Inference by Minimizing Size, Divergence, or their Sum

We speed up marginal inference by ignoring factors that do not significantly contribute to overall accuracy. In order to pick a suitable subset of factors to ignore, we propose three schemes: minimizing the number of model factors under a bound on the KL divergence between pruned and full models; minimizing the KL divergence under a bound on factor count; and minimizing the weighted sum of KL divergence and factor count. All three problems are solved using an approximation of the KL divergence than can be calculated in terms of marginals computed on a simple seed graph. Applied to synthetic image denoising and to three different types of NLP parsing models, this technique performs marginal inference up to 11 times faster than loopy BP, with graph sizes reduced up to 98%-at comparable error in marginals and parsing accuracy. We also show that minimizing the weighted sum of divergence and size is substantially faster than minimizing either of the other objectives based on the approximation to divergence presented here.Comment: Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010

BANYAN. IV. Fundamental parameters of low-mass star candidates in nearby young stellar kinematic groups - Isochronal Age determination using Magnetic evolutionary models

Based on high resolution optical spectra obtained with ESPaDOnS at CFHT, we determine fundamental parameters (\Teff, R, \Lbol, \logg\ and metallicity) for 59 candidate members of nearby young kinematic groups. The candidates were identified through the BANYAN Bayesian inference method of \citet{2013malo}, which takes into account the position, proper motion, magnitude, color, radial velocity and parallax (when available) to establish a membership probability. The derived parameters are compared to Dartmouth Magnetic evolutionary models and to field stars with the goal to constrain the age of our candidates. We find that, in general, low-mass stars in our sample are more luminous and have inflated radii compared to older stars, a trend expected for pre-main sequence stars. The Dartmouth Magnetic evolutionary models show a good fit to observations of field K and M stars assuming a magnetic field strength of a few kG, as typically observed for cool stars. Using the low-mass members of $\beta$Pictoris moving group, we have re-examined the age inconsistency problem between Lithium Depletion age and isochronal age (Hertzspring-Russell diagram). We find that the inclusion of the magnetic field in evolutionary models increase the isochronal age estimates for the K5V-M5V stars. Using these models and field strengths, we derive an average isochronal age between 15 and 28 Myr and we confirm a clear Lithium Depletion Boundary from which an age of 26$\pm$3~Myr is derived, consistent with previous age estimates based on this method.Comment: Accepted for publication in Ap

Degradation of small simple and large complex lunar craters: Not a simple scale dependence

The crater record of a planetary surface unit is often analyzed by its cumulative size‐frequency distribution (CSFD). Measuring CSFDs involves traditional approaches, such as traditional crater counting (TCC) and buffered crater counting (BCC), as well as geometric corrections, such as nonsparseness correction (NSC) and buffered nonsparseness correction (BNSC). NSC and BNSC consider the effects of geometric crater obliteration on the CSFD. On the Moon, crater obliteration leads to two distinct states in which obtained CSFDs do not match the production CSFD—crater equilibrium and nonsparseness. Crater equilibrium occurs when each new impact erases a preexisting crater of the same size. It is clearly observed on lunar terrains dominated by small simple craters with steep‐sloped production CSFDs, such as Imbrian to Eratosthenian‐era mare units. Nonsparseness, on the other hand, is caused by the geometric overlap of preexisting craters by a new impact, which is also known as “cookie cutting.” Cookie cutting is most clearly observed on lunar terrains dominated by large craters with shallow‐sloped production CSFDs, such as the pre‐Nectarian lunar highlands. We use the Cratered Terrain Evolution Model (CTEM) to simulate the evolution of a pre‐Nectarian surface unit. The model was previously used to simulate the diffusion‐induced equilibrium for small craters of the lunar maria. We find that relative to their size, large craters contribute less to the diffusion of the surrounding landscape than small craters. Thus, a simple scale dependence cannot account for the per‐crater contribution to degradation by small simple and large complex craters

The Solar Neighborhood. XXXIV. A Search for Planets Orbiting Nearby M Dwarfs using Astrometry

Astrometric measurements are presented for seven nearby stars with previously detected planets: six M dwarfs (GJ 317, GJ 667C, GJ 581, GJ 849, GJ 876, and GJ 1214) and one K dwarf (BD $-$10 3166). Measurements are also presented for six additional nearby M dwarfs without known planets, but which are more favorable to astrometric detections of low mass companions, as well as three binary systems for which we provide astrometric orbit solutions. Observations have baselines of three to thirteen years, and were made as part of the RECONS long-term astrometry and photometry program at the CTIO/SMARTS 0.9m telescope. We provide trigonometric parallaxes and proper motions for all 16 systems, and perform an extensive analysis of the astrometric residuals to determine the minimum detectable companion mass for the 12 M dwarfs not having close stellar secondaries. For the six M dwarfs with known planets, we are not sensitive to planets, but can rule out the presence of all but the least massive brown dwarfs at periods of 2 - 12 years. For the six more astrometrically favorable M dwarfs, we conclude that none have brown dwarf companions, and are sensitive to companions with masses as low as 1 $M_{Jup}$ for periods longer than two years. In particular, we conclude that Proxima Centauri has no Jovian companions at orbital periods of 2 - 12 years. These results complement previously published M dwarf planet occurrence rates by providing astrometrically determined upper mass limits on potential super-Jupiter companions at orbits of two years and longer. As part of a continuing survey, these results are consistent with the paucity of super-Jupiter and brown dwarf companions we find among the over 250 red dwarfs within 25 pc observed longer than five years in our astrometric program.Comment: 18 pages, 5 figures, 4 tables, accepted for publication in A

XSEDE Campus Bridging Use Cases

This document is both a user-­facing document (publicly accessible) and an internal working document intended to define user needs and use cases that fall under the general umbrella of Campus Bridging within the overall activities of XSEDE.XSEDE is supported by National Science Foundation Grant 1053575 (XSEDE: eXtreme Science and Engineering Discovery Environment)

Campus Bridging Use Case Quality Attribute Scenarios

This document is both a user-­facing document (publicly accessible) and an internal working document intended to define quality attributes associated with user needs and use cases that fall under the general umbrella of Campus Bridging within the overall activities of XSEDE.XSEDE is supported by National Science Foundation Grant 1053575 (XSEDE: eXtreme Science and Engineering Discovery Environment)

Investment under ambiguity with the best and worst in mind

Recent literature on optimal investment has stressed the difference between the impact of risk and the impact of ambiguity - also called Knightian uncertainty - on investors' decisions. In this paper, we show that a decision maker's attitude towards ambiguity is similarly crucial for investment decisions. We capture the investor's individual ambiguity attitude by applying alpha-MEU preferences to a standard investment problem. We show that the presence of ambiguity often leads to an increase in the subjective project value, and entrepreneurs are more eager to invest. Thereby, our investment model helps to explain differences in investment behavior in situations which are objectively identical