While the literature on learning at scale has largely focused on MOOCs, online degree programs, and AI techniques for supporting scalable learning experiences, informal learning communities have been relatively underrepresented. None-theless, these massive open online learning communities regularly draw far more engaged users than the typical MOOC. Their informal structure, however, makes them significantly more difficult to study. In this work, we take a first step toward attempting to understand these communi-ties specifically from the perspective of scale. Taking a sample of 62 such communities, we develop a tagging sys-tem for understanding the specific features and how they relate to scale. For example, just as a MOOC cannot man-ually grade every assignment, so also an informal learning community cannot approve every contribution; and just as MOOCs therefore employ autograding, informal learning communities employ crowd-sourced moderation or plat-form-driven enforcement. Using these tags, we then select several communities for deeper case studies. We also use these tags to make sense of learning-based subreddits from the popular community site Reddit, which offers an API for programmatic analysis. Based on these techniques, we offer findings about the performance of informal learning communities at scale and issue a call to include these envi-ronments more fully in future research on learning at scale.

Mapping iceberg locations and geometrical parameters is important for marine operational applications and climate science. The innovative TanDEM-X mission (TDM) was used for 3D mapping of icebergs in sea ice with the single-pass SAR interferometry (InSAR) method. The extracted digital elevation model (DEM) from TDM InSAR data over icebergs in sea ice was compared to a DEM generated from very-high-resolution (VHR) electro-optical stereo data. The comparison demonstrated a good correspondence between the electro-optical and InSAR-derived DEMs. A significant decrease in root-mean-squared error (RMSE) was achieved after applying spatial filtering. The resulting RMSE for the areas with selected icebergs in sea ice was 2 m.

The RADARSAT-2 satellite is an advanced C-band synthetic aperture radar (SAR) with a variety of new modes including options for polarization combinations, resolution, and swath width. This paper examines the potential of multi polarization data for detecting and discriminating ship and iceberg targets Data used in this study consist of well validated airborne Convair-580 SAR and spaceborne ASAR HH/HV and HH/VV. In total, the data set used for evaluating detection and discrimination consists of 901 validated iceberg and ship targets. Optimizing target detection is accomplished using receiver operator curves (ROC) as proposed by [6] and discrimination is conducted using a quadratic discriminant (QD) with feature selection based on sequential forward selection (SFS). In general it was found that detection and discrimination improve with more polarimetric information; however, HH/HV and VV/VH only had nominally less discrimination performance than the quad polarization modes evaluated.

Modern medical imaging modalities provide large amounts of information in both the spatial and temporal domains and the incorporation of this information in a coherent algorithmic framework is a significant challenge. In this paper, we present a novel and intuitive approach to combine 3-D spatial and temporal (3-D + time) magnetic resonance imaging (MRI) data in an integrated segmentation algorithm to extract the myocardium of the left ventricle. A novel level-set segmentation process is developed that simultaneously delineates and tracks the boundaries of the left ventricle muscle. By encoding prior knowledge about cardiac temporal evolution in a parametric framework, an expectation-maximization algorithm optimally tracks the myocardial deformation over the cardiac cycle. The expectation step deforms the level-set function while the maximization step updates the prior temporal model parameters to perform the segmentation in a nonrigid sense.

SUMMARY: The Genetic Simulation Library (GSL) is a set of C++ programs and classes that can be used in individual-based simulation models of plant and animal populations. The classes in this library build representations of genes for a wide variety of different types of organisms, from asexually reproducing single-cell organisms to chromosome-structured sexually reproducing species. AVAILABILITY: http://www.csi.uoregon.edu/projects/genetics/GSL . Free for non-commercial applications; others should contact the author.

article Technology in use (panel session) Share on Authors: Sharon Traweek Joint Appointment in Anthropology and The Program in Science, Technology and Society, MIT Joint Appointment in Anthropology and The Program in Science, Technology and Society, MITView Profile , Michael Lynch Department of Sociology, Whitman College Department of Sociology, Whitman CollegeView Profile , Richard Frankel Department of Medicine, Wayne State, University and consultant, National Aeronautics and Space Administration Department of Medicine, Wayne State, University and consultant, National Aeronautics and Space AdministrationView Profile , Brigitte Jordon Departments of Anthropology and Pediatrics, Michigan State University Departments of Anthropology and Pediatrics, Michigan State UniversityView Profile , Editor: Lucy A. Suchman Intelligent Systems Laboratory, Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA Intelligent Systems Laboratory, Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CAView Profile Authors Info & Claims ACM SIGCHI BulletinVolume 16Issue 4April 1985 pp 89–92https://doi.org/10.1145/1165385.317473Published:01 April 1985 1citation214DownloadsMetricsTotal Citations1Total Downloads214Last 12 Months5Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access