Provable Deterministic Leverage Score Sampling

We explain theoretically a curious empirical phenomenon: "Approximating a matrix by deterministically selecting a subset of its columns with the corresponding largest leverage scores results in a good low-rank matrix surrogate". To obtain provable guarantees, previous work requires randomized sampling of the columns with probabilities proportional to their leverage scores. In this work, we provide a novel theoretical analysis of deterministic leverage score sampling. We show that such deterministic sampling can be provably as accurate as its randomized counterparts, if the leverage scores follow a moderately steep power-law decay. We support this power-law assumption by providing empirical evidence that such decay laws are abundant in real-world data sets. We then demonstrate empirically the performance of deterministic leverage score sampling, which many times matches or outperforms the state-of-the-art techniques.Comment: 20th ACM SIGKDD Conference on Knowledge Discovery and Data Minin

Factors associated with attendance at the postpartum blood pressure visit in pregnancies complicated by hypertension.

OBJECTIVES: Women with hypertensive disorders of pregnancy should have a blood pressure evaluation no later than 7-10 days after delivery. The objective of this study was to identify the factors associated with patient attendance at the postpartum blood pressure follow-up visit. STUDY DESIGN: This was a retrospective cohort study of postpartum women who had a hypertensive disorder of pregnancy. Postpartum follow-up rates were recorded, and characteristics of women who attended a postpartum visit for blood pressure evaluation were compared to women who did not return for the visit. Multiple logistic regression was performed. MAIN OUTCOME MEASURES: Characteristics of women who returned for a blood pressure visit. RESULTS: There were 378 women who met inclusion criteria; 193(51.1%) attended the blood pressure visit. Women who returned were older and more likely to have preeclampsia, severe features, magnesium sulfate use, or severe hypertension during hospitalization. They were less likely to have gestational hypertension. Adjusted analysis demonstrated that black/non-Hispanic women (OR 0.53, 95% CI 0.34-0.83), the presence of any preeclampsia diagnosis (OR 2.19, 95% CI 1.03-4.81), and whether the woman underwent a cesarean delivery (OR 3.06, 95% CI 1.85-5.14) remained significant factors in predicting adherence. CONCLUSIONS: Women who returned for a blood pressure visit were more likely to have had significant hypertensive disease or a cesarean delivery. Non-Hispanic black women had the lowest rate of follow-up. Given black women have the highest rates of maternal morbidity and mortality nationwide, effective interventions to increase follow-up for them are needed

BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees

The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26x-629x while guaranteeing the same predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201

13-Series resolvins mediate the leukocyte-platelet actions of atorvastatin and pravastatin in inflammatory arthritis

This work was supported by funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program (Grant 677542), a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant 107613/Z/15/Z), and the Barts Charity (Grant MGU0343). This work was also funded, in part, by Medical Research Council Advance Course Masters (Grant MR/J015741/1). The authors declare no conflicts of interest

Designing informative warning signals: Effects of indicator type, modality, and task demand on recognition speed and accuracy

An experiment investigated the assumption that natural indicators which exploit existing learned associations between a signal and an event make more effective warnings than previously unlearned symbolic indicators. Signal modality (visual, auditory) and task demand (low, high) were also manipulated. Warning effectiveness was indexed by accuracy and reaction time (RT) recorded during training and dual task test phases. Thirty-six participants were trained to recognize 4 natural and 4 symbolic indicators, either visual or auditory, paired with critical incidents from an aviation context. As hypothesized, accuracy was greater and RT was faster in response to natural indicators during the training phase. This pattern of responding was upheld in test phase conditions with respect to accuracy but observed in RT only in test phase conditions involving high demand and the auditory modality. Using the experiment as a specific example, we argue for the importance of considering the cognitive contribution of the user (viz., prior learned associations) in the warning design process. Drawing on semiotics and cognitive psychology, we highlight the indexical nature of so-called auditory icons or natural indicators and argue that the cogniser is an indispensable element in the tripartite nature of signification

Fully spray-coated triple-cation perovskite solar cells

We use ultrasonic spray-coating to sequentially deposit thin films of tin oxide, a triple-cation perovskite and spiro-OMeTAD, allowing us fabricate perovskite solar cells (PSCs) with a champion reverse scan power conversion efficiency (PCE) of 19.4% on small-area substrates. We show that the use of spray-deposition permits us to rapidly (>80 mm s−1) coat 25 mm × 75 mm substrates that were divided into a series of devices each with an active area of 15.4 mm2, yielding an average PCE of 10.3% and a peak PCE of 16.3%. By connecting seven 15.4 mm2 devices in parallel on a single substrate, we create a device having an effective active area of 1.08 cm2 and a PCE of 12.7%. This work demonstrates the possibility for spray-coating to fabricate high efficiency and low-cost perovskite solar cells at speed

Self-assembled monolayers of alendronate on Ti6Al4V alloy surfaces enhance osteogenesis in mesenchymal stem cells

Phosphonates have emerged as an alternative for functionalization of titanium surfaces by the formation of homogeneous self-assembled monolayers (SAMs) via Ti-O-P linkages. This study presents results from an investigation of the modification of Ti6Al4V alloy by chemisorption of osseoinductive alendronate using a simple, effective and clean methodology. The modified surfaces showed a tailored topography and surface chemistry as determined by SEM microscopy and RAMAN spectroscopy. X-ray photoelectron spectroscopy revealed that an effective mode of bonding is created between the metal oxide surface and the phosphate residue of alendronate, leading to formation of homogenous drug distribution along the surface. In-vitro studies showed that alendronate SAMs induce differentiation of hMSC to a bone cell phenotype and promote bone formation on modified surfaces. Here we show that this novel method for the preparation of functional coatings on titanium-based medical devices provides osseoinductive bioactive molecules to promote enhanced integration at the site of implantation