Approximate Inference in Continuous Determinantal Point Processes

Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion. In machine learning, the focus of DPP-based models has been on diverse subset selection from a discrete and finite base set. This discrete setting admits an efficient sampling algorithm based on the eigendecomposition of the defining kernel matrix. Recently, there has been growing interest in using DPPs defined on continuous spaces. While the discrete-DPP sampler extends formally to the continuous case, computationally, the steps required are not tractable in general. In this paper, we present two efficient DPP sampling schemes that apply to a wide range of kernel functions: one based on low rank approximations via Nystrom and random Fourier feature techniques and another based on Gibbs sampling. We demonstrate the utility of continuous DPPs in repulsive mixture modeling and synthesizing human poses spanning activity spaces

Mining Missing Hyperlinks from Human Navigation Traces: A Case Study of Wikipedia

Hyperlinks are an essential feature of the World Wide Web. They are especially important for online encyclopedias such as Wikipedia: an article can often only be understood in the context of related articles, and hyperlinks make it easy to explore this context. But important links are often missing, and several methods have been proposed to alleviate this problem by learning a linking model based on the structure of the existing links. Here we propose a novel approach to identifying missing links in Wikipedia. We build on the fact that the ultimate purpose of Wikipedia links is to aid navigation. Rather than merely suggesting new links that are in tune with the structure of existing links, our method finds missing links that would immediately enhance Wikipedia's navigability. We leverage data sets of navigation paths collected through a Wikipedia-based human-computation game in which users must find a short path from a start to a target article by only clicking links encountered along the way. We harness human navigational traces to identify a set of candidates for missing links and then rank these candidates. Experiments show that our procedure identifies missing links of high quality

Tracking of fatness during childhood, adolescence and young adulthood: a 7-year follow-up study in Madeira Island, Portugal

Aims: Investigating tracking of fatness from childhood to adolescence, early adolescence to young adulthood and late adolescence to young adulthood. Subjects and methods: Participants from the Madeira Growth Study were followed during an average period of 7.2 years. Height, body mass, skin-folds and circumferences were measured, nine health- and performance-related tests were administered and the Baecke questionnaire was used to assess physical activity. Skeletal maturity was estimated using the TW3 method. Results: The prevalence of overweight plus obesity ranged from 8.2–20.0% at baseline and from 20.4–40.0% at followup, in boys. Corresponding percentages for girls were 10.6– 12.0% and 13.2–18.0%. Inter-age correlations for fatness indicators ranged from 0.43–0.77. BMI, waist circumference and sum of skin-folds at 8, 12 and 16-years old were the main predictors of these variables at 15, 19 and 23-years old, respectively. Strength, muscular endurance and aerobic fitness were negatively related to body fatness. Physical activity and maturation were independently associated with adolescent (15 years) and young adult (19 years) fatness. Conclusions: Over 7.2 years, tracking was moderate-to-high for fatness. Variance was explained by fatness indicators and to a small extent by physical fitness, physical activity and maturation

Learning to Infer Social Ties in Large Networks

Abstract. In online social networks, most relationships are lack of meaning labels (e.g., “colleague ” and “intimate friends”), simply because users do not take the time to label them. An interesting question is: can we automatically infer the type of social relationships in a large network? what are the fundamental factors that imply the type of social relation-ships? In this work, we formalize the problem of social relationship learn-ing into a semi-supervised framework, and propose a Partially-labeled Pairwise Factor Graph Model (PLP-FGM) for learning to infer the type of social ties. We tested the model on three different genres of data sets: Publication, Email and Mobile. Experimental results demonstrate that the proposed PLP-FGM model can accurately infer 92.7 % of advisor-advisee relationships from the coauthor network (Publication), 88.0 % of manager-subordinate relationships from the email network (Email), and 83.1 % of the friendships from the mobile network (Mobile). Finally, we develop a distributed learning algorithm to scale up the model to real large networks.

Computer-aided ventilator resetting is feasible on the basis of a physiological profile.

BACKGROUND: Ventilator resetting is frequently needed to adjust tidal volume, pressure and gas exchange. The system comprising lungs and ventilator is so complex that a trial and error strategy is often applied. Comprehensive characterization of lung physiology is feasible by monitoring. The hypothesis that the effect of ventilator resetting could be predicted by computer simulation based on a physiological profile was tested in healthy pigs. METHODS: Flow, pressure and CO2 signals were recorded in 7 ventilated pigs. Elastic recoil pressure was measured at postinspiratory and post-expiratory pauses. Inspiratory and expiratory resistance as a function of volume and compliance were calculated. CO2 elimination per breath was expressed as a function of tidal volume. Calculating pressure and flow moment by moment simulated the effect of ventilator action, when respiratory rate was varied between 10 and 30 min(-1) and minute volume was changed so as to maintain PaCO2. Predicted values of peak airway pressure, plateau pressure, and CO2 elimination were compared to values measured after resetting. RESULTS: With 95% confidence, predicted pressures and CO2 elimination deviated from measured values with < 1 cm H2O and < 6%, respectively. CONCLUSION: It is feasible to predict effects of ventilator resetting on the basis of a physiological profile at least in health

User Identity Linkage by Latent User Space Modelling

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ