Convex Modeling of Interactions with Strong Heredity

We consider the task of fitting a regression model involving interactions among a potentially large set of covariates, in which we wish to enforce strong heredity. We propose FAMILY, a very general framework for this task. Our proposal is a generalization of several existing methods, such as VANISH [Radchenko and James, 2010], hierNet [Bien et al., 2013], the all-pairs lasso, and the lasso using only main effects. It can be formulated as the solution to a convex optimization problem, which we solve using an efficient alternating directions method of multipliers (ADMM) algorithm. This algorithm has guaranteed convergence to the global optimum, can be easily specialized to any convex penalty function of interest, and allows for a straightforward extension to the setting of generalized linear models. We derive an unbiased estimator of the degrees of freedom of FAMILY, and explore its performance in a simulation study and on an HIV sequence data set.Comment: Final version accepted for publication in JCG

Short-Term Load Forecasting Using AMI Data

Accurate short-term load forecasting is essential for efficient operation of the power sector. Predicting load at a fine granularity such as individual households or buildings is challenging due to higher volatility and uncertainty in the load. In aggregate loads such as at grids level, the inherent stochasticity and fluctuations are averaged-out, the problem becomes substantially easier. We propose an approach for short-term load forecasting at individual consumers (households) level, called Forecasting using Matrix Factorization (FMF). FMF does not use any consumers' demographic or activity patterns information. Therefore, it can be applied to any locality with the readily available smart meters and weather data. We perform extensive experiments on three benchmark datasets and demonstrate that FMF significantly outperforms the computationally expensive state-of-the-art methods for this problem. We achieve up to 26.5% and 24.4 % improvement in RMSE over Regression Tree and Support Vector Machine, respectively and up to 36% and 73.2% improvement in MAPE over Random Forest and Long Short-Term Memory neural network, respectively

Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers

Analysis of the fairness of machine learning (ML) algorithms recently attracted many researchers' interest. Most ML methods show bias toward protected groups, which limits the applicability of ML models in many applications like crime rate prediction etc. Since the data may have missing values which, if not appropriately handled, are known to further harmfully affect fairness. Many imputation methods are proposed to deal with missing data. However, the effect of missing data imputation on fairness is not studied well. In this paper, we analyze the effect on fairness in the context of graph data (node attributes) imputation using different embedding and neural network methods. Extensive experiments on six datasets demonstrate severe fairness issues in missing data imputation under graph node classification. We also find that the choice of the imputation method affects both fairness and accuracy. Our results provide valuable insights into graph data fairness and how to handle missingness in graphs efficiently. This work also provides directions regarding theoretical studies on fairness in graph data.Comment: Accepted at IEEE International Conference on Big Data (IEEE Big Data

Teaching children road safety through storybooks: an approach to child health literacy in Pakistan

Background: Road traffic injuries (RTIs) commonly affect the younger population in low- and-middle-income countries. School children may be educated about road safety using storybooks with colorful pictures, which tends to increase the child’s interest in the text. Therefore, this study assessed the use of bilingual pictorial storybooks to improve RTI prevention knowledge among school children.Methods: This pretest-posttest study was conducted in eight public and nine private schools of Karachi, Pakistan, between February to May 2015. Children in grades four and five were enrolled at baseline (n = 410). The intervention was an interactive discussion about RTI prevention using a bilingual (Urdu and English) pictorial storybook. A baseline test was conducted to assess children’s pre-existing knowledge about RTI prevention followed by administration of the intervention. Two posttests were conducted: first immediately after the intervention, and second after 2 months. Test scores were analyzed using McNemar test and paired sample t-test. Results: There were 57% girls and 55% public school students; age range 8–16 years. Compared to the overall baseline score (5.1 ± 1.4), the number of correct answers increased in both subsequent tests (5.9 ± 1.2 and 6.1 ± 1.1 respectively, p-value \u3c 0.001). Statistically significant improvement in mean scores was observed based on gender, grades and school type over time (p-value \u3c 0.001).Conclusion: Discussions using bilingual pictorial storybooks helped primary school children in Pakistan grasp knowledge of RTI prevention. RTI education sessions may be incorporated into school curricula using storybooks as teaching tools. Potential exists to create similar models for other developing countries by translating the storybooks into local languages

Colorimetric sensing of uric acid based on sawdust-deposited silver nanoparticles via an eco-friendly and cost-effective approach

Uric acid is directly linked to gout, arthritis, neurological, cardiovascular, and kidney-related disorders. It is a byproduct obtained from the breakdown of purines and a significant indicator of hyperuricemia observed in both urine and blood. In the absence of any enzyme, it's quite difficult to develop a novel, cost-effective, and clinical method for uric acid detection. Herein, we report a very simple, low-cost, and non-enzymatic method for the selective identification and quantification of uric acid using green synthesized silver nanoparticles (Ag NPs). The desired Ag NPs were synthesized by the hydrothermal method using Erythrina suberosa sawdust as a deagglomeration agent and Psidium guajava extract as a reductant. The synthesis of the sensing platform, i.e., sawdust-deposited Ag NPs, was confirmed through different techniques such as UV-Vis spectrophotometer, FTIR, XRD, EDX, and scanning electron microscopy (SEM). Sawdust can offer a good, environmentally friendly, and cost-effective strategy to overcome the problem of agglomeration in nanoparticles. The enzyme mimic, with the help of H2O2, oxidizes the colorless 3,3′,5,5′-tetramethylbenzidine (TMB) to oxidized TMB with a blue-green color. The addition of uric acid reduces the oxidized TMB to a colorless product, resulting in a colorimetric change. For quality improvement, different reaction parameters, including pH, time, TMB, and NPs concentration, were optimized. Our proposed sensor responds in linear ranges of 0.04–0.360 μM, with a limit of quantification of 0.01 μM and a limit of detection of 0.004 μM. The suggested enzyme mimic detected uric acid in blood samples, with particular specificity in the presence of competitive analytes

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication