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The Whole Atmosphere Community Climate Model version 6 (WACCM6) is a major update of the whole atmosphere modeling capability in the Community Earth System Model (CESM), featuring enhanced physical, chemical and aerosol parameterizations. This work describes WACCM6 and some of the important features of the model. WACCM6 can reproduce many modes of variability and trends in the middle atmosphere, including the Quasi‐Biennial Oscillation, Stratospheric Sudden Warmings and the evolution of Southern Hemisphere springtime ozone depletion over the 20th century. WACCM6 can also reproduce the climate and temperature trends of the 20th century throughout the atmospheric column. The representation of the climate has improved in WACCM6, relative to WACCM4. In addition, there are improvements in high latitude climate variability at the surface and sea ice extent in WACCM6 over the lower top version of the model (CAM6) that come from the extended vertical domain and expanded aerosol chemistry in WACCM6, highlighting the importance of the stratosphere and tropospheric chemistry for high latitude climate variability.

Background When patients are admitted onto psychiatric wards, sleep problems are highly prevalent. We carried out the first trial testing a psychological sleep treatment at acute admission (Oxford Ward sLeep Solution, OWLS). Methods This assessor-blind parallel-group pilot trial randomised patients to receive sleep treatment at acute crisis [STAC, plus standard care (SC)], or SC alone (1 : 1). STAC included cognitive–behavioural therapy (CBT) for insomnia, sleep monitoring and light/dark exposure for circadian entrainment, delivered over 2 weeks. Assessments took place at 0, 2, 4 and 12 weeks. Feasibility outcomes assessed recruitment, retention of participants and uptake of the therapy. Primary efficacy outcomes were the Insomnia Severity Index and Warwick–Edinburgh Mental Wellbeing Scale at week 2. Analyses were intention-to-treat, estimating treatment effect with 95% confidence intervals. Results Between October 2015 and July 2016, 40 participants were recruited (from 43 assessed eligible). All participants offered STAC completed treatment (mean sessions received = 8.6, s.d. = 1.5). All participants completed the primary end point. Compared with SC, STAC led to large effect size (ES) reductions in insomnia at week 2 (adjusted mean difference −4.6, 95% CI −7.7 to −1.4, ES −0.9), a small improvement in psychological wellbeing (adjusted mean difference 3.7, 95% CI −2.8 to 10.1, ES 0.3) and patients were discharged 8.5 days earlier. One patient in the STAC group had an adverse event, unrelated to participation. Conclusions In this challenging environment for research, the trial was feasible. Therapy uptake was high. STAC may be a highly effective treatment for sleep disturbance on wards with potential wider benefits on wellbeing and admission length.

Summary Background Sleep disturbance occurs in most patients with delusions or hallucinations and should be treated as a clinical problem in its own right. However, cognitive behavioural therapy (CBT)—the best evidence-based treatment for insomnia—has not been tested in this patient population. We aimed to pilot procedures for a randomised trial testing CBT for sleep problems in patients with current psychotic experiences, and to provide a preliminary assessment of potential benefit. Methods We did this prospective, assessor-blind, randomised controlled pilot trial (Better Sleep Trial [BEST]) at two mental health centres in the UK. Patients (aged 18–65 years) with persistent distressing delusions or hallucinations in the context of insomnia and a schizophrenia spectrum diagnosis were randomly assigned (1:1), via a web-based randomisation system with minimisation to balance for sex, insomnia severity, and psychotic experiences, to receive either eight sessions of CBT plus standard care (medication and contact with the local clinical team) or standard care alone. Research assessors were masked to group allocation. Assessment of outcome was done at weeks 0, 12 (post-treatment), and 24 (follow-up). The primary efficacy outcomes were insomnia assessed by the Insomnia Severity Index (ISI) and delusions and hallucinations assessed by the Psychotic Symptoms Rating Scale (PSYRATS) at week 12. We did analysis by intention to treat, with an aim to provide confidence interval estimation of treatment effects. This study is registered with ISRCTN, number 33695128. Findings Between Dec 14, 2012, and May 22, 2013, and Nov 7, 2013, and Aug 26, 2014, we randomly assigned 50 patients to receive CBT plus standard care (n=24) or standard care alone (n=26). The last assessments were completed on Feb 10, 2015. 48 (96%) patients provided follow-up data. 23 (96%) patients offered CBT took up the intervention. Compared with standard care, CBT led to reductions in insomnia in the large effect size range at week 12 (adjusted mean difference 6·1, 95% CI 3·0–9·2, effect size d=1·9). By week 12, nine (41%) of 22 patients receiving CBT and one (4%) of 25 patients receiving standard care alone no longer had insomnia, with ISI scores lower than the cutoff for insomnia. The treatment effect estimation for CBT covered a range from reducing but also increasing delusions (adjusted mean difference 0·3, 95% CI −2·0 to 2·6) and hallucinations (−1·9, −6·5 to 2·7). Three patients, all in the CBT group, had five adverse events, although none were regarded as related to study treatment. Interpretation Our findings show that CBT for insomnia might be highly effective for improving sleep in patients with persistent delusions or hallucinations. A larger, suitably powered phase 3 study is now needed to provide a precise estimate of the effects of CBT for sleep problems, both on sleep and psychotic experiences. Funding Research for Patient Benefit Programme, National Institute for Health Research.

Variational inference (VI) combined with data subsampling enables approximate posterior inference over large data sets, but suffers from poor local optima. We first formulate a deterministic annealing approach for the generic class of conditionally conjugate exponential family models. This approach uses a decreasing temperature parameter which deterministically deforms the objective during the course of the optimization. A well-known drawback to this annealing approach is the choice of the cooling schedule. We therefore introduce variational tempering, a variational algorithm that introduces a temperature latent variable to the model. In contrast to related work in the Markov chain Monte Carlo literature, this algorithm results in adaptive annealing schedules. Lastly, we develop local variational tempering, which assigns a latent temperature to each data point; this allows for dynamic annealing that varies across data. Compared to the traditional VI, all proposed approaches find improved predictive likelihoods on held-out data., published version, 8 pages, 4 figures

In many developing countries, half the population lives in rural locations, where access to essentials such as school materials, mosquito nets, and medical supplies is restricted. We propose an alternative method of distribution (to standard road delivery) in which the existing mobility habits of a local population are leveraged to deliver aid, which raises two technical challenges in the areas optimisation and learning. For optimisation, a standard Markov decision process applied to this problem is intractable, so we provide an exact formulation that takes advantage of the periodicities in human location behaviour. To learn such behaviour models from sparse data (i.e., cell tower observations), we develop a Bayesian model of human mobility. Using real cell tower data of the mobility behaviour of 50,000 individuals in Ivory Coast, we find that our model outperforms the state of the art approaches in mobility prediction by at least 25% (in held-out data likelihood). Furthermore, when incorporating mobility prediction with our MDP approach, we find a 81.3% reduction in total delivery time versus routine planning that minimises just the number of participants in the solution path., Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)