4 resultados para Computer Supported Cooperative Work (CSCW)
em Duke University
Resumo:
Axisymmetric radiating and scattering structures whose rotational invariance is broken by non-axisymmetric excitations present an important class of problems in electromagnetics. For such problems, a cylindrical wave decomposition formalism can be used to efficiently obtain numerical solutions to the full-wave frequency-domain problem. Often, the far-field, or Fraunhofer region is of particular interest in scattering cross-section and radiation pattern calculations; yet, it is usually impractical to compute full-wave solutions for this region. Here, we propose a generalization of the Stratton-Chu far-field integral adapted for 2.5D formalism. The integration over a closed, axially symmetric surface is analytically reduced to a line integral on a meridional plane. We benchmark this computational technique by comparing it with analytical Mie solutions for a plasmonic nanoparticle, and apply it to the design of a three-dimensional polarization-insensitive cloak.
Resumo:
The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated which promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests that behavioral signs can be observed late in the first year of life. Many of these studies involve extensive frame-by-frame video observation and analysis of a child's natural behavior. Although nonintrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are burdensome for clinical and large population research purposes. This work is a first milestone in a long-term project on non-invasive early observation of children in order to aid in risk detection and research of neurodevelopmental disorders. We focus on providing low-cost computer vision tools to measure and identify ASD behavioral signs based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure responses to general ASD risk assessment tasks and activities outlined by the AOSI which assess visual attention by tracking facial features. We show results, including comparisons with expert and nonexpert clinicians, which demonstrate that the proposed computer vision tools can capture critical behavioral observations and potentially augment the clinician's behavioral observations obtained from real in-clinic assessments.
Resumo:
The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated that promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests behavioral markers can be observed late in the first year of life. Many of these studies involved extensive frame-by-frame video observation and analysis of a child's natural behavior. Although non-intrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are impractical for clinical and large population research purposes. Diagnostic measures for ASD are available for infants but are only accurate when used by specialists experienced in early diagnosis. This work is a first milestone in a long-term multidisciplinary project that aims at helping clinicians and general practitioners accomplish this early detection/measurement task automatically. We focus on providing computer vision tools to measure and identify ASD behavioral markers based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure three critical AOSI activities that assess visual attention. We augment these AOSI activities with an additional test that analyzes asymmetrical patterns in unsupported gait. The first set of algorithms involves assessing head motion by tracking facial features, while the gait analysis relies on joint foreground segmentation and 2D body pose estimation in video. We show results that provide insightful knowledge to augment the clinician's behavioral observations obtained from real in-clinic assessments.
Resumo:
*Designated as an exemplary master's project for 2015-16*
This paper examines how contemporary literature contributes to the discussion of punitory justice. It uses close analysis of three contemporary novels, Margaret Atwood’s The Heart Goes Last, Hillary Jordan’s When She Woke, and Joyce Carol Oates’s Carthage, to deconstruct different conceptions of punitory justice. This analysis is framed and supported by relevant social science research on the concept of punitivity within criminal justice. Each section examines punitory justice at three levels: macro, where media messages and the predominant social conversation reside; meso, which involves penal policy and judicial process; and micro, which encompasses personal attitudes towards criminal justice. The first two chapters evaluate works by Atwood and Jordan, examining how their dystopian schemas of justice shed light on top-down and bottom-up processes of punitory justice in the real world. The third chapter uses a more realistic novel, Oates’s Carthage, to examine the ontological nature of punitory justice. It explores a variety of factors that give rise to and legitimize punitory justice, both at the personal level and within a broader cultural consensus. This chapter also discusses how both victim and perpetrator can come to stand in as metaphors to both represent and distract from broader social issues. As a whole, analysis of these three novels illuminate how current and common conceptualizations of justice have little to do with the actual act of transgression itself. Instead, justice emerges as a set of specific, conditioned responses to perceived threats, mediated by complex social, cultural, and emotive forces.
