@article{29112,
  abstract     = {{<jats:p> Zusammenfassung. Die vorliegende Studie befasst sich mit der Übersetzung und Validierung des englischsprachigen Sport Emotion Questionnaire (SEQ; Jones et al., 2005 ), der vorwettbewerblichen Emotionen von Sporttreibenden misst. In einer ersten Teilstudie wurde mittels einer Hin-Rück-Übersetzung und des Bilingual-Retest-Verfahrens ( n = 32) eine deutsche Version des SEQ (SEQ-d) erzeugt. In Studie 2 (Tennisspieler/innen, n = 116) zeigte sich jedoch eine vom Original abweichende Faktorstruktur, woraufhin der SEQ-d als dreidimensionale Kurzskala entwickelt wurde. Diese wurde in Studie 3 (Läufern/innen, n = 271) validiert. Die Kurzskala besitzt einen akzeptablen Fit (CFI = .950, RMSEA = .069, SRMR = .063) und eine interne Konsistenz von α = .84 (negative Emotionen), α = .86 (positive Emotionen), α = .87 (Anspannung). Durch Korrelationen mit anderen Emotionsmerkmalen konnte die konvergente Validität bestätigt werden. Die Kriteriumsvalidität wurde anhand wettkampf- und personenbezogener Zusatzparameter untersucht (bspw. Alter, Wettkampferfahrung…). Mit der deutschsprachigen Version des SEQ liegt ein ökonomisches und validiertes Messinstrument zur Erfassung vorwettbewerblicher Emotionen vor. </jats:p>}},
  author       = {{Wetzel, Änne and Weigelt, Matthias and Klingsieck, Katrin B.}},
  issn         = {{0012-1924}},
  journal      = {{Diagnostica}},
  pages        = {{246--257}},
  title        = {{{Übersetzung und Validierung einer deutschsprachigen Version des Sport Emotion Questionnaire (SEQ)}}},
  doi          = {{10.1026/0012-1924/a000255}},
  year         = {{2020}},
}

@article{29109,
  abstract     = {{Das Schreiben von wissenschaftlichen Texten ist mit einer Vielzahl von Herausforderungen verbunden, die die Bewältigung einer Schreibaufgabe häufig mühselig erscheinen lassen. Dieser Beitrag führt das wissenschaftliche Schreiben als eine Form des komplexen Problemlösens ein. Er betrachtet das wissenschaftliche Schreiben als ein rhetorisches Problem und zeigt auf, welche Ressourcen und Strategien im Rahmen des Schreibprozesses eingesetzt werden, um dieses Problem zu lösen. Der Fokus liegt dabei auf der Rolle der Selbstregulation beim wissenschaftlichen Schreiben. Aus prominenten Kompetenz- und Phasenmodellen des Schreibens abgeleitet, stellt der Beitrag grundlegende Strategien einer erfolgreichen Selbstregulation beim wissenschaftlichen Schreiben vor, die individuell eingesetzt werden können, um ins Schreiben zu kommen und im Schreiben zu bleiben.}},
  author       = {{Klingsieck, Katrin B. and Golombek, Christiane}},
  journal      = {{HLZ – Herausforderung Lehrer*innenbildung}},
  pages        = {{655–672}},
  title        = {{{Schreibherausforderungen: Ins Schreiben kommen und im Schreiben bleiben –die Selbstregulation beim Schreiben wissenschaftlicher Texte in den Qualifizierungsphasen.}}},
  doi          = {{10.4119/HLZ-2499}},
  volume       = {{3}},
  year         = {{2020}},
}

@article{29111,
  author       = {{Svartdal, Frode and Klingsieck, Katrin B. and Steel, Piers and Gamst-Klaussen, Thor}},
  issn         = {{0191-8869}},
  journal      = {{Personality and Individual Differences}},
  title        = {{{Measuring implemental delay in procrastination: Separating onset and sustained goal striving}}},
  doi          = {{10.1016/j.paid.2019.109762}},
  year         = {{2020}},
}

@article{29110,
  author       = {{Svartdal, Frode and Dahl, Tove I. and Gamst-Klaussen, Thor and Koppenborg, Markus and Klingsieck, Katrin B.}},
  issn         = {{1664-1078}},
  journal      = {{Frontiers in Psychology}},
  title        = {{{How Study Environments Foster Academic Procrastination: Overview and Recommendations}}},
  doi          = {{10.3389/fpsyg.2020.540910}},
  year         = {{2020}},
}

@inbook{48813,
  author       = {{Luft, Sebastian and Janes, Jered}},
  booktitle    = {{Transcending Reason. Heidegger on Rationality,}},
  editor       = {{Burch, M and McMullin, I}},
  pages        = {{237--258}},
  title        = {{{Die angebliche Frage nach dem ‘Sein des Seienden’:  An Unknown Husserlian Response to Heidegger’s ‘Question of Being’}}},
  year         = {{2020}},
}

@inproceedings{20766,
  abstract     = {{Recently, the source separation performance was greatly improved by time-domain audio source separation based on dual-path recurrent neural network (DPRNN). DPRNN is a simple but effective model for a long sequential data. While DPRNN is quite efficient in modeling a sequential data of the length of an utterance, i.e., about 5 to 10 second data, it is harder to apply it to longer sequences such as whole conversations consisting of multiple utterances. It is simply because, in such a case, the number of time steps consumed by its internal module called inter-chunk RNN becomes extremely large. To mitigate this problem, this paper proposes a multi-path RNN (MPRNN), a generalized version of DPRNN, that models the input data in a hierarchical manner. In the MPRNN framework, the input data is represented at several (>_ 3) time-resolutions, each of which is modeled by a specific RNN sub-module. For example, the RNN sub-module that deals with the finest resolution may model temporal relationship only within a phoneme, while the RNN sub-module handling the most coarse resolution may capture only the relationship between utterances such as speaker information. We perform experiments using simulated dialogue-like mixtures and show that MPRNN has greater model capacity, and it outperforms the current state-of-the-art DPRNN framework especially in online processing scenarios.}},
  author       = {{Kinoshita, Keisuke and von Neumann, Thilo and Delcroix, Marc and Nakatani, Tomohiro and Haeb-Umbach, Reinhold}},
  booktitle    = {{Proc. Interspeech 2020}},
  pages        = {{2652--2656}},
  title        = {{{Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its Application to Speaker Stream Separation}}},
  doi          = {{10.21437/Interspeech.2020-2388}},
  year         = {{2020}},
}

@inproceedings{23518,
  author       = {{Gräßler, Iris and Roesmann, Daniel and Pottebaum, Jens}},
  booktitle    = {{Procedia Manufacturing - Proceedings of the 10th Conference on Learning Factories, CLF2020, Band 45;  16. - 17. Apr. 2020}},
  pages        = {{479--484}},
  publisher    = {{Elsevier }},
  title        = {{{Traceable learning effects by use of digital adaptive assistance in production}}},
  doi          = {{doi: 10.1016/j.promfg.2020.04.058}},
  volume       = {{45}},
  year         = {{2020}},
}

@inproceedings{23513,
  author       = {{Gräßler, Iris and Pottebaum, Jens and Scholle, Philipp and Thiele, Henrik}},
  booktitle    = {{ISPIM Conference Proceedings; 7. - 10. Jun. 2020}},
  pages        = {{1--9}},
  publisher    = {{International Society for Professional Innovation Management (ISPIM)}},
  title        = {{{Innovation management and strategic planning of innovative self-preparednes and self-Protection services}}},
  year         = {{2020}},
}

@article{23468,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>The value of models is well recognised in product and systems engineering. Modelling languages and diagrams are used to capture mental models and to handle model complexity. Literature research indicates that there are only very few approaches to utilise the potential of virtual and augmented reality for supporting tasks in model based (systems) engineering. The paper at hand contributes a new morphology of intuitive interaction for Immersive Abstraction as a holistic approach to extend that coverage. It presents a holistic framework to categorise solutions and future research directions.</jats:p>}},
  author       = {{Gräßler, Iris and Pottebaum, Jens}},
  issn         = {{2633-7762}},
  journal      = {{Proceedings of the Design Society: DESIGN Conference; 26. - 29. Okt. 2020}},
  pages        = {{1295--1304}},
  publisher    = {{Cambridge University Press}},
  title        = {{{Immersive Abstraction: A new Morphology of Intuitive Interaction with System Models}}},
  doi          = {{10.1017/dsd.2020.158}},
  volume       = {{Band 1}},
  year         = {{2020}},
}

@inproceedings{23520,
  author       = {{Gräßler, Iris and Roesmann, Daniel and Pottebaum, Jens}},
  booktitle    = {{Frühjahrskongress 2020 - Digitaler Wandel, digitale Arbeit, digitaler Mensch?; 16. - 18. Mrz. 2020}},
  isbn         = {{978-3-936804-27-0}},
  pages        = {{B.6.4}},
  publisher    = {{Gesellschaft für Arbeitswissenschaft e. V., Dortmund}},
  title        = {{{Entwicklung eines Prüfstands für die Bewertung von kompetenzbildenden Assistenzsystemen in Cyber-physischen Produktionssystemen}}},
  year         = {{2020}},
}

@inproceedings{23519,
  author       = {{Gräßler, Iris and Pottebaum, Jens and Taplick, Patrick and Roesmann, Daniel and Kamann, Markus}},
  booktitle    = {{Frühjahrskongress 2020 - Digitaler Wandel, digitale Arbeit, digitaler Mensch?; 16. - 18. Mrz. 2020}},
  isbn         = {{978-3-936804-27-0}},
  pages        = {{B.2.2}},
  publisher    = {{Gesellschaft für Arbeitswissenschaft e. V., Dortmund}},
  title        = {{{Produktdatenbasiertes, arbeitsgebundenes Lernen für und mit Augmented Reality in der Instandhaltung}}},
  year         = {{2020}},
}

@article{23530,
  author       = {{Pottebaum, Jens and Gräßler, Iris}},
  journal      = {{Konstruktion}},
  number       = {{(11-12)}},
  pages        = {{76--83}},
  publisher    = {{VDI-Verlag}},
  title        = {{{Informationsqualität in der Produktentwicklung: Modellbasiertes Systems Engineering mit expliziter Berücksichtigung von Unsicherheit}}},
  doi          = {{10.37544/0720-5953-2020-11-12-76}},
  volume       = {{72 (11-12)}},
  year         = {{2020}},
}

@inbook{48963,
  author       = {{Schulze, Max}},
  booktitle    = {{Provinz Editionen 2011-2020}},
  editor       = {{Gliem, Vera and Strsembski, Stephan}},
  keywords     = {{Editionen, Kunst, Fotografie, Siebdruck, Farbattentat}},
  title        = {{{Hunger (Eduardo Chillida) und Swingcolor}}},
  year         = {{2020}},
}

@inbook{48957,
  author       = {{Schulze, Max}},
  booktitle    = {{Salon #15}},
  editor       = {{Theewen, Gerhard }},
  isbn         = {{978-3-89770-523-4}},
  pages        = {{65--74}},
  publisher    = {{Salon Verlag}},
  title        = {{{Der Wunsch zu verschwinden}}},
  year         = {{2020}},
}

@book{29817,
  author       = {{Hartung, Olaf}},
  isbn         = {{978-3-17-022637-1}},
  pages        = {{178}},
  publisher    = {{W. Kohlhammer}},
  title        = {{{Museen und Geschichtsunterricht}}},
  year         = {{2020}},
}

@inbook{49006,
  author       = {{Moritz, Tilman}},
  booktitle    = {{Peter Paul Rubens und der Barock im Norden. Katalog zur Ausstellung im Erzbischöflichen Diözesanmuseum Paderborn}},
  editor       = {{Stiegemann, Christoph}},
  isbn         = {{978-3-7319-0956-9}},
  pages        = {{136–147}},
  publisher    = {{Michael Imhof}},
  title        = {{{Mehr Barock wagen. Neuordnungen des Fürstbistums Paderborn aus dem Dreißigjährigen Krieg}}},
  year         = {{2020}},
}

@inbook{17994,
  abstract     = {{In this work we review the novel framework for the computation of finite dimensional invariant sets of infinite dimensional dynamical systems developed in [6] and [36]. By utilizing results on embedding techniques for infinite dimensional systems we extend a classical subdivision scheme [8] as well as a continuation algorithm [7] for the computation of attractors and invariant manifolds of finite dimensional systems to the infinite dimensional case. We show how to implement this approach for the analysis of delay differential equations and partial differential equations and illustrate the feasibility of our implementation by computing the attractor of the Mackey-Glass equation and the unstable manifold of the one-dimensional Kuramoto-Sivashinsky equation.}},
  author       = {{Gerlach, Raphael and Ziessler, Adrian}},
  booktitle    = {{Advances in Dynamics, Optimization and Computation}},
  editor       = {{Junge, Oliver and Schütze, Oliver and Ober-Blöbaum, Sina and Padberg-Gehle, Kathrin}},
  isbn         = {{9783030512637}},
  issn         = {{2198-4182}},
  pages        = {{66--85}},
  publisher    = {{Springer International Publishing}},
  title        = {{{The Approximation of Invariant Sets in Infinite Dimensional Dynamical Systems}}},
  doi          = {{10.1007/978-3-030-51264-4_3}},
  volume       = {{304}},
  year         = {{2020}},
}

@article{16712,
  abstract     = {{We investigate self-adjoint matrices A∈Rn,n with respect to their equivariance properties. We show in particular that a matrix is self-adjoint if and only if it is equivariant with respect to the action of a group Γ2(A)⊂O(n) which is isomorphic to ⊗nk=1Z2. If the self-adjoint matrix possesses multiple eigenvalues – this may, for instance, be induced by symmetry properties of an underlying dynamical system – then A is even equivariant with respect to the action of a group Γ(A)≃∏ki=1O(mi) where m1,…,mk are the multiplicities of the eigenvalues λ1,…,λk of A. We discuss implications of this result for equivariant bifurcation problems, and we briefly address further applications for the Procrustes problem, graph symmetries and Taylor expansions.}},
  author       = {{Dellnitz, Michael and Gebken, Bennet and Gerlach, Raphael and Klus, Stefan}},
  issn         = {{1468-9367}},
  journal      = {{Dynamical Systems}},
  number       = {{2}},
  pages        = {{197--215}},
  title        = {{{On the equivariance properties of self-adjoint matrices}}},
  doi          = {{10.1080/14689367.2019.1661355}},
  volume       = {{35}},
  year         = {{2020}},
}

@book{49070,
  author       = {{Schulze, Max}},
  pages        = {{40}},
  publisher    = {{Eigenverlag}},
  title        = {{{Vandalene}}},
  year         = {{2020}},
}

@inproceedings{20753,
  abstract     = {{In this paper we present our system for the detection and classification of acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection and separation in domestic environments. We introduce two new models: the forward-backward convolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional neural network (CNN). The FBCRNN employs two recurrent neural network (RNN) classifiers sharing the same CNN for preprocessing. With one RNN processing a recording in forward direction and the other in backward direction, the two networks are trained to jointly predict audio tags, i.e., weak labels, at each time step within a recording, given that at each time step they have jointly processed the whole recording. The proposed training encourages the classifiers to tag events as soon as possible. Therefore, after training, the networks can be applied to shorter audio segments of, e.g., 200ms, allowing sound event detection (SED). Further, we propose a tag-conditioned CNN to complement SED. It is trained to predict strong labels while using (predicted) tags, i.e., weak labels, as additional input. For training pseudo strong labels from a FBCRNN ensemble are used. The presented system scored the fourth and third place in the systems and teams rankings, respectively. Subsequent improvements allow our system to even outperform the challenge baseline and winner systems in average by, respectively, 18.0% and 2.2% event-based F1-score on the validation set. Source code is publicly available at https://github.com/fgnt/pb_sed.}},
  author       = {{Ebbers, Janek and Haeb-Umbach, Reinhold}},
  booktitle    = {{Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)}},
  title        = {{{Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection}}},
  year         = {{2020}},
}