@inproceedings{45758,
  author       = {{Bauer, Anna Brigitte and Reinhold, Peter}},
  booktitle    = {{Lernen, Lehren und Forschen in einer digital geprägten Welt}},
  editor       = {{van Vorst, Helena}},
  location     = {{Aachen 2022}},
  title        = {{{PSФ: Entwicklung einer abgestimmten Studieneingangsphase (Physik) }}},
  year         = {{2023}},
}

@article{43827,
  abstract     = {{A series of new organic donor–π–acceptor dyes incorporating a diquat moiety as a novel electron-acceptor unit have been synthesized and characterized. The analytical data were supported by DFT calculations. These dyes were explored in the aerobic thiocyanation of indoles and pyrroles. Here they showed a high photocatalytic activity under visible light, giving isolated yields of up to 97 %. In addition, the photocatalytic activity of standalone diquat and methyl viologen through formation of an electron donor acceptor complex is presented.}},
  author       = {{Meier, Armin and Badalov, Sabuhi and Biktagirov, Timur and Schmidt, Wolf Gero and Wilhelm, René}},
  issn         = {{0947-6539}},
  journal      = {{Chemistry – A European Journal}},
  keywords     = {{General Chemistry, Catalysis, Organic Chemistry}},
  number       = {{22}},
  pages        = {{ e202203541}},
  publisher    = {{Wiley}},
  title        = {{{Diquat Based Dyes: A New Class of Photoredox Catalysts and Their Use in Aerobic Thiocyanation}}},
  doi          = {{10.1002/chem.202203541}},
  volume       = {{ 29}},
  year         = {{2023}},
}

@article{45764,
  abstract     = {{As a benchmark, the structural, electronic and optical properties of the three main phases of TiO$\rm{_2}$ crystals have been calculated using Hubbard U correction and hybrid functional methods in density-functional theory. These calculations are compared concerning the available experimental observations on pristine TiO$\rm{_2}$ crystals. Modified hybrid functionals, particularly the PBE0 functional with 11.4% fraction of exact exchange, are shown to provide highly accurate atomic structures and also accurate electronic structure data, including optical excitation energies. With $\rm{DFT+U}$, accurate optical spectra are also possible, but only if the Hubbard U is applied on the O $\rm2p$ electrons exclusively. Furthermore, both methods, the 11.4%-PBE0 hybrid functional and the $\rm{DFT+U_p}$ scheme have been used to study TiO$\rm{_2}$ amorphous ultra-thin films, confirming the agreement of the two methods even with respect to small details of the optical spectra. Our results show that the proposed $\rm{DFT+U_p}$ methodology is computationally efficient, but still accurate. It can be applied to well-ordered TiO$\rm{_2}$ polymorphs as well as to amorphous TiO$\rm{_2}$ and will allow for the calculations of complex titania-based structures.}},
  author       = {{Badalov, Sabuhi and Bocchini, Adriana and Wilhelm, Rene and Kozub, A. L. and Gerstmann, Uwe and Schmidt, Wolf Gero}},
  journal      = {{Materials Research Express}},
  publisher    = {{IOP Publishing}},
  title        = {{{Rutile, anatase, brookite and titania thin film from Hubbard corrected and hybrid DFT}}},
  doi          = {{10.1088/2053-1591/ace0fa}},
  year         = {{2023}},
}

@article{45782,
  abstract     = {{<jats:p>The development of automotive components with reduced greenhouse gas (GHG) emissions is needed to reduce overall vehicle emissions. Life Cycle Engineering (LCE) based on Life Cycle Assessment (LCA) supports this by providing holistic information and improvement potentials regarding eco-efficient products. Key factors influencing LCAs of automotive components, such as material production, will change in the future. First approaches for integrating future scenarios for these key factors into LCE already exist, but they only consider a limited number of parameters and scenarios. This work aims to develop a method that can be practically applied in the industry for integrating prospective LCAs (pLCA) into the LCE of automotive components, considering relevant parameters and consistent scenarios. Therefore, pLCA methods are further developed to investigate the influence of future scenarios on the GHG emissions of automotive components. The practical application is demonstrated for a vehicle component with different design options. This paper shows that different development paths of the foreground and background system can shift the ecological optimum of design alternatives. Therefore, future pathways of relevant parameters must be considered comprehensively to reduce GHG emissions of future vehicles. This work contributes to the methodological and practical integration of pLCA into automotive development processes and provides quantitative results.</jats:p>}},
  author       = {{Grenz, Julian and Ostermann, Moritz and Käsewieter, Karoline and Cerdas, Felipe and Marten, Thorsten and Herrmann, Christoph and Tröster, Thomas}},
  issn         = {{2071-1050}},
  journal      = {{Sustainability}},
  keywords     = {{prospective LCA, life cycle engineering (LCE), lightweight design, automotive components, body parts, circular economy, steel, aluminum, hybrid materials, fiber metal laminates}},
  number       = {{13}},
  publisher    = {{MDPI AG}},
  title        = {{{Integrating Prospective LCA in the Development of Automotive Components}}},
  doi          = {{10.3390/su151310041}},
  volume       = {{15}},
  year         = {{2023}},
}

@inproceedings{31880,
  abstract     = {{The notion of neural collapse refers to several emergent phenomena that have been empirically observed across various canonical classification problems. During the terminal phase of training a deep neural network, the feature embedding of all examples of the same class tend to collapse to a single representation, and the features of different classes tend to separate as much as possible. Neural collapse is often studied through a simplified model, called the unconstrained feature representation, in which the model is assumed to have "infinite expressivity" and can map each data point to any arbitrary representation. In this work, we propose a more realistic variant of the unconstrained feature representation that takes the limited expressivity of the network into account. Empirical evidence suggests that the memorization of noisy data points leads to a degradation (dilation) of the neural collapse. Using a model of the memorization-dilation (M-D) phenomenon, we show one mechanism by which different losses lead to different performances of the trained network on noisy data. Our proofs reveal why label smoothing, a modification of cross-entropy empirically observed to produce a regularization effect, leads to improved generalization in classification tasks.}},
  author       = {{Nguyen, Duc Anh and Levie, Ron and Lienen, Julian and Kutyniok, Gitta and Hüllermeier, Eyke}},
  booktitle    = {{International Conference on Learning Representations, ICLR}},
  location     = {{Kigali, Ruanda}},
  title        = {{{Memorization-Dilation: Modeling Neural Collapse Under Noise}}},
  year         = {{2023}},
}

@unpublished{45814,
  abstract     = {{Label noise poses an important challenge in machine learning, especially in
deep learning, in which large models with high expressive power dominate the
field. Models of that kind are prone to memorizing incorrect labels, thereby
harming generalization performance. Many methods have been proposed to address
this problem, including robust loss functions and more complex label correction
approaches. Robust loss functions are appealing due to their simplicity, but
typically lack flexibility, while label correction usually adds substantial
complexity to the training setup. In this paper, we suggest to address the
shortcomings of both methodologies by "ambiguating" the target information,
adding additional, complementary candidate labels in case the learner is not
sufficiently convinced of the observed training label. More precisely, we
leverage the framework of so-called superset learning to construct set-valued
targets based on a confidence threshold, which deliver imprecise yet more
reliable beliefs about the ground-truth, effectively helping the learner to
suppress the memorization effect. In an extensive empirical evaluation, our
method demonstrates favorable learning behavior on synthetic and real-world
noise, confirming the effectiveness in detecting and correcting erroneous
training labels.}},
  author       = {{Lienen, Julian and Hüllermeier, Eyke}},
  booktitle    = {{arXiv:2305.13764}},
  title        = {{{Mitigating Label Noise through Data Ambiguation}}},
  year         = {{2023}},
}

@inproceedings{33734,
  abstract     = {{Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis}},
  author       = {{KOUAGOU, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)}},
  editor       = {{Pesquita, Catia and Jimenez-Ruiz, Ernesto and McCusker, Jamie and Faria, Daniel and Dragoni, Mauro and Dimou, Anastasia and Troncy, Raphael and Hertling, Sven}},
  keywords     = {{Neural network, Concept learning, Description logics}},
  location     = {{Hersonissos, Crete, Greece}},
  pages        = {{209 -- 226}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Neural Class Expression Synthesis}}},
  doi          = {{https://doi.org/10.1007/978-3-031-33455-9_13}},
  volume       = {{13870}},
  year         = {{2023}},
}

@unpublished{45244,
  abstract     = {{Label noise poses an important challenge in machine learning, especially in
deep learning, in which large models with high expressive power dominate the
field. Models of that kind are prone to memorizing incorrect labels, thereby
harming generalization performance. Many methods have been proposed to address
this problem, including robust loss functions and more complex label correction
approaches. Robust loss functions are appealing due to their simplicity, but
typically lack flexibility, while label correction usually adds substantial
complexity to the training setup. In this paper, we suggest to address the
shortcomings of both methodologies by "ambiguating" the target information,
adding additional, complementary candidate labels in case the learner is not
sufficiently convinced of the observed training label. More precisely, we
leverage the framework of so-called superset learning to construct set-valued
targets based on a confidence threshold, which deliver imprecise yet more
reliable beliefs about the ground-truth, effectively helping the learner to
suppress the memorization effect. In an extensive empirical evaluation, our
method demonstrates favorable learning behavior on synthetic and real-world
noise, confirming the effectiveness in detecting and correcting erroneous
training labels.}},
  author       = {{Lienen, Julian and Hüllermeier, Eyke}},
  booktitle    = {{arXiv:2305.13764}},
  title        = {{{Mitigating Label Noise through Data Ambiguation}}},
  year         = {{2023}},
}

@article{45868,
  abstract     = {{Perfect vector vortex beams (PVVBs) have attracted considerable interest due to their peculiar optical features. PVVBs are typically generated through the superposition of perfect vortex beams, which suffer from the limited number of topological charges (TCs). Furthermore, dynamic control of PVVBs is desirable and has not been reported. We propose and experimentally demonstrate hybrid grafted perfect vector vortex beams (GPVVBs) and their dynamic control. Hybrid GPVVBs are generated through the superposition of grafted perfect vortex beams with a multifunctional metasurface. The generated hybrid GPVVBs possess spatially variant rates of polarization change due to the involvement of more TCs. Each hybrid GPVVB includes different GPVVBs in the same beam, adding more design flexibility. Moreover, these beams are dynamically controlled with a rotating half waveplate. The generated dynamic GPVVBs may find applications in the fields where dynamic control is in high demand, including optical encryption, dense data communication, and multiple particle manipulation.}},
  author       = {{Ahmed, Hammad and Ansari, Muhammad Afnan and Li, Yan and Zentgraf, Thomas and Mehmood, Muhammad Qasim and Chen, Xianzhong}},
  issn         = {{2041-1723}},
  journal      = {{Nature Communications}},
  keywords     = {{General Physics and Astronomy, General Biochemistry, Genetics and Molecular Biology, General Chemistry, Multidisciplinary}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Dynamic control of hybrid grafted perfect vector vortex beams}}},
  doi          = {{10.1038/s41467-023-39599-8}},
  volume       = {{14}},
  year         = {{2023}},
}

@inbook{45875,
  author       = {{Götte, Thorsten and Knollmann, Till and Meyer auf der Heide, Friedhelm and Scheideler, Christian and Werthmann, Julian}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{1----20}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Capabilities and Limitations of Local Strategies in Dynamic Networks}}},
  doi          = {{10.5281/zenodo.8060372}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45895,
  author       = {{Karl, Holger and Maack, Marten and Meyer auf der Heide, Friedhelm and Pukrop, Simon and Redder, Adrian}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{183--202}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{On-The-Fly Compute Centers II: Execution of Composed Services in Configurable Compute Centers}}},
  doi          = {{10.5281/zenodo.8068664}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45901,
  author       = {{Blömer, Johannes and Bobolz, Jan and Eidens, Fabian and Jager, Tibor and Kramer, Paul}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{237--246}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Practical Cryptograhic Techniques for Secure and Privacy-Preserving Customer Loyalty Systems}}},
  doi          = {{10.5281/zenodo.8068755}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45897,
  author       = {{Gottschalk, Sebastian and Vorbohle, Christian and Kundisch, Dennis and Engels, Gregor and Wünderlich, Nacy V.}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{203--224}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Architectural Management of OTF Computing Markets}}},
  doi          = {{10.5281/zenodo.8068691}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45891,
  author       = {{Blömer, Johannes and Eidens, Fabian and Jager, Tibor and Niehues, David and Scheideler, Christian}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{145--164}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Robustness and Security}}},
  doi          = {{10.5281/zenodo.8068629}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45882,
  author       = {{Bäumer, Frederik Simon and Chen, Wei-Fan and Geierhos, Michaela and Kersting, Joschka and Wachsmuth, Henning}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{65--84}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation}}},
  doi          = {{10.5281/zenodo.8068456}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45884,
  author       = {{Hanselle, Jonas Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{85--104}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Configuration and Evaluation}}},
  doi          = {{10.5281/zenodo.8068466}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45878,
  author       = {{Haake, Claus-Jochen and Hehenkamp, Burkhard and Polevoy, Gleb}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{21--44}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{The Market for Services: Incentives, Algorithms, Implementation}}},
  doi          = {{10.5281/zenodo.8068414}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{45886,
  author       = {{Wehrheim, Heike and Hüllermeier, Eyke and Becker, Steffen and Becker, Matthias and Richter, Cedric and Sharma, Arnab}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{105--123}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Composition Analysis in Unknown Contexts}}},
  doi          = {{10.5281/zenodo.8068510}},
  volume       = {{412}},
  year         = {{2023}},
}

@unpublished{45911,
  abstract     = {{Label noise poses an important challenge in machine learning, especially in
deep learning, in which large models with high expressive power dominate the
field. Models of that kind are prone to memorizing incorrect labels, thereby
harming generalization performance. Many methods have been proposed to address
this problem, including robust loss functions and more complex label correction
approaches. Robust loss functions are appealing due to their simplicity, but
typically lack flexibility, while label correction usually adds substantial
complexity to the training setup. In this paper, we suggest to address the
shortcomings of both methodologies by "ambiguating" the target information,
adding additional, complementary candidate labels in case the learner is not
sufficiently convinced of the observed training label. More precisely, we
leverage the framework of so-called superset learning to construct set-valued
targets based on a confidence threshold, which deliver imprecise yet more
reliable beliefs about the ground-truth, effectively helping the learner to
suppress the memorization effect. In an extensive empirical evaluation, our
method demonstrates favorable learning behavior on synthetic and real-world
noise, confirming the effectiveness in detecting and correcting erroneous
training labels.}},
  author       = {{Lienen, Julian and Hüllermeier, Eyke}},
  booktitle    = {{arXiv:2305.13764}},
  title        = {{{Mitigating Label Noise through Data Ambiguation}}},
  year         = {{2023}},
}

@inbook{45880,
  author       = {{Elrich, Alina and Kaimann, Daniel and Fahr, René and Kundisch, Dennis and Mir Djawadi, Behnud and Müller, Michelle and Poniatowski, Martin and Schäfers, Sabrina and Frick, Bernd}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{45--64}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Empirical Analysis in Markets for OTF Services}}},
  doi          = {{10.5281/zenodo.8068430}},
  volume       = {{412}},
  year         = {{2023}},
}