Fixed Point Actions for Lattice Fermions

The fixed point actions for Wilson and staggered lattice fermions are determined by iterating renormalization group transformations. In both cases a line of fixed points is found. Some points have very local fixed point actions. They can be used to construct perfect lattice actions for asymptotically free fermionic theories like QCD or the Gross-Neveu model. The local fixed point actions for Wilson fermions break chiral symmetry, while in the staggered case the remnant $U(1)_e \otimes U(1)_o$ symmetry is preserved. In addition, for Wilson fermions a nonlocal fixed point is found that corresponds to free chiral fermions. The vicinity of this fixed point is studied in the Gross-Neveu model using perturbation theory.Comment: 6 pages, figures 1 and 4 included, figures 2,3,5,6,7 can be obtained from [email protected]

Three-body effects in the Hoyle-state decay

We use a sequential $R$-matrix model to describe the breakup of the Hoyle state into three $\alpha$ particles via the ground state of $^8\mathrm{Be}$. It is shown that even in a sequential picture, features resembling a direct breakup branch appear in the phase-space distribution of the $\alpha$ particles. We construct a toy model to describe the Coulomb interaction in the three-body final state and its effects on the decay spectrum are investigated. The framework is also used to predict the phase-space distribution of the $\alpha$ particles emitted in a direct breakup of the Hoyle state and the possibility of interference between a direct and sequential branch is discussed. Our numerical results are compared to the current upper limit on the direct decay branch determined in recent experiments

Structural adaptive segmentation for statistical parametric mapping

Functional Magnetic Resonance Imaging inherently involves noisy measurements and a severe multiple test problem. Smoothing is usually used to reduce the effective number of multiple comparisons and to locally integrate the signal and hence increase the signal-to-noise ratio. Here, we provide a new structural adaptive segmentation algorithm (AS) that naturally combines the signal detection with noise reduction in one procedure. Moreover, the new method is closely related to a recently proposed structural adaptive smoothing algorithm and preserves shape and spatial extent of activation areas without blurring the borders

Local estimation of the noise level in MRI using structural adaptation

We present a method for local estimation of the signal-dependent noise level in magnetic resonance images. The procedure uses a multi-scale approach to adaptively infer on local neighborhoods with similar data distribution. It exploits a maximum-likelihood estimator for the local noise level. The validity of the method was evaluated on repeated diffusion data of a phantom and simulated data using T1-data corrupted with artificial noise. Simulation results are compared with a recently proposed estimate. The method was applied to a high-resolution diffusion dataset to obtain improved diffusion model estimation results and to demonstrate its usefulness in methods for enhancing diffusion data

Modeling the orientation distribution function by mixtures of angular central Gaussian distributions

In this paper we develop a tensor mixture model for diffusion weighted imaging data using an automatic model selection criterion for the order of tensor components in a voxel. We show that the weighted orientation distribution function for this model can be expanded into a mixture of angular central Gaussian distributions. We show properties of this model in extensive simulations and in a high angular resolution experimental data set. The results suggest that the model may improve imaging of cerebral fiber tracts. We demonstrate how inference on canonical model parameters may give rise to new clinical applications

High resolution fMRI: Overcoming the signal-to-noise problem

Increasing the spatial resolution in functional Magnetic Resonance Imaging (fMRI) inherently lowers the signal-to-noise ratio (SNR). In order to still detect functionally significant activations in high-resolution images, spatial smoothing of the data is required. However, conventional non-adaptive smoothing comes with a reduced effective resolution, foiling the benefit of the higher acquisition resolution. We show how our recently proposed structural adaptive smoothing procedure for functional MRI data can improve signal detection of high-resolution fMRI experiments regardless of the lower SNR. The procedure is evaluated on human visual and sensory-motor mapping experiments. In these applications, the higher resolution could be fully utilized and high-resolution experiments were outperforming normal resolution experiments by means of both statistical significance and information content

Diffusion Tensor Imaging: Structural adaptive smoothing

Diffusion Tensor Imaging (DTI) data is characterized by a high noise level. Thus, estimation errors of quantities like anisotropy indices or the main diffusion direction used for fiber tracking are relatively large and may significantly confound the accuracy of DTI in clinical or neuroscience applications. Besides pulse sequence optimization, noise reduction by smoothing the data can be pursued as a complementary approach to increase the accuracy of DTI. Here, we suggest an anisotropic structural adaptive smoothing procedure, which is based on the Propagation-Separation method and preserves the structures seen in DTI and their different sizes and shapes. It is applied to artificial phantom data and a brain scan. We show that this method significantly improves the quality of the estimate of the diffusion tensor and hence enables one either to reduce the number of scans or to enhance the input for subsequent analysis such as fiber tracking

Benthic microalgal production in the Arctic: Applied methods and status of the current database

The current database on benthic microalgal production in Arctic waters comprises 10 peer-reviewed and three unpublished studies. Here, we compile and discuss these datasets, along with the applied measurement approaches used. The latter is essential for robust comparative analysis and to clarify the often very confusing terminology in the existing literature. Our compilation demonstrates that i) benthic microalgae contribute significantly to coastal ecosystem production in the Arctic, and ii) benthic microalgal production on average exceeds pelagic productivity by a factor of 1.5 for water depths down to 30 m. We have established relationships between irradiance, water depth and benthic microalgal productivity that can be used to extrapolate results from quantitative experimental studies to the entire Arctic region. Two different approaches estimated that current benthic microalgal production in the Arctic is between 1.1 and 1.6×107 tons C year-1. Climate change is expected to increase the overall primary production and affect the balance between pelagic and benthic productivity in the Arctic. It is therefore imperative to get better quantitative understanding of the relationship between increased freshwater run-off, shrinking sea-ice cover, light availability and benthic primary production to assess future impact on the Arctic food web and trophic coupling. © 2009 by Walter de Gruyter