Bayesian optimization for materials design

We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian process regression, which allows predicting the performance of a new design based on previously tested designs. After providing a detailed introduction to Gaussian process regression, we introduce two Bayesian optimization methods: expected improvement, for design problems with noise-free evaluations; and the knowledge-gradient method, which generalizes expected improvement and may be used in design problems with noisy evaluations. Both methods are derived using a value-of-information analysis, and enjoy one-step Bayes-optimality

Identifying the participant characteristics that predict recruitment and retention of participants to randomised controlled trials involving children : a systematic review

Background Randomised controlled trials (RCTs) are recommended as the ‘gold standard’ in evaluating health care interventions. The conduct of RCTs is often impacted by difficulties surrounding recruitment and retention of participants in both adult and child populations. Factors influencing recruitment and retention of children to RCTs can be more complex than in adults. There is little synthesised evidence of what influences participation in research involving parents and children. Aim To identify predictors of recruitment and retention in RCTs involving children. Methods A systematic review of RCTs was conducted to synthesise the available evidence. An electronic search strategy was applied to four databases and restricted to English language publications. Quantitative studies reporting participant predictors of recruitment and retention in RCTs involving children aged 0–12 were identified. Data was extracted and synthesised narratively. Quality assessment of articles was conducted using a structured tool developed from two existing quality evaluation checklists. Results Twenty-eight studies were included in the review. Of the 154 participant factors reported, 66 were found to be significant predictors of recruitment and retention in at least one study. These were classified as parent, child, family and neighbourhood characteristics. Parent characteristics (e.g. ethnicity, age, education, socioeconomic status (SES)) were the most commonly reported predictors of participation for both recruitment and retention. Being young, less educated, of an ethnic minority and having low SES appear to be barriers to participation in RCTs although there was little agreement between studies. When analysed according to setting and severity of the child’s illness there appeared to be little variation between groups. The quality of the studies varied. Articles adhered well to reporting guidelines around provision of a scientific rationale for the study and background information as well as displaying good internal consistency of results. However, few studies discussed the external validity of the results or provided recommendations for future research. Conclusion Parent characteristics may predict participation of children and their families to RCTs; however, there was a lack of consensus. Whilst sociodemographic variables may be useful in identifying which groups are least likely to participate they do not provide insight into the processes and barriers to participation for children and families. Further studies that explore variables that can be influenced are warranted. Reporting of studies in this field need greater clarity as well as agreed definitions of what is meant by retention

Search for gravitational waves associated with gamma-ray bursts detected by Fermi and Swift during the LIGO–Virgo run O3b

We search for gravitational-wave signals associated with gamma-ray bursts (GRBs) detected by the Fermi and Swift satellites during the second half of the third observing run of Advanced LIGO and Advanced Virgo (2019 November 1 15:00 UTC–2020 March 27 17:00 UTC). We conduct two independent searches: a generic gravitational-wave transients search to analyze 86 GRBs and an analysis to target binary mergers with at least one neutron star as short GRB progenitors for 17 events. We find no significant evidence for gravitational-wave signals associated with any of these GRBs. A weighted binomial test of the combined results finds no evidence for subthreshold gravitational-wave signals associated with this GRB ensemble either. We use several source types and signal morphologies during the searches, resulting in lower bounds on the estimated distance to each GRB. Finally, we constrain the population of low-luminosity short GRBs using results from the first to the third observing runs of Advanced LIGO and Advanced Virgo. The resulting population is in accordance with the local binary neutron star merger rate