@inproceedings{61991, author = {{Seidenberg, Tobias and Stöhr, Bernd and Eckertz, Daniel and Jagnow, Jan and Dumitrescu, Roman}}, booktitle = {{Proceedings of the 2024 9th International Conference on Information Systems Engineering}}, publisher = {{ACM}}, title = {{{HD Mapping as enabler for autonomous systems on airports}}}, doi = {{10.1145/3711954.3711967}}, year = {{2025}}, } @article{62283, author = {{Rezat, Sebastian and Doligkeit, Nadja}}, journal = {{mathematik lehren}}, number = {{252}}, pages = {{16–22}}, title = {{{Zahlenmuster und Brüche: Eine Lernumgebung zum algebraischen Denken}}}, year = {{2025}}, } @inproceedings{62301, author = {{Dreiling, Dmitrij and Itner, Dominik and Gravenkamp, Hauke and Birk, Carolin and Henning, Bernd}}, booktitle = {{2025 International Congress on Ultrasonics}}, pages = {{102–105}}, publisher = {{AMA Service GmbH}}, title = {{{A Measurement Setup for the Determination of Temperature-Dependent Viscoelastic Material Parameters Using an Ultrasonic Pulse-Echo Technique}}}, doi = {{10.5162/ultrasonic2025/a12-c5}}, year = {{2025}}, } @article{62643, author = {{Schwabe, Tobias and Kress, Christian and Kruse, Stephan and Weizel, Maxim and Rhee, Hanjo and Scheytt, J. Christoph}}, journal = {{Journal of Lightwave Technology}}, keywords = {{Integrated circuit modeling, Capacitance, Silicon, Modulation, Adaptation models, Semiconductor device modeling, Bandwidth, Data communication, electrooptical transmitter, equalization, free-carrier-plasma dispersion effect, modelling, optical modulator, phase shifter, silicon photonics}}, number = {{1}}, pages = {{255--270}}, title = {{{Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation in a 250 nm EPIC BiCMOS Technology}}}, doi = {{10.1109/JLT.2024.3450949}}, volume = {{43}}, year = {{2025}}, } @inproceedings{62642, author = {{Kruse, Stephan and Brockmeier, Jan and Schwengelbeck, Max and Schwabe, Tobias and Scheytt, J. Christoph}}, booktitle = {{2025 55th European Microwave Conference (EuMC)}}, keywords = {{Phased arrays, Optical fibers, Optical fiber sensors, Laser radar, Optical variables measurement, Apertures, Light emitting diodes, Optical receivers, Optical transmitters, Optical modulation, Lidar, light detection and ranging, FMCW, frequency modulated contentious wave, visible light sensing (VLS), visible light communication (VLC), automotive headlights, light emitting diode (LED), microwave photonics, wireless sensing}}, pages = {{602--605}}, title = {{{A Photonic Assisted Visible Light FMCW Lidar System for Large Aperture Phased Array MIMO Based on LEDs}}}, doi = {{10.23919/EuMC65286.2025.11235259}}, year = {{2025}}, } @article{62644, author = {{Schwabe, Tobias and Kress, Christian and Sadiye, Babak and Kruse, Stephan and Scheytt, J. Christoph}}, journal = {{IEEE Access}}, keywords = {{Optical attenuators, Equalizers, Phase shifters, Optical modulation, Electro-optic modulators, Optical amplifiers, Circuits, Silicon photonics, Optical saturation, Integrated circuit modeling, Data communication, equalization, electro-optical transmitter, silicon photonics, phase shifter, optical modulator, free-carrier plasma dispersion effect, driver architectures, biasing schemes}}, pages = {{192433--192450}}, title = {{{Analysis and Design of Forward Biased Silicon Photonics Phase Shifter Equalizer Circuits}}}, doi = {{10.1109/ACCESS.2025.3629385}}, volume = {{13}}, year = {{2025}}, } @inproceedings{62641, author = {{Kruse, Stephan and Diri, Jabil and Mager, Thomas and Kress, Christian and Scheytt, J. Christoph}}, booktitle = {{2025 55th European Microwave Conference (EuMC)}}, keywords = {{Optical fibers, Integrated optics, Semiconductor device measurement, Laser radar, Optical device fabrication, Photonic integrated circuits, Microwave theory and techniques, Optical fiber devices, Plastics, Substrates, Microwave photonics, photonic radar, optical LO distribution, mechatronic integrated device (MID)}}, pages = {{127--130}}, title = {{{Electrooptical Integration of an Electronic Photonic Integrated Circuit Into Plastic Substrates Using Mid-Technology}}}, doi = {{10.23919/EuMC65286.2025.11235121}}, year = {{2025}}, } @misc{62638, author = {{Kruse, Stephan and Meinecke, Marc-Michael and Gisder, Thomas and Simoni, Renato and González-Huici, Maria Antonia and Greiff, Christian and Mateos-Nunez, David and Danklmayer, Andreas and Ladan, Julien and Kurz, Heiko Gustav}}, title = {{{Antennenvorrichtung zum Aussenden von elektromagnetischer Strahlung für ein Kraftfahrzeug sowie Kraftfahrzeug}}}, year = {{2025}}, } @misc{62639, author = {{Kruse, Stephan and Silberhorn, Christine and Brecht, Benjamin and Schwabe, Tobias}}, title = {{{Optisch basierter Digital-Analog-Umsetzer}}}, year = {{2025}}, } @inbook{62645, author = {{Häsel-Weide, Uta and Nührenbörger, Marcus}}, booktitle = {{Handbuch Lehrerinnen- und Lehrerbildung}}, editor = {{Cramer, C. and König, J. and Rothland, M.}}, pages = {{549--555}}, publisher = {{Klinkhardt}}, title = {{{ Mathematik (Primarstufe) in der Lehrerinnen- und Lehrerbildung. Qualifizierung für das Lehren von Mathematik in der Grundschule}}}, doi = {{10.35468/hblb2025-070}}, year = {{2025}}, } @inproceedings{62163, abstract = {{Zero-shot classifiers based on Contrastive Language-Audio Pretraining (CLAP) models enable classification of given audio into classes defined at test time using text. These models are costly to run with respect to computation and memory requirements. In this work, we propose to build a specialized low-resource classifier for classes pre-defined using text, using a two-stage procedure consisting of zero-shot data set pruning and model compression. First, relevant in-domain data is selected from a source dataset using class label embeddings obtained from a pre-trained CLAP model. This data is then used to distill the audio encoder of a CLAP model. The proposed compression method produces compact audio encoders with slightly reduced accuracy. Note that neither labeled nor unlabeled in-domain audio data is required for its development. We verify by cross-dataset tests that the resulting classifiers are indeed specialized to their task.}}, author = {{Werning, Alexander and Häb-Umbach, Reinhold}}, booktitle = {{Proceedings of the 16th ITG Conference on Speech Communication}}, editor = {{Möller, Sebastian and Gerkmann, Timo and Kolossa, Dorothea}}, location = {{Berlin}}, pages = {{76--80}}, title = {{{A Fully Zero-Shot Approach to Obtaining Specialized and Compact Audio Tagging Models}}}, year = {{2025}}, } @inproceedings{59900, abstract = {{Running state-of-the-art large-scale audio models on edge devices is often infeasible due to their limited storage and computing resources. It is therefore necessary to compress and tune the models for the specific target task and hardware. This is commonly achieved by distilling the audio model, the teacher, to a small target model, the student. However, this approach can be improved by prepending a dataset pruning stage and training the teacher on the pruned data set only, which contains examples relevant to the target task. Recently, CLAP models have emerged that embed audio and text examples in a common embedding space. We use the audio embeddings of the CLAP model for the above pruning stage, which is realized using a domain classifier. After knowledge distillation, the student is eventually fine-tuned on some data from the target domain. The CLAP architecture combines text and audio embedding spaces, which allows to search for data given only a textual description, such as a class label. We show how this can help data pruning.}}, author = {{Werning, Alexander and Häb-Umbach, Reinhold}}, booktitle = {{Proceedings of DAS|DAGA 2025}}, location = {{Copenhagen}}, title = {{{Distilling Efficient Audio Models using Data Pruning with CLAP}}}, year = {{2025}}, } @inbook{62701, abstract = {{Learning continuous vector representations for knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL firstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A final OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can effectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.}}, author = {{Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032060655}}, issn = {{0302-9743}}, keywords = {{Decision Tree, OWL Class Expression Learning, Description Logic, Knowledge Graph, Large Language Model, Verbalizer}}, location = {{Porto, Portugal}}, publisher = {{Springer Nature Switzerland}}, title = {{{Tree-Based OWL Class Expression Learner over Large Graphs}}}, doi = {{10.1007/978-3-032-06066-2_29}}, year = {{2025}}, } @inproceedings{62707, author = {{Heindorf, Stefan and Neib, Daniel}}, booktitle = {{Proceedings of the 34th ACM International Conference on Information and Knowledge Management}}, publisher = {{ACM}}, title = {{{Assessing Natural Language Explanations of Relational Graph Neural Networks}}}, doi = {{10.1145/3746252.3760918}}, year = {{2025}}, } @inproceedings{61041, abstract = {{Large Language Models (LLMs) are increasingly deployed in real-world applications that require access to up-to-date knowledge. However, retraining LLMs is computationally expensive. Therefore, knowledge editing techniques are crucial for maintaining current information and correcting erroneous assertions within pre-trained models. Current benchmarks for knowledge editing primarily focus on recalling edited facts, often neglecting their logical consequences. To address this limitation, we introduce a new benchmark designed to evaluate how knowledge editing methods handle the logical consequences of a single fact edit. Our benchmark extracts relevant logical rules from a knowledge graph for a given edit. Then, it generates multi-hop questions based on these rules to assess the impact on logical consequences. Our findings indicate that while existing knowledge editing approaches can accurately insert direct assertions into LLMs, they frequently fail to inject entailed knowledge. Specifically, experiments with popular methods like ROME and FT reveal a substantial performance gap, up to 24%, between evaluations on directly edited knowledge and on entailed knowledge. This highlights the critical need for semantics-aware evaluation frameworks in knowledge editing.}}, author = {{Moteu Ngoli, Tatiana and Kouagou, N'Dah Jean and Zahera, Hamada Mohamed Abdelsamee and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of the 24th International Semantic Web Conference (ISWC 2025)}}, isbn = {{978-3-032-09530-5}}, keywords = {{dice sailproject moteu kouagou zahera ngonga}}, location = {{Nara, Japan}}, pages = {{pp 41--56}}, publisher = {{Springer, Cham}}, title = {{{Benchmarking Knowledge Editing using Logical Rules}}}, doi = {{https://doi.org/10.1007/978-3-032-09530-5_3}}, year = {{2025}}, } @article{62731, author = {{Meschede, Henning and Knorr, Lukas and Piacentino, Antonio and Markovska, Natasa and Duic, Neven}}, issn = {{0360-5442}}, journal = {{Energy}}, publisher = {{Elsevier BV}}, title = {{{Integrated and demand-responsive energy futures in the context of sustainable development of energy systems}}}, doi = {{10.1016/j.energy.2025.139453}}, year = {{2025}}, } @phdthesis{62750, abstract = {{Diese Dissertation enthält Beiträge zum Bereich der Mehrzieloptimierung mit einem Fokus auf unbeschränkten Problemen, die auf einem allgemeinen Hilbertraum definiert sind. Für Mehrzieloptimierungsprobleme mit lokal Lipschitz-stetigen Zielfunktionen definieren wir ein multikriterielles Subdifferential, das wir erstmals im Kontext allgemeiner Hilberträume analysieren. Aufbauend auf diesen theoretischen Untersuchungen präsentieren wir ein Abstiegsverfahren, bei welchem in jeder Iteration eine Abstiegsrichtung mittels einer numerischen Approximation des multikriteriellen Subdifferentials bestimmt wird. Im Kontext konvexer, stetig differenzierbarer Zielfunktionen mit Lipschitz-stetigen Gradienten, führen wir eine Familie von dynamischen Gradientensystemen mit Trägheitsterm ein, die bekannte kontinuierliche Systeme aus der skalaren Optimierung verallgemeinern. Wir stellen drei neue Systeme vor: eines mit konstanter Dämpfung, eines mit asymptotisch abnehmender Dämpfung und eines, das zusätzlich eine zeitabhängige Tikhonov-Regularisierung beinhaltet. Aufbauend auf den Untersuchungen der neuen dynamischen Gradientensysteme, entwickeln wir ein beschleunigtes Gradientenverfahren zur Mehrzieloptimierung, das auf einer Diskretisierung des multikriteriellen Gradientensystems mit asymptotisch abnehmender Dämpfung beruht. Das hergeleitete Verfahren bewahrt die günstigen Konvergenzeigenschaften des kontinuierlichen Systems und erreicht eine schnellere Konvergenz als klassische Verfahren.}}, author = {{Sonntag, Konstantin}}, publisher = {{Paderborn University}}, title = {{{First-order methods and gradient dynamical systems for multiobjective optimization}}}, doi = {{10.17619/UNIPB/1-2457}}, year = {{2025}}, } @inproceedings{62007, abstract = {{Ensemble methods are widely employed to improve generalization in machine learning. This has also prompted the adoption of ensemble learning for the knowledge graph embedding (KGE) models in performing link prediction. Typical approaches to this end train multiple models as part of the ensemble, and the diverse predictions are then averaged. However, this approach has some significant drawbacks. For instance, the computational overhead of training multiple models increases latency and memory overhead. In contrast, model merging approaches offer a promising alternative that does not require training multiple models. In this work, we introduce model merging, specifically weighted averaging, in KGE models. Herein, a running average of model parameters from a training epoch onward is maintained and used for predictions. To address this, we additionally propose an approach that selectively updates the running average of the ensemble model parameters only when the generalization performance improves on a validation dataset. We evaluate these two different weighted averaging approaches on link prediction tasks, comparing the state-of-the-art benchmark ensemble approach. Additionally, we evaluate the weighted averaging approach considering literal-augmented KGE models and multi-hop query answering tasks as well. The results demonstrate that the proposed weighted averaging approach consistently improves performance across diverse evaluation settings.}}, author = {{Sapkota, Rupesh and Demir, Caglar and Sharma, Arnab and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025)}}, keywords = {{Knowledge Graphs, Embeddings, Ensemble Learning}}, location = {{Dayton, OH, USA}}, publisher = {{ACM}}, title = {{{Parameter Averaging in Link Prediction}}}, doi = {{https://doi.org/10.1145/3731443.3771365}}, year = {{2025}}, } @article{62973, abstract = {{Large Language Models (LLMs) are increasingly being explored for their potential in software engineering, particularly in static analysis tasks. In this study, we investigate the potential of current LLMs to enhance call-graph analysis and type inference for Python and JavaScript programs. We empirically evaluated 24 LLMs, including OpenAI's GPT series and open-source models like LLaMA and Mistral, using existing and newly developed benchmarks. Specifically, we enhanced TypeEvalPy, a micro-benchmarking framework for type inference in Python, with auto-generation capabilities, expanding its scope from 860 to 77,268 type annotations for Python. Additionally, we introduced SWARM-CG and SWARM-JS, comprehensive benchmarking suites for evaluating call-graph construction tools across multiple programming languages. Our findings reveal a contrasting performance of LLMs in static analysis tasks. For call-graph generation, traditional static analysis tools such as PyCG for Python and Jelly for JavaScript consistently outperform LLMs. While advanced models like mistral-large-it-2407-123b and gpt-4o show promise, they still struggle with completeness and soundness in call-graph analysis across both languages. In contrast, LLMs demonstrate a clear advantage in type inference for Python, surpassing traditional tools like HeaderGen and hybrid approaches such as HiTyper. These results suggest that, while LLMs hold promise in type inference, their limitations in call-graph analysis highlight the need for further research. Our study provides a foundation for integrating LLMs into static analysis workflows, offering insights into their strengths and current limitations.}}, author = {{Shivarpatna Venkatesh, Ashwin Prasad and Sunil, Rose and Sabu, Samkutty and Mir, Amir M. and Reis, Sofia and Bodden, Eric}}, journal = {{Empirical Software Engineering}}, number = {{6}}, publisher = {{Springer}}, title = {{{An Empirical Study of Large Language Models for Type and Call Graph Analysis in Python and JavaScript}}}, doi = {{10.48550/ARXIV.2410.00603}}, volume = {{30}}, year = {{2025}}, } @article{62980, abstract = {{We introduce a new classification of multimode states with a fixed number of photons. This classification is based on the factorizability of homogeneous multivariate polynomials and is invariant under unitary transformations. The classes physically correspond to field excitations in terms of single and multiple photons, each of which is in an arbitrary irreducible superposition of quantized modes. We further show how the transitions between classes are rendered possible by photon addition, photon subtraction, and photon-projection nonlinearities. We explicitly put forward a design for a multilayer interferometer in which the states for different classes can be generated with state-of-the-art experimental techniques. Limitations of the proposed designs are analyzed using the introduced classification, providing a benchmark for the robustness of certain states and classes.}}, author = {{Kopylov, Denis A. and Offen, Christian and Ares, Laura and Wembe Moafo, Boris Edgar and Ober-Blöbaum, Sina and Meier, Torsten and Sharapova, Polina R. and Sperling, Jan}}, issn = {{2643-1564}}, journal = {{Physical Review Research}}, number = {{3}}, publisher = {{American Physical Society (APS)}}, title = {{{Multiphoton, multimode state classification for nonlinear optical circuits}}}, doi = {{10.1103/sv6z-v1gk}}, volume = {{7}}, year = {{2025}}, }