Scotogenic Z2Z_2 or U(1)DU(1)_D Model of Neutrino Mass with Δ(27)\Delta(27) Symmetry

The scotogenic model of radiative neutrino mass with $Z_2$ or $U(1)_D$ dark matter is shown to accommodate $\Delta(27)$ symmetry naturally. The resulting neutrino mass matrix is identical to either of two forms, one proposed in 2006, the other in 2008. These two structures are studied in the context of present neutrino data, with predictions of $CP$ violation and neutrinoless double beta decay.Comment: 7 pages, 4 figure

Dark Matter with Flavor Symmetry and its Collider Signature

The notion that dark matter and standard-model matter are connected through flavor implies a generic collider signature of the type 2 jets + $\mu^\pm$ + $e^\mp$ + missing energy. We discuss the theoretical basis of this proposal and its verifiability at the Large Hadron Collider.Comment: 10 pages, 5 figure

Neutrino Mixing and CP Phase Correlations

A special form of the $3 \times 3$ Majorana neutrino mass matrix derivable from $\mu - \tau$ interchange symmetry accompanied by a generalized $CP$ transformation was obtained many years ago. It predicts $\theta_{23} = \pi/4$ as well as $\delta_{CP} = \pm \pi/2$, with $\theta_{13} \neq 0$. Whereas this is consistent with present data, we explore a deviation of this result which occurs naturally in a recent proposed model of radiative inverse seesaw neutrino mass.Comment: 9 pages, 7 figure

Scotogenic A4A_4 Neutrino Model for Nonzero θ13\theta_{13} and Large δCP\delta_{CP}

Assuming that neutrinos acquire radiative seesaw Majorana masses through their interactions with dark matter, i.e. scotogenic from the Greek 'scotos' meaning darkness, and using the non-Abelian discrete symmetry $A_4$, we propose a model of neutrino masses and mixing with nonzero $\theta_{13}$ and necessarily large leptonic CP violation, allowing both the normal and inverted hierarchies of neutrino masses, as well as quasi-degenerate solutions.Comment: 12 pages, 12 figure

Wellness from Diabetes: Community Health and Diabetes Assessment

The Republic of the Marshall Islands (RMI) is highly prevalent in type 2 diabetes mellitus (T2DM) with a prevalence rate of 37.37%, the highest in the world. T2DM dominates Majuro, the country’s capital, as a leading cause of mortality and morbidity, despite efforts of health care workers, local community organizations, and government. Income and education are social determinants of health. The correlations between good health and high income, and between good health and high education level, are positive. However, there is a continuous growth of T2DM incidence and prevalence on Majuro. Therefore, we hypothesized that there is no significant difference between healthful dietary and exercise practices of two groups of people on Majuro, RMI: those with high income and high education levels, and those with low income and low education levels. Community-based research conducted on Majuro helped test our hypothesis and gain knowledge of necessary steps to reverse this epidemic. During beginning stages of our research, related literature on diabetes, social determinants of health, and research methods were reviewed. To acquire qualitative data, focus group discussions (FGDs) and key informant interviews (KIIs) were conducted. FGDs were held with people grouped according to profession (health, education, community). With the KIIs, key members deeply involved or active in the community were interviewed one-on-one. The bulk of our quantitative data will be gathered by surveys on basic demographics, economics, and health-related perceptions. In collaboration with the Ministry of Health and local organizations, 400 surveys will be administered in Marshallese and English, and collected

Gradient descent for sparse rank-one matrix completion for crowd-sourced aggregation of sparsely interacting workers

We consider worker skill estimation for the singlecoin Dawid-Skene crowdsourcing model. In practice skill-estimation is challenging because worker assignments are sparse and irregular due to the arbitrary, and uncontrolled availability of workers. We formulate skill estimation as a rank-one correlation-matrix completion problem, where the observed components correspond to observed label correlation between workers. We show that the correlation matrix can be successfully recovered and skills identifiable if and only if the sampling matrix (observed components) is irreducible and aperiodic. We then propose an efficient gradient descent scheme and show that skill estimates converges to the desired global optima for such sampling matrices. Our proof is original and the results are surprising in light of the fact that even the weighted rank-one matrix factorization problem is NP hard in general. Next we derive sample complexity bounds for the noisy case in terms of spectral properties of the signless Laplacian of the sampling matrix. Our proposed scheme achieves state-of-art performance on a number of real-world datasets.Published versio