University of Mannheim

About: University of Mannheim is a education organization based out in Mannheim, Germany. It is known for research contribution in the topics: Context (language use) & Politics. The organization has 4448 authors who have published 12918 publications receiving 446557 citations. The organization is also known as: Uni Mannheim & UMA.

The Effect of Attribute Ranges on Weights in Multiattribute Utility Measurements

Abstract: Multiattribute utility theory requires a specific relation between the range of outcomes of each attribute and the weight for that attribute. The greater the range, the greater the weight has to be. Experimental results show that subjects do not adjust their judgments properly if the range is varied. For the two methods tested the adjustment is smaller than required by theory. The bias was smaller for a regression procedure than for the direct ratio method. Weights based on an intuitive range were not found to be superior to those elicited over different ranges.

Code Conjurer: Pulling Reusable Software out of Thin Air

Abstract: For many years, the IT industry has sought to accelerate the software development process by assembling new applications from existing software assets. However, true component-based reuse of the form Douglas Mcllroy envisaged in the 1960s is still the exception rather than the rule, and most of the systematic software reuse practiced today uses heavyweight approaches such as product-line engineering or domain-specific frameworks. By component, we mean any cohesive and compact unit of software functionality with a well-defined interface - from simple programming language classes to more complex artifacts such as Web services and Enterprise JavaBeans.

Nonlinear Shape Statistics in Mumford-Shah Based Segmentation

Abstract: We present a variational integration of nonlinear shape statistics into a Mumford-Shah based segmentation process. The non-linear statistics are derived from a set of training silhouettes by a novel method of density estimation which can be considered as an extension of kernel PCA to a stochastic framework.The idea is to assume that the training data forms a Gaussian distribution after a nonlinear mapping to a potentially higher-dimensional feature space. Due to the strong nonlinearity, the corresponding density estimate in the original space is highly non-Gaussian. It can capture essentially arbitrary data distributions (e.g. multiple clusters, ring- or banana-shaped manifolds).Applications of the nonlinear shape statistics in segmentation and tracking of 2D and 3D objects demonstrate that the segmentation process can incorporate knowledge on a large variety of complex real-world shapes. It makes the segmentation process robust against misleading information due to noise, clutter and occlusion.

Measuring Interest Group Influence Using Quantitative Text Analysis

Abstract: The analysis of interest group influence is crucial in order to explain policy outcomes and to assess the democratic legitimacy of the European Union. However, owing to methodological difficulties ...