Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Encapsulation of small magnetic clusters in fullerene cages: A density functional theory investigation within van der Waals corrections

The encapsulation of magnetic transition-metal (TM) clusters inside carbon cages (fullerenes, nanotubes) has been of great interest due to the wide range of applications, which spread from medical sensors in magnetic resonance imaging to photonic crystals. Several theoretical studies have been reported; however, our atomistic understanding of the physical properties of encapsulated magnetic TM 3d clusters is far from satisfactory. In this work, we will report general trends, derived from density functional theory within the generalized gradient approximation proposed by Perdew, Burke, and Ernzerhof (PBE), for the encapsulation properties of the TMm@C-n (TM = Fe, Co, Ni; m = 2-6, n = 60,70,80,90) systems. Furthermore, to understand the role of the van der Waals corrections to the physical properties, we employed the empirical Grimme's correction (PBE + D2). We found that both PBE and PBE + D2 functionals yield almost the same geometric parameters, magnetic and electronic properties, however, PBE + D2 strongly enhances the encapsulation energy. We found that the center of mass of the TMm clusters is displaced towards the inside C-n surfaces, except for large TMm clusters (m = 5 and 6). For few cases, e. g., Co-4 and Fe-4, the encapsulation changes the putative lowest-energy structure compared to the isolated TMm clusters. We identified few physical parameters that play an important role in the sign and magnitude of the encapsulation energy, namely, cluster size, fullerene equatorial diameter, shape, curvature of the inside C-n surface, number of TM atoms that bind directly to the inside C-n surface, and the van der Waals correction. The total magnetic moment of encapsulated TMm clusters decreases compared with the isolated TMm clusters, which is expected due to the hybridization of the d-p states, and strongly depends on the size and shape of the fullerene cages.

Texture analysis of high resolution MRI allows discrimination between febrile and afebrile initial precipitating injury in mesial temporal sclerosis

A computational pipeline combining texture analysis and pattern classification algorithms was developed for investigating associations between high-resolution MRI features and histological data. This methodology was tested in the study of dentate gyrus images of sclerotic hippocampi resected from refractory epilepsy patients. Images were acquired using a simple surface coil in a 3.0T MRI scanner. All specimens were subsequently submitted to histological semiquantitative evaluation. The computational pipeline was applied for classifying pixels according to: a) dentate gyrus histological parameters and b) patients' febrile or afebrile initial precipitating insult history. The pipeline results for febrile and afebrile patients achieved 70% classification accuracy, with 78% sensitivity and 80% specificity [area under the reader observer characteristics (ROC) curve: 0.89]. The analysis of the histological data alone was not sufficient to achieve significant power to separate febrile and afebrile groups. Interesting enough, the results from our approach did not show significant correlation with histological parameters (which per se were not enough to classify patient groups). These results showed the potential of adding computational texture analysis together with classification methods for detecting subtle MRI signal differences, a method sufficient to provide good clinical classification. A wide range of applications of this pipeline can also be used in other areas of medical imaging. Magn Reson Med, 2012. (c) 2012 Wiley Periodicals, Inc.

The processing of polyelectrolyte-covered magnetite nanoparticles in the form of nanostructured thin films

Magnetic nanoparticles are promising for a variety of applications, such as biomedical devices, spin electronics, magnetic data storage media, to name a few. However, these goals may only be reached if stable and organized structures are fabricated. In this article, we report on a single-step synthetic route with the coprecipitation method, in which iron oxide magnetic nanoparticles (Fe3O4 NPs) were stabilized in aqueous media using the poly(diallyldimethylammonium chloride) (PDAC) polyelectrolyte. The Fe3O4 NPs had a diameter of ca. 5 nm, according to transmission electron microscopy (TEM) images, being arranged in an inverse spinel structure typical of magnetite. An investigation with infrared spectroscopy indicated that the mechanisms of stabilization in the polymer matrix were based on the interaction between quaternary amide groups from PDAC and the nanoparticle surface. The Fe3O4-PDAC NPs exhibited considerable magnetic susceptibility, with a monotonic increase in the magnetization with decreasing temperature. These Fe3O4-PDAC NPs were immobilized in layer-by-layer (LbL) films, being alternated with layers of poly(vinylsulfonic acid) (PVS). The LbL films were much rougher than typical films made with polyelectrolytes, and Fe3O4-PDAC NPs have been responsible for the high electrocatalytic activity toward H2O2 reduction, with an overpotential shift of 0.69 V. Overall, the stability, magnetic properties and film-forming ability indicate that the Fe3O4-PDAC NPs may be used for nanoelectronics and bioelectrochemical devices requiring reversible and magnetic redox materials.

Effect of low-level laser therapy on pain levels in patients with temporomandibular disorders: a systematic review

Temporomandibular disorders (TMD) are characterized by the presence of temporomandibular joint (TMJ) and/or masticatory muscle pain and dysfunction. Low-level laser is presented as an adjuvant therapeutic modality for the treatment of TMD, especially when the presence of inflammatory pain is suspected. Objective: To systematically review studies that investigated the effect of low level laser therapy (LLLT) on the pain levels in individuals with TMD. Material and Methods: The databases Scopus, embase, ebsco and PubMed were reviewed from January/2003 to October/2010 with the following keywords: laser therapy, low-level laser therapy, temporomandibular joint disorders, temporomandibular joint dysfunction syndrome, temporomandibular joint, temporomandibular, facial pain and arthralgia, with the inclusion criteria for intervention studies in humans. exclusion criteria adopted were intervention studies in animals, studies that were not written in english, Spanish or Portuguese, theses, monographs, and abstracts presented in scientific events. Results: After a careful review, 14 studies fit the criteria for inclusion, of which, 12 used a placebo group. As for the protocol for laser application, the energy density used ranged from 0.9 to 105 J/cm², while the power density ranged from 9.8 to 500 mW. The number of sessions varied from 1 to 20 and the frequency of applications ranged from daily for 10 days to 1 time per week for 4 weeks. A reduction in pain levels was reported in 13 studies, with 9 of these occurring only in the experimental group, and 4 studies reporting pain relief for both the experimental group and for the placebo. Conclusion: Most papers showed that LLLT seemed to be effective in reducing pain from TMD. However, the heterogeneity of the standardization regarding the parameters of laser calls for caution in interpretation of these results. Thus, it is necessary to conduct further research in order to obtain a consensus regarding the best application protocol for pain relief in patients with TMD.

Effects of specimen geometry and loading mode on crack growth resistance curves of a high-strength pipeline girth weld

This work presents an investigation of the ductile tearing properties for a girth weld made of an API 5L X80 pipeline steel using experimentally measured crack growth resistance curves. Use of these materials is motivated by the increasing demand in the number of applications for manufacturing high strength pipes for the oil and gas industry including marine applications and steel catenary risers. Testing of the pipeline girth welds employed side-grooved, clamped SE(T) specimens and shallow crack bend SE(B) specimens with a weld centerline notch to determine the crack growth resistance curves based upon the unloading compliance (UC) method using the single specimen technique. Recently developed compliance functions and η-factors applicable for SE(T) and SE(B) fracture specimens with homogeneous material and overmatched welds are introduced to determine crack growth resistance data from laboratory measurements of load-displacement records.

Inhomogeneity in optical properties of rat brain: a study for LLLT dosimetry.

Over the last few years, low-level light therapy (LLLT) has shown an incredible suitability for a wide range of applications for central nervous system (CNS) related diseases. In this therapeutic modality light dosimetry is extremely critical so the study of light propagation through the CNS organs is of great importance. To better understand how light intensity is delivered to the most relevant neural sites we evaluated optical transmission through slices of rat brain point by point. We experimented red (λ = 660 nm) and near infrared (λ = 808 nm) diode laser light analyzing the light penetration and distribution in the whole brain. A fresh Wistar rat (Rattus novergicus) brain was cut in sagittal slices and illuminated with a broad light beam. A high-resolution digital camera was employed to acquire data of transmitted light. Spatial profiles of the light transmitted through the sample were obtained from the images. Peaks and valleys in the profiles show sites where light was less or more attenuated. The peak intensities provide information about total attenuation and the peak widths are correlated to the scattering coefficient at that individual portion of the sample. The outcomes of this study provide remarkable information for LLLT dose-dependent studies involving CNS and highlight the importance of LLLT dosimetry in CNS organs for large range of applications in animal and human diseases.

Towards a hybrid approach for adapting web graphical user interfaces to heterogeneous devices using context

Ubiquitous Computing promises seamless access to a wide range of applications and Internet based services from anywhere, at anytime, and using any device. In this scenario, new challenges for the practice of software development arise: Applications and services must keep a coherent behavior, a proper appearance, and must adapt to a plenty of contextual usage requirements and hardware aspects. Especially, due to its interactive nature, the interface content of Web applications must adapt to a large diversity of devices and contexts. In order to overcome such obstacles, this work introduces an innovative methodology for content adaptation of Web 2.0 interfaces. The basis of our work is to combine static adaption - the implementation of static Web interfaces; and dynamic adaptation - the alteration, during execution time, of static interfaces so as for adapting to different contexts of use. In hybrid fashion, our methodology benefits from the advantages of both adaptation strategies - static and dynamic. In this line, we designed and implemented UbiCon, a framework over which we tested our concepts through a case study and through a development experiment. Our results show that the hybrid methodology over UbiCon leads to broader and more accessible interfaces, and to faster and less costly software development. We believe that the UbiCon hybrid methodology can foster more efficient and accurate interface engineering in the industry and in the academy.

A comparative study on multiscale fractal dimension descriptors

Fractal theory presents a large number of applications to image and signal analysis. Although the fractal dimension can be used as an image object descriptor, a multiscale approach, such as multiscale fractal dimension (MFD), increases the amount of information extracted from an object. MFD provides a curve which describes object complexity along the scale. However, this curve presents much redundant information, which could be discarded without loss in performance. Thus, it is necessary the use of a descriptor technique to analyze this curve and also to reduce the dimensionality of these data by selecting its meaningful descriptors. This paper shows a comparative study among different techniques for MFD descriptors generation. It compares the use of well-known and state-of-the-art descriptors, such as Fourier, Wavelet, Polynomial Approximation (PA), Functional Data Analysis (FDA), Principal Component Analysis (PCA), Symbolic Aggregate Approximation (SAX), kernel PCA, Independent Component Analysis (ICA), geometrical and statistical features. The descriptors are evaluated in a classification experiment using Linear Discriminant Analysis over the descriptors computed from MFD curves from two data sets: generic shapes and rotated fish contours. Results indicate that PCA, FDA, PA and Wavelet Approximation provide the best MFD descriptors for recognition and classification tasks. (C) 2012 Elsevier B.V. All rights reserved.