DTI denoising for data with low signal to noise ratios

Low signal to noise ratio (SNR) experiments in diffusion tensor imaging (DTI) give key information about tracking and anisotropy, e. g., by measurements with small voxel sizes or with high b values. However, due to the complicated and dominating impact of thermal noise such data are still seldom analysed. In this paper Monte Carlo simulations are presented which investigate the distributions of noise for different DTI variables in low SNR situations. Based on this study a strategy for the application of spatial smoothing is derived. Optimal prerequisites for spatial filters are unbiased, bell shaped distributions with uniform variance, but, only few variables have a statistics close to that. To construct a convenient filter a chain of nonlinear Gaussian filters is adapted to peculiarities of DTI and a bias correction is introduced. This edge preserving three dimensional filter is then validated via a quasi realistic model. Further, it is shown that for small sample sizes the filter is as effective as a maximum likelihood estimator and produces reliable results down to a local SNR of approximately 1. The filter is finally applied to very recent data with isotropic voxels of the size 1×1×1mm^3 which corresponds to a spatially mean SNR of 2.5. This application demonstrates the statistical robustness of the filter method. Though the Rician noise model is only approximately realized in the data, the gain of information by spatial smoothing is considerable

Probing the cosmic web: inter-cluster filament detection using gravitational lensing

The problem of detecting dark matter filaments in the cosmic web is considered. Weak lensing is an ideal probe of dark matter, and therefore forms the basis of particularly promising detection methods. We consider and develop a number of weak lensing techniques that could be used to detect filaments in individual or stacked cluster fields, and apply them to synthetic lensing data sets in the fields of clusters from the Millennium Simulation. These techniques are multipole moments of the shear and convergence, mass reconstruction, and parameterized fits to filament mass profiles using a Markov Chain Monte Carlo approach. In particular, two new filament detection techniques are explored (multipole shear filters and Markov Chain Monte Carlo mass profile fits), and we outline the quality of data required to be able to identify and quantify filament profiles. We also consider the effects of large scale structure on filament detection. We conclude that using these techniques, there will be realistic prospects of detecting filaments in data from future space-based missions. The methods presented in this paper will be of great use in the identification of dark matter filaments in future surveys.Comment: 12 pages, 4 figures, MNRAS accepted, (replacement due to corrupted end of pdf file

Epitaxial strain adaption in chemically disordered FeRh thin films

Strain and strain adaption mechanisms in modern functional materials are of crucial importance for their performance. Understanding these mechanisms will advance innovative approaches for material properties engineering. Here we study the strain adaption mechanism in a thin film model system as function of epitaxial strain. Chemically disordered FeRh thin films are deposited on W-V buffer layers, which allow for large variation of the preset lattice constants, e.g. epitaxial boundary condition. It is shown by means of high resolution X-ray reciprocal space maps and transmission electron microscopy that the system reacts with a tilting mechanism of the structural units in order to adapt to the lattice constants of the buffer layer. This response explained by density functional theory calculations, which evidence an energetic minimum for structures with a distortion of c/a =0.87. The experimentally observed tilting mechanism is induced by this energy gain and allows the system to remain in the most favorable structure. In general, it is shown that the use of epitaxial model heterostructures consisting of alloy buffer layers of fully miscible elements and the functional material of interest allows to study strain adaption behaviors in great detail. This approach makes even small secondary effects observable, such as the directional tilting of the structural domains identified in the present case study

The value of theoretical multiplicity for steering transitions towards sustainability

Transition management, as a theory of directing structural societal changes towards sustainable system innovations, has become a major topic in scientific research over the last years. In this paper we focus on the question how transitions towards sustainability can be steered, governed or managed, in particular by governmental actors. We suggest an approach of theoretical multiplicity, arguing that multiple theories will be needed simultaneously for dealing with the complex societal sustainability issues. Therefore, we address the steering question by theoretically comparing transition management theory to a number of related theories on societal change and intervention, such as multi-actor collaboration, network governance, configuration management, policy agenda setting, and adaptive management. We conclude that these related theories put the managerial assumptions of transition management into perspective, by adding other steering roles and leadership mechanisms to the picture. Finally we argue that new modes of steering inevitable have consequences for the actual governance institutions. New ways of governing change ask for change within governance systems itself and vice versa. Our argument for theoretical multiplicity implicates the development of multiple, potentially conflicting, governance capacitie