The taxonomic distribution of asteroids from multi-filter all-sky photometric surveys

The distribution of asteroids across the Main Belt has been studied for decades to understand the compositional distribution and what that tells us about the formation and evolution of our solar system. All-sky surveys now provide orders of magnitude more data than targeted surveys. We present a method to bias-correct the asteroid population observed in the Sloan Digital Sky Survey (SDSS) according to size, distance, and albedo. We taxonomically classify this dataset consistent with the Bus and Bus-DeMeo systems and present the resulting taxonomic distribution. The dataset includes asteroids as small as 5 km, a factor of three in diameter smaller than in previous works. Because of the wide range of sizes in our sample, we present the distribution by number, surface area, volume, and mass whereas previous work was exclusively by number. While the distribution by number is a useful quantity and has been used for decades, these additional quantities provide new insights into the distribution of total material. We find evidence for D-types in the inner main belt where they are unexpected according to dynamical models of implantation of bodies from the outer solar system into the inner solar system during planetary migration (Levison et al. 2009). We find no evidence of S-types or other unexpected classes among Trojans and Hildas, albeit a bias favoring such a detection. Finally, we estimate for the first time the total amount of material of each class in the inner solar system. The main belt's most massive classes are C, B, P, V and S in decreasing order. Excluding the four most massive asteroids, Ceres, Pallas, Vesta and Hygiea that heavily skew the values, primitive material (C-, P-types) account for more than half main-belt and Trojan asteroids by mass, most of the remaining mass being in the S-types. All the other classes are minor contributors to the material between Mars and Jupiter.Comment: Accepted for publication in Icarus -- 43 pages, 15 figures, 7 table

A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

RNeXML: a package for reading and writing richly annotated phylogenetic, character, and trait data in R

NeXML is a powerful and extensible exchange standard recently proposed to better meet the expanding needs for phylogenetic data and metadata sharing. Here we present the RNeXML package, which provides users of the R programming language with easy-to-use tools for reading and writing NeXML documents, including rich metadata, in a way that interfaces seamlessly with the extensive library of phylogenetic tools already available in the R ecosystem

A fine-tuned global distribution dataset of marine forests

Species distribution records are a prerequisite to follow climate-induced range shifts across space and time. However, synthesizing information from various sources such as peer-reviewed literature, herbaria, digital repositories and citizen science initiatives is not only costly and time consuming, but also challenging, as data may contain thematic and taxonomic errors and generally lack standardized formats. We address this gap for important marine ecosystem-structuring species of large brown algae and seagrasses. We gathered distribution records from various sources and provide a fine-tuned dataset with ~2.8 million dereplicated records, taxonomically standardized for 682 species, and considering important physiological and biogeographical traits. Specifically, a flagging system was implemented to signal potentially incorrect records reported on land, in regions with limiting light conditions for photosynthesis, and outside the known distribution of species, as inferred from the most recent published literature. We document the procedure and provide a dataset in tabular format based on Darwin Core Standard (DwC), alongside with a set of functions in R language for data management and visualization.FCT: (SFRH/BPD/111003/2015) / (SFRH/BSAB/150485/2019) / (SFRH/BD/144878/2019)(PTDC/MAREST/6053/2014) / MARFOR (BIODIVERSA/004/2015) / UIDB/04326/2020info:eu-repo/semantics/publishedVersio

Asteroids in GALEX: Near-ultraviolet photometry of the major taxonomic groups

We present ultraviolet photometry (NUV band, 180--280 nm) of 405 asteroids observed serendipitously by the Galaxy Evolution Explorer (GALEX) from 2003--2012. All asteroids in this sample were detected by GALEX at least twice. Unambiguous visible-color-based taxonomic labels (C type versus S type) exist for 315 of these asteroids; of these, thermal-infrared-based diameters are available for 245. We derive NUV-V color using two independent models to predict the visual magnitude V at each NUV-detection epoch. Both V models produce NUV-V distributions in which the S types are redder than C types with more than 8-sigma confidence. This confirms that the S types' redder spectral slopes in the visible remain redder than the C types' into the NUV, this redness being consistent with absorption by silica-containing rocks. The GALEX asteroid data confirm earlier results from the International Ultraviolet Explorer, which two decades ago produced the only other sizeable set of UV asteroid photometry. The GALEX-derived NUV-V data also agree with previously published Hubble Space Telescope (HST) UV observations of asteroids 21 Lutetia and 1 Ceres. Both the HST and GALEX data indicate that NUV band is less useful than u band for distinguishing subgroups within the greater population of visible-color-defined C types (notably, M types and G types).Comment: 13 pages, 11 figures, accepted 2015-May-6 to The Astrophysical Journal. Includes one machine-readable table of NUV asteroid detections. Version 2 includes a corrected citation to Waszczak et al. (2015) arXiv abstrac

NEOWISE Observations of Near-Earth Objects: Preliminary Results

With the NEOWISE portion of the \emph{Wide-field Infrared Survey Explorer} (WISE) project, we have carried out a highly uniform survey of the near-Earth object (NEO) population at thermal infrared wavelengths ranging from 3 to 22 $\mu$m, allowing us to refine estimates of their numbers, sizes, and albedos. The NEOWISE survey detected NEOs the same way whether they were previously known or not, subject to the availability of ground-based follow-up observations, resulting in the discovery of more than 130 new NEOs. The survey's uniformity in sensitivity, observing cadence, and image quality have permitted extrapolation of the 428 near-Earth asteroids (NEAs) detected by NEOWISE during the fully cryogenic portion of the WISE mission to the larger population. We find that there are 981$\pm$19 NEAs larger than 1 km and 20,500$\pm$3000 NEAs larger than 100 m. We show that the Spaceguard goal of detecting 90% of all 1 km NEAs has been met, and that the cumulative size distribution is best represented by a broken power law with a slope of 1.32$\pm$0.14 below 1.5 km. This power law slope produces $\sim13,200\pm$1,900 NEAs with $D>$140 m. Although previous studies predict another break in the cumulative size distribution below $D\sim$50-100 m, resulting in an increase in the number of NEOs in this size range and smaller, we did not detect enough objects to comment on this increase. The overall number for the NEA population between 100-1000 m are lower than previous estimates. The numbers of near-Earth comets will be the subject of future work.Comment: Accepted to Ap

Utilising ontology-based modelling for learning content management

Learning content management needs to support a variety of open, multi-format Web-based software applications. We propose multidimensional, model-based semantic annotation as a way to support the management of access to and change of learning content. We introduce an information architecture model as the central contribution that supports multi-layered learning content structures. We discuss interactive query access, but also change management for multi-layered learning content management. An ontology-enhanced traceability approach is the solution