The Lives of Stars: Insights From the TGAS-RAVE-LAMOST Dataset

In this paper we investigate how the chemical and kinematic properties of stars vary as a function of age. Using data from a variety of photometric, astrometric and spectroscopic surveys, we calculate the ages, phase space information and orbits for $\sim$125,000 stars covering a wide range of stellar parameters. We find indications that the inner regions of the disk reached high levels of enrichment early, while the outer regions were more substantially enriched in intermediate and recent epochs. We consider these enrichment histories through comparison of the ages of stars, their metallicities, and kinematic properties, such as their angular momentum in the solar neighborhood (which is a proxy for orbital radius). We calculate rates at which the velocity dispersions evolve, investigate the Oort constants for different aged populations (finding a slightly negative $\partial V_{C} / \partial R$ and $\partial V_{R} / \partial R$ for all ages, being most negative for the oldest stars), as well as examine the behavior of the velocity vertex deviation angle as a function of age (which we find to fall from $\sim$15 degrees for the 2 Gyr aged population to $\sim$6 degrees at around 6.5 Gyr of age, after which it remains unchanged). We find evidence for stellar churning, and find that the churned stars have a slightly younger age distribution than the rest of the data.Comment: 18 Pages, 14 Figures, Accepted Ap

Using self-categorization theory to uncover the framing of the 2015 Rugby World Cup: a cross-cultural comparison of three nations’ newspapers

Research into the framing of sporting events has been extensively studied to uncover newspaper bias in the coverage of global sporting events. Through discourse, the media attempt to capture, build, and maintain audiences for the duration of sporting events through the use of multiple narratives and/or storylines. Little research has looked at the ways in which the same event is reported across different nations, and media representations of the Rugby World Cup have rarely featured in discussions of the framing of sport events. The present study highlights the different ways in which rugby union is portrayed across the three leading Southern Hemisphere nations in the sport. It also shows the prominence of nationalistic discourse across those nations and importance of self-categorizations in newspaper narratives.</jats:p

Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates

We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression. The method is simple to describe: it randomly partitions a dataset of size N into m subsets of equal size, computes an independent kernel ridge regression estimator for each subset, then averages the local solutions into a global predictor. This partitioning leads to a substantial reduction in computation time versus the standard approach of performing kernel ridge regression on all N samples. Our two main theorems establish that despite the computational speed-up, statistical optimality is retained: as long as m is not too large, the partition-based estimator achieves the statistical minimax rate over all estimators using the set of N samples. As concrete examples, our theory guarantees that the number of processors m may grow nearly linearly for finite-rank kernels and Gaussian kernels and polynomially in N for Sobolev spaces, which in turn allows for substantial reductions in computational cost. We conclude with experiments on both simulated data and a music-prediction task that complement our theoretical results, exhibiting the computational and statistical benefits of our approach

Randomized Smoothing for Stochastic Optimization

We analyze convergence rates of stochastic optimization procedures for non-smooth convex optimization problems. By combining randomized smoothing techniques with accelerated gradient methods, we obtain convergence rates of stochastic optimization procedures, both in expectation and with high probability, that have optimal dependence on the variance of the gradient estimates. To the best of our knowledge, these are the first variance-based rates for non-smooth optimization. We give several applications of our results to statistical estimation problems, and provide experimental results that demonstrate the effectiveness of the proposed algorithms. We also describe how a combination of our algorithm with recent work on decentralized optimization yields a distributed stochastic optimization algorithm that is order-optimal.Comment: 39 pages, 3 figure