Incompressible Euler Equations and the Effect of Changes at a Distance

Because pressure is determined globally for the incompressible Euler equations, a localized change to the initial velocity will have an immediate effect throughout space. For solutions to be physically meaningful, one would expect such effects to decrease with distance from the localized change, giving the solutions a type of stability. Indeed, this is the case for solutions having spatial decay, as can be easily shown. We consider the more difficult case of solutions lacking spatial decay, and show that such stability still holds, albeit in a somewhat weaker form.Comment: Revised statement of Theorem 1 to include a missing definitio

Outcome Measurement in Nonprofit Organizations: Current Practices and Recommendations

This report provides a view of the state of outcome measurement as implemented in a number of private nonprofit service organizations engaged in outcome measurement. It provides examples of procedures that some organizations have been able to implement and use for outcome measurement.For this effort, we selected a sample of organizations that responded to INDEPENDENT SECTOR's 1998 Measures Survey, specifically those that reported collecting outcome information on a regular basis. Our sample included only nonprofit organizations that provided services directly to clients (end services), not those whose primary function was to provide services to other organizations. We included organizations that provided human services (including vocational rehabilitation, employment training, youth services, housing and homeless services, and meals/nutrition programs) and health and mental health services (excluding hospitals), as well as environmental and animal protection organizations. We conducted telephone interviews with, and reviewed documents from, thirty-six organizations

The interaction between gaze and facial expression in the amygdala and extended amygdala is modulated by anxiety

Behavioral evidence indicates that angry faces are seen as more threatening, and elicit greater anxiety, when directed at the observer, whereas the influence of gaze on the processing of fearful faces is less consistent. Recent research has also found inconsistent effects of expression and gaze direction on the amygdala response to facial signals of threat. However, such studies have failed to consider the important influence of anxiety on the response to signals of threat; an influence that is well established in behavioral research and recent neuroimaging studies. Here, we investigated the way in which individual differences in anxiety would influence the interactive effect of gaze and expression on the response to angry and fearful faces in the human extended amygdala. Participants viewed images of fearful, angry and neutral faces, either displaying an averted or direct gaze. We found that state anxiety predicted an increased response in the dorsal amygdala/substantia innominata (SI) to angry faces when gazing at, relative to away from the observer. By contrast, high state anxious individuals showed an increased amygdala response to fearful faces that was less dependent on gaze. In addition, the relationship between state anxiety and gaze on emotional intensity ratings mirrored the relationship between anxiety and the amygdala/SI response. These results have implications for understanding the functional role of the amygdala and extended amygdala in processing signals of threat, and are consistent with the proposed role of this region in coding the relevance or significance of a stimulus to the observer

Making a Business Case for Reducing Racial and Ethnic Disparities in Health Care: Key Issues and Observations

Offers lessons from RWJF's Finding Answers program around issues involved in laying out financial reasons for providers, caregivers, and others to adopt ongoing, effective interventions to improve quality of care for minority patients

Patterns of Scalable Bayesian Inference

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with few clear overarching principles. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. We review existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, we characterize the general principles that have proven successful for designing scalable inference procedures and comment on the path forward

A FACTOR ANALYSIS OF SUPERMARKET MANAGEMENT PRACTICES

Empirically based management practice indices are constructed using results from factor analysis of data from 344 stores in the 2000 Supermarket Panel. These indices are compared to six management indices based on expert opinion. The empirical indices group variables differently and provide a more compact summary of supermarket management practices.Agribusiness,

Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge

This paper describes the winning entry to the IJCNN 2011 Social Network Challenge run by Kaggle.com. The goal of the contest was to promote research on real-world link prediction, and the dataset was a graph obtained by crawling the popular Flickr social photo sharing website, with user identities scrubbed. By de-anonymizing much of the competition test set using our own Flickr crawl, we were able to effectively game the competition. Our attack represents a new application of de-anonymization to gaming machine learning contests, suggesting changes in how future competitions should be run. We introduce a new simulated annealing-based weighted graph matching algorithm for the seeding step of de-anonymization. We also show how to combine de-anonymization with link prediction---the latter is required to achieve good performance on the portion of the test set not de-anonymized---for example by training the predictor on the de-anonymized portion of the test set, and combining probabilistic predictions from de-anonymization and link prediction.Comment: 11 pages, 13 figures; submitted to IJCNN'201