Quando o currículo se torna passarela para a diferença

Viver de múltiplas formas (n)o currículo é o tema principal deste trabalho. A partir de quatro cenas narrativas sobre desfiles de moda realizados por alunos do ensino médio numa escola pública do Recife, tecemos as linhas principais do texto buscando recompor a forma como aquelas ações coletivas habitavam o currículo de maneira singular, afetando significativamente as experiências de gênero e sexualidade daquelas pessoas. Compreendemos currículo como significante que remete a um espaço-tempo enunciativo em constante (re)formulação a partir de processos de hibridização cultural. Pontuamos a importância das interpelações subjetivas para a composição de elementos “novos” e/ou “desconhecidos” no tecido curricular e como isso pode ser entendido enquanto uma abertura desse campo à passagem da diferença, que desloca e recria os ideais de educação a partir da significação de outras vivências

Resonant tunneling magnetoresistance in epitaxial metal-semiconductor heterostructures

We report on resonant tunneling magnetoresistance via localized states through a ZnSe semiconducting barrier which can reverse the sign of the effective spin polarization of tunneling electrons. Experiments performed on Fe/ZnSe/Fe planar junctions have shown that positive, negative or even its sign-reversible magnetoresistance can be obtained, depending on the bias voltage, the energy of localized states in the ZnSe barrier and spatial symmetry. The averaging of conduction over all localized states in a junction under resonant condition is strongly detrimental to the magnetoresistance

K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection

Dynamic Ensemble Selection (DES) techniques aim to select locally competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample (its the nearest neighbors in the validation set). The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers that correctly classify all samples in the region of competence of the test sample, if such classifier exists, otherwise, it removes from the region of competence the sample that is furthest from the test sample, and the process repeats. When the region of competence has samples of different classes, KNORA-E can reduce the region of competence in such a way that only samples of a single class remain in the region of competence, leading to the selection of locally incompetent classifiers that classify all samples in the region of competence as being from the same class. In this paper, we propose two DES techniques: K-Nearest Oracles Borderline (KNORA-B) and K-Nearest Oracles Borderline Imbalanced (KNORA-BI). KNORA-B is a DES technique based on KNORA-E that reduces the region of competence but maintains at least one sample from each class that is in the original region of competence. KNORA-BI is a variation of KNORA-B for imbalance datasets that reduces the region of competence but maintains at least one minority class sample if there is any in the original region of competence. Experiments are conducted comparing the proposed techniques with 19 DES techniques from the literature using 40 datasets. The results show that the proposed techniques achieved interesting results, with KNORA-BI outperforming state-of-art techniques.Comment: Paper accepted for publication on IJCNN 201

An Ensemble Generation Method Based on Instance Hardness

In Machine Learning, ensemble methods have been receiving a great deal of attention. Techniques such as Bagging and Boosting have been successfully applied to a variety of problems. Nevertheless, such techniques are still susceptible to the effects of noise and outliers in the training data. We propose a new method for the generation of pools of classifiers based on Bagging, in which the probability of an instance being selected during the resampling process is inversely proportional to its instance hardness, which can be understood as the likelihood of an instance being misclassified, regardless of the choice of classifier. The goal of the proposed method is to remove noisy data without sacrificing the hard instances which are likely to be found on class boundaries. We evaluate the performance of the method in nineteen public data sets, and compare it to the performance of the Bagging and Random Subspace algorithms. Our experiments show that in high noise scenarios the accuracy of our method is significantly better than that of Bagging.Comment: Paper accepted for publication on IJCNN 201

2-Methyl-5-(4-tol­yl)-7-(trifluoro­meth­yl)pyrazolo[1,5-a]pyrimidine

In the title compound, C15H12F3N3, the pyrazolo[1,5-a]pyrimidine system ring is essentially planar with a maximum deviation from the mean plane of 0.014 (1) Å. The 4-tolyl group makes a dihedral angle of 14.1 (1)° with the pyrazolo[1,5-a]pyrimidine ring system. The crystal packing is stabilized mainly by van der Waals forces

Making Maps Of The Cosmic Microwave Background: The MAXIMA Example

This work describes Cosmic Microwave Background (CMB) data analysis algorithms and their implementations, developed to produce a pixelized map of the sky and a corresponding pixel-pixel noise correlation matrix from time ordered data for a CMB mapping experiment. We discuss in turn algorithms for estimating noise properties from the time ordered data, techniques for manipulating the time ordered data, and a number of variants of the maximum likelihood map-making procedure. We pay particular attention to issues pertinent to real CMB data, and present ways of incorporating them within the framework of maximum likelihood map-making. Making a map of the sky is shown to be not only an intermediate step rendering an image of the sky, but also an important diagnostic stage, when tests for and/or removal of systematic effects can efficiently be performed. The case under study is the MAXIMA data set. However, the methods discussed are expected to be applicable to the analysis of other current and forthcoming CMB experiments.Comment: Replaced to match the published version, only minor change

Results of a phase I-II study of fenretinide and rituximab for patients with indolent B-cell lymphoma and mantle cell lymphoma.

Fenretinide, a synthetic retinoid, induces apoptotic cell death in B-cell non-Hodgkin lymphoma (B-NHL) and acts synergistically with rituximab in preclinical models. We report results from a phase I-II study of fenretinide with rituximab for B-NHLs. Eligible diagnoses included indolent B-NHL or mantle cell lymphoma. The phase I design de-escalated from fenretinide at 900 mg/