SKilL language server

Language analysis features offered by integrated development environments (IDEs) can ease and accelerate the task of writing code, but are often not available for domain-specific languages. The Language Server Protocol (LSP) aims to solve this problem by allowing language servers that support these features for a certain programming language to be used portably in a number of IDEs. A language server for Serialization Killer Language (SKilL) was implemented that supports a multitude of language features including automatic formatting, completion suggestions, and display of references and documentation associated with symbols. This thesis presents how the language server was implemented and discusses associated challenges that arose due to the nature of the SKilL and LSP specification

Improved RAFT architectures for optical flow estimation

The estimation of optical flow, that is computing the displacement field between two images, is a useful tool in computer vision that has many applications as part of larger frameworks. RAFT [70], a recent method for optical flow estimation, has significantly improved the quality of results on realistic benchmarks over previous approaches, while simultaneously reducing model complexity and training cost. Despite these advancements, RAFT still has several shortcomings including its flow upsampling that can only capture high-resolution details to a limited extent, simple cost volume without normalization, and limited incorporation of multiple frames in sequences. Due to its novelty, the method has also not been applied to related tasks, such as unsupervised optical flow estimation. To address this, we propose several remedies to these mentioned shortcomings of RAFT, including different cost volume normalization strategies and alternative matching cost functions, as well as different flow upsampling strategies that can capture more high-resolution details. We also extend the method to unsupervised training as well as online training, which involves multiple frames of sequences. In the context of unsupervised training, we introduce learned losses that can be applied to arbitrary model architectures and improve results over traditional photometric and smoothness losses. Our online learning approaches yield an improvement over RAFT’s warm start and use multi-frame consistency to improve performance on video sequences. We evaluate our approaches on optical flow benchmarks and find that our modifications represent improvements over RAFT when working within a limited computational budget. We also argue that these result should scale for training configurations without such limitations

Martin Luthers reformatorische Hauptschriften von 1520

2017 ist vergangen – Luther aber bleibt. So auch in seinen reformatorischen Hauptschriften von 1520, die wir Ihnen in diesem Hörbeitrag vorstellen. In einer einzigartigen schriftstellerischen Leistung hat Luther 1520 die zentralen Themen der Theologie in seinen Schriften behandelt: die anstehenden Reformen in der Gesellschaft, die großen Fragen nach dem, was in der Kirche gilt, und das Leben des Einzelnen vor Gott und vor den Menschen. Die „Freiheit eines Christenmenschen“ ist seitdem Thema und Aufgabe. Ein Beitrag der Luther-Gesellschaft (https://www.luther-gesellschaft.de/). Es sprechen: Prof. Dr. Dr. Dr. h.c. Johannes Schilling, Kiel, Erster Präsident der Luther-Gesellschaft Prof. Dr. Christopher Spehr, Lehrstuhl für Kirchengeschichte, Friedrich-Schiller-Universität Jena Prof. Dr. Wolf-Friedrich Schäufele, Fachgebiet Kirchengeschichte, Philipps-Universität Marburg Schnitt: Benjamin Loschek, Multimediazentrum der Friedrich-Schiller-Universität Jen