Non-dispersive optics using storage of light

We demonstrate the non-dispersive deflection of an optical beam in a Stern-Gerlach magnetic field. An optical pulse is initially stored as a spin-wave coherence in thermal rubidium vapour. An inhomogeneous magnetic field imprints a phase gradient onto the spin wave, which upon reacceleration of the optical pulse leads to an angular deflection of the retrieved beam. We show that the obtained beam deflection is non-dispersive, i.e. its magnitude is independent of the incident optical frequency. Compared to a Stern-Gerlach experiment carried out with propagating light under the conditions of electromagnetically induced transparency, the estimated suppression of the chromatic aberration reaches 10 orders of magnitude.Comment: 11 pages, 4 figures, accepted for publication in Physical Review

Fixed points and limit cycles in the population dynamics of lysogenic viruses and their hosts

Starting with stochastic rate equations for the fundamental interactions between microbes and their viruses, we derive a mean field theory for the population dynamics of microbe-virus systems, including the effects of lysogeny. In the absence of lysogeny, our model is a generalization of that proposed phenomenologically by Weitz and Dushoff. In the presence of lysogeny, we analyze the possible states of the system, identifying a novel limit cycle, which we interpret physically. To test the robustness of our mean field calculations to demographic fluctuations, we have compared our results with stochastic simulations using the Gillespie algorithm. Finally, we estimate the range of parameters that delineate the various steady states of our model.Comment: 20 pages, 16 figures, 4 table

Microwave Dielectric Heating of Drops in Microfluidic Devices

We present a technique to locally and rapidly heat water drops in microfluidic devices with microwave dielectric heating. Water absorbs microwave power more efficiently than polymers, glass, and oils due to its permanent molecular dipole moment that has a large dielectric loss at GHz frequencies. The relevant heat capacity of the system is a single thermally isolated picoliter drop of water and this enables very fast thermal cycling. We demonstrate microwave dielectric heating in a microfluidic device that integrates a flow-focusing drop maker, drop splitters, and metal electrodes to locally deliver microwave power from an inexpensive, commercially available 3.0 GHz source and amplifier. The temperature of the drops is measured by observing the temperature dependent fluorescence intensity of cadmium selenide nanocrystals suspended in the water drops. We demonstrate characteristic heating times as short as 15 ms to steady-state temperatures as large as 30 degrees C above the base temperature of the microfluidic device. Many common biological and chemical applications require rapid and local control of temperature, such as PCR amplification of DNA, and can benefit from this new technique.Comment: 6 pages, 4 figure

Study of effects of fuel properties in turbine-powered business aircraft

Increased interest in research and technology concerning aviation turbine fuels and their properties was prompted by recent changes in the supply and demand situation of these fuels. The most obvious change is the rapid increase in fuel price. For commercial airplanes, fuel costs now approach 50 percent of the direct operating costs. In addition, there were occasional local supply disruptions and gradual shifts in delivered values of certain fuel properties. Dwindling petroleum reserves and the politically sensitive nature of the major world suppliers make the continuation of these trends likely. A summary of the principal findings, and conclusions are presented. Much of the material, especially the tables and graphs, is considered in greater detail later. The economic analysis and examination of operational considerations are described. Because some of the assumptions on which the economic analysis is founded are not easily verified, the sensitivity of the analysis to alternates for these assumptions is examined. The data base on which the analyses are founded is defined in a set of appendices

Heterogeneity in susceptibility dictates the order of epidemiological models

The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but do not incorporate population-level heterogeneity in infection susceptibility. We show that variation strongly influences the rate of infection, while the infection process simultaneously sculpts the susceptibility distribution. These joint dynamics influence the force of infection and are, in turn, influenced by the shape of the initial variability. Intriguingly, we find that certain susceptibility distributions (the exponential and the gamma) are unchanged through the course of the outbreak, and lead naturally to power-law behavior in the force of infection; other distributions often tend towards these "eigen-distributions" through the process of contagion. The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order epidemic models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak

Avalanche statistics and time-resolved grain dynamics for a driven heap

We probe the dynamics of intermittent avalanches caused by steady addition of grains to a quasi-two dimensional heap. To characterize the time-dependent average avalanche flow speed v(t), we image the top free surface. To characterize the grain fluctuation speed dv(t), we use Speckle-Visibility Spectroscopy. During an avalanche, we find that the fluctuation speed is approximately one-tenth the average flow speed, and that these speeds are largest near the beginning of an event. We also find that the distribution of event durations is peaked, and that event sizes are correlated with the time interval since the end of the previous event. At high rates of grain addition, where successive avalanches merge into smooth continuous flow, the relationship between average and fluctuation speeds changes to dv Sqrt[v]

A multi-color fast-switching microfluidic droplet dye laser

We describe a multi-color microfluidic dye laser operating in whispering gallery mode based on a train of alternating droplets containing solutions of different dyes; this laser is capable of switching the wavelength of its emission between 580 nm and 680 nm at frequencies up to 3.6 kHz -— the fastest among all dye lasers reported; it has potential applications in on-chip spectroscopy and flow cytometry

COMPARING THE PERFORMANCE OF REGRESSION AND NEURAL NETWORKS AS DATA QUALITY VARIES: A BUSINESS VALUE APPROACH

Under circumstances where data quality may vary (due to inaccuracies or lack of timeliness, for example), knowledge about the potential performance of alternate predictive models can help a decision maker to design a business value-maximizing information system. This paper examines a real-world example from the field of finance to illustrate a comparison of alternative modeling tools. Two modeling alternatives are used in this example: regression analysis and neural network analysis. There are two main results: (1) Linear regression outperformed neural nets in terms of forecasting accuracy, but the opposite was true when we considered the business value of the forecast. (2) Neural net-based forecasts tended to be more robust than linear regression forecasts as data accuracy degraded. Managerial implications for financial risk management of MBS portfolios are drawn from the results.Information Systems Working Papers Serie

QUANTIFYING THE VALUE OF MODELS AND DATA: A COMPARISON OF THE PERFORMANCE OF REGRESSION AND NEURAL NETS WHEN DATA QUALITY VARIES

Under circumstances where data quality may vary, knowledge about the potential performance of alternate predictive models can enable a decision maker to design an information system whose value is optimized in two ways. The decision maker can select a model which is least sensitive to predictive degradation in the range of observed data quality variation. And, once the "right" model has been selected, the decision maker can select the appropriate level of data quality in view of the costs of acquiring it. This paper examines a real-world example from the field of finance -- prepayments in mortgage-backed securities (MBS) portfolio management -- to illustrate a methodology that enables such evaluations to be made for two modeling alternative: regression analysis and neural network analysis. The methodology indicates that with "perfect data," the neural network approach outperforms regression in terms of predictive accuracy and utility in a prepayment risk management forecasting system (RMFS). Further, the performance of the neural network model is more robust under conditions of data quality degradation.Information Systems Working Papers Serie