Log-Concave Duality in Estimation and Control

In this paper we generalize the estimation-control duality that exists in the linear-quadratic-Gaussian setting. We extend this duality to maximum a posteriori estimation of the system's state, where the measurement and dynamical system noise are independent log-concave random variables. More generally, we show that a problem which induces a convex penalty on noise terms will have a dual control problem. We provide conditions for strong duality to hold, and then prove relaxed conditions for the piecewise linear-quadratic case. The results have applications in estimation problems with nonsmooth densities, such as log-concave maximum likelihood densities. We conclude with an example reconstructing optimal estimates from solutions to the dual control problem, which has implications for sharing solution methods between the two types of problems

A late-time transition in the equation of state versus Lambda-CDM

We study a model of the dark energy which exhibits a rapid change in its equation of state w(z), such as occurs in vacuum metamorphosis. We compare the model predictions with CMB, large scale structure and supernova data and show that a late-time transition is marginally preferred over standard Lambda-CDM.Comment: 4 pages, 1 figure, to appear in the proceedings of XXXVIIth Rencontres de Moriond, "The Cosmological Model", March 200

Machine Learning Classification of SDSS Transient Survey Images

We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient science pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per cent, while only incorrectly classifying at most 18 per cent of artefacts as real objects, corresponding to a precision (purity) of 84 per cent. In general, random forests performed best, followed by the k-nearest neighbour and the SkyNet artificial neural net algorithms, compared to other methods such as na\"ive Bayes and kernel support vector machine. Our results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise.Comment: 14 pages, 8 figures. In this version extremely minor adjustments to the paper were made - e.g. Figure 5 is now easier to view in greyscal

Adiabatic Gravitational Perturbation During Reheating

We study the possibilities of parametric amplification of the gravitational perturbation during reheating in single-field inflation models. Our result shows that there is no additional growth of the super-horizon modes beyond the usual predictions.Comment: Refs added; New version to appear in PR

Electrical Tuning of Single Nitrogen-Vacancy Center Optical Transitions Enhanced by Photoinduced Fields

We demonstrate precise control over the zero-phonon optical transition energies of individual nitrogen-vacancy (NV) centers in diamond by applying multiaxis electric fields, via the dc Stark effect. The Stark shifts display surprising asymmetries that we attribute to an enhancement and rectification of the local electric field by photoionized charge traps in the diamond. Using this effect, we tune the excited-state orbitals of strained NV centers to degeneracy and vary the resulting degenerate optical transition frequency by >10 GHz, a scale comparable to the inhomogeneous frequency distribution. This technique will facilitate the integration of NV-center spins within photonic networks.Comment: 10 pages, 6 figure

A mechanistic model of connector hubs, modularity, and cognition

The human brain network is modular--comprised of communities of tightly interconnected nodes. This network contains local hubs, which have many connections within their own communities, and connector hubs, which have connections diversely distributed across communities. A mechanistic understanding of these hubs and how they support cognition has not been demonstrated. Here, we leveraged individual differences in hub connectivity and cognition. We show that a model of hub connectivity accurately predicts the cognitive performance of 476 individuals in four distinct tasks. Moreover, there is a general optimal network structure for cognitive performance--individuals with diversely connected hubs and consequent modular brain networks exhibit increased cognitive performance, regardless of the task. Critically, we find evidence consistent with a mechanistic model in which connector hubs tune the connectivity of their neighbors to be more modular while allowing for task appropriate information integration across communities, which increases global modularity and cognitive performance

Gravitational waves in preheating

We study the evolution of gravitational waves through the preheating era that follows inflation. The oscillating inflaton drives parametric resonant growth of scalar field fluctuations, and although super-Hubble tensor modes are not strongly amplified, they do carry an imprint of preheating. This is clearly seen in the Weyl tensor, which provides a covariant description of gravitational waves.Comment: 8 pages, 8 figures, Revte