Momentum-dependence of charmonium spectral functions from lattice QCD

We compute correlators and spectral functions for J/psi and eta_c mesons at nonzero momentum on anisotropic lattices with Nf=2. We find no evidence of significant momentum dependence at the current level of precision. In the pseudoscalar channel, the ground state appears to survive up to T~450MeV or 2.1T_c. In the vector channel, medium modifications may occur at lower temperatures.Comment: 5 pages, 12 figure

Accurate Checks of Universality for Dyson's Hierarchical Model

Using recently developed methods, we perform high-accuracy calculations of the susceptibility near beta_c for the D=3 version of Dyson's hierarchical model. Using linear fits, we estimate the leading gamma and subleading Delta exponents. Independent estimates are obtained by calculating the first two eigenvalues of the linearized renormalization group transformation. We found gamma = 1.29914073 (with an estimated error of 10^{-8}) and, Delta=0.4259469 (with an estimated error of 10^{-7}) independently of the choice of local integration measure (Ising or Landau-Ginzburg). After a suitable rescaling, the approximate fixed points for a large class of local measure coincide accurately with a fixed point constructed by Koch and Wittwer.Comment: 9 pages, Revtex, 1 figur

The spectrum of radial, orbital and gluonic excitations of charmonium

We present results for the charmonium spectrum from $N_f=2$ dynamical QCD simulations on $12^3\times 80$ anisotropic lattices. Using all-to-all propagators we determine the ground and excited states of S, P and D waves and hybrids. We also evaluate the disconnected (OZI suppressed) contribution to the $\eta_c$ and $J/\Psi$Comment: 6 pages, 3 figures, Presented at 24th International Symposium on Lattice Field Theory (Lattice 2006), Tucson, Arizona, 23-28 Jul 200

High-accuracy critical exponents of O(N) hierarchical sigma models

We perform high-accuracy calculations of the critical exponent gamma and its subleading exponent for the 3D O(N) Dyson's hierarchical model, for N up to 20. We calculate the critical temperatures for the nonlinear sigma model measure. We discuss the possibility of extracting the first coefficients of the 1/N expansion from our numerical data. We show that the leading and subleading exponents agreewith Polchinski equation and the equivalent Litim equation, in the local potential approximation, with at least 4 significant digits.Comment: 4 pages, 2 Figs., uses revte

q-Analogue of Shock Soliton Solution

By using Jackson's q-exponential function we introduce the generating function, the recursive formulas and the second order q-differential equation for the q-Hermite polynomials. This allows us to solve the q-heat equation in terms of q-Kampe de Feriet polynomials with arbitrary N moving zeroes, and to find operator solution for the Initial Value Problem for the q-heat equation. By the q-analog of the Cole-Hopf transformation we construct the q-Burgers type nonlinear heat equation with quadratic dispersion and the cubic nonlinearity. In q -> 1 limit it reduces to the standard Burgers equation. Exact solutions for the q-Burgers equation in the form of moving poles, singular and regular q-shock soliton solutions are found.Comment: 13 pages, 5 figure

Charmonium spectral functions in Nf=2 QCD

We report on a study of charmonium at high temperature in 2-flavour QCD. This is the first such study with dynamical fermions. Using an improved anisotropic lattice action, spectral functions are extracted from correlators in the vector and pseudoscalar channels. No signs of medium-induced suppression of the ground states are seen for temperatures up to 1.5T_c, while at T~2T_c there are clear signs of modifications. The current systematic and statistical uncertainties in our data, in particular the relatively coarse lattice and small volume, do not allow us to draw a firm conclusion at this stage.Comment: 6 pages, talk by JIS at Lattice 2005 (Non-zero temperature and density

Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution

In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance SR of diffusion MR brain images in terms of errors compared to ground truth. We further show that the reduced error scores produce tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use.Comment: Accepted paper at MICCAI 201

Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research