Robust CERN LHC Higgs boson search in weak boson fusion

We demonstrate that a CERN LHC Higgs boson search in weak boson fusion production with subsequent decay to weak boson pairs is robust against extensions of the standard model or minimal supersymmetric standard model involving a large number of Higgs doublets. We also show that the transverse mass distribution provides unambiguous discrimination of a continuum Higgs signal from the standard model.

Centric fusion in goats (Capra hircus): Identification of a 6/15 translocation by high resolution chromosome banding

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

NONLOCAL EFFECTS IN THE NUCLEUS-NUCLEUS FUSION CROSS-SECTION

Effects of the nonlocality of factorizable potentials are taken into account in the calculation of nucleus-nucleus fusion cross section through an effective mass approach. This cross section makes use of the tunneling factor calculated for the nonlocal barrier, without the explicit introduction of any result coming from coupled channel calculation, besides the approximations of Hill-Wheeler and Wong. Its new expression embodies the nonlocal effects in a factor which redefines the local potential barrier curvature. Applications to different systems, namely O-16 + Co-59, O-16,O-18 + Ni-58,Ni-60,Ni-64, and O-16,O-18 + Cu-63,Cu-65 are presented, where the nonlocal range is treated as a free parameter.

Optimum-Path Forest Classifier for Large Scale Biometric Applications

This paper addresses biometric identification using large databases, in particular, iris databases. In such applications, it is critical to have low response time, while maintaining an acceptable recognition rate. Thus, the trade-off between speed and accuracy must be evaluated for processing and recognition parts of an identification system. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. The existing Gauss-Laguerre Wavelet based coding scheme is used for iris encoding. The performance of the OPF and two other - Hamming and Bayesian - classifiers, is compared using small, medium, and large-scale databases. Such a comparison shows that the OPF has faster response for large-scale databases, thus performing better than the more accurate, but slower, classifiers.

Chlorine evolution during the fusion of chlorofluoroindate glasses

Samples with a composition of 40InF 3-20ZnF 2-5MCl- xBaF 2-ySrF 2, where M=Na, Li and x+y=35 mol%, were prepared. The thermal properties related to the Ba/Sr ratio and to the remaining chlorine content in the glasses were studied. Thermal stability is improved with the addition of chlorine. However, chlorine concentration is regulated by the sublimation of indium fluorides which takes place at about 600°C. Indium fluorides arc formed during glass fusion. The mechanisms of chlorine sublimation were studied. © 2005 Akadémiai Kiadó, Budapest.

Inactivation of vesicular stomatitis virus through inhibition of membrane fusion by chemical modification of the viral glycoprotein

Membrane fusion is an essential step in the entry of enveloped viruses into their host cells triggered by conformational changes in viral glycoproteins. We have demonstrated previously that modification of vesicular stomatitis virus (VSV) with diethylpyrocarbonate (DEPC) abolished conformational changes on VSV glycoprotein and the fusion reaction catalyzed by the virus. In the present study, we evaluated whether treatment with DEPC was able to inactivate the virus. Infectivity and viral replication were abolished by viral treatment with 0.5 mM DEPC. Mortality profile and inflammatory response in the central nervous system indicated that G protein modification with DEPC eliminates the ability of the virus to cause disease. In addition, DEPC treatment did not alter the conformational integrity of surface proteins of inactivated VSV as demonstrated by transmission electron microscopy and competitive ELISA. Taken together, our results suggest a potential use of histidine (His) modification to the development of a new process of viral inactivation based on fusion inhibition. © 2006 Elsevier B.V. All rights reserved.

Fusion of fingerprint recognition methods for robust human identification

Biometrics is one of the biggest tendencies in human identification. The fingerprint is the most widely used biometric. However considering the automatic fingerprint recognition a completely solved problem is a common mistake. The most popular and extensively used methods, the minutiae-based, do not perform well on poor-quality images and when just a small area of overlap between the template and the query images exists. The use of multibiometrics is considered one of the keys to overcome the weakness and improve the accuracy of biometrics systems. This paper presents the fusion of a minutiae-based and a ridge-based fingerprint recognition method at rank, decision and score level. The fusion techniques implemented leaded to a reduction of the Equal Error Rate by 31.78% (from 4.09% to 2.79%) and a decreasing of 6 positions in the rank to reach a Correct Retrieval (from rank 8 to 2) when assessed in the FVC2002-DB1A database. © 2008 IEEE.

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.

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.