Ultraluminous X-ray sources with the SKA

The discovery almost three decades ago of non-nuclear, point-like X-ray sources with X-ray luminosities LX ≥ 3 × 1039 erg s−1 revolutionized the physics of black hole accretion. If of stellar origin, such Ultraluminous X-ray sources (ULXs) would have to accrete at super-Eddington rates in order to reach the observed high X-ray luminosities. Alternatively, ULXs could host sub-Eddington accreting intermediate-mass black holes, which are the long-time sought missing link between stellar and supermassive black holes and the possible seeds of the supermassive black holes that formed in the early Universe. The nature of ULXs can be better investigated in those cases for which a radio counterpart is detected. Radio observations of ULXs have revealed a wide variety of morphologies and source types, from compact and extended jets to radio nebulae and transient behaviours, providing the best observational evidence for the presence of an intermediate-mass black hole in some of them. The high sensitivity of the SKA will allow us to study the faintest ULX radio counterparts in the Local Universe as well as to detect new sources at much larger distances. It will thus perform a leap step in understanding ULXs, their accretion physics, and their possible role as seed black holes in supermassive black hole and galaxy growth.

A connectionist computational method for face recognition

In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.