The ultraviolet limit and sum rule for the shear correlator in hot Yang-Mills theory

We determine a next-to-leading order result for the correlator of the shear stress operator in high-temperature Yang-Mills theory. The computation is performed via an ultraviolet expansion, valid in the limit of small distances or large momenta, and the result is used for writing operator product expansions for the Euclidean momentum and coordinate space correlators as well as for the Minkowskian spectral density. In addition, our results enable us to confirm and refine a shear sum rule originally derived by Romatschke, Son and Meyer.Comment: 16 pages, 2 figures. v2: small clarifications, one reference added, published versio

Quark mass thresholds in QCD thermodynamics

We discuss radiative corrections to how quark mass thresholds are crossed, as a function of the temperature, in basic thermodynamic observables such as the pressure, the energy and entropy densities, and the heat capacity of high temperature QCD. The indication from leading order that the charm quark plays a visible role at surprisingly low temperatures, is confirmed. We also sketch a way to obtain phenomenological estimates relevant for generic expansion rate computations at temperatures between the QCD and electroweak scales, pointing out where improvements over the current knowledge are particularly welcome.Comment: 14 pages. v2: minor additions and clarifications; published versio

Gauge Dependence of the High-Temperature 2-Loop Effective Potential for the Higgs Field

The high-temperature limit of the 2-loop effective potential for the Higgs field is calculated from an effective 3d theory, in a general covariant gauge. It is shown explicitly that a gauge-independent result can be extracted for the equation of state from the gauge-dependent effective potential. The convergence of perturbation theory is estimated in the broken phase, utilizing the gauge dependence of the effective potential.Comment: 13 LaTeX-pages + 2 ps-figure (Instructions added to uudecode the ps-file.

Bayesian Transductive Markov Random Fields for Interactive Segmentation in Retinal Disorders

In the realm of computer aided diagnosis (CAD) interactive segmentation schemes have been well received by physicians, where the combination of human and machine intelligence can provide improved segmentation efficacy with minimal expert intervention [1-3]. Transductive learning (TL) or semi-supervised learning (SSL) is a suitable framework for learning-based interactive segmentation given the scarce label problem. In this paper we present extended work on Bayesian transduction and regularized conditional mixtures for interactive segmentation [3]. We present a Markov random field model integrating a semi-parametric conditional mixture model within a Bayesian transductive learning and inference setting. The model allows efficient learning and inference in a semi-supervised setting given only minimal approximate label information. Preliminary experimental results on multimodal images of retinal disorders such as drusen, geographic atrophy (GA), and choroidal neovascularisation (CNV) with exudates and subretinal fibrosis show promising segmentation performance

Quarkonium in Hot Medium

I review recent progress in studying quarkonium properties in hot medium as well as possible consequences for quarkonium production in heavy ion collisions.Comment: Invited talk at SQM 2009, Buzios, Brazil, Sep. 27 -Oct. 2 2009, LaTeX, 8 pages,3 figures; typos corrected, references adde

Learning Non-Homogenous Textures and the Unlearning Problem with Application to Drusen Detection in Retinal Images

In this work we present a novel approach for learning non- homogenous textures without facing the unlearning problem. Our learning method mimics the human behavior of selective learning in the sense of fast memory renewal. We perform probabilistic boosting and structural similarity clustering for fast selective learning in a large knowledge domain acquired over different time steps. Applied to non- homogenous texture discrimination, our learning method is the first approach that deals with the unlearning problem applied to the task of drusen segmentation in retinal imagery, which itself is a challenging problem due to high variability of non-homogenous texture appearance. We present preliminary results

Real-time identification of sliding friction using LabVIEW FPGA

Friction is present in all mechanical systems, and can greatly affect system stability and control in precision motion applications. In this paper, we present application of a frictional model to trajectory planning of a part centering system with real-time identification of model parameters through system force and position response. This identification is carried out using LabVIEW motion control software and digital signal processing (DSP) and field-programmable gate array (FPGA) hardware. A comparison of hardware performance for force measurement is also made

Improving PET-Based Physiological Quantification Through Methods of Wavelet Denoising

The goal of this study was to evaluate methods of multidimensional wavelet denoising on restoring the fidelity of biological signals hidden within dynamic positron emission tomography (PET) images. A reduction of noise within pixels, between adjacent regions, and time-serial frames was achieved via redundant multiscale representations. In analyzing dynamic PET data of healthy volunteers, a multiscale method improved the estimate-to-error ratio of flows fivefold without loss of detail. This technique also maintained accuracy of flow estimates in comparison with the "gold standard," using dynamic PET with O15-water. In addition, in studies of coronary disease patients, flow patterns were preserved and infarcted regions were well differentiated from normal regions. The results show that a wavelet-based noise-suppression method produced reliable approximations of salient underlying signals and led to an accurate quantification of myocardial perfusion. The described protocol can be generalized to other temporal biomedical imaging modalities including functional magnetic resonance imaging and ultrasound

Management of varices and variceal hemorrhage in cirrhosis

Variceal hemorrhage is a lethal complication of cirrhosis, particularly in patients in whom clinical decompensation (i.e., ascites, encephalopathy, a previous episode of hemorrhage, or jaundice) has already developed. Practice guidelines for the management of varices and variceal hemorrhage1 in cirrhosis are mostly based on evidence in the literature that has been summarized and prioritized at consensus conferences..