Monte Carlo Simulation of the Short-time Behaviour of the Dynamic XY Model

Dynamic relaxation of the XY model quenched from a high temperature state to the critical temperature or below is investigated with Monte Carlo methods. When a non-zero initial magnetization is given, in the short-time regime of the dynamic evolution the critical initial increase of the magnetization is observed. The dynamic exponent $\theta$ is directly determined. The results show that the exponent $\theta$ varies with respect to the temperature. Furthermore, it is demonstrated that this initial increase of the magnetization is universal, i.e. independent of the microscopic details of the initial configurations and the algorithms.Comment: 14 pages with 5 figures in postscrip

Microscopic Non-Universality versus Macroscopic Universality in Algorithms for Critical Dynamics

We study relaxation processes in spin systems near criticality after a quench from a high-temperature initial state. Special attention is paid to the stage where universal behavior, with increasing order parameter emerges from an early non-universal period. We compare various algorithms, lattice types, and updating schemes and find in each case the same universal behavior at macroscopic times, despite of surprising differences during the early non-universal stages.Comment: 9 pages, 3 figures, RevTeX, submitted to Phys. Rev. Let

First lattice evidence for a non-trivial renormalization of the Higgs condensate

General arguments related to ``triviality'' predict that, in the broken phase of $(\lambda\Phi^4)_4$ theory, the condensate re-scales by a factor $Z_{\phi}$ different from the conventional wavefunction-renormalization factor, $Z_{prop}$. Using a lattice simulation in the Ising limit we measure $Z_{\phi}=m^2 \chi$ from the physical mass and susceptibility and $Z_{prop}$ from the residue of the shifted-field propagator. We find that the two $Z$'s differ, with the difference increasing rapidly as the continuum limit is approached. Since $Z_{\phi}$ affects the relation of to the Fermi constant it can sizeably affect the present bounds on the Higgs mass.Comment: 10 pages, 3 figures, 1 table, Latex2

Dynamical Relaxation and Universal Short-Time Behavior in Finite Systems: The Renormalization Group Approach

We study how the finite-sized n-component model A with periodic boundary conditions relaxes near its bulk critical point from an initial nonequilibrium state with short-range correlations. Particular attention is paid to the universal long-time traces that the initial condition leaves. An approach based on renormalization-group improved perturbation theory in 4-epsilon space dimensions and a nonperturbative treatment of the q=0 mode of the fluctuating order-parameter field is developed. This leads to a renormalized effective stochastic equation for this mode in the background of the other q=0 modes; we explicitly derive it to one-loop order, show that it takes the expected finite-size scaling form at the fixed point, and solve it numerically. Our results confirm for general n that the amplitude of the magnetization density m(t) in the linear relaxation-time regime depends on the initial magnetization in the universal fashion originally found in our large-$n$ analysis [J.\ Stat. Phys. 73 (1993) 1]. The anomalous short-time power-law increase of m(t) also is recovered. For n=1, our results are in fair agreement with recent Monte Carlo simulations by Li, Ritschel, and Zheng [J. Phys. A 27 (1994) L837] for the three-dimensional Ising model.Comment: 27 pages, 7 postscript figures, REVTEX 3.0, submitted to Nucl. Phys.

{HDR} Denoising and Deblurring by Learning Spatio-temporal Distortion Model

We seek to reconstruct sharp and noise-free high-dynamic range (HDR) video from a dual-exposure sensor that records different low-dynamic range (LDR) information in different pixel columns: Odd columns provide low-exposure, sharp, but noisy information; even columns complement this with less noisy, high-exposure, but motion-blurred data. Previous LDR work learns to deblur and denoise (DISTORTED->CLEAN) supervised by pairs of CLEAN and DISTORTED images. Regrettably, capturing DISTORTED sensor readings is time-consuming; as well, there is a lack of CLEAN HDR videos. We suggest a method to overcome those two limitations. First, we learn a different function instead: CLEAN->DISTORTED, which generates samples containing correlated pixel noise, and row and column noise, as well as motion blur from a low number of CLEAN sensor readings. Second, as there is not enough CLEAN HDR video available, we devise a method to learn from LDR video in-stead. Our approach compares favorably to several strong baselines, and can boost existing methods when they are re-trained on our data. Combined with spatial and temporal super-resolution, it enables applications such as re-lighting with low noise or blur