Autobiographical memory specificity, depression, and trauma in bipolar disorder

Objectives. In a bipolar disorder (BD) sample, the present study investigated: (i) the prevalence of trauma; (ii) the specificity of autobiographical memory (AM); (iii) the influence of childhood trauma on AM specificity, current inter-episode depressive mood, and BD severity; (iv) if AM specificity moderates the influence of childhood trauma on current inter-episode depressive mood and BD severity.

Methods. Fifty-two participants were recruited from a geographically well-defined mental health service in Northern Ireland. The AM test, self-report measures of lifetime experience of trauma, childhood trauma, and depression were administered. Severity of BD was estimated utilizing a systematic tool for reviewing all available clinical data of participants.

Results. A high prevalence of trauma was found. A total of 94.2% (49/52) of participants reported experiencing a traumatic event in either childhood or adulthood. AM specificity was significantly lower than previous reports of such in major depression. However, whilst childhood trauma predicted current inter-episode depressive mood, childhood trauma was not predictive of BD severity or AM specificity. Moreover, the association between childhood trauma and depressed mood was not moderated by AM specificity.

Conclusions. The findings of this study suggest a relationship between early psychosocial adversity and current inter-episode depressive mood in BD. In addition, levels of overgeneral AM are similar to that reported for depression, but are unrelated to childhood trauma, current inter-episode depressive mood, or BD severity. Clinical implications include the importance of routine assessment of trauma in BD and the need for adjunctive evidenced-based psychological therapies.

A Comparison of Programming Models for Multiprocessors with Explicitly Managed Memory Hierarchies

On multiprocessors with explicitly managed memory hierarchies (EMM), software has the responsibility of moving data in and out of fast local memories. This task can be complex and error-prone even for expert programmers. Before we can allow compilers to handle the complexity for us, we must identify the abstractions that are general enough to allow us to write applications with reasonable effort, yet speci?c enough to exploit the vast on-chip memory bandwidth of EMM multi-processors. To this end, we compare two programming models against hand-tuned codes on the STI Cell, paying attention to programmability and performance. The ?rst programming model, Sequoia, abstracts the memory hierarchy as private address spaces, each corresponding to a parallel task. The second, Cellgen, is a new framework which provides OpenMP-like semantics and the abstraction of a shared address spaces divided into private and shared data. We compare three applications programmed using these models against their hand-optimized counterparts in terms of abstractions, programming complexity, and performance.

Modified Information Criteria and Selection of Long Memory Time Series Models

The problem of model selection of a univariate long memory time series is investigated once a semi parametric estimator for the long memory parameter has been used. Standard information criteria are not consistent in this case. A Modified Information Criterion (MIC) that overcomes these difficulties is introduced and proofs that show its asymptotic validity are provided. The results are general and cover a wide range of short memory processes. Simulation evidence compares the new and existing methodologies and empirical applications in monthly inflation and daily realized volatility are presented.