Combat Exposure, Agency, Perceived Threat, Guilt, and Posttraumatic Stress Disorder among Iraq and Afghanistan War Veterans

This study assessed how various combat experiences related to post-deployment adjustment among 289 Iraq/Afghanistan veterans. The study examined the relationships among three predictor variables (combat exposure, agency, perceived threat), one mediator (guilt), and two criterion factors (posttraumatic stress disorder/PTSD, and psychological wellbeing/PWB). It distinguished agency (e.g., firing or killing) from combat exposure (e.g., being fired at or witnessing). The study sought to: a) examine whether combat exposure differs from agency as constructs of combat experiences; b) determine the contributions of three predictors to the degree of PTSD and PWB; and c) determine whether guilt mediated the relationships between the three predictors and the two criterion factors. Instruments used included the Combat Experiences Subscale, the Post-Battle Subscale, and the Perceived Threat Subscale from the Deployment Risk and Resilience Inventory (DRRI), the Atrocities Exposure Subscale, the Laufer-Parson Guilt Inventory, the PTSD Checklist (PCL – Military), the Satisfaction With Life Scale, the Self-Acceptance Subscale and the Purpose in Life Subscale developed by Ryff (1989). Factor analyses, correlational analyses, hierarchical regression analyses, and Sobel Tests were used to analyze the data. Results indicated that exposure and agency were two constructs with shared commonalties (especially those involving injuring and killing of enemy combatants). Agency-Civilian-Casualties emerged as a new variable that merits further exploration due to the increases in civilian causalities in modern warfare. Atrocity also appeared to be a distinct variable that needs further examination. About 96% of participants reported having been under fire. However, 41% reported never having fired at the enemies. About 72% reported having at least one moderate PTSD symptom, and 43% could be identified as PTSD positive. All three predictors were highly correlated with guilt, PTSD, and PWB. PTSD was found to be highly (negatively) correlated with PWB. Together, the three predictors accounted for 58% of the total variance for PTSD, and 46% for PWB. When guilt was included in the regression, the four variables accounted for 78% of the total variance for PTSD, and 64% for PWB. Guilt mediated between exposure and PTSD, agency and PTSD, and agency and PWB. Implications of these findings were discussed

Regularized Principal Component Analysis for Spatial Data

In many atmospheric and earth sciences, it is of interest to identify dominant spatial patterns of variation based on data observed at $p$ locations and $n$ time points with the possibility that $p>n$. While principal component analysis (PCA) is commonly applied to find the dominant patterns, the eigenimages produced from PCA may exhibit patterns that are too noisy to be physically meaningful when $p$ is large relative to $n$. To obtain more precise estimates of eigenimages, we propose a regularization approach incorporating smoothness and sparseness of eigenimages, while accounting for their orthogonality. Our method allows data taken at irregularly spaced or sparse locations. In addition, the resulting optimization problem can be solved using the alternating direction method of multipliers, which is easy to implement, and applicable to a large spatial dataset. Furthermore, the estimated eigenfunctions provide a natural basis for representing the underlying spatial process in a spatial random-effects model, from which spatial covariance function estimation and spatial prediction can be efficiently performed using a regularized fixed-rank kriging method. Finally, the effectiveness of the proposed method is demonstrated by several numerical example

Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders

An effective approach to non-parallel voice conversion (VC) is to utilize deep neural networks (DNNs), specifically variational auto encoders (VAEs), to model the latent structure of speech in an unsupervised manner. A previous study has confirmed the ef- fectiveness of VAE using the STRAIGHT spectra for VC. How- ever, VAE using other types of spectral features such as mel- cepstral coefficients (MCCs), which are related to human per- ception and have been widely used in VC, have not been prop- erly investigated. Instead of using one specific type of spectral feature, it is expected that VAE may benefit from using multi- ple types of spectral features simultaneously, thereby improving the capability of VAE for VC. To this end, we propose a novel VAE framework (called cross-domain VAE, CDVAE) for VC. Specifically, the proposed framework utilizes both STRAIGHT spectra and MCCs by explicitly regularizing multiple objectives in order to constrain the behavior of the learned encoder and de- coder. Experimental results demonstrate that the proposed CD- VAE framework outperforms the conventional VAE framework in terms of subjective tests.Comment: Accepted to ISCSLP 201