The shortest period M-dwarf eclipsing system BW3 V38, II: determination of absolute elements

The spectroscopic data for the short-period (0.1984 d)eclipsing binary V38, discovered by the OGLE micro-lensing team in Baade's Window field BW3, are analyzed. Radial velocity curves are derived from mid-resolution spectra obtained with EMMI-NTT at ESO - La Silla, and a simultaneous solution of the existing light curve by OGLE and of the new radial velocity curves is obtained. The system is formed by almost twin M3e dwarf components that are very close, but not yet in contact. The spectra of both dwarfs show signatures of the presence of strong chromospheres. Spectroscopy definitely confirms, therefore, what was suggested on the basis of photometry: BW3 V38 is indeed a unique system, as no other similar binary with M components and in such a tight orbit is known. Within the limits posed by the relatively large errors, due to the combined effect of system faintness and of the constraints on exposure time, the derived physical parameters seem to agree with the relations obtained from the other few known eclipsing binaries with late type components (which indicate a discrepancy between the available evolutionary models and the data at ~ 10% level). A possible explanation is the presence of strong magnetic fields and fast rotation (that applies to the BW3 V38 case as well). A simple computation of the system secular evolution by angular momentum loss and spin orbit synchronization shows that the evolution of a system with M dwarfs components is rather slow, and indicates as well a possible reason why systems similar to BW3 V38 are so rare.Comment: 9 pages, 7 figures, 3 tables, accepted for publication in A&

An Arabic Optical Braille Recognition System

Technology has shown great promise in providing access to textual information for visually impaired people. Optical Braille Recognition (OBR) allows people with visual impairments to read volumes of typewritten documents with the help of flatbed scanners and OBR software. This project looks at developing a system to recognize an image of embossed Arabic Braille and then convert it to text. It particularly aims to build fully functional Optical Arabic Braille Recognition system. It has two main tasks, first is to recognize printed Braille cells, and second is to convert them to regular text. Converting Braille to text is not simply a one to one mapping, because one cell may represent one symbol (alphabet letter, digit, or special character), two or more symbols, or part of a symbol. Moreover, multiple cells may represent a single symbol

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings