Roland Preuss

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Cell movement--for example, during embryogenesis or tumor metastasis--is a complex dynamical process resulting from an intricate interplay of multiple components of the cellular migration machinery. At first sight, the paths of migrating cells resemble those of thermally driven Brownian particles. However, cell migration is an active biological process(More)
In the Biomedical Medical Research laboratory in St. Louis Missouri there is an ongoing project to characterize water diffusion in fixed baboon brain using diffusion weighted magnetic resonance imaging as a means of monitoring development throughout gestation. Magnetic resonance images can be made sensitive to diffusion by applying magnetic field gradients(More)
The spectral properties of the 1-D Hubbard model are obtained from quantum Monte Carlo simulations using the maximum entropy method. The one-particle excitations are characterized by dispersive cosine-like bands. Velocities for spin-and charge excitations are obtained that lead to a conformal charge c = 0:98 0:05 for the largest system simulated (N = 84).(More)
In a collaboration of eight experiments the dependence of the chemical erosion yield of carbon on the ion flux, U, was established to U À0.54 at high ion fluxes. With this flux dependence a comprehensive description for chemical erosion is available as function of energy, temperature and flux. With this description the erosion and re-deposition of carbon in(More)
Bayesian probability theory along with the maximum entropy concept is widely used for inferential problems, particularly to infer dynamic properties of strongly correlated fermion systems from Quantum-Monte-Carlo (QMC) imaginary time data. In current QMC-applications, however, the error-covariance of the QMC data is not treated consistently. Here we present(More)
On the basis of Quantum-Monte-Carlo results the evolution of the spectral weight A(k, ω) of the two-dimensional Hubbard model is studied from insulating to metallic behavior. As observed in recent photoemission experiments for cuprates, the electronic excitations display essentially doping-independent features: a quasiparticle-like dispersive narrow band of(More)
We examine a first order differential equation with respect to time used to describe magnetic islands in magnetically confined plasmas. The free parameters of this equation are obtained by employing Bayesian probability theory. Additionally, a typical Bayesian change point is solved in the process of obtaining the data.
We present a method for the decomposition of the mass spectra of mixed gases using Bayesian probability theory. The method works without any calibration measurement and therefore applies also to the analysis of spectra containing unstable species. For the example of mixtures of three different hydrocarbon gases the algorithm provides concentrations and(More)
Energy connnement data of large fusion experiments have been analyzed in terms of dimensionless form free scaling functions. Several possible physical scenarios lead to diierent models with a certain number of degrees of freedom. These are used to set up the connnement function as a linear combination of di-mensionless power law terms. Then one has to solve(More)