Research in computer science

Synopses are given for NASA supported work in computer science at the University of Virginia. Some areas of research include: error seeding as a testing method; knowledge representation for engineering design; analysis of faults in a multi-version software experiment; implementation of a parallel programming environment; two computer graphics systems for visualization of pressure distribution and convective density particles; task decomposition for multiple robot arms; vectorized incomplete conjugate gradient; and iterative methods for solving linear equations on the Flex/32

Research in computer science

Several short summaries of the work performed during this reporting period are presented. Topics discussed in this document include: (1) resilient seeded errors via simple techniques; (2) knowledge representation for engineering design; (3) analysis of faults in a multiversion software experiment; (4) implementation of parallel programming environment; (5) symbolic execution of concurrent programs; (6) two computer graphics systems for visualization of pressure distribution and convective density particles; (7) design of a source code management system; (8) vectorizing incomplete conjugate gradient on the Cyber 203/205; (9) extensions of domain testing theory and; (10) performance analyzer for the pisces system

Intrarater Agreement of Elbow Extension Range of Motion in the Upper Limb Neurodynamic Test 1 Using a Smartphone Application

To estimate the intrarater agreement of the Compass application of a smartphone in the assessment of elbow extension range of motion (EE-ROM) at pain onset and maximum tolerable point during the Upper Limb Neurodynamic Test 1 (ULNT1).info:eu-repo/semantics/publishedVersio

Research in computer science

Various graduate research activities in the field of computer science are reported. Among the topics discussed are: (1) failure probabilities in multi-version software; (2) Gaussian Elimination on parallel computers; (3) three dimensional Poisson solvers on parallel/vector computers; (4) automated task decomposition for multiple robot arms; (5) multi-color incomplete cholesky conjugate gradient methods on the Cyber 205; and (6) parallel implementation of iterative methods for solving linear equations

Measuring category intuitiveness in unconstrained categorization tasks

What makes a category seem natural or intuitive? In this paper, an unsupervised categorization task was employed to examine observer agreement concerning the categorization of nine different stimulus sets. The stimulus sets were designed to capture different intuitions about classification structure. The main empirical index of category intuitiveness was the frequency of the preferred classification, for different stimulus sets. With 169 participants, and a within participants design, with some stimulus sets the most frequent classification was produced over 50 times and with others not more than two or three times. The main empirical finding was that cluster tightness was more important in determining category intuitiveness, than cluster separation. The results were considered in relation to the following models of unsupervised categorization: DIVA, the rational model, the simplicity model, SUSTAIN, an Unsupervised version of the Generalized Context Model (UGCM), and a simple geometric model based on similarity. DIVA, the geometric approach, SUSTAIN, and the UGCM provided good, though not perfect, fits. Overall, the present work highlights several theoretical and practical issues regarding unsupervised categorization and reveals weaknesses in some of the corresponding formal models

Effectiveness of slow motion video compared to real time video in improving the accuracy and consistency of subjective gait analysis in dogs

Objective measures of canine gait quality via force plates, pressure mats or kinematic analysis are considered superior to subjective gait assessment (SGA). Despite research demonstrating that SGA does not accurately detect subtle lameness, it remains the most commonly performed diagnostic test for detecting lameness in dogs. This is largely because the financial, temporal and spatial requirements for existing objective gait analysis equipment makes this technology impractical for use in general practice. The utility of slow motion video as a potential tool to augment SGA is currently untested. To evaluate a more accessible way to overcome the limitations of SGA, a slow motion video study was undertaken. Three experienced veterinarians reviewed video footage of 30 dogs, 15 with a diagnosis of primary limb lameness based on history and physical examination, and 15 with no indication of limb lameness based on history and physical examination. Four different videos were made for each dog, demonstrating each dog walking and trotting in real time, and then again walking and trotting in 50% slow motion. For each video, the veterinary raters assessed both the degree of lameness, and which limb(s) they felt represented the source of the lameness. Spearman’s rho, Cramer’s V, and t-tests were performed to determine if slow motion video increased either the accuracy or consistency of raters’ SGA relative to real time video. Raters demonstrated no significant increase in consistency or accuracy in their SGA of slow motion video relative to real time video. Based on these findings, slow motion video does not increase the consistency or accuracy of SGA values. Further research is required to determine if slow motion video will benefit SGA in other ways

In praise of tedious anatomy

Functional neuroimaging is fundamentally a tool for mapping function to structure, and its success consequently requires neuroanatomical precision and accuracy. Here we review the various means by which functional activation can be localized to neuroanatomy and suggest that the gold standard should be localization to the individual’s or group’s own anatomy through the use of neuroanatomical knowledge and atlases of neuroanatomy. While automated means of localization may be useful, they cannot provide the necessary accuracy, given variability between individuals. We also suggest that the field of functional neuroimaging needs to converge on a common set of methods for reporting functional localization including a common “standard” space and criteria for what constitutes sufficient evidence to report activation in terms of Brodmann’s areas

Measuring Motivation Orientation and School Readiness in Children Served by Head Start

Currently, the most widely used direct assessment of motivation orientation for preschoolers has little to no research on its reliability and validity. This study examined the test–retest reliability and concurrent and predictive validity of this direct assessment. Results highlight potential limitations of this measure in capturing motivation orientation in preschoolers from low-income families