184 research outputs found
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
Clinical predictors of radiographic abnormalities among infants with bronchiolitis in a paediatric emergency department
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