Gas distribution, kinematics and star formation in faint dwarf galaxies

We compare the gas distribution, kinematics and the current star formation in a sample of 10 very faint (-13.37 < M_B < -9.55) dwarf galaxies. For 5 of these galaxies we present fresh, high sensitivity, GMRT HI 21cm observations. For all our galaxies we construct maps of the HI column density at a constant linear resolution of ~300 pc; this forms an excellent data set to check for the presence of a threshold column density for star formation. We find that while current star formation (as traced by Halpha emission) is confined to regions with relatively large (N_HI > (0.4 -1.7) X 10^{21} atoms cm^{-2}) HI column density, the morphology of the Halpha emission is in general not correlated with that of the high HI column density gas. Thus, while high column density gas may be necessary for star formation, in this sample at least, it is not sufficient to ensure that star formation does in fact occur. We examine the line profiles of the HI emission, but do not find a simple relation between regions with complex line profiles and those with on-going star formation. Finally, we examine the very fine scale (~20-100 pc) distribution of the HI gas, and find that at these scales the emission exhibits a variety of shell like, clumpy and filamentary features. The Halpha emission is sometimes associated with high density HI clumps, sometimes the Halpha emission lies inside a high density shell, and sometimes there is no correspondence between the Halpha emission and the HI clumps. In summary, the interplay between star formation and gas density in these galaxy does not seem to show the simple large scale patterns observed in brighter galaxies (abridged).Comment: 15 pages, 6 tables, 13 figures. Accepted for publication in MNRA

Illuminating the past 8 billion years of cold gas towards two gravitationally lensed quasars

Using the Boolardy Engineering Test Array of the Australian Square Kilometre Array Pathfinder (ASKAP BETA), we have carried out the first z = 0-1 survey for HI and OH absorption towards the gravitationally lensed quasars PKS B1830-211 and MGJ0414+0534. Although we detected all previously reported intervening systems towards PKS B1830-211, in the case of MG J0414+0534, three systems were not found, indicating that the original identifications may have been confused with radio frequency interference. Given the sensitivity of our data, we find that our detection yield is consistent with the expected frequency of intervening HI systems estimated from previous surveys for 21-cm emission in nearby galaxies and z ~ 3 damped Lyman a absorbers. We find spectral variability in the z = 0.886 face-on spiral galaxy towards PKS B1830-211 from observations undertaken with the Westerbork Synthesis Radio Telescope in 1997/1998 and ASKAP BETA in 2014/2015. The HI equivalent width varies by a few per cent over approximately yearly time-scales. This long-term spectral variability is correlated between the north-east and south-west images of the core, and with the total flux density of the source, implying that it is observationally coupled to intrinsic changes in the quasar. The absence of any detectable variability in the ratio of HI associated with the two core images is in stark contrast to the behaviour previously seen in the molecular lines. We therefore infer that coherent opaque HI structures in this galaxy are larger than the parsec-scale molecular clouds found at mm-wavelengths

Common characteristics of open source software development and applicability for drug discovery: a systematic review

<p>Abstract</p> <p>Background</p> <p>Innovation through an open source model has proven to be successful for software development. This success has led many to speculate if open source can be applied to other industries with similar success. We attempt to provide an understanding of open source software development characteristics for researchers, business leaders and government officials who may be interested in utilizing open source innovation in other contexts and with an emphasis on drug discovery.</p> <p>Methods</p> <p>A systematic review was performed by searching relevant, multidisciplinary databases to extract empirical research regarding the common characteristics and barriers of initiating and maintaining an open source software development project.</p> <p>Results</p> <p>Common characteristics to open source software development pertinent to open source drug discovery were extracted. The characteristics were then grouped into the areas of participant attraction, management of volunteers, control mechanisms, legal framework and physical constraints. Lastly, their applicability to drug discovery was examined.</p> <p>Conclusions</p> <p>We believe that the open source model is viable for drug discovery, although it is unlikely that it will exactly follow the form used in software development. Hybrids will likely develop that suit the unique characteristics of drug discovery. We suggest potential motivations for organizations to join an open source drug discovery project. We also examine specific differences between software and medicines, specifically how the need for laboratories and physical goods will impact the model as well as the effect of patents.</p

Big-bang Nucleosynthesis Enters the Precision Era

The last parameter of big-bang nucleosynthesis, the baryon density, is being pinned down by measurements of the deuterium abundance in high-redshift hydrogen clouds. When it is determined, it will fix the primeval light-element abundances. D, ^3He and ^7Li will become ``tracers'' for the study of Galactic and stellar chemical evolution, and big-bang nucleosynthesis will become an even sharper probe of particle physics, e.g., the bound to the number of light neutrino species will be tightened significantly. Two key tests of the consistency of the standard theory are on the horizon: an independent, high-precision determination of the baryon density from anisotropy of the cosmic background radiation and a precision determination of the primeval $^4$He abundance.Comment: 19 pages Latex; 8 eps figures; submitted to Rev Mod Phys (Colloquia

Attributed algebraic specifications

Determining what factors can influence the successful outcome of a software project has been labeled by many scholars and software engineers as a difficult problem. In this paper we use machine learning to create a model that can determine the stage a software project has obtained with some accuracy. Our model uses 8 Open Source project metrics to determine the stage a project is in. We validate our model using two performance measures; the exact success rate of classifying an Open Source Software project and the success rate over an interval of one stage of its actual performance using different scales of our dependent variable. In all cases we obtain an accuracy of above 70% with one away classification (a classification which is away by one) and about 40% accuracy with an exact classification. We also determine the factors (according to one classifier) that uses only eight variables among all the variables available in SourceForge, that determine the health of an OSS project

What Attracts Newcomers to Onboard on OSS Projects? TL;DR: Popularity

Part 3: FLOSS AdoptionInternational audienceVoluntary contributions play an important role in maintaining Open Source Software (OSS) projects active. New volunteers feel motivated to contribute to OSS projects based on a set of motivations. In this study, we aim to understand which factors OSS projects usually maintain that might influence their new contributors’ onboarding. Using a set of 450 repositories, we investigated mixed factors, such as the project age, the number of stars, the programming language used, or the presence of text files that aid contributors (e.g., templates for pull-requests or license files). We used a K-Spectral Centroid (KSC) clustering algorithm to investigated the newcomers’ growth rate for the analyzed projects. We could found three common patterns: a logarithmic, an exponential, and a linear growth pattern. Based on these patterns, we used a Random Forest classifier to understand how each factor could explain the growth rates. We found that popularity of the project (in terms of stars), time to review pull requests, project age, and programming languages are the factors that best explain the newcomers’ growth patterns