Probing neutralino properties in minimal supergravity with bilinear R-parity violation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

On the meaning of the minimal Wess-Zumino term

The physical meaning of the recently proposed minimal Wess-Zumino (MWZ) term is discussed. It is shown that the only relativistically acceptable MWZ corresponds to a gauged Floreanini-Jackiw chiral boson. This leads to the conclusion that the very mechanism in action is that of closing families like it happens in the standard model, and not that of the WZ term, in the spirit of Faddeev-Shatashvilli.

The δ expansion and the principle of minimal sensitivity

The δ-expansion is a nonperturbative approach for field theoretic models which combines the techniques of perturbation theory and the variational principle. Different ways of implementing the principle of minimal sensitivity to the δ-expansion produce in general different results for observables. For illustration we use the Nambu-Jona-Lasinio model for chiral symmetry restoration at finite density and compare results with those obtained with the Hartree-Fock approximation.

Minimal cross-subsidies approach for loss allocation in distribution networks with open access

Open access philosophy applied by regulatory agencies may lead to a scenario where captive consumers will solely face the responsibility on distribution network's losses even with Independent Energy Producers (also known as Distributed Generation) and Independent Energy Consumers connected to the system. This work proposes the utilization of a loss allocation method in distribution systems where open access is allowed, in which cross-subsidies, that appear due to the influence the generators have over the system losses, are minimized. Thus, guaranteeing to some extent the efficiency and transparency of the economic signals of the market. Results obtained through the Zbus loss allocation method adapted for distribution networks are processed in such a way that the corresponding allocation to the generation buses is divided among the consumer buses, while still considering consumers spatial characteristics. © 2007 IEEE.

The gyromagnetic factor of elementary particles and non-minimal coupling

In this work we investigate a possible magnetic moment generation for massive neutral particles with spins-1 and -2 coupled non-minimally, in a specific way, to an external electromagnetic field. It is found that, in the nonrelativistic limit, these particles present g = 1. This result, worked out in the framework of Relativistic Quantum Mechanics, seems to suggest that g = 1 for all massive and neutral particles of any spin ≤ 2. We also compare with the results obtained for massive charged particles of spins-1 and -2, in the same regime (nonrelativistic), in order to investigate the role played by the spin separetely from the charge. Copyright © owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike Licence.

The higgs boson mass in minimal technicolor models

Recently a Minimal and an Ultraminimal Technicolor models were proposed where the presence of TC fermions in other representations than the fundamental one led to viable models without conflict with the known value for the measured S parameter. In this work we apply the results of [5] to compute the masses of the Higgs boson in the case of the Minimal and Ultraminimal Technicolor models. © 2010 American Institute of Physics.

Improving the accuracy of the optimum-path forest supervised classifier for large datasets

In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time. © 2010 Springer-Verlag.

Search for neutral minimal supersymmetric standard model Higgs bosons decaying to tau pairs produced in association with b quarks in pp̄ collisions at √s=1.96TeV

We report results from a search for neutral Higgs bosons produced in association with b quarks using data recorded by the D0 experiment at the Fermilab Tevatron Collider and corresponding to an integrated luminosity of 7.3fb-1. This production mode can be enhanced in several extensions of the standard model (SM) such as in its minimal supersymmetric extension (MSSM) at high tan β. We search for Higgs bosons decaying to tau pairs with one tau decaying to a muon and neutrinos and the other to hadrons. The data are found to be consistent with SM expectations, and we set upper limits on the cross section times branching ratio in the Higgs boson mass range from 90 to 320GeV/c2. We interpret our result in the MSSM parameter space, excluding tan β values down to 25 for Higgs boson masses below 170GeV/c2. © 2011 American Physical Society.

Improving accuracy and speed of optimum-path forest classifier using combination of disjoint training subsets

The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.

Intrusion detection system using optimum-path forest

Intrusion detection systems that make use of artificial intelligence techniques in order to improve effectiveness have been actively pursued in the last decade. Neural networks and Support Vector Machines have been also extensively applied to this task. However, their complexity to learn new attacks has become very expensive, making them inviable for a real time retraining. In this research, we introduce a new pattern classifier named Optimum-Path Forest (OPF) to this task, which has demonstrated to be similar to the state-of-the-art pattern recognition techniques, but extremely more efficient for training patterns. Experiments on public datasets showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, as well as allow the algorithm to learn new attacks faster than the other techniques. © 2011 IEEE.

A snoring classifier based on heart rate variability analysis

The effect of snoring on the cardiovascular system is not well-known. In this study we analyzed the Heart Rate Variability (HRV) differences between light and heavy snorers. The experiments are done on the full-whole-night polysomnography (PSG) with ECG and audio channels from patient group (heavy snorer) and control group (light snorer), which are gender- and age-paired, totally 30 subjects. A feature Snoring Density (SND) of audio signal as classification criterion and HRV features are computed. Mann-Whitney statistical test and Support Vector Machine (SVM) classification are done to see the correlation. The result of this study shows that snoring has close impact on the HRV features. This result can provide a deeper insight into the physiological understand of snoring. © 2011 CCAL.

Fractal dimension and Shannon's entropy analyses of the architectural complexity caused by the inflammatory reactions induced by highly crystalline polyvinyl alcohol) microspheres implanted in subcutaneous tissues of the Wistar rats

The results of the histopathological analyses after the implantation of highly crystalline PVA microspheres in subcutaneous tissues of Wistar rats are here in reported. Three different groups of PVA microparticles were systematically studied: highly crystalline, amorphous, and commercial ones. In addition to these experiments, complementary analyses of architectural complexity were performed using fractal dimension (FD), and Shannon's entropy (SE) concepts. The highly crystalline microspheres induced inflammatory reactions similar to the ones observed for the commercial ones, while the inflammatory reactions caused by the amorphous ones were less intense. Statistical analyses of the subcutaneous tissues of Wistar rats implanted with the highly crystalline microspheres resulted in FD and SE values significantly higher than the statistical parameters observed for the amorphous ones. The FD and SE parameters obtained for the subcutaneous tissues of Wistar rats implanted with crystalline and commercial microparticles were statistically similar. Briefly, the results indicated that the new highly crystalline microspheres had biocompatible behavior comparable to the commercial ones. In addition, statistical tools such as FD and SE analyses when combined with histopathological analyses can be useful tools to investigate the architectural complexity tissues caused by complex inflammatory reactions. © 2012 WILEY PERIODICALS, INC.

Nilpotency of the b ghost in the non-minimal pure spinor formalism

The b ghost in the non-minimal pure spinor formalism is not a fundamental field. It is based on a complicated chain of operators and proving its nilpotency is nontrivial. Chandia proved this property in arXiv:1008.1778, but with an assumption on the nonminimal variables that is not valid in general. In this work, the b ghost is demonstrated to be nilpotent without this assumption. © 2013 SISSA, Trieste, Italy.

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.