Would Gaze-Contingent Rendering Improve Depth Perception in Virtual and Augmented Reality?

Near distances are overestimated in virtual reality, and far distances are underestimated, but an explanation for these distortions remains elusive. One potential concern is that whilst the eye rotates to look at the virtual scene, the virtual cameras remain static. Could using eye-tracking to change the perspective of the virtual cameras as the eye rotates improve depth perception in virtual reality? This paper identifies 14 distinct perspective distortions that could in theory occur from keeping the virtual cameras fixed whilst the eye rotates in the context of near-eye displays. However, the impact of eye movements on the displayed image depends on the optical, rather than physical, distance of the display. Since the optical distance of most head-mounted displays is over 1m, most of these distortions will have only a negligible effect. The exception are 'gaze-contingent disparities', which will leave near virtual objects looking displaced from physical objects that are meant to be at the same distance in augmented reality.Comment: 5 page

Efficient estimation of generalized additive nonparametric regression models.

We define new procedures for estimating generalized additive nonparametric regression models that are more efficient than the Linton and Härdle (1996, Biometrika 83, 529–540) integration-based method and achieve certain oracle bounds. We consider criterion functions based on the Linear exponential family, which includes many important special cases. We also consider the extension to multiple parameter models like the gamma distribution and to models for conditional heteroskedasticity.

Nonparametric Inference for Unbalanced Time Series Data

This paper is concerned with the practical problem of conducting inference in a vector time series setting when the data is unbalanced or incomplete. In this case, one can work only with the common sample, to which a standard HAC/Bootstrap theory applies, but at the expense of throwing away data and perhaps losing efficiency. An alternative is to use some sort of imputation method, but this requires additional modelling assumptions, which we would rather avoid. We show how the sampling theory changes and how to modify the resampling algorithms to accommodate the problem of missing data. We also discuss efficiency and power. Unbalanced data of the type we consider are quite common in financial panel data, see, for example, Connor and Korajczyk (1993). These data also occur in cross-country studies.Bootstrap, efficient, HAC estimation, missing data, subsampling.

Nonparametric inference for unbalance time series data

Estimation of heteroskedasticity and autocorrelation consistent covariance matrices (HACs) is a well established problem in time series. Results have been established under a variety of weak conditions on temporal dependence and heterogeneity that allow one to conduct inference on a variety of statistics, see Newey and West (1987), Hansen (1992), de Jong and Davidson (2000), and Robinson (2004). Indeed there is an extensive literature on automating these procedures starting with Andrews (1991). Alternative methods for conducting inference include the bootstrap for which there is also now a very active research program in time series especially, see Lahiri (2003) for an overview. One convenient method for time series is the subsampling approach of Politis, Romano, andWolf (1999). This method was used by Linton, Maasoumi, andWhang (2003) (henceforth LMW) in the context of testing for stochastic dominance. This paper is concerned with the practical problem of conducting inference in a vector time series setting when the data is unbalanced or incomplete. In this case, one can work only with the common sample, to which a standard HAC/bootstrap theory applies, but at the expense of throwing away data and perhaps losing effciency. An alternative is to use some sort of imputation method, but this requires additional modelling assumptions, which we would rather avoid.1 We show how the sampling theory changes and how to modify the resampling algorithms to accommodate the problem of missing data. We also discuss effciency and power. Unbalanced data of the type we consider are quite common in financial panel data, see for example Connor and Korajczyk (1993). These data also occur in cross-country studies.

Evaluating hedge fund performance: a stochastic dominance approach

We introduce a general and flexible framework for hedge fund performance evaluation and asset allocation: stochastic dominance (SD) theory. Our approach utilizes statistical tests for stochastic dominance to compare the returns of hedge funds. We form hedge fund portfolios by using SD criteria and examine the out-of-sample performance of these hedge fund portfolios. Compared to performance of portfolios of randomly selected hedge funds and mean-variance e¢ cient hedge funds, our results show that fund selection method based on SD criteria greatly improves the performance of hedge fund portfolio

Gas-dynamic shock heating of post-flare loops due to retraction following localized, impulsive reconnection

We present a novel model in which shortening of a magnetic flux tube following localized, three-dimensional reconnection generates strong gas-dynamic shocks around its apex. The shortening releases magnetic energy by progressing away from the reconnection site at the Alfven speed. This launches inward flows along the field lines whose collision creates a pair of gas-dynamic shocks. The shocks raise both the mass density and temperature inside the newly shortened flux tube. Reconnecting field lines whose initial directions differ by more that 100 degrees can produce a concentrated knot of plasma hotter that 20 MK, consistent with observations. In spite of these high temperatures, the shocks convert less than 10% of the liberated magnetic energy into heat - the rest remains as kinetic energy of bulk motion. These gas-dynamic shocks arise only when the reconnection is impulsive and localized in all three dimensions; they are distinct from the slow magnetosonic shocks of the Petschek steady-state reconnection model

Capillary forces in the acoustics of patchy-saturated porous media

A linearized theory of the acoustics of porous elastic formations, such as rocks, saturated with two different viscous fluids is generalized to take into account a pressure discontinuity across the fluid boundaries. The latter can arise due to the surface tension of the membrane separating the fluids. We show that the frequency-dependent bulk modulus $\tilde{K}(\omega)$ for wave lengths longer than the characteristic structural dimensions of the fluid patches has a similar analytic behavior as in the case of a vanishing membrane stiffness and depends on the same parameters of the fluid-distribution topology. The effect of the capillary stiffness can be accounted by renormalizing the coefficients of the leading terms in the low-frequency asymptotic of $\tilde{K}(\omega)$.Comment: 27 pages, 3 figure

The Passive Optical Sample Assembly (POSA) on STS-1

The passive optical sample assembly (POSA) hardware, scheduled for the flight on orbital flight test 1 is described. The function of the instrument is aid in the assessment contamination hazards to sensitive payloads in the shuttle cargo bay. It consists of an array of passively deployed samples mounted on the development flight instrumentation pallet in the shuttle cargo bay. The directory of samples together with their intended measurements are presented. The plan for POSA data analysis is also given

Estimating Semiparametric ARCH (8) Models by Kernel Smoothing Methods

We investigate a class of semiparametric ARCH(8) models that includes as a special case the partially nonparametric (PNP) model introduced by Engle and Ng (1993) and which allows for both flexible dynamics and flexible function form with regard to the 'news impact' function. We propose an estimation method that is based on kernel smoothing and profiled likelihood. We establish the distribution theory of the parametric components and the pointwise distribution of the nonparametric component of the model. We also discuss efficiency of both the parametric and nonparametric part. We investigate the performance of our procedures on simulated data and on a sample of S&P500 daily returns. We find some evidence of asymmetric news impact functions in the data.ARCH, inverse problem, kernel estimation, news impact curve, nonparametric regression, profile likelihood, semiparametric estimation, volatility