Particle acceleration and magnetic field structure in PKS 2155-304: optical polarimetric observations

In this paper, we present multiband optical polarimetric observations of the very-high energy blazar PKS 2155-304 made simultaneously with a HESS/Fermi high-energy campaign in 2008, when the source was found to be in a low state. The intense daily coverage of the data set allowed us to study in detail the temporal evolution of the emission, and we found that the particle acceleration time-scales are decoupled from the changes in the polarimetric properties of the source. We present a model in which the optical polarimetric emission originates at the polarized mm-wave core and propose an explanation for the lack of correlation between the photometric and polarimetric fluxes. The optical emission is consistent with an inhomogeneous synchrotron source in which the large-scale field is locally organized by a shock in which particle acceleration takes place. Finally, we use these optical polarimetric observations of PKS 2155-304 at a low state to propose an origin for the quiescent gamma-ray flux of the object, in an attempt to provide clues for the source of its recently established persistent TeV emission.

MODELING VERY LONG BASELINE INTERFEROMETRIC IMAGES WITH THE CROSS-ENTROPY GLOBAL OPTIMIZATION TECHNIQUE

We present a new technique for obtaining model fittings to very long baseline interferometric images of astrophysical jets. The method minimizes a performance function proportional to the sum of the squared difference between the model and observed images. The model image is constructed by summing N(s) elliptical Gaussian sources characterized by six parameters: two-dimensional peak position, peak intensity, eccentricity, amplitude, and orientation angle of the major axis. We present results for the fitting of two main benchmark jets: the first constructed from three individual Gaussian sources, the second formed by five Gaussian sources. Both jets were analyzed by our cross-entropy technique in finite and infinite signal-to-noise regimes, the background noise chosen to mimic that found in interferometric radio maps. Those images were constructed to simulate most of the conditions encountered in interferometric images of active galactic nuclei. We show that the cross-entropy technique is capable of recovering the parameters of the sources with a similar accuracy to that obtained from the very traditional Astronomical Image Processing System Package task IMFIT when the image is relatively simple (e. g., few components). For more complex interferometric maps, our method displays superior performance in recovering the parameters of the jet components. Our methodology is also able to show quantitatively the number of individual components present in an image. An additional application of the cross-entropy technique to a real image of a BL Lac object is shown and discussed. Our results indicate that our cross-entropy model-fitting technique must be used in situations involving the analysis of complex emission regions having more than three sources, even though it is substantially slower than current model-fitting tasks (at least 10,000 times slower for a single processor, depending on the number of sources to be optimized). As in the case of any model fitting performed in the image plane, caution is required in analyzing images constructed from a poorly sampled (u, v) plane.

An analysis of the composite stellar population in M32

We obtained long-slit spectra of high signal-to-noise ratio of the galaxy M32 with the Gemini Multi-Object Spectrograph at the Gemini-North telescope. We analysed the integrated spectra by means of full spectral fitting in order to extract the mixture of stellar populations that best represents its composite nature. Three different galactic radii were analysed, from the nuclear region out to 2 arcmin from the centre. This allows us to compare, for the first time, the results of integrated light spectroscopy with those of resolved colour-magnitude diagrams from the literature. As a main result we propose that an ancient and an intermediate-age population co-exist in M32, and that the balance between these two populations change between the nucleus and outside one effective radius (1r(eff)) in the sense that the contribution from the intermediate population is larger at the nuclear region. We retrieve a smaller signal of a young population at all radii whose origin is unclear and may be a contamination from horizontal branch stars, such as the ones identified by Brown et al. in the nuclear region. We compare our metallicity distribution function for a region 1 to 2 arcmin from the centre to the one obtained with photometric data by Grillmair et al. Both distributions are broad, but our spectroscopically derived distribution has a significant component with [Z/Z(circle dot)] <= -1, which is not found by Grillmair et al.

GEMINI near-infrared spectroscopic observations of young massive stars embedded in molecular clouds

K-band spectra of young stellar candidates in four Southern hemisphere clusters have been obtained with the Gemini Near-Infrared Spectrograph in Gemini South. The clusters are associated with IRAS sources that have colours characteristic of ultracompact H II regions. Spectral types were obtained by comparison of the observed spectra with those of a near-infrared (NIR) library; the results include the spectral classification of nine massive stars and seven objects confirmed as background late-type stars. Two of the studied sources have K-band spectra compatible with those characteristic of very hot stars, as inferred from the presence of C IV, N III and N V emission lines at 2.078, 2.116 and 2.100 mu m, respectively. One of them, I16177_IRS1, has a K-band spectrum similar to that of Cyg OB2 7, an O3If* supergiant star. The nebular K-band spectrum of the associated Ultra-Compact (UC) H II region shows the s-process [Kr III] and [Se IV] high excitation emission lines, previously identified only in planetary nebula. One young stellar object was found in each cluster, associated with either the main IRAS source or a nearby resolved Midecourse Space eXperiment (MSX) component, confirming the results obtained from previous NIR photometric surveys. The distances to the stars were derived from their spectral types and previously determined JHK magnitudes; they agree well with the values obtained from the kinematic method, except in the case of IRAS 15408-5356, for which the spectroscopic distance is about a factor of 2 smaller than the kinematic value.

Topological triangle characterization with application to object detection from images

A novel mathematical framework inspired on Morse Theory for topological triangle characterization in 2D meshes is introduced that is useful for applications involving the creation of mesh models of objects whose geometry is not known a priori. The framework guarantees a precise control of topological changes introduced as a result of triangle insertion/removal operations and enables the definition of intuitive high-level operators for managing the mesh while keeping its topological integrity. An application is described in the implementation of an innovative approach for the detection of 2D objects from images that integrates the topological control enabled by geometric modeling with traditional image processing techniques. (C) 2008 Published by Elsevier B.V.

Chaotic phase synchronization and desynchronization in an oscillator network for object selection

Object selection refers to the mechanism of extracting objects of interest while ignoring other objects and background in a given visual scene. It is a fundamental issue for many computer vision and image analysis techniques and it is still a challenging task to artificial Visual systems. Chaotic phase synchronization takes place in cases involving almost identical dynamical systems and it means that the phase difference between the systems is kept bounded over the time, while their amplitudes remain chaotic and may be uncorrelated. Instead of complete synchronization, phase synchronization is believed to be a mechanism for neural integration in brain. In this paper, an object selection model is proposed. Oscillators in the network representing the salient object in a given scene are phase synchronized, while no phase synchronization occurs for background objects. In this way, the salient object can be extracted. In this model, a shift mechanism is also introduced to change attention from one object to another. Computer simulations show that the model produces some results similar to those observed in natural vision systems.

Shape classification using complex network and Multi-scale Fractal Dimension

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.

Minimizing the object dimensions in circle and sphere packing problems

Given a fixed set of identical or different-sized circular items, the problem we deal with consists on finding the smallest object within which the items can be packed. Circular, triangular, squared, rectangular and also strip objects are considered. Moreover, 2D and 3D problems are treated. Twice-differentiable models for all these problems are presented. A strategy to reduce the complexity of evaluating the models is employed and, as a consequence, instances with a large number of items can be considered. Numerical experiments show the flexibility and reliability of the new unified approach. (C) 2007 Elsevier Ltd. All rights reserved.

Influence of education and depressive symptoms on cognitive function in the elderly

Objective: The purpose of the present study was to investigate the influence that education and depression have on the performance of elderly people in neuropsychological tests. Methods: The study was conducted at the Institute of Psychiatry, University of Sao Paulo School of Medicine, Hospital das Clinicas. All of the individuals evaluated were aged 60 or older. The study sample consisted of 59 outpatients with depressive disorders and 51 healthy controls. We stratified the sample by level of education: low = 1-4 years of schooling; high = 5 or more years of schooling. Evaluations consisted of psychiatric assessment, cognitive assessment, laboratory tests and cerebral magnetic resonance imaging. Results: We found that level of education influenced all the measures of cognitive domains investigated (intellectual efficiency, processing speed, attention, executive function and memory) except the Digit Span Forward and Fuld Object Memory Evaluation (immediate and delayed recall), whereas depressive symptoms influenced some measures of memory, attention, executive function and processing speed. Although the combination of a low level of education and depression had a significant negative influence on Stroop Test part B, Trail Making Test part B and Logical Memory (immediate recall), we found no other significant effects of the interaction between level of education and depression. Conclusion: The results of this study underscore the importance of considering the level of education in the analysis of cognitive performance in depressed elderly patients, as well as the relevance of developing new cognitive function tests in which level of education has a reduced impact on the results.