Accelerating MCMC via Parallel Predictive Prefetching

We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. Our approach exploits fast, iterative approximations to the target density to speculatively evaluate many potential future steps of the chain in parallel. The approach can accelerate computation of the target distribution of a Bayesian inference problem, without compromising exactness, by exploiting subsets of data. It takes advantage of whatever parallel resources are available, but produces results exactly equivalent to standard serial execution. In the initial burn-in phase of chain evaluation, it achieves speedup over serial evaluation that is close to linear in the number of available cores

Effects of Resistance Training on Smoking Abstinence Self-Efficacy in Sedentary Smokers

ABSTRACT Introduction: Cigarette smoking is prevalent with 40 million Americans smoking and one in five deaths attributed to this behavior. Aims: This study examined effects of a resistance training (RT) program on smoking abstinence self-efficacy. Methods: Forty (40) participants were randomized into intervention or control groups. Smoking abstinence self-efficacy, withdrawal symptoms, and smoking behavior were measured. Results/Findings: RT group presented with statistically significant greater increases in self-efficacy compared to control. RT group participants also presented with non-significantly greater decreases in smoking behavior and withdrawal symptoms compared to control. Conclusions: This study demonstrates effectiveness of RT in improving smoking abstinence self-efficacy

Food Insecurity and Nutrition in Franklinton

In this report, Fit For Franklinton documents the problem of food insecurity in Franklinton, suggests ways to alleviate the problem, and calls for people to get involved in implementing those solutions

ASC: Automatically Scalable Computation

We present an architecture designed to transparently and automatically scale the performance of sequential programs as a function of the hardware resources available. The architecture is predicated on a model of computation that views program execution as a walk through the enormous state space composed of the memory and registers of a single-threaded processor. Each instruction execution in this model moves the system from its current point in state space to a deterministic subsequent point. We can parallelize such execution by predictively partitioning the complete path and speculatively executing each partition in parallel. Accurately partitioning the path is a challenging prediction problem. We have implemented our system using a functional simulator that emulates the x86 instruction set, including a collection of state predictors and a mechanism for speculatively executing threads that explore potential states along the execution path. While the overhead of our simulation makes it impractical to measure speedup relative to native x86 execution, experiments on three benchmarks show scalability of up to a factor of 256 on a 1024 core machine when executing unmodified sequential programs.Engineering and Applied Science

Safety and efficacy of glucagon-like peptide-1 receptor agonists in children and adolescents with obesity: A meta-analysis

To determine the weight, body mass index (BMI), cardiometabolic, and gastrointestinal effects of glucagon-like peptide-1 (GLP-1) receptor agonists in children with obesity. Study design: Web of Science, PubMed/MEDLINE, and Scopus databases from 01/01/1994-01/01/2021 for randomized control trials examining the weight, BMI, cardiometabolic, or gastrointestinal effects of GLP-1 receptor agonists in children and adolescents with obesity. Data were extracted by 2 independent surveyors and a random effects model was applied to meta-analyze generic inverse variance outcomes. Primary outcomes were related to weight and cardiometabolic profile, and secondary outcomes of interest were gastrointestinal-related treatment-emergent adverse events. Results: Nine studies involving 574 participants were identified, of which 3 involved exenatide and 6 involved liraglutide. GLP-1 receptor agonists use caused a modest reduction in body weight (mean difference [MD] -1.50 [-2.50,-0.50] kg, I2 64%), BMI (MD -1.24 [-1.71,-0.77] kg/m2, I2 0%), and BMI z score (MD -0.14 [-0.23,-0.06], I2 43%). Glycemic control was improved in children with proven insulin resistance (glycated hemoglobin A1c MD -1.05 [-1.93,-0.18] %, I2 76%). Although no lipid profile improvements were noted, a modest decrease in systolic blood pressure was detected (MD -2.30 [-4.11,-0.49] mm Hg; I2 0%). Finally, analysis of gastrointestinal-related treatment-emergent adverse events revealed an increased risk of nausea (risk ratio 2.11 [1.44, 3.09]; I2 0%), without significant increases in other gastrointestinal symptoms. Conclusions: This meta-analysis indicates that GLP-1 receptor agonists are safe and effective in modestly reducing weight, BMI, glycated hemoglobin A1c, and systolic blood pressure in children and adolescents with obesity in a clinical setting, albeit with increased rates of nausea

Computational Caches

Caching is a well-known technique for speeding up computation. We cache data from file systems and databases; we cache dynamically generated code blocks; we cache page translations in TLBs. We propose to cache the act of computation, so that we can apply it later and in different contexts. We use a state-space model of computation to support such caching, involving two interrelated parts: speculatively memoized predicted/resultant state pairs that we use to accelerate sequential computation, and trained probabilistic models that we use to generate predicted states from which to speculatively execute. The key techniques that make this approach feasible are designing probabilistic models that automatically focus on regions of program execution state space in which prediction is tractable and identifying state space equivalence classes so that predictions need not be exact.Engineering and Applied Science

Predictors and moderators of quality of life in caregivers of amputee patients by type 2 diabetes

BackgroundThe amputation of a foot or a leg is one of the complications caused by diabetes that creates fear. After the amputation, the patient becomes dependent on a caregiver, who is often not prepared for this new phase of life. Knowing the factors that influence care delivery in caregivers of amputee type 2 diabetes patients is important from an heuristic point of view, since very few studies have focused on this population.ObjectivesThis study analysed the predictors and moderators of quality of life, in caregivers of amputee patients due to type 2 diabetes.MethodsThis study has a cross-sectional design. All ethical standards were followed in the conduct of this study. The sample comprised 101 caregivers who answered the following instruments: Carer's Assessment of Managing Index, Burden Assessment Scale, Depression Anxiety Stress Scales, Revised Impact of Events Scale, Family Assessment Device, Family Disruption from Illness Scale and the Short Form Health Survey-36.ResultsThe practice of physical activity, lower burden, better family functioning and less traumatic symptoms were predictors of better mental quality of life. Having no chronic disease and less physical symptoms predicted better physical quality of life. Duration of care moderated the relationship between traumatic symptoms and mental quality of life, but not with physical quality of life. Receiving help in caregiving moderated the relationship between traumatic symptoms and mental quality of life. The limitations of this study include the exclusive use of self-report instruments and the fact that the caregivers who have participated in this study were those who accompanied the patient to the hospital.ConclusionIn order to promote physical quality of life, future intervention programmes should consider the presence of chronic disease in the caregiver and the duration of care, as well as the caregivers' physical symptoms.- This study was supported by a scholarship from the Coordination Foundation for the Improvement of Higher Education Personnel/Full Doctoral Program Abroad Financed by CAPES - Brazil (reference number 1010-14)