The supergiant B[e] star LHA 115-S 18 - binary and/or luminous blue variable?

Context. The mechanism by which supergiant (sg)B[e] stars support cool, dense dusty discs/tori and their physical relationship with other evolved, massive stars such as luminous blue variables is uncertain. Aims. In order to investigate both issues we have analysed the long term behaviour of the canonical sgB[e] star LHA 115-S 18. Methods. We employed the OGLE II-IV lightcurve to search for (a-)periodic variability and supplemented these data with new and historic spectroscopy. Results. In contrast to historical expectations for sgB[e] stars, S18 is both photometrically and spectroscopically highly variable. The lightcurve is characterised by rapid aperiodic ` aring' throughout the 16 years of observations. Changes in the high excitation emission line component of the spectrum imply evolution in the stellar temperature - as expected for luminous blue variables - although somewhat surprisingly, spectroscopic and photometric variability appears not to be correlated. Characterised by emission in low excitation metallic species, the cool circumstellar torus appears largely unaffected by this behaviour. Finally, in conjunction with intense, highly variable He ii emission, X-ray emission implies the presence of an unseen binary companion. Conclusions. S18 provides observational support for the putative physical association of (a subset of) sgB[e] stars and luminous blue variables. Given the nature of the circumstellar environment of S18 and that luminous blue variables have been suggested as SN progenitors, it is tempting to draw a parallel to the progenitors of SN1987A and SN2009ip. Moreover the likely binary nature of S18 strengthens the possibility that the dusty discs/tori that characterise sgB[e] stars are the result of binary-driven mass-loss; consequently such stars may provide a window on the short lived phase of mass-transfer in massive compact binaries.

Where Are We After Five Editions?: Robot Vision Challenge, a Competition that Evaluates Solutions for the Visual Place Classification Problem

This article describes the Robot Vision challenge, a competition that evaluates solutions for the visual place classification problem. Since its origin, this challenge has been proposed as a common benchmark where worldwide proposals are measured using a common overall score. Each new edition of the competition introduced novelties, both for the type of input data and subobjectives of the challenge. All the techniques used by the participants have been gathered up and published to make it accessible for future developments. The legacy of the Robot Vision challenge includes data sets, benchmarking techniques, and a wide experience in the place classification research that is reflected in this article.