922017 research outputs found
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Speech-Driven Robot Face Action Generation with Deep Generative Model for Social Robots
No full textThe natural co-speech facial action as a kind of non-verbal behavior plays an essential role in human communication, which also leads to a natural and friendly human-robot interaction. However, a lot of previous works for robot speech-based behaviour generation are rule-based or handcrafted methods, which are time-consuming and with limited synchronization levels between the speech and the facial action. Based on the Generative Adversarial Networks (GAN) model, this paper developed an effective speech-driven facial action synthesizer, i.e., given an acoustic speech, a synchronous and realistic 3D facial action sequence is generated. In addition, a mapping between the 3D human facial action to the real robot facial action that regulates Zeno robot facial expressions is also completed. The evaluation results show the model has potential for natural human-robot interaction
Association of vocational interventions and work-related factors with disease and work outcomes in people with RMDs: A Systematic Review
No full textObjective: A EULAR taskforce was convened to develop recommendations for lifestyle behaviours among people with rheumatic and musculoskeletal diseases (RMDs). This paper reviews the literature on work-related factors and disease-specific outcomes for people with osteoarthritis, rheumatoid arthritis (RA), systemic lupus erythematosus, axial spondyloarthritis (axSpA), psoriatic arthritis, systemic sclerosis (SSc) and gout.Methods: Two separate systematic literature reviews (SLRs) were conducted. The first identified SLRs, published between 01/2013 and 09/2018. The second identified original observational and intervention studies published before 05/2019. Manuscripts were included if they assessed the effects of vocational interventions on disease-specific outcomes (i.e. clinical outcomes, patient-reported outcomes, and work outcomes) or if they assessed the association between work-related factors and these outcomes. Medline, Embase, Cochrane Library of systematic reviews and CENTRAL databases were searched. Results: Two SLRs were identified including individuals with SSc and inflammatory arthritis. Subsequently, 23 original manuscripts were identified, with most of them (43.5%) including people with RA and no manuscripts on gout. Most observational studies evaluated the association between work-related factors and work outcomes while limited information was available on the impact of work on clinical outcomes. A few studies suggested that physically demanding jobs have a small detrimental effect on radiographic progression in axSpA and PsA. Intervention studies showed beneficial effects of vocational interventions for disease-specific outcomes, but with small effect sizes.Conclusion: Many studies indicated that work participation is not likely to be detrimental and, in some cases, may be beneficial for RMD-specific outcomes and should therefore receive attention within healthcare consultations.<br/
Customized Multi-Energy Pricing in Smart Grids: A Bilevel and Evolutionary Computation Approach
No full textThis paper proposes a customized energy pricing scheme for energy retailers in multi-energy (i.e., electricity and natural gas) retail markets. Microgrids with distributed energy resources (DERs) and demand response (DR) programs are considered on the demand side. We adopt a bilevel single-leader multi-follower model to analyze the customized multi-energy pricing decisions where the retailer’s profit maximization problem is formulated at the upper level, and the microgrids’ operation costs minimization problems are considered at the lower level. A particle swarm optimization (PSO) based evolutionary solution approach is developed to solve the proposed bilevel decision-making problem. Through a numerical case study, we demonstrate the feasibility and effectiveness of the proposed bilevel model and the solution algorithm. We reveal that the proposed customized pricing scheme could offer differentiated optimal pricing decisions to various microgrids characterized by their energy conversion efficiencies
A genome-wide association study with tissue transcriptomics identifies genetic drivers for classic bladder exstrophy
No full textClassic bladder exstrophy represents the most severe end of all human congenital anomalies of the kidney and urinary tract and is associated with bladder cancer susceptibility. Previous genetic studies identified one locus to be involved in classic bladder exstrophy, but were limited to a restrict number of cohort. Here we show the largest classic bladder exstrophy genome-wide association analysis to date where we identify eight genome-wide significant loci, seven of which are novel. In these regions reside ten coding and four non-coding genes. Among the coding genes is EFNA1, strongly expressed in mouse embryonic genital tubercle, urethra, and primitive bladder. Re-sequence of EFNA1 in the investigated classic bladder exstrophy cohort of our study displays an enrichment of rare protein altering variants. We show that all coding genes are expressed and/or significantly regulated in both mouse and human embryonic developmental bladder stages. Furthermore, nine of the coding genes residing in the regions of genome-wide significance are differentially expressed in bladder cancers. Our data suggest genetic drivers for classic bladder exstrophy, as well as a possible role for these drivers to relevant bladder cancer susceptibility
Students’ views about alternates to traditional dissertation for Master in Public Health: results of a virtual focus group
No full textA traditional dissertation remains a component of most Master in Public Health programmes, but there is increasing interest and use of alternatives. A virtual focus group of six students studying an online distance Master in Public Health explored their perceptions about dissertations and preferences for alternatives. Students value the dissertation as a means of consolidating learning and focusing in-depth on chosen topics. Alternatives were viewed as attractive but not always practical. Participants felt that additional options need to be accompanied by guidance to ensure students make the most appropriate choice for their future. Three types of students with different needs emerged; students aspiring to progress to a PhD should do a traditional dissertation, students already employed in a relevant role may choose a work-based dissertation or replace the dissertation with taught units, and students doing the master to increase their employability could choose a placement-based dissertation or replace the dissertation with work experience reflection. Innovations that introduce alternatives to the traditional dissertation would be welcomed by MPH students studying online, but they would need to be accompanied by suitable career advice to ensure students choose the most appropriate route for their future aspirations
ESBMC-CHERI: Towards Verification of C Programs for CHERI Platforms with ESBMC
No full textIn this paper we present ESBMC-CHERI – first bounded model checker capable of formally verifying C programs for CHERI-enabled platforms. CHERI provides run-time protection for the memory unsafe programming languages such as C/C++ at the hardware level. At the same time, it introduces new semantics to C programs, making some safe C programs cause hardware exceptions on CHERI-extended platforms. Hence, it is crucial to detect memory safety violations and compatibility issues ahead of compilation. However, there are no verification tools currently available for reasoning over CHERI-C programs. We demonstrate the work undertaken towards implementing support for CHERI-C in our state-of-the-art bounded model checker ESBMC and the plans for future work and extensive evaluation of ESBMC-CHERI. The ESBMC-CHERI demonstration and the source code are available at https://github.com/esbmc/esbmc/tree/cheri-clang
Staff training to improve participant recruitment into surgical randomised controlled trials: a feasibility Study Within A Trial (SWAT) across four host randomised controlled trials simultaneously
No full textObjectiveTo test the feasibility of undertaking a simultaneous Study Within A Trial (SWAT) to train staff who recruit participants into surgical randomised controlled trials (RCTs), by assessing key uncertainties around recruitment, randomisation, intervention delivery and data collection. Study design and settingTwelve surgical RCTs were eligible. Interested sites (clusters) were randomised 1:1, with recruiting staff (surgeons and nurses) offered training or no training. The primary outcome was the feasibility of recruiting sites across multiple surgical trials simultaneously. Secondary outcomes included numbers/types of staff enrolled, attendance at training, training acceptability, confidence in recruiting and participant recruitment rates six months later. ResultsFour RCTs (33%) comprising 91 sites participated. Of these, 29 sites agreed to participate (32%) and were randomised to intervention (15 sites, 29 staff) or control (14 sites, 29 staff). Research nurses attended and found the training to be acceptable; no surgeons attended. In the intervention group, there was evidence of increased confidence when pre and post training scores were compared (mean difference in change 1.42; 95% CI 0.56, 2.27; p = 0.002) – there was no effect on recruitment rate. ConclusionIt was feasible to randomise sites across four surgical RCTs in a simultaneous SWAT design. However, as small numbers of trials and sites participated, and no surgeons attended training, strategies to improve these aspects are needed for future evaluations. Trial registrationISRCTN registry: DISC (ISRCTN18254597), registered on 4th April 2017; PROFHER 2 (ISRCTN76296703), registered on 5th April 2018; IntAct (ISRCTN13334746), registered on 10th April 2017; and START:REACTS (ISRCTN17825590), registered on 5th March 2018. The training SWAT has been submitted to the MRC SWAT repository (SWAT111) <br/
Embodied Attention in Word-Object Mapping: A Developmental Cognitive Robotics Model
No full textDevelopmental Robotics models provide useful tools to study and understand the language learning process in infants and robots. These models allow us to describe key mechanisms of language development, such as statistical learning, the role of embodiment, and the impact of the attention payed to an object while learning its name. Robots can be particularly well suited for this type of problems, because they cover both a physical manipulation of the environment and mathematical modeling of the temporal changes of the learned concepts. In this work we present a computational representation of the impact of embodiment and attention on word learning, relying on sensory data collected with a real robotic agent in a real world scenario. Results show that the cognitive architecture designed for this scenario is able to capture the changes underlying the moving object in the field of view of the robot. The architecture successfully handles the temporal relationship in moving items and manages to show the effects of the embodied attention on word-object mapping
Measuring Risk of Re-identification in Microdata: State-of-the Art and New Directions
No full textWe review the influential research carried out by Chris Skinner in the area of statistical disclosure control, and in particular quantifying the risk of re-identification in sample microdata from a random survey drawn from a finite population. We use the sample microdata to infer population parameters when the population is unknown, and estimate the risk of re-identification based on the notion of population uniqueness using probabilistic modelling. We also introduce a new approach to measure the risk of re-identification for a subpopulation in a register that is not representative of the general population, for example a register of cancer patients. In addition, we can use the additional information from the register to measure the risk of re-identification for the sample microdata. This new approach was developed by the two authors and is published here for the first time. We demonstrate this approach in an application study based on UK census data where we can compare the estimated risk measures to the known truth. <br/
Stein's Method Meets Computational Statistics: A Review of Some Recent Developments
No full textStein's method compares probability distributions through the study of a class of linear operators called Stein operators. While mainly studied in probability and used to underpin theoretical statistics, Stein's method has led to significant advances in computational statistics in recent years. The goal of this survey is to bring together some of these recent developments and, in doing so, to stimulate further research into the successful field of Stein's method and statistics. The topics we discuss include tools to benchmark and compare sampling methods such as approximate Markov chain Monte Carlo, deterministic alternatives to sampling methods, control variate techniques, parameter estimation and goodness-of-t testing.<br/