to double crops during the study period. (C) 2007 Elsevier Inc. All rights reserved.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Multifractal regime transition in a modified minority game model

The search for more realistic modeling of financial time series reveals several stylized facts of real markets. In this work we focus on the multifractal properties found in price and index signals. Although the usual minority game (MG) models do not exhibit multifractality, we study here one of its variants that does. We show that the nonsynchronous MG models in the nonergodic phase is multifractal and in this sense, together with other stylized facts, constitute a better modeling tool. Using the structure function (SF) approach we detected the stationary and the scaling range of the time series generated by the MG model and, from the linear (non-linear) behavior of the SF we identified the fractal (multifractal) regimes. Finally, using the wavelet transform modulus maxima (WTMM) technique we obtained its multifractal spectrum width for different dynamical regimes. (C) 2009 Elsevier Ltd. All rights reserved.

Multifractality in the random parameter model for multivariate time series

The Random Parameter model was proposed to explain the structure of the covariance matrix in problems where most, but not all, of the eigenvalues of the covariance matrix can be explained by Random Matrix Theory. In this article, we explore the scaling properties of the model, as observed in the multifractal structure of the simulated time series. We use the Wavelet Transform Modulus Maxima technique to obtain the multifractal spectrum dependence with the parameters of the model. The model shows a scaling structure compatible with the stylized facts for a reasonable choice of the parameter values. (C) 2009 Elsevier B.V. All rights reserved.

Coconut coir as biosorbent for Cr(VI) removal from laboratory wastewater

A high cost-effective treatment of sulphochromic waste is proposed employing a raw coconut coir as biosorbent for Cr(VI) removal. The ideal pH and sorption kinetic, sorption capacities, and sorption sites were the studied biosorbent parameters. After testing five different isotherm models with standard solutions, Redlich-Peterson and Toth best fitted the experimental data, obtaining a theoretical Cr(VI) sorption capacity (SC) of 6.3 mg g(-1). Acid-base potentiometric titration indicated around of 73% of sorption sites were from phenolic compounds, probably lignin. Differences between sorption sites in the coconut coir before and after Cr adsorption identified from Fourier transform infrared spectra suggested a modification of sorption sites after sulphochromic waste treatment, indicating that the sorption mechanism involves organic matter oxidation and chromium uptake. For sulphocromic waste treatment, the SC was improved to 26.8 +/- 0.2 mg g(-1), and no adsorbed Cr(VI) was reduced, remaining only Cr(III) in the final solution. The adsorbed material was calcinated to obtain Cr2O3, with a reduction of more than 60% of the original mass. (c) 2008 Elsevier B.V. All rights reserved.

An alternative application to the Portuguese agro-industrial residue: Wheat straw

The effects of alkaline treatments of the wheat straw with sodium hydroxide were investigated. The optimal condition for extraction of hemicelluloses was found to be with 0.50 mol/l sodium hydroxide at 55C for 2 h. This resulted in the release of 17.3% of hemicellulose (% dry starting material), corresponding to the dissolution of 49.3% of the original hemicellulose. The yields were determined by gravimetric analysis and expressed as a proportion of the starting material. Chemical composition and physico-chemical properties of the samples of hemicelluloses were elucidated by a combination of sugar analyses, Fourier transform infrared (FTIR), and thermal analysis. The results showed that the treatments were very effective on the extraction of hemicelluloses from wheat straw and that the extraction intensity (expressed in terms of alkali concentration) had a great influence on the yield and chemical features of the hemicelluloses. The FTIR analysis revealed typical signal pattern for the hemicellulosic fraction in the 1,200-1,000 cm(-1) region. Bands between 1,166 and 1,000 cm(-1) are typical of xylans.

Synthesis of Nb(2)O(5)center dot nH(2)O nanoparticles by water-in-oil microemulsion

Hydrous niobium oxide (Nb(2)O(5)center dot nH(2)O) nanoparticles had been Successfully prepared by water-in-oil microemulsion. They were characterized by X-ray diffraction (XRD), thermal analysis (TG/DTG), Fourier transform infrared spectroscopy (FTIR), BET surface area measurement, transmission electron microscopy (TEM), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). The results showed that the nanoparticle was exactly Nb(2)O(5)center dot nH(2)O with spherical shape. Their BET surface area was 60 m(2) g(-1). XRD results showed that Nb(2)O(5)center dot nH(2)O nanoparticles with crystallite size in nanometer scale were formed. The crystallinity and crystallity size increased with increasing annealing temperature. TT-phase of Nb(2)O(5) was obtained when the sample is annealed at 550 degrees C. (C) 2009 Elsevier B.V. All rights reserved.

Cellulosic Films Obtained from the Treatment of Sugarcane Bagasse Fibers with N-methylmorpholine-N-oxide (NMMO)

Ethanol/water organosolv pulping was used to obtain sugarcane bagasse pulp that was bleached with sodium chlorite. This bleached pulp was used to obtain cellulosic films that were further evaluated by Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and scanning electron microscopy (SEM). A good film formation was observed when temperature of 74 degrees C and baths of distilled water were used, which after FTIR, TGA, and SEM analysis indicated no significant difference between the reaction times. The results showed this to be an interesting and promising process, combining the prerequisites for a more efficient utilization of agro-industrial residues.

Human gait recognition using extraction and fusion of global motion features

This paper proposes a novel computer vision approach that processes video sequences of people walking and then recognises those people by their gait. Human motion carries different information that can be analysed in various ways. The skeleton carries motion information about human joints, and the silhouette carries information about boundary motion of the human body. Moreover, binary and gray-level images contain different information about human movements. This work proposes to recover these different kinds of information to interpret the global motion of the human body based on four different segmented image models, using a fusion model to improve classification. Our proposed method considers the set of the segmented frames of each individual as a distinct class and each frame as an object of this class. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction based on the Wavelet Transform (WT) and feature extraction by Principal Component Analysis (PCA). We propose four new schemas for motion information capture: the Silhouette-Gray-Wavelet model (SGW) captures motion based on grey level variations; the Silhouette-Binary-Wavelet model (SBW) captures motion based on binary information; the Silhouette-Edge-Binary model (SEW) captures motion based on edge information and the Silhouette Skeleton Wavelet model (SSW) captures motion based on skeleton movement. The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique. The results suggest excellent performance in terms of recognising people by their gait.

Digital frequency relaying based on the modified least mean square method

This research presents a method for frequency estimation in power systems using an adaptive filter based on the Least Mean Square Algorithm (LMS). In order to analyze a power system, three-phase voltages were converted into a complex signal applying the alpha beta-transform and the results were used in an adaptive filtering algorithm. Although the use of the complex LMS algorithm is described in the literature, this paper deals with some practical aspects of the algorithm implementation. In order to reduce computing time, a coefficient generator was implemented. For the algorithm validation, a computing simulation of a power system was carried Out using the ATP software. Many different situations were Simulated for the performance analysis of the proposed methodology. The results were compared to a commercial relay for validation, showing the advantages of the new method. (C) 2009 Elsevier Ltd. All rights reserved.