Effects of workplace representation on firm-provided further training in Germany

"Unions are an important indicator of various measures of firm performance in Anglo-Saxon countries. The same holds for the German analogue of workplace unionism - the works council. Using the IAB Establishment Panel I examine the impact of works councils and shop-floor participation on further training and training intensity. As some studies suggest that the impact of workplace representation varies with firm size, I also test for differences between large and small/medium-sized establishments. Pooled logit and count data models are employed to analyze firms' further training activity and training intensity. Because the treatment variables may suffer from endogeneity I also adopt linear and nonlinear instrumental variables techniques. The analysis reveals a positive impact of works councils on firm-provided further training, but provides slightly weaker evidence of firm-size differentials of workplace representation." (Author's abstract, IAB-Doku) ((en))betriebliche Weiterbildung, Interessenvertretung, IAB-Betriebspanel, Betriebsrat - Auswirkungen

Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines

Many automatically analyzable scientific questions are well-posed and offer a variety of information about the expected outcome a priori. Although often being neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and the direct information about the ambiguity inherent in the extracted data. We present a new concept for the estimation and propagation of uncertainty involved in image analysis operators. This allows using simple processing operators that are suitable for analyzing large-scale 3D+t microscopy images without compromising the result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it enhance the result quality of various processing operators. All presented concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. Furthermore, the functionality of the proposed approach is validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. Especially, the automated analysis of terabyte-scale microscopy data will benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout. The generality of the concept, however, makes it also applicable to practically any other field with processing strategies that are arranged as linear pipelines.Comment: 39 pages, 12 figure

European Parliament Electoral Turnout in Post-Communist Europe

The relatively low voter turnout rates in the June 2004 European Parliamentary elections in many of the post-communist states surprised observers. While the average turnout rate for these new-EU member states barely surpassed 30%, turnout exhibited much variance at the national and sub-national levels. In this article, we study the determinants of European Parliamentary election voter turnout rates in the post-communist countries at the regional level. Our central hypothesis is that regional turnout rates may be related to regional economic conditions and that in areas experiencing economic hardship, turnout will be lower. We also assess the extent that EU attitudes matter for turnout. A unique data set, compiled at the NUTS-3.Economics of voting;participation;European Parliamentary election; post-communist countries.

Normative Praxis: konstitutions- und konstruktionsanalytische Grundlagen

Talcott Parsons gründet seine funktionale Handlungstheorie in Anschluss an Émile Durkheim auf die Annahme, dass kulturelle Werte und Normen ein internalisiertes System von Symbolen bilden, das von allen Gesellschaftsmitgliedern geteilt wird und ebenso den Verlauf wie den Sinn und die Legitimation von Handlungen steuere. Alfred Schütz konterte diese Auffassung von Parsons zu ›normativen Werten‹ bekannterweise mit dem Hinweis, es gebe keine Norm, die nicht in Bestandteile oder Mittel zerlegt und auch Gegenstand der subjektiven Wahl zwischen Realisieren oder Verwerfen werden kann (Schütz & Parsons 1977: 47). Ebenfalls im Gegensatz zu Parsons Annahme, dass Gesellschaft auf Normativität gründet, und nun mit Blick auf die soziohistorische Dimension, führt (1987) die These des Funktionsverlustes der universalen Moral an, wonach moderne, hochdifferenzierte Gesellschaften gerade das Auseinandertreten divergierender Gruppen- und Sondermoralen verkraften müssen (vgl. Bergmann & Luckmann 1999: 33). Wenn dies zutrifft, kann sich eine moderne Gesellschaft freilich nicht auf einer streng eindimensionalen ›Generalmoral‹ gründen, obwohl normative gesellschaftliche Faktoren, wenn sie effektiv funktionieren, eine relativ spontane soziale Ordnung garantieren

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced datasets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present thesis introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images.Comment: 218 pages, 58 figures, PhD thesis, Department of Mechanical Engineering, Karlsruhe Institute of Technology, published online with KITopen (License: CC BY-SA 3.0, http://dx.doi.org/10.5445/IR/1000057821