k+=0k^+=0 Modes in Light-Cone Quantization

We investigate the light-cone quantization of $\phi^3$ theory in 1+1 dimensions with a regularization of discretized light-cone momentum $k^+$. Solving a second-class constraint associated with the $k^+=0$ mode, we show that the $k^+=0$ mode propagates along the internal lines of Feynman diagrams in any order of perturbation, hence our theory recovers the Lorentz invariance.Comment: 14p

Strong-Field QED and High Power Lasers

This contribution presents an overview of fundamental QED processes in the presence of an external field produced by an ultra-intense laser. The discussion focusses on the basic intensity effects on vacuum polarisation and the prospects for their observation. Some historical remarks are added where appropriate.Comment: 14 pages, 9 figures; plenary talk, Euler-Heisenberg session of QFEXT11, Benasque, Spain, September 18-24, 201

Noncommutativity and the lightfront

We discuss various limits which transform configuration space into phase space, with emphasis on those related to lightfront field theory, and show that they are unified by spectral flow. Examples include quantising in `almost lightfront' coordinates and the appearance of lightlike noncommutativity from a strong background laser field. We compare this with the limit of a strong magnetic field, and investigate the role played by lightfront zero modes.Comment: 7 pages, 2 eps figures. V2: minor changes, references adde

Light Front Quantisation of Gauge Theories in a Finite Volume

We discuss the light-front formulation of SU(2) Yang-Mills theory on a torus. The gauge choice we use allows for an exact and unambiguous solution of Gauss's law.Comment: 4 pages, Latex; talk presented at QCD 94, Montpellier, France, July 7-13, and at the Fourth International Workshop on Light Cone Quantization and Non- Perturbative Dynamics, Polana Zgorzelisko, Poland, August 15-25, TPR 94-2

Clustering in Additive Mixed Models with Approximate Dirichlet Process Mixtures using the EM Algorithm

We consider additive mixed models for longitudinal data with a nonlinear time trend. As random effects distribution an approximate Dirichlet process mixture is proposed that is based on the truncated version of the stick breaking presentation of the Dirichlet process and provides a Gaussian mixture with a data driven choice of the number of mixture components. The main advantage of the specification is its ability to identify clusters of subjects with a similar random effects structure. For the estimation of the trend curve the mixed model representation of penalized splines is used. AnExpectation-Maximization algorithm is given that solves the estimation problem and that exhibits advantages over Markov chain Monte Carlo approaches, which are typically used when modeling with Dirichlet processes. The method is evaluated in a simulation study and applied to body mass index profiles of children

Clustering in linear mixed models with Dirichlet process mixtures using EM algorithm

In linear mixed models the assumption of normally distributed random effects is often inappropriate and unnecessary restrictive. The proposed Dirichlet process mixture assumes a hierarchical Gaussian mixture. In addition to the weakening of distributions assumptions the specification allows to estimate clusters of observations with a similar random effects structure identified. An Expectation-Maximization algorithm is given that solves the estimation problem and that exhibits advantages over in this framework usually used Markov chain Monte Carlo approaches. The method is evaluated in a simulation study and applied to dynamics of unemployment in Germany as well as lung function growth data