Measured and predicted shock shapes for AFE configuration at Mach 6 in air and in CF4

Shock shapes and stand-off distances were obtained for the Aeroassist Flight Experiment configuration from Mach 6 tests in air and in CF4. Results were plotted for an angle-of attack range from -10 to 10 degrees and comparisons were made at selected angles with inviscid-flow predictions. Tests were performed in the Langley Research Center (LaRC) 20 inch Mach 6 Tunnel (air) at unit free-stream Reynolds numbers (N sub Re, infinity) of 2 million/ft and 0.6 million/ft and in the LaRC Hypersonic CF4 Tunnel at N sub Re, infinity = 0.5 million/ft and 0.3 million/ft. Within the range of these tests, N sub Re, infinity did not affect the shock shape or stand off distance, and the predictions were in good agreement with the measurements. The shock stand-off distance in CF4 was approximately half of that in air. This effect resulted from the differences in density ratio across the normal shock, which was approximately 12 in CF4 and 5 in air. In both test gases, the shock lay progressively closer to the body as angle of attack decreased

Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models

Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image-noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating the uncertainty in label assignment is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. On the other hand, most computationally efficient methods fail to estimate label uncertainty. We therefore propose in this paper the Active Mean Fields (AMF) approach, a technique based on Bayesian modeling that uses a mean-field approximation to efficiently compute a segmentation and its corresponding uncertainty. Based on a variational formulation, the resulting convex model combines any label-likelihood measure with a prior on the length of the segmentation boundary. A specific implementation of that model is the Chan-Vese segmentation model (CV), in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary. Furthermore, the Euler-Lagrange equations derived from the AMF model are equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image denoising. Solutions to the AMF model can thus be implemented by directly utilizing highly-efficient ROF solvers on log-likelihood ratio fields. We qualitatively assess the approach on synthetic data as well as on real natural and medical images. For a quantitative evaluation, we apply our approach to the icgbench dataset

Direct Evidence for the Source of Reported Magnetic Behavior in "CoTe"

In order to unambiguously identify the source of magnetism reported in recent studies of the Co-Te system, two sets of high-quality, epitaxial CoTe$_x$ films (thickness $\simeq$ 300 nm) were prepared by pulse laser deposition (PLD). X-ray diffraction (XRD) shows that all of the films are epitaxial along the [001] direction and have the hexagonal NiAs structure. There is no indication of any second phase metallic Co peaks (either $fcc$ or $hcp$) in the XRD patterns. The two sets of CoTe$_x$ films were grown on various substrates with PLD targets having Co:Te in the atomic ratio of 50:50 and 35:65. From the measured lattice parameters $c = 5.396 \AA$ for the former and $c = 5.402\AA$ for the latter, the compositions CoTe$_{1.71}$ (63.1% Te) and CoTe$_{1.76}$ (63.8% Te), respectively, are assigned to the principal phase. Although XRD shows no trace of metallic Co second phase, the magnetic measurements do show a ferromagnetic contribution for both sets of films with the saturation magnetization values for the CoTe$_{1.71}$ films being approximately four times the values for the CoTe$_{1.76}$ films. $^{59}$Co spin-echo nuclear magnetic resonance (NMR) clearly shows the existence of metallic Co inclusions in the films. The source of weak ferromagnetism reported in several recent studies is due to the presence of metallic Co, since the stoichiometric composition "CoTe" does not exist.Comment: 19 pages, 7 figure

Letters between Heber M. Wells and William Kerr

Letters concerning the scholarships created under the will of the late Cecil John Rhodes

Combining spatial priors and anatomical information for fMRI detection

In this paper, we analyze Markov Random Field (MRF) as a spatial regularizer in fMRI detection. The low signal-to-noise ratio (SNR) in fMRI images presents a serious challenge for detection algorithms, making regularization necessary to achieve good detection accuracy. Gaussian smoothing, traditionally employed to boost SNR, often produces over-smoothed activation maps. Recently, the use of MRF priors has been suggested as an alternative regularization approach. However, solving for an optimal configuration of the MRF is NP-hard in general. In this work, we investigate fast inference algorithms based on the Mean Field approximation in application to MRF priors for fMRI detection. Furthermore, we propose a novel way to incorporate anatomical information into the MRF-based detection framework and into the traditional smoothing methods. Intuitively speaking, the anatomical evidence increases the likelihood of activation in the gray matter and improves spatial coherency of the resulting activation maps within each tissue type. Validation using the receiver operating characteristic (ROC) analysis and the confusion matrix analysis on simulated data illustrates substantial improvement in detection accuracy using the anatomically guided MRF spatial regularizer. We further demonstrate the potential benefits of the proposed method in real fMRI signals of reduced length. The anatomically guided MRF regularizer enables significant reduction of the scan length while maintaining the quality of the resulting activation maps.National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.)/National Alliance for Medical Image Computing (U.S.) Grant U54-EB005149)National Science Foundation (U.S.) (Grant IIS 9610249)National Institutes of Health (U.S.) (National Center for Research Resources (U.S.)/Biomedical Informatics Research Network Grant U24-RR021382)National Institutes of Health (U.S.) (National Center for Research Resources (U.S.)/Neuroimaging Analysis Center (U.S.) Grant P41-RR13218)National Institutes of Health (U.S.) (National Institute of Neurological Disorders and Stroke (U.S.) Grant R01-NS051826)National Science Foundation (U.S.) (CAREER Grant 0642971)National Science Foundation (U.S.). Graduate Research FellowshipNational Center for Research Resources (U.S.) (FIRST-BIRN Grant)Neuroimaging Analysis Center (U.S.