Surface temperature of a magnetized neutron star and interpretation of the ROSAT data. II

We complete our study of pulsars' non-uniform surface temperature and of its effects on their soft X-ray thermal emission. Our previous work had shown that, due to gravitational lensing, dipolar fields cannot reproduce the strong pulsations observed in Vela, Geminga, PSR 0656+14, and PSR 1055-52. Assuming a standard neutron star mass of 1.4 Msol, we show here that the inclusion of a quadrupolar component, if it is suitably oriented, is sufficient to increase substantially the pulsed fraction, Pf, up to, or above, the observed values if the stellar radius is 13 km or even 10 km. For models with a radius of 7 km the maximum pulsed fraction obtainable with (isotropic) blackbody emission is of the order of 15% for orthogonal rotators (Vela, Geminga and PSR 1055-52) and only 5% for an inclined rotator as PSR 0656+14. Given the observed values this indicates that the neutron stars in Geminga and PSR 0656+14 have radii significantly larger than 7 km and, given the very specific quadrupole components required to increase Pf, even radii of the order of 10 km may be unlikely in all four cases. We confirm our previous finding that the pulsed fraction always increases with photon energy, below about 1 keV, when blackbody emission is used and show that it is due to the hardenning of the blackbody spectrum with increasing temperature. The observed decrease of pulsed fraction may thus suggest that the emitted spectrum softens with increasing temperature. Finally, we apply our model to reassess the magnetic field effect on the outer boundary condition used in neutron star cooling models and show that, in contradistinction to several previous claims, it is very small and most probably results in a slight reduction of the heat flow through the envelope.Comment: 17 pages with 8 figures. Uses AASTeX v4.0 macro. Submitted to Ap.

Breathers and Thermal Relaxation in Fermi-Pasta-Ulam Arrays

Breather stability and longevity in thermally relaxing nonlinear arrays depend sensitively on their interactions with other excitations. We review the relaxation of breathers in Fermi-Pasta-Ulam arrays, with a specific focus on the different relaxation channels and their dependence on the interparticle interactions, dimensionality, initial condition, and system parameters

Probability and Common-Sense: Tandem Towards Robust Robotic Object Recognition in Ambient Assisted Living

The suitable operation of mobile robots when providing Ambient Assisted Living (AAL) services calls for robust object recognition capabilities. Probabilistic Graphical Models (PGMs) have become the de-facto choice in recognition systems aiming to e ciently exploit contextual relations among objects, also dealing with the uncertainty inherent to the robot workspace. However, these models can perform in an inco herent way when operating in a long-term fashion out of the laboratory, e.g. while recognizing objects in peculiar con gurations or belonging to new types. In this work we propose a recognition system that resorts to PGMs and common-sense knowledge, represented in the form of an ontology, to detect those inconsistencies and learn from them. The utilization of the ontology carries additional advantages, e.g. the possibility to verbalize the robot's knowledge. A primary demonstration of the system capabilities has been carried out with very promising results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

OLT: A Toolkit for Object Labeling Applied to Robotic RGB-D Datasets

In this work we present the Object Labeling Toolkit (OLT), a set of software components publicly available for helping in the management and labeling of sequential RGB-D observations collected by a mobile robot. Such a robot can be equipped with an arbitrary number of RGB-D devices, possibly integrating other sensors (e.g. odometry, 2D laser scanners, etc.). OLT first merges the robot observations to generate a 3D reconstruction of the scene from which object segmentation and labeling is conveniently accomplished. The annotated labels are automatically propagated by the toolkit to each RGB-D observation in the collected sequence, providing a dense labeling of both intensity and depth images. The resulting objects’ labels can be exploited for many robotic oriented applications, including high-level decision making, semantic mapping, or contextual object recognition. Software components within OLT are highly customizable and expandable, facilitating the integration of already-developed algorithms. To illustrate the toolkit suitability, we describe its application to robotic RGB-D sequences taken in a home environment.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Spanish grant pro- gram FPU-MICINN 2010 and the Spanish projects TAROTH: New developments toward a Robot at Home (DPI2011-25483) and PROMOVE: Advances in mobile robotics for promoting independent life of elders (DPI2014-55826-R

Experiences on a motivational learning approach for robotics in undergraduate courses

This paper presents an educational experience carried out in robotics undergraduate courses from two different degrees: Computer Science and Industrial Engineering, having students with diverse capabilities and motivations. The experience compares two learning strategies for the practical lessons of such courses: one relies on code snippets in Matlab to cope with typical robotic problems like robot motion, localization, and mapping, while the second strategy opts for using the ROS framework for the development of algorithms facing a competitive challenge, e.g. exploration algorithms. The obtained students’ opinions were instructive, reporting, for example, that although they consider harder to master ROS when compared to Matlab, it might be more useful in their (robotic related) professional careers, which enhanced their disposition to study it. They also considered that the challenge-exercises, in addition to motivate them, helped to develop their skills as engineers to a greater extent than the skeleton-code based ones. These and other conclusions will be useful in posterior courses to boost the interest and motivation of the students.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Cara un modelo tecnolóxico na intervención orientadora: ontoloxía, tarefa xenérica e metodoloxía KADS no proceso de avaliación psicopedagóxica

[Resumo] o propósito deste traballo que presentamos é amosa-la facticidade dunha "transferencia" de metodoloxías entre dous campos, a primeira vista, epistemoloxicamente moi distantes como son a intelixencia artificial e a orientación educativa. Esa "transferencia" entrámbolos dous diferentes saberes científicos , neste artigo, restrínxese ás seguintes cuestións: 1.- creación de ontoloxías e pseudo-ontoloxías que expliciten o coñecemento que se encerra no concepto "avaliación psicopedagóxica" ,2.- introducción no discurso da orientación e da avaliación psicopedagóxica de termos como "tarefaxenérica" ou metodoloxías (especificamente KADS) para adquiri-Io coñecemento dun experto, 3.- posibilidade de trasvase do coñecemento adquirido mediante as metodoloxías e procesos anteriores nun sistema informático. Trátase de abordar, dende logo de modo preliminar, algunhas cuestións en tomo ó problema seguinte ¿ É posible, plausible, necesario, oportuno un modelo tecnolóxico nos procesos de intervenciónna orientación educativa

UPGMpp: a Software Library for Contextual Object Recognition

Object recognition is a cornerstone task towards the scene understanding problem. Recent works in the field boost their perfor- mance by incorporating contextual information to the traditional use of the objects’ geometry and/or appearance. These contextual cues are usually modeled through Conditional Random Fields (CRFs), a partic- ular type of undirected Probabilistic Graphical Model (PGM), and are exploited by means of probabilistic inference methods. In this work we present the Undirected Probabilistic Graphical Models in C++ library (UPGMpp), an open source solution for representing, training, and per- forming inference over undirected PGMs in general, and CRFs in par- ticular. The UPGMpp library supposes a reliable and comprehensive workbench for recognition systems exploiting contextual information, in- cluding a variety of inference methods based on local search, graph cuts, and message passing approaches. This paper illustrates the virtues of the library, i.e. it is efficient, comprehensive, versatile, and easy to use, by presenting a use-case applied to the object recognition problem in home scenes from the challenging NYU2 dataset.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Spanish grant program FPU-MICINN 2010 and the Spanish projects “TAROTH: New developments toward a robot at home” (Ref. DPI2011-25483) and “PROMOVE: Advances in mobile robotics for promoting independent life of elders” (Ref. DPI2014-55826-R

An evaluation of plume tracking as a strategy for gas source localization in turbulent wind flows

Gas source localization is likely the most direct application of a mobile robot endowed with gas sensing capabilities. Multiple algorithms have been proposed to locate the gas source within a known environment, ranging from bio-inspired to probabilistic ones. However, their application to real-world conditions still remains a major issue due to the great difficulties those scenarios bring, among others, the common presence of obstacles which hamper the movement of the robot and notably ncrease the complexity of the gas dispersion. In this work, we consider a plume tracking algorithm based on the well-known silkworm moth strategy and analyze its performance when facing different realistic environments characterized by the presence of obstacles and turbulent wind flows. We rely on computational fluid dynamics and the open source gas dispersion simulator GADEN to generate realistic gas distributions in scenarios where the presence of obstacles breaks down the ideal downwind plume. We first propose some modifications to the original silkworm moth algorithm in order to deal with the presence of obstacles in the environment (avoiding collisions) and then analyze its performance within four challenging environments.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Proyecto de excelencia de la Junata de Andalucia TEP2012-53

Centroid-Based Clustering with ab-Divergences

Centroid-based clustering is a widely used technique within unsupervised learning algorithms in many research fields. The success of any centroid-based clustering relies on the choice of the similarity measure under use. In recent years, most studies focused on including several divergence measures in the traditional hard k-means algorithm. In this article, we consider the problem of centroid-based clustering using the family of ab-divergences, which is governed by two parameters, a and b. We propose a new iterative algorithm, ab-k-means, giving closed-form solutions for the computation of the sided centroids. The algorithm can be fine-tuned by means of this pair of values, yielding a wide range of the most frequently used divergences. Moreover, it is guaranteed to converge to local minima for a wide range of values of the pair (a, b). Our theoretical contribution has been validated by several experiments performed with synthetic and real data and exploring the (a, b) plane. The numerical results obtained confirm the quality of the algorithm and its suitability to be used in several practical applications.MINECO TEC2017-82807-