Efficient, Near Complete and Often Sound Hybrid Dynamic Data Race Prediction (extended version)

Dynamic data race prediction aims to identify races based on a single program run represented by a trace. The challenge is to remain efficient while being as sound and as complete as possible. Efficient means a linear run-time as otherwise the method unlikely scales for real-world programs. We introduce an efficient, near complete and often sound dynamic data race prediction method that combines the lockset method with several improvements made in the area of happens-before methods. By near complete we mean that the method is complete in theory but for efficiency reasons the implementation applies some optimizations that may result in incompleteness. The method can be shown to be sound for two threads but is unsound in general. We provide extensive experimental data that shows that our method works well in practice.Comment: typos, appendi

Attention bias and anxiety in young children exposed to family violence

Background—Attention bias towards threat is associated with anxiety in older youth and adults and has been linked with violence exposure. Attention bias may moderate the relationship between violence exposure and anxiety in young children. Capitalizing on measurement advances, the current study examines these relationships at a younger age than previously possible. Methods—Young children (mean age 4.7, ±0.8) from a cross-sectional sample oversampled for violence exposure (N = 218) completed the dot-probe task to assess their attention biases. Observed fear/anxiety was characterized with a novel observational paradigm, the Anxiety Diagnostic Observation Schedule. Mother-reported symptoms were assessed with the Preschool-Age Psychiatric Assessment and Trauma Symptom Checklist for Young Children. Violence exposure was characterized with dimensional scores reflecting probability of membership in two classes derived via latent class analysis from the Conflict Tactics Scales: Abuse and Harsh Parenting. Results—Family violence predicted greater child anxiety and trauma symptoms. Attention bias moderated the relationship between violence and anxiety

Diary reports of concerns in mothers of infant siblings of children with autism across the first year of life

We examined the home-based concerns reported by mothers of infant siblings of children with autism across the first year of life. At all three ages measured, mothers of high-risk infants were significantly more likely than mothers of low-risk infants to report language, social communication, and restricted and repetitive behavior concerns but were not more likely to report general, medically based concerns. At 6 and 9 months of age, maternal concerns were poorly related to infant or family variables. At 12 months of age, there were moderate correlations between maternal concerns and infant behavior, and concerns were associated with the proband's autism symptoms and mothers' concurrent depressive symptoms. These findings highlight the need to examine high-risk infants' development in the family context.R21 DC 08637 - NIDCD NIH HHS; R01-DC010290 - NIDCD NIH HHS; AS1323 - Autism Speaks; R21 DC008637 - NIDCD NIH HHS; R01 DC010290 - NIDCD NIH HH

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Predictors and characteristics of an ultra-distance mountain bike race performance

http://www.ester.ee/record=b468968

Quantitative trait variation in ASD probands and toddler sibling outcomes at 24 months

BACKGROUND: Younger siblings of children with autism spectrum disorder (ASD) are at increased likelihood of receiving an ASD diagnosis and exhibiting other developmental concerns. It is unknown how quantitative variation in ASD traits and broader developmental domains in older siblings with ASD (probands) may inform outcomes in their younger siblings. METHODS: Participants included 385 pairs of toddler siblings and probands from the Infant Brain Imaging Study. ASD probands (mean age 5.5 years, range 1.7 to 15.5 years) were phenotyped using the Autism Diagnostic Interview-Revised (ADI-R), the Social Communication Questionnaire (SCQ), and the Vineland Adaptive Behavior Scales, Second Edition (VABS-II). Siblings were assessed using the ADI-R, VABS-II, Mullen Scales of Early Learning (MSEL), and Autism Diagnostic Observation Schedule (ADOS) and received a clinical best estimate diagnosis at 24 months using DSM-IV-TR criteria (n = 89 concordant for ASD; n = 296 discordant). We addressed two aims: (1) to determine whether proband characteristics are predictive of recurrence in siblings and (2) to assess associations between proband traits and sibling dimensional outcomes at 24 months. RESULTS: Regarding recurrence risk, proband SCQ scores were found to significantly predict sibling 24-month diagnostic outcome (OR for a 1-point increase in SCQ = 1.06; 95% CI = 1.01, 1.12). Regarding quantitative trait associations, we found no significant correlations in ASD traits among proband-sibling pairs. However, quantitative variation in proband adaptive behavior, communication, and expressive and receptive language was significantly associated with sibling outcomes in the same domains; proband scores explained 9-18% of the variation in cognition and behavior in siblings with ASD. Receptive language was particularly strongly associated in concordant pairs (ICC = 0.50, p \u3c 0.001). CONCLUSIONS: Proband ASD symptomology, indexed by the SCQ, is a predictor of familial ASD recurrence risk. While quantitative variation in social communication and restricted and repetitive behavior were not associated among sibling pairs, standardized ratings of proband language and communication explained significant variation in the same domains in the sibling at 24 months, especially among toddlers with an ASD diagnosis. These data suggest that proband characteristics can alert clinicians to areas of developmental concern for young children with familial risk for ASD

The relations of metabolic syndrome to anxiety and depression symptoms in children and adults

Metabolic syndrome is a cluster of five factors (elevated systolic blood pressure, elevated blood glucose, elevated triglycerides, large waist circumference, and decreased HDL) that are related to a greater chance of heart disease, stroke, and diabetes. There is evidence that metabolic syndrome is correlated with depression, but the directionality and mechanism is unclear. There is also dispute in the literature as to whether there is a correlation with anxiety and metabolic syndrome. In this study, levels of depression and anxiety determined from questionnaires and interviews (Adult Self Report, Child Behavior Checklist, Kiddie Schedule for Affective Disorders and Schizophrenia-Present and Lifetime, and the Composite International Diagnostic Interview) were compared with the five factors of metabolic syndrome in 100 three-person families. In children and adolescents, elevated triglycerides were predictive of elevated depressive behavior above the age of 12.68 (pppp \u3c .05 respectively). Additionally, a lower SES, older age, greater anxious behavior, and being male were all predictive of greater overall metabolic risk. Results implicate an age-moderated difference in how metabolic factors affect depression in children, possibly having a mechanism coinciding or affected by puberty. In adults, the directionality seems to reverse, with the anxious behavior having an effect on the metabolic syndrome factor, possibly related to stress and inflammation. Further research is needed to study these mechanisms and elucidate the connections between the disorders