On bending angles by gravitational lenses in motion

The bending of lightrays by the gravitational field of a ``lens'' that is moving relative to the observer is calculated within the approximation of weak fields, small angles and thin lenses. Up to first order in $v/c$ -- and assuming the acceleration to be much smaller than $v/c$ -- the bending angle, time delay and redshift of the images are found to be affected by the component of the speed of the deflector along the line of sight. The correction takes the form of an overall factor of $1+v/c$ accompanying the mass of the deflector, leading to an indeterminacy of the order of $v/c$ in the mass of the lens inferred on the basis of the separation of multiple images. The consequent correction to the microlensing lightcurve is pointed out, as well as scenarios where the correction is potentially relevant.Comment: 6 pages, to appear in MNRA

The U.S. Dynamic Taylor Rule With Multiple Breaks, 1984-2001.

This paper combines two major strands of literature: structural breaks and Taylor rules. At first, I propose a nonstandard t-test statistic for detecting multiple level and trend breaks of I(0) series by supplying theoretical and limit-distribution critical values obtained from Montecarlo experimentation. Thereafter, I introduce a forward-looking Taylor rule expressed as a dynamic model which allows for multiple breaks and reaction-function coefficients of the leads of inflation, of the output gap and of an equity market index. Sequential GMM estimation of the model, applied to the Effective Federal Funds Rate for the period 1984:01-2001:06, produces three main interesting results: the existence of significant structural breaks, the substantial role played by inflation in the FOMC decisions and a marked equity targeting policy approach. Such results reveal departures from rationality, determined by structured and unstructured uncertainty, which the Fed systematically attempts at reducing by administering inflation scares and misinformation about the actual Phillips curve, in order to keep the output and equity markets under control.Generalized Method of Moments; Monetary Policy Rules; Multiple Breaks

EFFECTS OF RISK, DISEASE, AND NITROGEN SOURCE ON OPTIMAL NITROGEN FERTILIZATION RATES IN WINTER WHEAT PRODUCTION

Interactions among nitrogen (N) fertilization rate, N source, and disease severity can affect mean yield and yield variance in conservation tillage wheat production. A Just-Pope model was used to evaluate the effects of N rate, N source, and disease on the spring N-fertilization decision. Ammonium nitrate (AN) was the utility-maximizing N source regardless of risk preferences. The net-return-maximizing AN rate was 92 lb N/acre, providing $0.52/acre higher net returns than the best alternative N source (urea). If a farmer could anticipate a higher than average Take-All infection, the difference in optimal net-returns between AN and urea would increase to$35.11/acre.Crop Production/Industries,

Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions

To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents. Pedestrians are particularly challenging to model, as their motion patterns are often uncertain and/or unknown a priori. This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection of changes in intent and online learning of motion patterns not seen in prior training data. The resulting long-term movement predictions demonstrate improved accuracy relative to offline learning alone, in terms of both intent and trajectory prediction. By embedding these predictions within a chance-constrained motion planner, trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware experiments demonstrate that this approach can accurately predict pedestrian motion patterns from onboard sensor/perception data and facilitate robust navigation within a dynamic environment.Comment: Submitted to 2014 International Workshop on the Algorithmic Foundations of Robotic