Online Distributed Sensor Selection

A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to constraints (e.g., on power and bandwidth). In many applications the utility function is not known a priori, must be learned from data, and can even change over time. Furthermore for large sensor networks solving a centralized optimization problem to select sensors is not feasible, and thus we seek a fully distributed solution. In this paper, we present Distributed Online Greedy (DOG), an efficient, distributed algorithm for repeatedly selecting sensors online, only receiving feedback about the utility of the selected sensors. We prove very strong theoretical no-regret guarantees that apply whenever the (unknown) utility function satisfies a natural diminishing returns property called submodularity. Our algorithm has extremely low communication requirements, and scales well to large sensor deployments. We extend DOG to allow observation-dependent sensor selection. We empirically demonstrate the effectiveness of our algorithm on several real-world sensing tasks

Training Gaussian Mixture Models at Scale via Coresets

How can we train a statistical mixture model on a massive data set? In this work we show how to construct coresets for mixtures of Gaussians. A coreset is a weighted subset of the data, which guarantees that models fitting the coreset also provide a good fit for the original data set. We show that, perhaps surprisingly, Gaussian mixtures admit coresets of size polynomial in dimension and the number of mixture components, while being independent of the data set size. Hence, one can harness computationally intensive algorithms to compute a good approximation on a significantly smaller data set. More importantly, such coresets can be efficiently constructed both in distributed and streaming settings and do not impose restrictions on the data generating process. Our results rely on a novel reduction of statistical estimation to problems in computational geometry and new combinatorial complexity results for mixtures of Gaussians. Empirical evaluation on several real-world datasets suggests that our coreset-based approach enables significant reduction in training-time with negligible approximation error

IRT assessment of readiness for interprofessional learning scale (RIPLS) : dimensionality, reliability, and item function

This poster presentation aims to bring evidence from modern psychometric methods to bear on a popularly deployed questionnaire in interprofessional education (IPE) assessment. Specifically, three interrelated problems raised against the Readiness for Interprofessional Learning Scale (RIPLS) are examined in a study with n =280 medical and nursing student participants. Firstly, findings indicate a strong, general factor underlying the RIPLS that supports unidimensional interpretations. Secondly, findings support RIPLS overall reliability, but fail to support subscale reliabilities. Thirdly, findings support the RIPLS potential sensitivity to changes with appropriate lower ranges for our pre-training student sample. Recommendations for refinement to the RIPLS include: use of more appropriate reliability indices; factor generalizability; and a subset of items. More generally, refinement is possible, whereas RIPLS disuse or continued misuse with problematic scales is likely to hinder progress in the field of IPE research

Electronic health literacy in Swiss-German parents : cross-sectional study of eHealth literacy scale unidimensionality

Parents often use digital media to search for information related to their children's health. As the quantity and quality of digital sources meant specifically for parents expand, parents' digital health literacy is increasingly important to process the information they retrieve. One of the earliest developed and widely used instruments to assess digital health literacy is the self-reported eHealth Literacy Scale (eHEALS). However, the eHEALS has not been psychometrically validated in a sample of parents. Given the inconsistency of the eHEALS underlying factor structure across previous reports, it is particularly important for validation to occur

Learning organizational ambidexterity : a joint-variance synthesis of exploration-exploitation modes on performance

Purpose: The purpose of this paper is to reexamine exploration-exploitation’s reciprocality in organizational ambidexterity (OA) research. OA figures prominently in a variety of organization science phenomena. Introduced as a two-stage model for innovation, theory specifies reciprocal reinforcement between the OA processes of exploration (eR) and exploitation (eT). In this study, the authors argue that previous analyses of OA necessarily neglect this reciprocality in favor of conceptualizations that conform to common statistical techniques. Design/Methodology/approach: The authors propose joint-variance (JV) as a soluble estimator of exploration–exploitation (eR-eT) reciprocality. An updated systematic literature synthesis yielded K = 50 studies (53 independent samples, N = 11,743) for further testing. Findings: Three primary findings are as follows: JV reduced negative confounding, explaining 45 per cent of between-study variance. JV quantified the positive confounding in separate meta-analytic estimates of eR and eT on performance because of double-counting (37.6 per cent), and substantive application of JV to hypothesis testing supported OA theoretical predictions. Research limitations/implications: The authors discuss practical consideration for eR-eT reciprocality, as well as theoretical contributions for cohering the OA empirical literature. Practical implications: The authors discuss design limitations and JV measurement extensions for the future. Social implications: Learning in OA literature has been neglected or underestimated. Originality/value: Because reciprocality is theorized, yet absent in current models, existing results represent confounded or biased evidence of the OA’s effect on firm performance. Subsequently, the authors propose JV as a soluble estimator of eR-eT learning modes

The Changing Financial Structure of the U.S. Farm Sector

Agricultural Finance,

Community Seismic Network

The article describes the design of the Community Seismic Network, which is a dense open seismic network based on low cost sensors. The inputs are from sensors hosted by volunteers from the community by direct connection to their personal computers, or through sensors built into mobile devices. The server is cloud-based for robustness and to dynamically handle the load of impulsive earthquake events. The main product of the network is a map of peak acceleration, delivered within seconds of the ground shaking. The lateral variations in the level of shaking will be valuable to first responders, and the waveform information from a dense network will allow detailed mapping of the rupture process. Sensors in buildings may be useful for monitoring the state-of-health of the structure after major shaking