Molecular velocity auto-correlation of simple liquids observed by NMR MGSE method

The velocity auto-correlation spectra of simple liquids obtained by the NMR method of modulated gradient spin echo show features in the low frequency range up to a few kHz, which can be explained reasonably well by a $t^{-3/2}$ long time tail decay only for non-polar liquid toluene, while the spectra of polar liquids, such as ethanol, water and glycerol, are more congruent with the model of diffusion of particles temporarily trapped in potential wells created by their neighbors. As the method provides the spectrum averaged over ensemble of particle trajectories, the initial non-exponential decay of spin echoes is attributed to a spatial heterogeneity of molecular motion in a bulk of liquid, reflected in distribution of the echo decays for short trajectories. While at longer time intervals, and thus with longer trajectories, heterogeneity is averaged out, giving rise to a spectrum which is explained as a combination of molecular self-diffusion and eddy diffusion within the vortexes of hydrodynamic fluctuations.Comment: 8 pages, 6 figur

Lessons learned from the 1st Ariel Machine Learning Challenge: Correcting transiting exoplanet light curves for stellar spots

The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots. The current practice in the literature is to identify the effects of spots visually and correct for them manually or discard the affected data. This paper explores a first step towards fully automating the efficient and precise derivation of transit depths from transit light curves in the presence of stellar spots. The primary focus of the paper is to present in detail a diverse arsenal of methods for doing so. The methods and results we present were obtained in the context of the 1st Machine Learning Challenge organized for the European Space Agency’s upcoming Ariel mission. We first present the problem, the simulated Ariel-like data and outline the Challenge while identifying best practices for organizing similar challenges in the future. Finally, we present the solutions obtained by the top-5 winning teams, provide their code and discuss their implications. Successful solutions either construct highly non-linear (w.r.t. the raw data) models with minimal preprocessing –deep neural networks and ensemble methods– or amount to obtaining meaningful statistics from the light curves, constructing linear models on which yields comparably good predictive performance

Mediators in Psychological Treatments for Anxiety and Depression in Adolescents and Young People: A Protocol of a Systematic Review

Introduction Anxiety and depressive disorders are a significant problem that starts in childhood or adolescence and should be addressed early to avoid chronic mental conditions. There is strong evidence to demonstrate that psychological treatments are effective for these disorders, however, little is known on mediators and mechanisms of change of psychological treatment in adolescents and young adults. Understanding the pathways through which psychological treatments operate will facilitate more effective treatments. Aim We aim to conduct a systematic review, exploring the available evidence on mediators of psychological treatments for anxiety and depression in adolescents and young adults. Methods A systematic search has been performed on PubMed and PsycINFO databases to identify studies from inception to 23rd February 2020. Eligible studies include randomized controlled trials and trials (quasi-experimental) designs that have enrolled adolescents and young adults presenting with depression and/or anxiety and that have examined mediators of psychological treatments. A group of 20 reviewers from the COST-Action TREATme (CA16102) divided into 10 pairs independently screen studies for inclusion, extract information from the included studies, and assess the methodological quality of the included studies and the requirements for mediators. The methodological quality will be assessed by The Mixed Methods Appraisal Tool. Extracted data from the included studies will be collected and presented using a narrative approach. Discussion This systematic review will summarize and provide a comprehensive overview of the current evidence on mediators of psychological treatments for anxiety and depression for adolescents and young adults. Results will allow the identification of strategies to optimize intervention to enhance clinical outcomes. Ethics and dissemination Ethics approval is not required. Findings from this systematic review will be published in a peer-reviewed journal and disseminated at conferences and meetings. PROSPERO registration number: CRD42021234641.This review is based upon work from COST Action European Network on Individualized Psychotherapy Treatment of Young People with Mental Disorders (TREATme; CA16102), supported by COST (European Cooperation in Science and Technology) (www.cost.eu)