Alexithymia Moderates the Association Between Maternal Depressive Symptoms and Perceived Adolescent Adjustment

Rates of suicide among African American youth are increasing faster than any other ethnic group (Bridge et al., 2015). With mental illness associated with suicide rates, it is essential to understand how symptoms manifest during adolescence. Although the association between maternal depression and poor adolescent adjustment is well established, there is a dearth of evidence examining the impact of maternal alexithymia on adolescent adjustment, particularly among low-income youth. The goal of the study was to elucidate the role of maternal alexithymia (difficulty understanding and expressing emotion) in the association between maternal depressive symptoms and adolescent adjustment within a sample of low-income urban youth. Data from the current sample were drawn from Project COPE, a 4-year longitudinal study of low-income urban youth from the eastern United States. The analytic sample consisted of youth (N = 351, Mage=12.20 years, SD=0.68 years at baseline) and their maternal caregivers from Time 1 of the study. The youth identified as 91% African American and 53% male. Maternal depression and Alexithymia was assessed using self-reports from the Brief Symptoms Inventory and the Toronto-Alexithymia scale respectively. Adolescent adjustment (anxiety and depressive symptoms) was assessed via caregiver reports from the Child Behavior Checklist. Results from moderation analyses revealed that maternal alexithymia moderated the association between maternal depression and perceived adolescent adjustment. Specifically, the association between maternal depressive symptoms and decreased perception of youth’s adjustment was stronger in mothers with high alexithymia. These findings illustrate the negative impact of maternal alexithymia on youth adjustment and subsequent poor outcomes.https://scholarscompass.vcu.edu/uresposters/1262/thumbnail.jp

Engineering studies related to Skylab program

The relationship between the S-193 Automatic Gain Control data and the magnitude of received signal power was studied in order to characterize performance parameters for Skylab equipment. The r-factor was used for the assessment and is defined to be less than unity, and a function of off-nadir angle, ocean surface roughness, and receiver signal to noise ratio. A digital computer simulation was also used to assess to additive receiver, or white noise. The system model for the digital simulation is described, along with intermediate frequency and video impulse response functions used, details of the input waveforms, and results to date. Specific discussion of the digital computer programs used is also provided

Wallops waveform analysis of SEASAT-1 radar altimeter data

Fitting a six parameter model waveform to over ocean experimental data from the waveform samplers in the SEASAT-1 radar altimeter is described. The fitted parameters include a waveform risetime, skewness, and track point; from these can be obtained estimates of the ocean surface significant waveheight, the surface skewness, and a correction to the altimeter's on board altitude measurement, respectively. Among the difficulties encountered are waveform sampler gains differing from calibration mode data, and incorporating the actual SEASAT-1 sampled point target response in the fitted wave form. There are problems in using the spacecraft derived attitude angle estimates, and a different attitude estimator is developed. Points raised in this report have consequences for the SEASAT-1 radar altimeter's ocean surface measurements are for the design and calibration of radar altimeters in future oceanographic satellites

Characteristics of ocean-reflected short radar pulses with application to altimetry and surface roughness determination

Current work related to geodetic altimetry is summarized. Special emphasis is placed on the effects of pulse length on both altimetry and sea-state estimation. Some discussion is also given of system tradeoff parameters and sea truth requirements to support scattering studies. The problem of analyzing signal characteristics and altimeter waveforms arising from rough surface backscattering is also considered

Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces

To enable safe and efficient human-robot collaboration in shared workspaces it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative re-planning with the above cost function. The key to our approach is thus to learn a cost function which "explains" the motion of the human. To do this, we gather example trajectories from pairs of participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively re-planning a trajectory using the learned cost function. Our planning algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF human kinematic model and accounts for the presence of a moving collaborator and obstacles in the environment. Our results suggest that in most cases, our method outperforms baseline methods when predicting motions. We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201

Barking Up the Right Tree: Are Small Groups Rational Agents?

Both mainstream economics and its critics have focused on models of individual rational agents even though most important decisions are made by small groups. Little systematic work has been done to study the behavior of small groups as decision-making agents in markets and other strategic games. This may limit the relevance of both economics and its critics to the objective of developing an understanding of how most important decisions are made. In order to gain some insight into this issue, this paper compares group and individual economic behavior. The objective of the research is to learn whether there are systematic differences between decisions made by groups and individual agents in market environments characterized by risky outcomes. A quantitative measure of deviation from minimallyrational decisions is used to compare group and individual behavior in common value auctions.