University of Stuttgart

About: University of Stuttgart is a education organization based out in Stuttgart, Germany. It is known for research contribution in the topics: Laser & Finite element method. The organization has 27715 authors who have published 56370 publications receiving 1363382 citations. The organization is also known as: Universität Stuttgart.

Cyclization of Propargylic Amides: Mild Access to Oxazole Derivatives

Abstract: The substrate scope, the mechanistic aspects of the gold-catalyzed oxazole synthesis, and substrates with different aliphatic, aromatic, and functional groups in the side chain were investigated. Even molecules with several propargyl amide groups could easily be converted, delivering di- and trioxazoles with interesting optical properties. Furthermore, the scope of the gold(I)-catalyzed alkylidene synthesis was investigated. Further functionalizations of these isolable intermediates of the oxazole synthesis were developed and chelate ligands can be obtained. The use of Barluenga's reagent offers a new and mild access to the synthetically valuable iodoalkylideneoxazoles from propargylic amides, this reagent being superior to other sources of halogens.

The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Papers

Abstract: Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to keep track of all the activities in the area. We present a computer-assisted literature review, where we utilize both text mining and qualitative coding, and analyze 6,996 papers from Scopus. We find that the roots of sentiment analysis are in the studies on public opinion analysis at the beginning of 20th century and in the text subjectivity analysis performed by the computational linguistics community in 1990's. However, the outbreak of computer-based sentiment analysis only occurred with the availability of subjective texts on the Web. Consequently, 99% of the papers have been published after 2004. Sentiment analysis papers are scattered to multiple publication venues, and the combined number of papers in the top-15 venues only represent ca. 30% of the papers in total. We present the top-20 cited papers from Google Scholar and Scopus and a taxonomy of research topics. In recent years, sentiment analysis has shifted from analyzing online product reviews to social media texts from Twitter and Facebook. Many topics beyond product reviews like stock markets, elections, disasters, medicine, software engineering and cyberbullying extend the utilization of sentiment analysis

The crisis of democracy and the science of deliberation.

Abstract: Citizens can avoid polarization and make sound decisions That there are more opportunities than ever for citizens to express their views may be, counterintuitively, a problem facing democracy—the sheer quantitative overabundance overloads policymakers and citizens, making it difficult to detect the signal amid the noise. This overload has been accompanied by marked decline in civility and argumentative complexity. Uncivil behavior by elites and pathological mass communication reinforce each other. How do we break this vicious cycle? Asking elites to behave better is futile so long as there is a public ripe to be polarized and exploited by demagogues and media manipulators. Thus, any response has to involve ordinary citizens; but are they up to the task? Social science on “deliberative democracy” offers reasons for optimism about citizens' capacity to avoid polarization and manipulation and to make sound decisions. The real world of democratic politics is currently far from the deliberative ideal, but empirical evidence shows that the gap can be closed.

Efficient Higher-Order CRFs for Morphological Tagging

Abstract: Training higher-order conditional random fields is prohibitive for huge tag sets. We present an approximated conditional random field using coarse-to-fine decoding and early updating. We show that our implementation yields fast and accurate morphological taggers across six languages with different morphological properties and that across languages higher-order models give significant improvements over 1-order models.