Super-Poincaré covariant quantization of the superstring

Using pure spinors, the superstring is covariantly quantized. For the first time, massless vertex operators are constructed and scattering amplitudes are computed in a manifestly ten-dimensional super-Poincaré covariant manner. Quantizable non-linear sigma model actions are constructed for the superstring in curved backgrounds, including the AdS 5 × S 5 background with Ramond-Ramond flux.

Consistency of super-poincaré covariant superstring tree amplitudes

Using pure spinors, the superstring was recently quantized in a manifestly ten-dimensional super-Poincaré covariant manner and a covariant prescription was given for tree-level scattering amplitudes. In this paper, we prove that this prescription is cyclically symmetric and, for the scattering of an arbitrary number of massless bosons and up to four massless fermions, it agrees with the standard Ramond-Neveu-Schwarz prescription.

Self-dual super-Yang-Mills as a string theory in (x, θ) space

Different string theories in twistor space have recently been proposed for describing N = 4 super-Yang-Mills. In this paper, a string theory in (x, θ) space is constructed for self-dual N = 4 super-Yang-Mills. It is hoped that these results will be useful for understanding the twistor-string proposals and their possible relation with the pure spinor formalism of the D = 10 superstring. © SISSA/ISAS 2004.

Conformational basis for the biological activity of TOAC-labeled angiotensin II and bradykinin: Electron paramagnetic resonance, circular dichroism, and fluorescence studies

N-Terminally and internally labeled analogues of the hormones angiotensin (AII, DRVYIHPF) and bradykinin (BK, RPPGFSPFR) were synthesized containing the paramagnetic amino acid 2,2,6,6-tetramethylpiperidine-1-oxyl-4-amino-4- carboxylic acid (TOAC). TOAC replaced Asp 1 (TOAC 1-AII) and Val 3 (TOAC 3-AII) in AII and was inserted prior to Arg 1 (TOAC 0-BK) and replacing Pro 3 (TOAC 3-BK) in BK. The peptide conformational properties were examined as a function of trifluoroethanol (TFE) content and pH. Electron paramagnetic resonance spectra were sensitive to both variables and showed that internally labeled analogues yielded rotational correlation times (TC) considerably larger than N-terminally labeled ones, evincing the greater freedom of motion of the N-terminus. In TFE, τ C increased due to viscosity effects. Calculation of τ Cpeptide/τ CTOAC ratios indicated that the peptides acquired more folded conformations. Circular dichroism spectra showed that, except for TOAC 1-AII in TFE, the N-terminally labeled analogues displayed a conformational behavior similar to that of the parent peptides. In contrast, under all conditions, the TOAC 3 derivatives acquired more restricted conformations. Fluorescence spectra of All and its derivatives were especially sensitive to the ionization of Tyr 4. Fluorescence quenching by the nitroxide moiety was much more pronounced for TOAC 3-AII The conformational behavior of the TOAC derivatives bears excellent correlation with their biological activity, since, while the N-terminally labeled peptides were partially active, their internally labeled counterparts were inactive [Nakaie, C. R., et al., Peptides 2002, 23, 65-70]. The data demonstrate that insertion of TOAC in the middle of the peptide chain induces conformational restrictions that lead to loss of backbone flexibility, not allowing the peptides to acquire their receptor-bound conformation. © 2004 Wiley Periodicals, Inc.

Super-Poincaré covariant two-loop superstring amplitudes

The super-Poincaré covariant formalism for the superstring is used to compute massless four-point two-loop amplitudes in ten-dimensional superspace. The computations are much simpler than in the RNS formalism and include both external bosons and fermions. © SISSA 2006.

Digestibilidade de uma dieta caseira e dois alimentos comerciais, econômico e super-prêmio, para cães

They were compared the coefficients of apparent digestibility (CAD) and the fecal quality of dogs fed a home-made diet and two dog foods, standard and super-premium. Six adult dogs were distributed in a double latin square desing (3 × 3), with three treatments and three periods, in a total of six replications per treatment. Tukey's test was used to compare the means. The home-made diet presented the highest CAD, not differing only of the CAD of acid ether extract of the super-premium food. The standard food was the lowest digestive, producting more feces. Fecal dry matter of dogs fed home-made diet was lower than in the standard and super-premium foods, which did not differ among then. Fecal score presented low variability among treatments, remaining in the considered ideal range. Home-made diet can be an alternative in the feeding of dogs.

Land use image classification through optimum-path forest clustering

Land use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this problem, we propose here the land use recognition by means of Optimum-Path Forest Clustering (OPF), which has never been applied to this context up to date. Experiments among Optimum-Path Forest, Mean Shift and K-Means demonstrated the robustness of OPF for automatic land use classification of images obtained by CBERS-2B and Ikonos-2 satellites. © 2011 IEEE.

Spatial clustering applied to health area

The significant volume of work accidents in the cities causes an expressive loss to society. The development of Spatial Data Mining technologies presents a new perspective for the extraction of knowledge from the correlation between conventional and spatial attributes. One of the most important techniques of the Spatial Data Mining is the Spatial Clustering, which clusters similar spatial objects to find a distribution of patterns, taking into account the geographical position of the objects. Applying this technique to the health area, will provide information that can contribute towards the planning of more adequate strategies for the prevention of work accidents. The original contribution of this work is to present an application of tools developed for Spatial Clustering which supply a set of graphic resources that have helped to discover knowledge and support for management in the work accidents area. © 2011 IEEE.

Post-processing association rules with clustering and objective measures

The post-processing of association rules is a difficult task, since a large number of patterns can be obtained. Many approaches have been developed to overcome this problem, as objective measures and clustering, which are respectively used to: (i) highlight the potentially interesting knowledge in domain; (ii) structure the domain, organizing the rules in groups that contain, somehow, similar knowledge. However, objective measures don't reduce nor organize the collection of rules, making the understanding of the domain difficult. On the other hand, clustering doesn't reduce the exploration space nor direct the user to find interesting knowledge, making the search for relevant knowledge not so easy. This work proposes the PAR-COM (Post-processing Association Rules with Clustering and Objective Measures) methodology that, combining clustering and objective measures, reduces the association rule exploration space directing the user to what is potentially interesting. Thereby, PAR-COM minimizes the user's effort during the post-processing process.

Characterization of the temperature, load and damage effects using piezoelectric transducer patches based on fuzzy clustering

Structural Health Monitoring (SHM) denotes a system with the ability to detect and interpret adverse changes in a structure. One of the critical challenges for practical implementation of SHM system is the ability to detect damage under changing environmental conditions. This paper aims to characterize the temperature, load and damage effects in the sensor measurements obtained with piezoelectric transducer (PZT) patches. Data sets are collected on thin aluminum specimens under different environmental conditions and artificially induced damage states. The fuzzy clustering algorithm is used to organize the sensor measurements into a set of clusters, which can attribute the variation in sensor data due to temperature, load or any induced damage.

Electrical consumers data clustering through optimum-path forest

Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.

QK-Means: A clustering technique based on community detection and K-Means for deployment of cluster head nodes

Wireless Sensor Networks (WSN) are a special kind of ad-hoc networks that is usually deployed in a monitoring field in order to detect some physical phenomenon. Due to the low dependability of individual nodes, small radio coverage and large areas to be monitored, the organization of nodes in small clusters is generally used. Moreover, a large number of WSN nodes is usually deployed in the monitoring area to increase WSN dependability. Therefore, the best cluster head positioning is a desirable characteristic in a WSN. In this paper, we propose a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique: the QK-Means algorithm. Simulation results show that QK-Means detect communities and sub-communities thus lost message rate is decreased and WSN coverage is increased. © 2012 IEEE.

Labeling methods for association rule clustering

Although association mining has been highlighted in the last years, the huge number of rules that are generated hamper its use. To overcome this problem, many post-processing approaches were suggested, such as clustering, which organizes the rules in groups that contain, somehow, similar knowledge. Nevertheless, clustering can aid the user only if good descriptors be associated with each group. This is a relevant issue, since the labels will provide to the user a view of the topics to be explored, helping to guide its search. This is interesting, for example, when the user doesn't have, a priori, an idea where to start. Thus, the analysis of different labeling methods for association rule clustering is important. Considering the exposed arguments, this paper analyzes some labeling methods through two measures that are proposed. One of them, Precision, measures how much the methods can find labels that represent as accurately as possible the rules contained in its group and Repetition Frequency determines how the labels are distributed along the clusters. As a result, it was possible to identify the methods and the domain organizations with the best performances that can be applied in clusters of association rules.

Boosting optimum-path forest clustering through harmony Search and its applications for intrusion detection in computer networks

In this paper we propose a nature-inspired approach that can boost the Optimum-Path Forest (OPF) clustering algorithm by optimizing its parameters in a discrete lattice. The experiments in two public datasets have shown that the proposed algorithm can achieve similar parameters' values compared to the exhaustive search. Although, the proposed technique is faster than the traditional one, being interesting for intrusion detection in large scale traffic networks. © 2012 IEEE.

Automatic visual dictionary generation through Optimum-Path Forest clustering

Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation. © 2012 IEEE.