High precision electrostatic potential calculations for cylindrically symmetric lenses

A method is developed for a potential calculation within cylindrically symmetric electrostatic lenses using mesh relaxation techniques, and it is capable of considerably higher accuracies than currently available. The method involves (i) creating very high order algorithms (orders of 6, 8, and 10) for determining the potentials at points in the net using surrounding point values, (ii) eliminating the effect of the large errors caused by singular points, and (iii) reducing gradients in the high gradient regions of the geometry, thereby allowing the algorithms used in these regions to achieve greater precisions-(ii) and (iii) achieved by the use of telescopic multiregions. In addition, an algorithm for points one unit from a metal surface is developed, allowing general mesh point algorithms to be used in these situations, thereby taking advantage of the enhanced precision of the latter. A maximum error function dependent on a sixth order gradient of the potential is defined. With this the single point algorithmic errors are able to be viewed over the entire net. Finally, it is demonstrated that by utilizing the above concepts and procedures, the potential of a point in a reasonably high gradient region of a test geometry can realize a precision of less than 10(-10).     

Practical considerations for high spatial and temporal resolution dynamic transmission electron microscopy

Although recent years have seen significant advances in the spatial resolution possible in the transmission electron microscope (TEM), the temporal resolution of most microscopes is limited to video rate at best. This lack of temporal resolution means that our understanding of dynamic processes in materials is extremely limited. High temporal resolution in the TEM can be achieved, however, by replacing the normal thermionic or field emission source with a photoemission source. In this case the temporal resolution is limited only by the ability to create a short pulse of photoexcited electrons in the source, and this can be as short as a few femtoseconds. The operation of the photo-emission source and the control of the subsequent pulse of electrons (containing as many as 5 x 10(7) electrons) create significant challenges for a standard microscope column that is designed to operate with a single electron in the column at any one time. In this paper, the generation and control of electron pulses in the TEM to obtain a temporal resolution <10(-6)s will be described and the effect of the pulse duration and current density on the spatial resolution of the instrument will be examined. The potential of these levels of temporal and spatial resolution for the study of dynamic materials processes will also be discussed.     

Polarized light imaging of white matter architecture

Polarized light imaging (PLI) is a method to image fiber orientation in gross histological brain sections based on the birefringent properties of the myelin sheaths. The method uses the transmission of polarized light to quantitatively estimate the fiber orientation and inclination angles at every point of the imaged section. Multiple sections can be assembled into a 3D volume, from which the 3D extent of fiber tracts can be extracted. This article describes the physical principles of PLI and describes two major applications of the method: the imaging of white matter orientation of the rat brain and the generation of fiber orientation maps of the human brain in white and gray matter. The strengths and weaknesses of the method are set out.     

Mild body cooling impairs attention via distraction from skin cooling

Many contemporary workers are routinely exposed to mild cold stress, which may compromise mental function and lead to accidents. A study investigated the effect of mild body cooling of 1.0 degree C rectal temperature (Tre) on vigilance (i.e. sustained attention) and the orienting of spatial attention (i.e. spatially selective processing of visual information). Vigilance and spatial attention tests were administered to 14 healthy males and six females at four stages (pre-immersion, deltaTre = 0, -0.5 and - 1.0 degree C ) of a gradual, head-out immersion cooling session (18-25 deltaC water), and in four time-matched stages of a contrast session, in which participants sat in an empty tub and no cooling took place. In the spatial attention test, target discrimination times were similar for all stages of the contrast session, but increased significantly in the cooling phase upon immersion (deltaTre = 0 degrees C), with no further increases at deltaTre = -0.5 and - 1.0 degree C. Despite global response slowing, cooling did not affect the normal pattern of spatial orienting. In the vigilance test, the variability of detection time was adversely affected in the cooling but not the contrast trials: variability increased at immersion but did not increase further with additional cooling. These findings suggest that attentional impairments are more closely linked to the distracting effects of cold skin temperature than decreases in body core temperature.     

The utility of a virtual reality locomotion interface for studying gait behavior

OBJECTIVE: To investigate the effect of optic flow on gait behavior during treadmill walking using an immersive virtual reality (VR) setup and compare it with conventional treadmill walking (TW) and overground walking (OW). BACKGROUND: Previous research comparing TW with OW speculated that a lack of optic flow (relative visual movement between a walker and the environment) during TW may have led to perceptual cue conflicts, resulting in differences in gait behavior, as compared with OW. METHOD: Participants walked under three locomotion conditions (OW, TW, and TW with VR [TWVR]) under three walking constraint conditions (no constraint, a temporal/pacing constraint, and a spatial/path-following constraint). Presence questionnaires (PQs) were administered at the close of the TWVR trials. Trials were subjected to video analysis to determine spatiotemporal and kinematics variables used for comparison of locomotion conditions. RESULTS: ANOVA revealed gait behavior during TWVR to be between that of OW and TW. Speed and cadence during TWVR were significantly different from those of TW, whereas knee angle was comparable to that of OW. Correlation analysis of PQ scores with gait measures revealed a positive linear association of the distraction subfactor of the PQ with walking speed during TWVR, suggesting an increase in the sense of presence in the virtual environment led to increases in walking speed. CONCLUSION: The results demonstrate that providing optic flow during TW through VR has an impact on gait behavior. APPLICATION: This study provides a basis for developing simple VR locomotion interface setups for gait research.     

Surface dependent representations for illumination insensitive image comparison

We consider the problem of matching images to tell whether they come from the same scene viewed under different lighting conditions. We show that the surface characteristics determine the type of image comparison method that should be used. Previous work has shown the effectiveness of comparing the image gradient direction for surfaces with material properties that change rapidly in one direction. We show analytically that two other widely used methods, normalized correlation of small windows and comparison of multiscale oriented filters, essentially compute the same thing. Then, we show that for surfaces whose properties change more slowly, comparison of the output of whitening filters is most effective. This suggests that a combination of these strategies should be employed to compare general objects. We discuss indications that Gabor jets use such a mixed strategy effectively, and we propose a new mixed strategy. We validate our results on synthetic and real images     

Improving the estimation of Kendall's tau when censoring affects only one of the variables

This paper considers the estimation of Kendall's tau for bivariate data (X,Y) when only Y is subject to right-censoring. Although τ is estimable under weak regularity conditions, the estimators proposed by Brown et al. [1974. Nonparametric tests of independence for censored data, with applications to heart transplant studies. Reliability and Biometry, 327-354], Weier and Basu [1980. An investigation of Kendall's τ modified for censored data with applications. J. Statist. Plann. Inference 4, 381-390] and Oakes [1982. A concordance test for independence in the presence of censoring. Biometrics 38, 451-455], which are standard in this context, fail to be consistent when τ≠0 because they only use information from the marginal distributions. An exception is the renormalized estimator of Oakes [2006. On consistency of Kendall's tau under censoring. Technical Report, Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY], whose consistency has been established for all possible values of τ, but only in the context of the gamma frailty model. Wang and Wells [2000. Estimation of Kendall's tau under censoring. Statist. Sinica 10, 1199-1215] were the first to propose an estimator which accounts for joint information. Four more are developed here: the first three extend the methods of Brown et al. [1974. Nonparametric tests of independence for censored data, with applications to heart transplant studies. Reliability and Biometry, 327-354], Weier and Basu [1980, An investigation of Kendall's τ modified for censored data with applications. J. Statist. Plann. Inference 4, 381-390] and Oakes [1982, A concordance test for independence in the presence of censoring. Biometrics 38, 451-455] to account for information provided by X, while the fourth estimator inverts an estimation of Pr(Y_i?y|X_i=x_i,Y_i>c_i) to get an imputation of the value of Y_i censored at C_i=c_i. Following Lim [2006. Permutation procedures with censored data. Comput. Statist. Data Anal. 50, 332-345], a nonparametric estimator is also considered which averages the obtained from a large number of possible configurations of the observed data (X₁,Z₁),…,(X_n,Z_n), where Z_i=min(Y_i,C_i). Simulations are presented which compare these various estimators of Kendall's tau. An illustration involving the well-known Stanford heart transplant data is also presented.     

A practical approximation algorithm for the LMS line estimator

The problem of fitting a straight line to a finite collection of points in the plane is an important problem in statistical estimation. Robust estimators are widely used because of their lack of sensitivity to outlying data points. The least median-of-squares (LMS) regression line estimator is among the best known robust estimators. Given a set of n points in the plane, it is defined to be the line that minimizes the median squared residual or, more generally, the line that minimizes the residual of any given quantile q, where 0<q?1. This problem is equivalent to finding the strip defined by two parallel lines of minimum vertical separation that encloses at least half of the points.The best known exact algorithm for this problem runs in O(n²) time. We consider two types of approximations, a residual approximation, which approximates the vertical height of the strip to within a given error bound ε_r?0, and a quantile approximation, which approximates the fraction of points that lie within the strip to within a given error bound ε_q?0. We present two randomized approximation algorithms for the LMS line estimator. The first is a conceptually simple quantile approximation algorithm, which given fixed q and ε_q>0 runs in O(nlogn) time. The second is a practical algorithm, which can solve both types of approximation problems or be used as an exact algorithm. We prove that when used as a quantile approximation, this algorithm's expected running time is . We present empirical evidence that the latter algorithm is quite efficient for a wide variety of input distributions, even when used as an exact algorithm.     

On classification with incomplete data

We address the incomplete-data problem in which feature vectors to be classified are missing data (features). A (supervised) logistic regression algorithm for the classification of incomplete data is developed. Single or multiple imputation for the missing data is avoided by performing analytic integration with an estimated conditional density function (conditioned on the observed data). Conditional density functions are estimated using a Gaussian mixture model (GMM), with parameter estimation performed using both expectation-maximization (EM) and variational Bayesian EM (VB-EM). The proposed supervised algorithm is then extended to the semisupervised case by incorporating graph-based regularization. The semisupervised algorithm utilizes all available data-both incomplete and complete, as well as labeled and unlabeled. Experimental results of the proposed classification algorithms are shown