Task granularity analysis in logic programs

While logic programming languages offer a great deal of scope for parallelism, there is usually some overhead associated with the execution of goals in parallel because of the work involved in task creation and scheduling. In practice, therefore, the "granularity" of a goal, i.e. an estimate of the work available under it, should be taken into account when deciding whether or not to execute a goal concurrently as a sepárate task. This paper describes a method for estimating the granularity of a goal at compile time. The runtime overhead associated with our approach is usually quite small, and the performance improvements resulting from the incorporation of grainsize control can be quite good. This is shown by means of experimental results.

Dysfunctional and compensatory duality in mild cognitive impairment during a continuous recognition memory task

One of the current issues of debate in the study of mild cognitive impairment (MCI) is deviations of oscillatory brain responses from normal brain states and its dynamics. This work aims to characterize the differences of power in brain oscillations during the execution of a recognition memory task in MCI subjects in comparison with elderly controls. Magnetoencephalographic (MEG) signals were recorded during a continuous recognition memory task performance. Oscillatory brain activity during the recognition phase of the task was analyzed by wavelet transform in the source space by means of minimum norm algorithm. Both groups obtained a 77% hit ratio. In comparison with healthy controls, MCI subjects showed increased theta (p < 0.001), lower beta reduction (p < 0.001) and decreased alpha and gamma power (p < 0.002 and p < 0.001 respectively) in frontal, temporal and parietal areas during early and late latencies. Our results point towards a dual pattern of activity (increase and decrease) which is indicative of MCI and specific to certain time windows, frequency bands and brain regions. These results could represent two neurophysiological sides of MCI. Characterizing these opposing processes may contribute to the understanding of the disorder.

Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using spectral entropies and statistical complexity measures

Alzheimer's disease (AD) is the most common cause of dementia. Over the last few years, a considerable effort has been devoted to exploring new biomarkers. Nevertheless, a better understanding of brain dynamics is still required to optimize therapeutic strategies. In this regard, the characterization of mild cognitive impairment (MCI) is crucial, due to the high conversion rate from MCI to AD. However, only a few studies have focused on the analysis of magnetoencephalographic (MEG) rhythms to characterize AD and MCI. In this study, we assess the ability of several parameters derived from information theory to describe spontaneous MEG activity from 36 AD patients, 18 MCI subjects and 26 controls. Three entropies (Shannon, Tsallis and Rényi entropies), one disequilibrium measure (based on Euclidean distance ED) and three statistical complexities (based on Lopez Ruiz–Mancini–Calbet complexity LMC) were used to estimate the irregularity and statistical complexity of MEG activity. Statistically significant differences between AD patients and controls were obtained with all parameters (p < 0.01). In addition, statistically significant differences between MCI subjects and controls were achieved by ED and LMC (p < 0.05). In order to assess the diagnostic ability of the parameters, a linear discriminant analysis with a leave-one-out cross-validation procedure was applied. The accuracies reached 83.9% and 65.9% to discriminate AD and MCI subjects from controls, respectively. Our findings suggest that MCI subjects exhibit an intermediate pattern of abnormalities between normal aging and AD. Furthermore, the proposed parameters provide a new description of brain dynamics in AD and MCI.

DAEDALUS at SemEval-2014 Task 9: Comparing approaches for sentiment analysis in twitter

This paper describes our participation at SemEval- 2014 sentiment analysis task, in both contextual and message polarity classification. Our idea was to com- pare two different techniques for sentiment analysis. First, a machine learning classifier specifically built for the task using the provided training corpus. On the other hand, a lexicon-based approach using natural language processing techniques, developed for a ge- neric sentiment analysis task with no adaptation to the provided training corpus. Results, though far from the best runs, prove that the generic model is more robust as it achieves a more balanced evaluation for message polarity along the different test sets.

Anomalous Consistency in Mild Cognitive Impairment: A Complex Networks Approach

Increased variability in performance has been associated with the emergence of several neurological and psychiatric pathologies. However, whether and how consistency of neuronal activity may also be indicative of an underlying pathology is still poorly understood. Here we propose a novel method for evaluating consistency from non-invasive brain recordings. We evaluate the consistency of the cortical activity recorded with magnetoencephalography in a group of subjects diagnosed with Mild Cognitive Impairment (MCI), a condition sometimes prodromal of dementia, during the execution of a memory task. We use metrics coming from nonlinear dynamics to evaluate the consistency of cortical regions. A representation known as parenclitic networks is constructed, where atypical features are endowed with a network structure, the topological properties of which can be studied at various scales. Pathological conditions correspond to strongly heterogeneous networks, whereas typical or normative conditions are characterized by sparsely connected networks with homogeneous nodes. The analysis of this kind of networks allows identifying the extent to which consistency is affected in the MCI group and the focal points where MCI is especially severe. To the best of our knowledge, these results represent the first attempt at evaluating the consistency of brain functional activity using complex networks theory.

The impact of cognitive control on children's goal monitoring in a time-based prospective memory task

Peer reviewed

Differences in frontal cortical activation by a working memory task after substitution of risperidone for typical antipsychotic drugs in patients with schizophrenia

Antipsychotic drug treatment of schizophrenia may be complicated by side effects of widespread dopaminergic antagonism, including exacerbation of negative and cognitive symptoms due to frontal cortical hypodopaminergia. Atypical antipsychotics have been shown to enhance frontal dopaminergic activity in animal models. We predicted that substitution of risperidone for typical antipsychotic drugs in the treatment of schizophrenia would be associated with enhanced functional activation of frontal cortex. We measured cerebral blood oxygenation changes during periodic performance of a verbal working memory task, using functional MRI, on two occasions (baseline and 6 weeks later) in two cohorts of schizophrenic patients. One cohort (n = 10) was treated with typical antipsychotic drugs throughout the study. Risperidone was substituted for typical antipsychotics after baseline assessment in the second cohort (n = 10). A matched group of healthy volunteers (n = 10) was also studied on a single occasion. A network comprising bilateral dorsolateral prefrontal and lateral premotor cortex, the supplementary motor area, and posterior parietal cortex was activated by working memory task performance in both the patients and comparison subjects. A two-way analysis of covariance was used to estimate the effect of substituting risperidone for typical antipsychotics on power of functional response in the patient group. Substitution of risperidone increased functional activation in right prefrontal cortex, supplementary motor area, and posterior parietal cortex at both voxel and regional levels of analysis. This study provides direct evidence for significantly enhanced frontal function in schizophrenic patients after substitution of risperidone for typical antipsychotic drugs, and it indicates the potential value of functional MRI as a tool for longitudinal assessment of psychopharmacological effects on cerebral physiology.

Confirming the Association Between Affective Disturbance and Perceived Cognitive Dysfunction Among Breast Cancer Patients: A Meta-Analysis of Available Literature

Purpose: Breast cancer is the most frequently diagnosed cancer among women worldwide. While undergoing chemotherapy treatment for breast cancer, patients often report experiencing "chemobrain." Previous literature reports correlations between psychological distress and these perceived cognitive problems. The aim of the present study was to examine the strength of the association between affective disturbance and subjective cognitive dysfunction.Methods: This study included a meta-analysis of the literature reporting a correlation between mood and subjective cognitive dysfunction. Eight studies with 1344 breast cancer patients treated with chemotherapy were selected based on stringent study inclusion criteria. Studies reporting a correlation coefficient between mood and subjective cognitive dysfunction were included.Results: In these data, there was no significant correlation between affective disturbance and subjective cognitive dysfunction. A random effects model yielded an overall weighted mean effect size of 0.12.Conclusion: Although this meta-analysis did not confirm the correlation between mood and subjective cognitive dysfunction, there was a clear association between these factors in the original disaggregated analyses, and they are clearly impactful from the time of diagnosis through long-term after care. The clinical implications of the present study and future directions for research are discussed.

The Impact of Prefrontal Cortex "Warm Up" on Immediate Cognitive Reappraisal Ability in Older Adolescents with Elevated Symptoms of Depression

Cognitive Reappraisal (CR) is a central component of Cognitive Behavioral Therapy for adolescent depression. Yet, previous research indicates that a brain region highly associated with successful CR in adults, the Prefrontal Cortex (PFC), is not fully developed until early adulthood. Thus, there is growing concern that CBT interventions directed at building CR abilities in depressed teens might be constrained by PFC immaturity. However, CR is an effective strategy for regulating affect. The current study evaluated an intervention aimed at enhancing CR performance through PFC “warm up” with a working memory task. Additionally, the study examined moderators of intervention response, as well as cognitive correlates of self-reported CR use. Participants included 48 older adolescents (mean age=19.1, 89% female) with elevated symptoms of depression who were randomly assigned to a lab-based WM or control activity followed by a CR task. Overall, results failed to support the effectiveness of “warm up” to augment CR performance. However, current level of depression predicted negative bias and sadness ratings after CR instructions, and this effect was qualified by an interaction with condition. The moderator analysis showed that depressive symptoms interacted with condition such that in the control condition, participants with higher depressive symptoms had significantly lower negative bias scores than individuals with lower depressive symptoms, but this pattern was not found in the experimental condition. Contrary to hypotheses, history of depression did not moderate treatment response. Additional analyses explored alternative explanations for the lack of intervention effects. There was some evidence to suggest that the WM task was frustrating and cognitively taxing. However, irritation scores and overall WM task accuracy did not predict subsequent CR performance. Lastly, multiple cognitive variables emerged as correlates of self-reported CR use, with cognitive flexibility contributing unique variance to self-reported CR use. Results pointed to new directions for improving CR performance among youth with elevated symptoms of depression.

Improved commercial vehicle conspicuity and signalling systems. Task I: accident analysis and functional requirements. Interim report.

National Highway Traffic Safety Administration, Washington, D.C.

Automatic audio signing - literature survey analysis. Interim report, phase 1, task A.

Federal Highway Administration, Washington, D.C.

National Crash Severity Study - quality control. Task V: analysis to refine spinout aspects of CRASH. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

An analysis of the effectiveness of written driver license examinations in evaluating applicant driving abilities. Task 1 - revisions of test and manual.

Arizona Department of Transportation, Phoenix

An analysis of the effectiveness of written driver license examinations in evaluating applicant driving abilities. Task 2 - Arizona driver licensing for the 90's.

Arizona Department of Transportation, Phoenix

An analysis of the effectiveness of written driver license examinations in evaluating applicant driving abilities. Task 3 - Arizona classified license system.

Arizona Department of Transportation, Phoenix