Worry and problem-solving skills and beliefs in primary school children

Objective. To examine the association between worry and problem-solving skills and beliefs (confidence and perceived control) in primary school children. Method. Children (8–11 years) were screened using the Penn State Worry Questionnaire for Children. High (N ¼ 27) and low (N ¼ 30) scorers completed measures of anxiety, problem-solving skills (generating alternative solutions to problems, planfulness, and effectiveness of solutions) and problem-solving beliefs(confidence and perceived control). Results. High and low worry groups differed significantly on measures of anxiety and problem-solving beliefs (confidence and control) but not on problem-solving skills. Conclusions. Consistent with findings with adults, worry in children was associated with cognitive distortions, not skills deficits. Interventions for worried children may benefit froma focus on increasing positive problem-solving beliefs

Blind correction of the EB-leakage in the pixel domain

We study the problem of EB-leakage that is associated with incomplete polarized CMB sky. In the blind case that assumes no additional information about the statistical properties and amplitudes of the signal from the missing sky region, we prove that the recycling method (Liu et al.~2018) gives the unique best estimate of the EB-leakage. Compared to the previous method, this method reduces the uncertainties in the BB power spectrum due to EB-leakage by more than one order of magnitude in the most interesting domain of multipoles, where $\ell$ is between $80$ and $200$. This work also provides a useful guideline for observational design of future CMB experiments.Comment: Minor modification

The impact of maternal control on children’s anxious cognitions, behaviours and affect: an experimental study

Controlling parenting is associated with child anxiety however the direction of effects remains unclear. The present study implemented a Latin-square experimental design to assess the impact of parental control on children’s anxious affect, cognitions and behaviour. A non-clinical sample of 24 mothers of children aged 4-5 years were trained to engage in (a) controlling and (b) autonomy-granting behaviours in interaction with their child during the preparation of a speech. When mothers engaged in controlling parenting behaviours, children made more negative predictions about their performance prior to delivering their speech and reported feeling less happy about the task, and this was moderated by child trait anxiety. In addition, children with higher trait anxiety displayed a significant increase in observed child anxiety in the controlling condition. The pattern of results was maintained when differences in mothers’ levels of negativity and habitual levels of control were accounted for. These findings are consistent with theories that suggest that controlling parenting is a risk factor in the development of childhood anxiety

Improving Sampling from Generative Autoencoders with Markov Chains

We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. We define generative autoencoders as autoencoders which are trained to softly enforce a prior on the latent distribution learned by the model. However, the model does not necessarily learn to match the prior. We formulate a Markov chain Monte Carlo (MCMC) sampling process, equivalent to iteratively encoding and decoding, which allows us to sample from the learned latent distribution. Using this we can improve the quality of samples drawn from the model, especially when the learned distribution is far from the prior. Using MCMC sampling, we also reveal previously unseen differences between generative autoencoders trained either with or without the denoising criterion

Systematic review and meta-analysis: anxiety and depressive disorders in offspring of parents with anxiety disorders

Objective: We conducted meta-analyses to assess risk of anxiety disorders among offspring of parents with anxiety disorders and to establish whether there is evidence of specificity of risk for anxiety disorders as opposed to depression in offspring, and whether particular parent anxiety disorders confer risks for particular child anxiety disorders. We also examined whether risk was moderated by offspring age, gender, temperament and the presence of depressive disorders in parents. Method: We searched PsycINFO, PubMed and Web of Science in June, 2016 and July, 2017 (PROSPERO CRD42016048814). Study inclusion criteria: published in peer-reviewed journals; contained at least one group of parents with anxiety disorders and at least one comparison group of parents who did not have anxiety disorders; reported rates of anxiety disorders in offspring, and used validated diagnostic tools to ascertain diagnoses. We used random and mixed-effects models and evaluated study quality. Results: We included 25 studies (7285 offspring). Where parents had an anxiety disorder, offspring were significantly more likely to have anxiety (RR: 1.76, 95% CI = 1.58-1.96) and depressive disorders (RR: 1.31, 95% CI = 1.13-1.52) than offspring of parents without anxiety disorders. Parent Panic Disorder and Generalized Anxiety Disorder appeared to confer particular risk. Risk was greater for offspring anxiety than depressive disorders (RR: 2.50, 95% CI = 1.50-4.16), and specifically for offspring Generalized Anxiety Disorder, Separation Anxiety Disorder and Specific Phobia, but there was no evidence that children of parents with particular anxiety disorders were at increased risk for the same particular anxiety disorders. Moderation analyses were possible only for offspring age, gender and parental depressive disorder; none were significant. Conclusions: Parent anxiety disorders pose specific risks of anxiety disorders to offspring. However, there is limited support for transmission of the same particular anxiety disorder. These results support the potential for targeted prevention of anxiety disorders

Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data

We propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but labelled data is in limited supply. We consider the specific case of classifying skin lesions as either malignant or benign. In this setting, the proposed approach -- the semi-supervised, denoising adversarial autoencoder -- is able to utilise vast amounts of unlabelled data to learn a representation for skin lesions, and small amounts of labelled data to assign class labels based on the learned representation. We analyse the contributions of both the adversarial and denoising components of the model and find that the combination yields superior classification performance in the setting of limited labelled training data.Comment: Under consideration for the IET Computer Vision Journal special issue on "Computer Vision in Cancer Data Analysis