28,459 research outputs found
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 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 and 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 15 degrees for the 2 Gyr aged population to 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
Impredicative Encodings of (Higher) Inductive Types
Postulating an impredicative universe in dependent type theory allows System
F style encodings of finitary inductive types, but these fail to satisfy the
relevant {\eta}-equalities and consequently do not admit dependent eliminators.
To recover {\eta} and dependent elimination, we present a method to construct
refinements of these impredicative encodings, using ideas from homotopy type
theory. We then extend our method to construct impredicative encodings of some
higher inductive types, such as 1-truncation and the unit circle S1
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