Can Effects of Dark Matter be Explained by the Turbulent Flow of Spacetime?

For the past forty years the search for dark matter has been one of the primary foci of astrophysics, although there has yet to be any direct evidence for its existence (Porter et al. 2011). Indirect evidence for the existence of dark matter is largely rooted in the rotational speeds of stars within their host galaxies, where, instead of having a ~ r^1/2 radial dependence, stars appear to have orbital speeds independent of their distance from the galactic center, which led to proposed existence of dark matter (Porter et al. 2011; Peebles 1993). We propose an alternate explanation for the observed stellar motions within galaxies, combining the standard treatment of a fluid-like spacetime with the possibility of a "bulk flow" of mass through the Universe. The differential "flow" of spacetime could generate vorticies capable of providing the "perceived" rotational speeds in excess of those predicted by Newtonian mechanics. Although a more detailed analysis of our theory is forthcoming, we find a crude "order of magnitude" calculation can explain this phenomena. We also find that this can be used to explain the graviational lensing observed around globular clusters like "Bullet Cluster".Comment: 5 pages, Accepted for publication in Journal of Modern Physics: Gravitation and Cosmology (Sept. 2012

Assessing the performance of the Asian/Pacific islander identification algorithm to infer Hmong ethnicity from electronic health records in California.

OBJECTIVE:This study assesses the performance of the North American Association of Central Cancer Registries Asian/Pacific Islander Identification Algorithm (NAPIIA) to infer Hmong ethnicity. DESIGN AND SETTING:Analyses of electronic health records (EHRs) from 1 January 2011 to 1 October 2015. The NAPIIA was applied to the EHR data, and self-reported Hmong ethnicity from a questionnaire was used as the gold standard. Sensitivity, specificity, positive (PPV) and negative predictive values (NPVs) were calculated comparing the source data ethnicity inferred by the algorithm with the self-reported ethnicity from the questionnaire. PARTICIPANTS:EHRs indicating Hmong, Chinese, Vietnamese and Korean ethnicity who met the original study inclusion criteria were analysed. RESULTS:The NAPIIA had a sensitivity of 78%, a specificity of 99.9%, a PPV of 96% and an NPV of 99%. The prevalence of Hmong population in the sample was 3.9%. CONCLUSION:The high sensitivity of the NAPIIA indicates its effectiveness in detecting Hmong ethnicity. The applicability of the NAPIIA to a multitude of Asian subgroups can advance Asian health disparity research by enabling researchers to disaggregate Asian data and unmask health challenges of different Asian subgroups

Spectra: Robust Estimation of Distribution Functions in Networks

Distributed aggregation allows the derivation of a given global aggregate property from many individual local values in nodes of an interconnected network system. Simple aggregates such as minima/maxima, counts, sums and averages have been thoroughly studied in the past and are important tools for distributed algorithms and network coordination. Nonetheless, this kind of aggregates may not be comprehensive enough to characterize biased data distributions or when in presence of outliers, making the case for richer estimates of the values on the network. This work presents Spectra, a distributed algorithm for the estimation of distribution functions over large scale networks. The estimate is available at all nodes and the technique depicts important properties, namely: robust when exposed to high levels of message loss, fast convergence speed and fine precision in the estimate. It can also dynamically cope with changes of the sampled local property, not requiring algorithm restarts, and is highly resilient to node churn. The proposed approach is experimentally evaluated and contrasted to a competing state of the art distribution aggregation technique.Comment: Full version of the paper published at 12th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS), Stockholm (Sweden), June 201