@article{55999, abstract = {{Clean hydrogen is a key aspect of carbon neutrality, necessitating robust methods for monitoring hydrogen concentration in critical infrastructures like pipelines or power plants. While semiconducting metal oxides such as In2O3 can monitor gas concentrations down to the ppm range, they often exhibit cross-sensitivity to other gases like H2O. In this study, we investigated whether cyclic optical illumination of a gas-sensitive In2O3 layer creates identifiable changes in a gas sensor´s electronic resistance that can be linked to H2 and H2O concentrations via machine learning. We exposed nanostructured In2O3 with a large surface area of 95 m2 g-1 to H2 concentrations (0-800 ppm) and relative humidity (0-70%) under cyclic activation utilizing blue light. The sensors were tested for 20 classes of gas combinations. A support vector machine achieved classification rates up to 92.0%, with reliable reproducibility (88.2 ± 2.7%) across five individual sensors using 10-fold cross-validation. Our findings suggest that cyclic optical activation can be used as a tool to classify H2 and H2O concentrations.}}, author = {{Baier, Dominik and Krüger, Alexander and Wagner, Thorsten and Tiemann, Michael and Weinberger, Christian}}, issn = {{2227-9040}}, journal = {{Chemosensors}}, keywords = {{resistive gas sensor, chemiresistor, semiconductor, metal oxide, In2O3, mesoporous, hydrogen, humidtiy, machine learning, sustainable}}, number = {{9}}, pages = {{178}}, publisher = {{MDPI}}, title = {{{Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O}}}, doi = {{10.3390/chemosensors12090178}}, volume = {{12}}, year = {{2024}}, } @article{56020, abstract = {{Hass im Netz ist nicht nur ein Problem, sondern ermöglicht es einer bürgerlichen Position, hegemoniale Ansprüche zu bekräftigen und zu legitimieren. Der Text geht somit dem nach, was im Gegensatz zum abzulehnenden Diskurs als ‚gut‘ gezeichnet wird. In diesem Gegensatz steht die bürgerliche Welt des Etablierten/Status quo als Ort, in dem der ‚gute‘ Diskurs bereits erfüllt ist. Sie dient sich somit als Standard und Vorbild für die sozialen Medien an und kann dabei gesellschaftliche Kräfte mobilisieren, indem sie sich als bedroht und schützenswert darstellt.}}, author = {{Althoff, Sebastian}}, journal = {{merzWissenschaft}}, number = {{6}}, pages = {{113--125}}, title = {{{Hass im Netz und die Konstruktion des 'guten' Diskurses: Eine machtkritische Analyse}}}, doi = {{https://doi.org/10.21240/merz/2024.6.10}}, volume = {{68}}, year = {{2024}}, } @misc{59223, author = {{Schwabe, Tobias and Mallick, Khaleda and Singh, Karanveer and Schneider, Thomas and Scheytt, J. Christoph}}, publisher = {{Zenodo}}, title = {{{Precise optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter presentation 2024 SPP 2111 }}}, doi = {{10.5281/zenodo.15114897}}, year = {{2024}}, } @misc{59224, author = {{Schwabe, Tobias and Singh, Karanveer and Schneider, Thomas and Scheytt, J. Christoph}}, publisher = {{Zenodo}}, title = {{{Precise optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter (PONyDAC II) 2024 SPP 2111 }}}, doi = {{10.5281/zenodo.15114631}}, year = {{2024}}, } @inproceedings{57103, author = {{Surendranath Shroff, Vijayalakshmi and Bahmanian, Meysam and Kruse, Stephan and Scheytt, J. Christoph}}, booktitle = {{2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) }}, location = {{Fort Lauderdale, Florida}}, publisher = {{IEEE}}, title = {{{Design of an Ultra-Low Phase Noise Broadband Amplifier in 130 nm SiGe BiCMOS Technology}}}, doi = {{10.1109/BCICTS59662.2024.10745663}}, year = {{2024}}, } @inproceedings{57160, abstract = {{Large audio tagging models are usually trained or pre-trained on AudioSet, a dataset that encompasses a large amount of different sound classes and acoustic environments. Knowledge distillation has emerged as a method to compress such models without compromising their effectiveness. There are many different applications for audio tagging, some of which require a specialization to a narrow domain of sounds to be classified. For these scenarios, it is beneficial to distill the large audio tagger with respect to a specific subset of sounds of interest. A method to prune a general dataset with respect to a target dataset is presented. By distilling with such a specialized pruned dataset, we obtain a compressed model with better classification accuracy in the specific target domain than with target-agnostic distillation.}}, author = {{Werning, Alexander and Haeb-Umbach, Reinhold}}, booktitle = {{32nd European Signal Processing Conference (EUSIPCO 2024)}}, keywords = {{data pruning, knowledge distillation, audio tagging}}, location = {{Lyon}}, title = {{{Target-Specific Dataset Pruning for Compression of Audio Tagging Models}}}, year = {{2024}}, } @inbook{62702, abstract = {{Clifford algebras are a natural extension of division algebras, including real numbers, complex numbers, quaternions, and octonions. Previous research in knowledge graph embeddings has focused exclusively on Clifford algebras of a specific type, which do not include nilpotent base vectors—elements that square to zero. In this work, we introduce a novel approach by incorporating nilpotent base vectors with a nilpotency index of two, leading to a more general form of Clifford algebras named degenerate Clifford algebras. This generalization to degenerate Clifford algebras does allow for covering dual numbers and as such include translations and rotations models under the same generalization paradigm for the first time. We develop two models to determine the parameters that define the algebra: one using a greedy search and another predicting the parameters based on neural network embeddings of the input knowledge graph. Our evaluation on seven benchmark datasets demonstrates that this incorporation of nilpotent vectors enhances the quality of embeddings. Additionally, our method outperforms state-of-the-art approaches in terms of generalization, particularly regarding the mean reciprocal rank achieved on validation data. Finally, we show that even a simple greedy search can effectively discover optimal or near-optimal parameters for the algebra.}}, author = {{Kamdem Teyou, Louis Mozart and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Frontiers in Artificial Intelligence and Applications}}, isbn = {{9781643685489}}, issn = {{0922-6389}}, location = {{Santiago de Compostela}}, publisher = {{IOS Press}}, title = {{{Embedding Knowledge Graphs in Degenerate Clifford Algebras}}}, doi = {{10.3233/faia240627}}, year = {{2024}}, } @inproceedings{62703, abstract = {{We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge graph embedding techniques. Initially employing polynomial functions to compute embeddings, we progress to more intricate representations using neural networks with varying layer complexities. We argue that employing functions for embedding computation enhances expressiveness and allows for more degrees of freedom, enabling operations such as composition, derivatives and primitive of entities representation. Additionally, we meticulously outline the step-by-step construction of our approach and provide code for reproducibility, thereby facilitating further exploration and application in the field.}}, author = {{Kamdem Teyou, Louis Mozart and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management}}, location = {{Boise}}, publisher = {{ACM}}, title = {{{Embedding Knowledge Graphs in Function Spaces}}}, doi = {{10.1145/3627673.3679819}}, year = {{2024}}, } @article{56267, author = {{Serino, Laura and Ridder, Werner and Bhattacharjee, Abhinandan and Gil López, Jano and Brecht, Benjamin and Silberhorn, Christine}}, issn = {{2837-6714}}, journal = {{Optica Quantum}}, publisher = {{Optica Publishing Group}}, title = {{{Orchestrating time and color: a programmable source of high-dimensional entanglement}}}, doi = {{10.1364/opticaq.532334}}, year = {{2024}}, } @techreport{62739, author = {{Bornemann, Tobias and Novotny–Farkas, Zoltán}}, title = {{{Does the Accounting Classification of Hybrid Financial Instruments as Debt or Equity Matter?}}}, doi = {{10.2139/ssrn.4821642}}, year = {{2024}}, } @inproceedings{57278, author = {{Morim da Silva, Ana Alexandra and Srivastava, Nikit and Moteu Ngoli, Tatiana and Röder, Michael and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)}}, publisher = {{Association for Computational Linguistics}}, title = {{{Benchmarking Low-Resource Machine Translation Systems}}}, doi = {{10.18653/v1/2024.loresmt-1.18}}, year = {{2024}}, } @inbook{54784, author = {{Janzen, Thomas and Vogelsang, Christoph and Rumlich, Dominik}}, booktitle = {{Wissen, Können und Handeln von Fremdsprachenlehrpersonen}}, editor = {{Gerlach, David}}, pages = {{221--234}}, publisher = {{Lang}}, title = {{{Feedbackkompetenz handlungsnah prüfen: Die Entwicklung einer rollenspielbasierten Simulation als Prüfungsformat für angehende Englischlehrkräfte}}}, year = {{2024}}, } @inproceedings{54811, author = {{Pollmeier, Pascal and Vogelsang, Christoph and Rogge, Tim}}, booktitle = {{Frühe naturwissenschaftliche Bildung}}, editor = {{van Vorst, Helena}}, location = {{Hamburg}}, title = {{{Eigenvideografien als Instrument zur Professionalisierung angehender Lehrkräfte}}}, volume = {{44}}, year = {{2024}}, } @article{62767, abstract = {{Abstract In this study, we develop a novel multi-fidelity deep learning approach that transforms low-fidelity solution maps into high-fidelity ones by incorporating parametric space information into an autoencoder architecture. This method’s integration of parametric space information significantly reduces the amount of training data needed to effectively predict high-fidelity solutions from low-fidelity ones. In this study, we examine a two-dimensional steady-state heat transfer analysis within a heterogeneous materials microstructure. The heat conductivity coefficients for two different materials are condensed from a 101  $$\times $$ ×  101 grid to smaller grids. We then solve the boundary value problem on the coarsest grid using a pre-trained physics-informed neural operator network known as Finite Operator Learning (FOL). The resulting low-fidelity solution is subsequently upscaled back to a 101  $$\times $$ ×  101 grid using a newly designed enhanced autoencoder. The novelty of the developed enhanced autoencoder lies in the concatenation of heat conductivity maps of different resolutions to the decoder segment in distinct steps. Hence the developed algorithm is named microstructure-embedded autoencoder (MEA). We compare the MEA outcomes with those from finite element methods, the standard U-Net, and an interpolation approach as an upscaling technique. Our analysis shows that MEA outperforms these methods in terms of computational efficiency and error on representative test cases. As a result, the MEA serves as a potential supplement to neural operator networks, effectively upscaling low-fidelity solutions to high-fidelity while preserving critical details often lost in traditional upscaling methods, such as sharp interfaces features lost in the context of interpolation approaches.}}, author = {{Najafi Koopas, Rasoul and Rezaei, Shahed and Rauter, Natalie and Ostwald, Richard and Lammering, Rolf}}, issn = {{0178-7675}}, journal = {{Computational Mechanics}}, number = {{4}}, pages = {{1377--1406}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space}}}, doi = {{10.1007/s00466-024-02568-z}}, volume = {{75}}, year = {{2024}}, } @article{62770, abstract = {{AbstractThe open-source parameter identification tool ADAPT (A diversely applicable parameter identification Tool) is integrated with a machine learning-based approach for start value prediction in order to calibrate a Gurson–Tvergaard–Needleman (GTN) and a Lemaitre damage model. As representative example case-hardened steel 16MnCrS5 is elaborated. An artificial neural network (ANN) is initially trained by using load–displacement curves derived from simulations of a boundary value problem—instead of using data generated for homogeneous states of deformation at material point or one-element level—with varying material parameter combinations. The ANN is then employed so as to predict sets of material parameters that already provide close solutions to the experiment. These predicted parameter sets serve as starting values for a subsequent multi-objective parameter identification by using ADAPT. ADAPT allows for the consideration of input data from multiple scales, including integral data such as load–displacement curves, full-field data such as displacement and strain fields, and high-resolution experimental void data at the micro-scale. The influence of each data set on prediction quality is analyzed. Using various types of input data introduces additional information, enhancing prediction accuracy. The validation is carried out with respect to experimental void measurements of forward rod extruded parts. The results demonstrate, by incorporating void measurements in the optimization process, that it is possible to improve the quantitative prediction of ductile damage in the sense of void area fractions by factor 28 in forward rod extrusion.}}, author = {{Gerlach, Jan and Schulte, Robin and Schowtjak, Alexander and Clausmeyer, Till and Ostwald, Richard and Tekkaya, A. Erman and Menzel, Andreas}}, issn = {{0939-1533}}, journal = {{Archive of Applied Mechanics}}, number = {{8}}, pages = {{2217--2242}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Enhancing damage prediction in bulk metal forming through machine learning-assisted parameter identification}}}, doi = {{10.1007/s00419-024-02634-1}}, volume = {{94}}, year = {{2024}}, } @article{62768, author = {{Najafi Koopas, Rasoul and Rezaei, Shahed and Rauter, Natalie and Ostwald, Richard and Lammering, Rolf}}, issn = {{0013-7944}}, journal = {{Engineering Fracture Mechanics}}, publisher = {{Elsevier BV}}, title = {{{A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: Efficient mapping of concrete microstructures to its fracture properties}}}, doi = {{10.1016/j.engfracmech.2024.110675}}, volume = {{314}}, year = {{2024}}, } @inproceedings{54589, author = {{Brennig, Katharina and Löhr, Bernd and Brock, Jonathan and Reineke, Malte Fabian and Bartelheimer, Christian}}, booktitle = {{Americas Conference on Information Systems (AMCIS)}}, title = {{{Maximizing the Impact of Process Mining Research: Four Strategic Guidelines}}}, year = {{2024}}, } @inproceedings{62796, author = {{Reineke, Malte Fabian and Bartelheimer, Christian}}, booktitle = {{AMCIS 2024 TREOs}}, location = {{Salt Lake City, Utah, USA}}, title = {{{Assessing the impact of organizational culture on workarounds: A maturity model}}}, year = {{2024}}, } @article{62670, author = {{André, Rémi F. and Brandt, Jessica and Schmidt, Johannes and López-Salas, Nieves and Odziomek, Mateusz and Antonietti, Markus}}, issn = {{0008-6223}}, journal = {{Carbon}}, publisher = {{Elsevier BV}}, title = {{{Inductively coupled plasma spectroscopy for heteroatom-doped carbonaceous materials: Limitations and acid choice for digestion}}}, doi = {{10.1016/j.carbon.2024.118946}}, volume = {{223}}, year = {{2024}}, } @inbook{57767, author = {{Pollmeier, Pascal and Stroop, Dietlinde and Fechner, Sabine}}, booktitle = {{Lehrkräftebildung in der digitalen Welt - Zukunftsorientierte Forschungs- und Praxisperspektiven}}, editor = {{Herzig, Bardo and Eickelmann, Birgit and Schwabl, Franszika and Schulze, Johanna and Niemann, Jan}}, pages = {{53--64}}, publisher = {{Waxmann}}, title = {{{Digitale Messwerterfassung im Chemieunterricht}}}, year = {{2024}}, }