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: Population & European union. The organization has 4448 authors who have published 12918 publications receiving 446557 citations. The organization is also known as: Uni Mannheim & UMA.

DeepDeSRT: Deep Learning for Detection and Structure Recognition of Tables in Document Images

Abstract: This paper presents a novel end-to-end system for table understanding in document images called DeepDeSRT In particular, the contribution of DeepDeSRT is two-fold First, it presents a deep learning-based solution for table detection in document images Secondly, it proposes a novel deep learning-based approach for table structure recognition, ie identifying rows, columns, and cell positions in the detected tables In contrast to existing rule-based methods, which rely on heuristics or additional PDF metadata (like, for example, print instructions, character bounding boxes, or line segments), the presented system is data-driven and does not need any heuristics or metadata to detect as well as to recognize tabular structures in document images Furthermore, in contrast to most existing table detection and structure recognition methods, which are applicable only to PDFs, DeepDeSRT processes document images, which makes it equally suitable for born-digital PDFs (as they can automatically be converted into images) as well as even harder problems, eg scanned documents To gauge the performance of DeepDeSRT, the system is evaluated on the publicly available ICDAR 2013 table competition dataset containing 67 documents with 238 pages overall Evaluation results reveal that DeepDeSRT outperforms state-of-the-art methods for table detection and structure recognition and achieves F1-measures of 9677% and 9144% for table detection and structure recognition, respectively Additionally, DeepDeSRT is evaluated on a closed dataset from a real use case of a major European aviation company comprising documents which are highly unlike those in ICDAR 2013 Tested on a randomly selected sample from this dataset, DeepDeSRT achieves high detection accuracy for tables which demonstrates the sound generalization capabilities of our system

The European Bond Markets Under Emu

Abstract: In this paper, we document how in the wake of monetary unification the markets for Euro-area sovereign and private-sector bonds have become increasingly integrated. Issuers and investors alike have come to regard the Euro-area bond market as a single one. Primary and secondary bond markets have become increasingly integrated on a pan-European scale. Issuance of corporate bonds has taken off on an unprecedented scale in continental Europe. In the process, both investors and issuers have reaped the considerable benefits afforded by greater competition in the underwriting of private bonds and auctioning of public ones, and by the greater liquidity of secondary markets. Bond yields have converged dramatically in the transition to EMU. The persistence of small and variable yield differentials for sovereign debt under EMU indicates that Euro-area bonds are still not perfect substitutes. However, to a large extent this does not reflect persistent market segmentation but rather small differentials in fundamental risk. Liquidity differences play at most a minor role, and this role appears to arise partly from their interaction with fundamental risk. The challenges still lying ahead are numerous. They include the unbalance between the German-dominated futures and the underlying cash market; the vulnerability of the cash markets' prices to free-riding and manipulation by large financial institutions; the possibility of joint bond issuance by Euro-area countries; the integration of clearing and settlement systems in the Euro-area bond market, and the participation of new accession countries' issuers to this market.

Removing Multiplicative Noise by Douglas-Rachford Splitting Methods

Abstract: In this paper, we consider a variational restoration model consisting of the I-divergence as data fitting term and the total variation semi-norm or nonlocal means as regularizer for removing multiplicative Gamma noise. Although the I-divergence is the typical data fitting term when dealing with Poisson noise we substantiate why it is also appropriate for cleaning Gamma noise. We propose to compute the minimizers of our restoration functionals by applying Douglas-Rachford splitting techniques, resp. alternating direction methods of multipliers. For a particular splitting, we present a semi-implicit scheme to solve the involved nonlinear systems of equations and prove its Q-linear convergence. Finally, we demonstrate the performance of our methods by numerical examples.

The EU, NATO and the Integration of Europe: Rules and Rhetoric

Abstract: Why did Western European states agree to the enlargement of the EU and NATO? Frank Schimmelfennig analyzes the history of the enlargement process and develops a theoretical approach of 'rhetorical action' to explain why it occurred. While rationalist theory explains the willingness of East European states to join the NATO and EU, it does not explain why member states decided to admit them. Using original data, Schimmelfennig shows that expansion to the East can be understood in terms of liberal democratic community building. Drawing on the works of Jon Elster and Erving Goffman, he demonstrates that the decision to expand was the result of rhetorical action. Candidates and their supporters used arguments based on collective identity, norms and values of the Western community to shame opponents into acquiescing to enlargement. This landmark book makes an enormous contribution to theory in international relations and to the study of European politics.