DataWarp: Building Applications which Make Progress in an Inconsistent World

The usual approach to dealing with imperfections in data is to attempt to eliminate them. However, the nature of modern systems means this is often futile. This paper describes an approach which permits applications to operate notwithstanding inconsistent data. Instead of attempting to extract a single, correct view of the world from its data, a DataWarp application constructs a collection of interpretations. It adopts one of these and continues work. Since it acts on assumptions, the DataWarp application considers its recent work to be provisional, expecting eventually most of these actions will become definitive. Should the application decide to adopt an alternative data view, it may then need to void provisional actions before resuming work. We describe the DataWarp architecture, discuss its implementation and describe an experiment in which a DataWarp application in an environment containing inconsistent data achieves better results than its conventional counterpart

Heartbeat Anomaly Detection using Adversarial Oversampling

Cardiovascular diseases are one of the most common causes of death in the world. Prevention, knowledge of previous cases in the family, and early detection is the best strategy to reduce this fact. Different machine learning approaches to automatic diagnostic are being proposed to this task. As in most health problems, the imbalance between examples and classes is predominant in this problem and affects the performance of the automated solution. In this paper, we address the classification of heartbeats images in different cardiovascular diseases. We propose a two-dimensional Convolutional Neural Network for classification after using a InfoGAN architecture for generating synthetic images to unbalanced classes. We call this proposal Adversarial Oversampling and compare it with the classical oversampling methods as SMOTE, ADASYN, and RandomOversampling. The results show that the proposed approach improves the classifier performance for the minority classes without harming the performance in the balanced classes

Early-life neighborhood context, perceived stress, and preterm birth in African American Women.

Stressors from multiple sources, across the life-course, may have independent and joint associations with preterm birth (PTB) risk in African American women. Using data from the Life-course Influences on Fetal Environments Study (LIFE; 2009-2011) of post-partum African American women from Metropolitan Detroit, Michigan (n=1365), we examined the association between perceived stress and PTB, and effect modification by perceptions of early-life neighborhood social control and disorder. We defined PTB as birth before 37 completed weeks of gestation. We used Cohen's Perceived Stress scale, and valid and reliable scales of early-life (age 10) neighborhood social control and social disorder to quantify exposures. We estimated prevalence ratios (PR) and 95% confidence intervals (CI) with log binomial regression models- with separate interaction terms for perceived stress and each early-life neighborhood scale. We considered p < 0.10 significant for interaction terms. PTB occurred in 16.4% (n=224) of the study participants. In the total sample, perceived stress was not associated with PTB rates. However, there was suggestive evidence of a joint association between perceived stress and early-life neighborhood social disorder (p for interaction = 0.06), such that among women who reported high early-life neighborhood social disorder (n=660), perceived stress was positively associated with PTB (adjusted PR: 1.31; 95% CI: 1.05, 1.63). There was no association between perceived stress and PTB for women in the low early-life neighborhood social disorder strata (n=651) (adjusted PR: 0.95, 95% CI: 0.75, 1.21). There was no evidence that early-life neighborhood social control modified the association between perceived stress and PTB. Our results suggest that early-life neighborhood stressors may magnify the association between current perceived stress and PTB rates, in African American women. More research to confirm and explicate the biologic and/or psychosocial mechanisms of the reported association is warranted

Properties of real metallic surfaces: Effects of density functional semilocality and van der Waals nonlocality

We have computed the surface energies, work functions, and interlayer surface relaxations of clean (111), (110), and (100) surfaces of Al, Cu, Ru, Rh, Pd, Ag, Pt, and Au. Many of these metallic surfaces have technological or catalytic applications. We compare experimental reference values to those of the local density approximation (LDA), the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA), the PBEsol (PBE for solids) GGA, the SCAN meta-GGA, and SCAN+rVV10 (SCAN with a long-range van der Waals or vdW correction). The closest agreement with uncertain experimental values is achieved by the simplest density functional (LDA) and by the most sophisticated general-purpose one (SCAN+rVV10). The long-range vdW interaction increases the surface energies by about 10%, and the work functions by about 1%. LDA works for metal surfaces through a stronger-than-usual error cancellation. PBE yields the most-underestimated and presumably least accurate surface energies and work functions. Surface energies within the random phase approximation (RPA) are also reported. Interlayer relaxations from different functionals are in reasonable agreement with one another, and usually with experiment

Historical Motivations for the Siege of Minas Tirith

Traces possible historical models for the Siege of Minas Tirith, particularly the fall of Constantinople in 1453