@inbook{62067, abstract = {{Most FPGA boards in the HPC domain are well-suited for parallel scaling because of the direct integration of versatile and high-throughput network ports. However, the utilization of their network capabilities is often challenging and error-prone because the whole network stack and communication patterns have to be implemented and managed on the FPGAs. Also, this approach conceptually involves a trade-off between the performance potential of improved communication and the impact of resource consumption for communication infrastructure, since the utilized resources on the FPGAs could otherwise be used for computations. In this work, we investigate this trade-off, firstly, by using synthetic benchmarks to evaluate the different configuration options of the communication framework ACCL and their impact on communication latency and throughput. Finally, we use our findings to implement a shallow water simulation whose scalability heavily depends on low-latency communication. With a suitable configuration of ACCL, good scaling behavior can be shown to all 48 FPGAs installed in the system. Overall, the results show that the availability of inter-FPGA communication frameworks as well as the configurability of framework and network stack are crucial to achieve the best application performance with low latency communication.}}, author = {{Meyer, Marius and Kenter, Tobias and Petrica, Lucian and O’Brien, Kenneth and Blott, Michaela and Plessl, Christian}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031697654}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Optimizing Communication for Latency Sensitive HPC Applications on up to 48 FPGAs Using ACCL}}}, doi = {{10.1007/978-3-031-69766-1_9}}, year = {{2024}}, } @article{56604, abstract = {{This manuscript makes the claim of having computed the 9th Dedekind number, D(9). This was done by accelerating the core operation of the process with an efficient FPGA design that outperforms an optimized 64-core CPU reference by 95x. The FPGA execution was parallelized on the Noctua 2 supercomputer at Paderborn University. The resulting value for D(9) is 286386577668298411128469151667598498812366. This value can be verified in two steps. We have made the data file containing the 490 M results available, each of which can be verified separately on CPU, and the whole file sums to our proposed value. The paper explains the mathematical approach in the first part, before putting the focus on a deep dive into the FPGA accelerator implementation followed by a performance analysis. The FPGA implementation was done in Register-Transfer Level using a dual-clock architecture and shows how we achieved an impressive FMax of 450 MHz on the targeted Stratix 10 GX 2,800 FPGAs. The total compute time used was 47,000 FPGA hours.}}, author = {{Van Hirtum, Lennart and De Causmaecker, Patrick and Goemaere, Jens and Kenter, Tobias and Riebler, Heinrich and Lass, Michael and Plessl, Christian}}, issn = {{1936-7406}}, journal = {{ACM Transactions on Reconfigurable Technology and Systems}}, number = {{3}}, pages = {{1--28}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{A Computation of the Ninth Dedekind Number Using FPGA Supercomputing}}}, doi = {{10.1145/3674147}}, volume = {{17}}, year = {{2024}}, } @inproceedings{53503, author = {{Olgu, Kaan and Kenter, Tobias and Nunez-Yanez, Jose and Mcintosh-Smith, Simon}}, booktitle = {{Proceedings of the 12th International Workshop on OpenCL and SYCL}}, publisher = {{ACM}}, title = {{{Optimisation and Evaluation of Breadth First Search with oneAPI/SYCL on Intel FPGAs: from Describing Algorithms to Describing Architectures}}}, doi = {{10.1145/3648115.3648134}}, year = {{2024}}, } @inbook{57834, author = {{Vernholz, Mats}}, booktitle = {{Jahrbuch der berufs- und wirtschaftspädagogischen Forschung 2024}}, editor = {{Kögler, Kristina and Kremer, H.-Hugo and Herkner, Volkmar}}, pages = {{132--147}}, publisher = {{Verlag Barbara Budrich}}, title = {{{Gewerblich-technische Lehrkräftebildung in Deutschland - Analyse der Einflüsse auf das akademische Selbstkonzept von Lehramtsstudierenden technischer (beruflicher) Fachrichtungen}}}, doi = {{10.3224/84743054}}, year = {{2024}}, } @article{54690, abstract = {{In basketball, an attacking player often plays a pass to one side while looking to the other side. This head fake provokes a conflict in the observing opponent, as the processing of the head orientation interferes with the processing of the pass direction. Accordingly, responses to passes with head fakes are slower and result in more errors than responses to passes without head fakes (head-fake effect). The head-fake effect and structurally similar interference effects (e.g., Stroop effect) are modulated by the frequency of conflicting trials. Previous studies mostly applied a block-wise manipulation of proportion congruency. However, in basketball (and also in other team sports), where different individual opponents can be encountered, it might be important to take the individual frequency (e.g., 20% vs. 80%) of these opponents into account. Therefore, the present study investigates the possibility to quickly (i.e., on a trial-by-trial basis) reconfigure the response behavior to different proportions of incongruent trials, which are contingent on different basketball players. Results point out that participants indeed adapted to the fake-frequency of different basketball players, which could be the result of strategic adaptation processes. Multi-level analyses, however, indicate that a substantial portion of the player-specific adaptation to fake frequencies is accounted by episodic retrieval processes, suggesting that item-specific proportion congruency effects can be explained in terms of stimulus-response binding and retrieval: The head orientation (e.g., to the right) of a current stimulus retrieves the last episode with the same head orientation including the response that was part of this last episode. Thus, from a theoretical perspective, an attacking player would provoke the strongest detrimental effect on an opponent if s/he repeats the same head movement but changes the direction of the pass. Whether it is at all possible to strategically apply this recommendation in practice needs still to be answered.}}, author = {{Güldenpenning, Iris and Böer, Nils Tobias and Kunde, Wilfried and Giesen, Carina G. and Rothermund, Klaus and Weigelt, Matthias}}, journal = {{Psychological Research}}, pages = {{1702--1711}}, title = {{{Context-specific adaptation for head fakes in basketball: a study on player-specific fake-frequency schedules}}}, doi = {{10.1007/s00426-024-01977-2}}, volume = {{88}}, year = {{2024}}, } @article{57311, author = {{Yang, Keke and Sowada, Matthias and Olfert, Viktoria and Seitz, Georg and Schreiber, Vincent and Heitmann, Marcel and Hein, David and Biegler, Max and Jüttner, Sven and Rethmeier, Michael and Meschut, Gerson}}, issn = {{2238-7854}}, journal = {{Journal of Materials Research and Technology}}, publisher = {{Elsevier BV}}, title = {{{Influence of liquid metal embrittlement on the failure behavior of dissimilar spot welds with advanced high-strength steel: A component study}}}, doi = {{10.1016/j.jmrt.2024.11.166}}, year = {{2024}}, } @inproceedings{56670, abstract = {{Systems Engineering is becoming increasingly important in the engineering of complex technical systems. Its introduction is forcing companies to undertake major transformation initiatives. As established change management approaches show, the corporate culture is an important key criterion for success of transformation. Therefore, when introducing Systems Engineering into an organization, transformation initiatives must be tailored to an existing corporate culture or the corporate culture itself must be changed in order to enable Systems Engineering. In literature and in industrial practice, different approaches for assessment of corporate culture exist. Within this research, a systematic literature review on methods and models for corporate culture assessment is conducted. Core elements are collected and combined with the fundamentals and success factors of Systems Engineering to develop a model for corporate culture assessment. The developed model is applied to the industrial practice of an ongoing Systems Engineering transformation of a large car manufacturer. The results of the assessment are compared with the emerging project challenges. Based on this model and its supporting tool and templates, organizations and transformation leaders are enabled to rapidly obtain an orientation of hindering or supporting currently established cultural aspects with regard to Systems Engineering transformation and to provide a decision basis for further measures.}}, author = {{Graessler, Iris and Grewe, Benedikt}}, booktitle = {{AHFE International}}, issn = {{2771-0718}}, publisher = {{AHFE International}}, title = {{{Importance of cultural change in Systems Engineering Transformation: A model for cultural assessment}}}, doi = {{10.54941/ahfe1005551}}, volume = {{158}}, year = {{2024}}, } @inproceedings{56346, author = {{Gräßler, Iris and Özcan, Deniz}}, booktitle = {{AHFE International}}, location = {{Split}}, publisher = {{AHFE International}}, title = {{{Quality Key Figures for Developing Future Scenarios}}}, doi = {{10.54941/ahfe1005553}}, volume = {{158}}, year = {{2024}}, } @article{62255, abstract = {{AbstractCellular stress and ageing involve an increase in crowding and aggregation of amylogenic proteins. We here investigate if crowding is the intrinsic cause of aggregation and utilise a previously established non-protein aggregation sensor, namely pseudoisocyanine chloride (PIC). PIC shows fibrillization in cells into a highly fluorescent J-aggregated state and is sensitive to crowding. Surprisingly, cell stress conditions stabilise the monomeric rather than the aggregated state of PIC both in the cytoplasm and in stress granules. Regarding the different physiochemical changes of the cytoplasm occurring upon cell stress, involving volume reduction, phase separation and solidification, the intrinsic crowding effect is not the key factor to drive associated self-assembly processes.}}, author = {{Pollak, Roland and Koch, Leon and König, Benedikt and Ribeiro, Sara S. and Samanta, Nirnay and Huber, Klaus and Ebbinghaus, Simon}}, issn = {{2399-3669}}, journal = {{Communications Chemistry}}, number = {{1}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Cell stress and phase separation stabilize the monomeric state of pseudoisocyanine chloride employed as a self-assembly crowding sensor}}}, doi = {{10.1038/s42004-024-01315-y}}, volume = {{7}}, year = {{2024}}, } @article{62252, author = {{Alfano, Caterina and Fichou, Yann and Huber, Klaus and Weiss, Matthias and Spruijt, Evan and Ebbinghaus, Simon and De Luca, Giuseppe and Morando, Maria Agnese and Vetri, Valeria and Temussi, Piero Andrea and Pastore, Annalisa}}, issn = {{0009-2665}}, journal = {{Chemical Reviews}}, number = {{6}}, pages = {{3186--3219}}, publisher = {{American Chemical Society (ACS)}}, title = {{{Molecular Crowding: The History and Development of a Scientific Paradigm}}}, doi = {{10.1021/acs.chemrev.3c00615}}, volume = {{124}}, year = {{2024}}, } @article{62251, author = {{Müller, Wenke and Sroka, Weronika and Schweins, Ralf and Nöcker, Bernd and Poon, Jia-Fei and Huber, Klaus}}, issn = {{0743-7463}}, journal = {{Langmuir}}, number = {{17}}, pages = {{8872--8885}}, publisher = {{American Chemical Society (ACS)}}, title = {{{Impact of Additive Hydrophilicity on Mixed Dye-Nonionic Surfactant Micelles: Micelle Morphology and Dye Localization}}}, doi = {{10.1021/acs.langmuir.4c00012}}, volume = {{40}}, year = {{2024}}, } @article{62250, author = {{Saha, Sanjib and Büngeler, Anne and Hense, Dominik and Strube, Oliver I. and Huber, Klaus}}, issn = {{0743-7463}}, journal = {{Langmuir}}, number = {{8}}, pages = {{4152--4163}}, publisher = {{American Chemical Society (ACS)}}, title = {{{On the Mechanism of Self-Assembly of Fibrinogen in Thrombin-free Aqueous Solution}}}, doi = {{10.1021/acs.langmuir.3c03132}}, volume = {{40}}, year = {{2024}}, } @article{62254, author = {{Koch, Leon and Saha, Sanjib and Huber, Klaus}}, issn = {{1948-7185}}, journal = {{The Journal of Physical Chemistry Letters}}, number = {{39}}, pages = {{9987--9993}}, publisher = {{American Chemical Society (ACS)}}, title = {{{Impact of Temperature on the Self-Assembly of Fibrinogen in Thrombin-Free Solutions}}}, doi = {{10.1021/acs.jpclett.4c02180}}, volume = {{15}}, year = {{2024}}, } @article{62253, author = {{Koch, Leon and Pollak, Roland and Ebbinghaus, Simon and Huber, Klaus}}, issn = {{0743-7463}}, journal = {{Langmuir}}, number = {{31}}, pages = {{16151--16159}}, publisher = {{American Chemical Society (ACS)}}, title = {{{Early Stages of FUS Droplet Formation via Liquid–Liquid Phase Separation}}}, doi = {{10.1021/acs.langmuir.4c01243}}, volume = {{40}}, year = {{2024}}, } @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}}, } @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{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}}, } @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}}, }