Vacua and Exact Solutions in Lower-DD Limits of EGB

We consider the action principles that are the lower dimensional limits of the Einstein-Gauss-Bonnet gravity {\it via} the Kaluza-Klein route. We study the vacua and obtain some exact solutions. We find that the reality condition of the theories may select one vacuum over the other from the two vacua that typically arise in Einstein-Gauss-Bonnet gravity. We obtain exact black hole and cosmological solutions carrying scalar hair, including scalar hairy BTZ black holes with both mass and angular momentum turned on. We also discuss the holographic central charges in the asymptotic AdS backgrounds.Comment: Latex, 19 page

Probing motion of fast radio burst sources by timing strongly lensed repeaters

Given the possible repetitive nature of fast radio bursts (FRBs), their cosmological origin, and their high occurrence, detection of strongly lensed sources due to intervening galaxy lenses is possible with forthcoming radio surveys. We show that if multiple images of a repeating source are resolved with VLBI, using a method independent of lens modeling, accurate timing could reveal non-uniform motion, either physical or apparent, of the emission spot. This can probe the physical nature of FRBs and their surrounding environments, constraining scenarios including orbital motion around a stellar companion if FRBs require a compact star in a special system, and jet-medium interactions for which the location of the emission spot may randomly vary. The high timing precision possible for FRBs ($\sim {\rm ms}$) compared to the typical time delays between images in galaxy lensing ($\gtrsim 10\, {\rm days}$) enables the measurement of tiny fractional changes in the delays ($\sim 10^{-9}$), and hence the detection of time-delay variations induced by relative motions between the source, the lens, and the Earth. We show that uniform cosmic peculiar velocities only cause the delay time to drift linearly, and that the effect from the Earth's orbital motion can be accurately subtracted, thus enabling a search for non-trivial source motion. For a timing accuracy of $\sim 1\,$ms and a repetition rate (of detected bursts) $\sim 0.05$ per day of a single FRB source, non-uniform displacement $\gtrsim 0.1 - 1\,$AU of the emission spot perpendicular to the line of sight is detectable if repetitions are seen over a period of hundreds of days.Comment: 21 pages, 6 figures, 1 table. New version accepted to ApJ with abstract revised, typo corrected, and references adde

Weather Forecast Based Conditional Pest Management: A Stochastic Optimal Control Investigation

In this paper, we examine conditional, forecast-based dynamic pest management in agricultural crop production given stochastic pest infestations and stochastic climate dynamics throughout the growing season. Using stochastic optimal control we show that correlation between forecast error for climate prediction and forecast error for pest outbreaks can be used to improve pesticide application efficiency. In the general setting, we apply modified Hamiltonian approach to discuss the steady state equilibrium. Given specific functional forms, a closed form solution can be found for the stochastic optimal control problem. Moreover, we find conditions for model parameters so that the optimal pesticide usage path will be monotonically increasing or decreasing in the correlation coefficient between climate forecast errors and pest growth disturbances.Pest Management, Stochastic Optimal Control, Production Economics,

Noise adaptive training for subspace Gaussian mixture models

Noise adaptive training (NAT) is an effective approach to normalise the environmental distortions in the training data. This paper investigates the model-based NAT scheme using joint uncertainty decoding (JUD) for subspace Gaussian mixture models (SGMMs). A typical SGMM acoustic model has much larger number of surface Gaussian components, which makes it computationally infeasible to compensate each Gaussian explicitly. JUD tackles the problem by sharing the compensation parameters among the Gaussians and hence reduces the computational and memory demands. For noise adaptive training, JUD is reformulated into a generative model, which leads to an efficient expectation-maximisation (EM) based algorithm to update the SGMM acoustic model parameters. We evaluated the SGMMs with NAT on the Aurora 4 database, and obtained higher recognition accuracy compared to systems without adaptive training. Index Terms: adaptive training, noise robustness, joint uncertainty decoding, subspace Gaussian mixture model