--- _id: '64979' abstract: - lang: eng text: We investigate homogeneous coupled cell systems with high-dimensional internal dynamics. In many studies on network dynamics, the analysis is restricted to networks with one-dimensional internal dynamics. Here, we show how symmetry explains the relation between dynamical behavior of systems with one-dimensional internal dynamics and with higher dimensional internal dynamics, when the underlying network topology is the same. Fundamental networks of homogeneous coupled cell systems (B. Rink, J. Sanders. Coupled Cell Networks and Their Hidden Symmetries. SIAM J. Math. Anal. 46.2 (2014)) can be expressed in terms of monoid representations, which uniquely decompose into indecomposable subrepresentations. In the high-dimensional internal dynamics case, these subrepresentations are isomorphic to multiple copies of those one computes in the one-dimensional internal dynamics case. This has interesting implications for possible center subspaces in bifurcation analysis. We describe the effect on steady state and Hopf bifurcations in l-parameter families of network vector fields. The main results in that regard are that (1) generic one-parameter steady state bifurcations are qualitatively independent of the dimension of the internal dynamics and that, (2) in order to observe all generic l-parameter bifurcations that may occur for internal dynamics of any dimension, the internal dynamics has to be at least l-dimensional for steady state bifurcations and 2l-dimensional for Hopf bifurcations. Furthermore, we illustrate how additional structure in the network can be exploited to obtain even greater understanding of bifurcation scenarios in the high-dimensional case beyond qualitative statements about the collective dynamics. One-parameter steady state bifurcations in feedforward networks exhibit an unusual amplification in the asymptotic growth rates of individual cells, when these are one-dimensional (S. von der Gracht, E. Nijholt, B. Rink. Amplified steady state bifurcations in feedforward networks. Nonlinearity 35.4 (2022)). As another main result, we prove that (3) the same cells exhibit this amplifying effect with the same growth rates when the internal dynamics is high-dimensional. article_number: '118196' article_type: original author: - first_name: Sören full_name: von der Gracht, Sören id: '97359' last_name: von der Gracht orcid: 0000-0002-8054-2058 - first_name: Eddie full_name: Nijholt, Eddie last_name: Nijholt - first_name: Bob full_name: Rink, Bob last_name: Rink citation: ama: von der Gracht S, Nijholt E, Rink B. Homogeneous coupled cell systems with high-dimensional internal dynamics. Chaos, Solitons & Fractals. 2026;208. doi:10.1016/j.chaos.2026.118196 apa: von der Gracht, S., Nijholt, E., & Rink, B. (2026). Homogeneous coupled cell systems with high-dimensional internal dynamics. Chaos, Solitons & Fractals, 208, Article 118196. https://doi.org/10.1016/j.chaos.2026.118196 bibtex: '@article{von der Gracht_Nijholt_Rink_2026, title={Homogeneous coupled cell systems with high-dimensional internal dynamics}, volume={208}, DOI={10.1016/j.chaos.2026.118196}, number={118196}, journal={Chaos, Solitons & Fractals}, publisher={Elsevier BV}, author={von der Gracht, Sören and Nijholt, Eddie and Rink, Bob}, year={2026} }' chicago: Gracht, Sören von der, Eddie Nijholt, and Bob Rink. “Homogeneous Coupled Cell Systems with High-Dimensional Internal Dynamics.” Chaos, Solitons & Fractals 208 (2026). https://doi.org/10.1016/j.chaos.2026.118196. ieee: 'S. von der Gracht, E. Nijholt, and B. Rink, “Homogeneous coupled cell systems with high-dimensional internal dynamics,” Chaos, Solitons & Fractals, vol. 208, Art. no. 118196, 2026, doi: 10.1016/j.chaos.2026.118196.' mla: von der Gracht, Sören, et al. “Homogeneous Coupled Cell Systems with High-Dimensional Internal Dynamics.” Chaos, Solitons & Fractals, vol. 208, 118196, Elsevier BV, 2026, doi:10.1016/j.chaos.2026.118196. short: S. von der Gracht, E. Nijholt, B. Rink, Chaos, Solitons & Fractals 208 (2026). date_created: 2026-03-16T08:39:07Z date_updated: 2026-03-16T08:42:56Z ddc: - '510' department: - _id: '101' - _id: '841' doi: 10.1016/j.chaos.2026.118196 external_id: arxiv: - '2510.06740' file: - access_level: closed content_type: application/pdf creator: svdg date_created: 2026-03-16T08:40:04Z date_updated: 2026-03-16T08:40:04Z file_id: '64980' file_name: homogeneous-coupled-cell-systems-with-high-dimensional-internal-dynamics.pdf file_size: 1951746 relation: main_file success: 1 file_date_updated: 2026-03-16T08:40:04Z has_accepted_license: '1' intvolume: ' 208' keyword: - Coupled cell systems - Network dynamics - Dimension reduction - Bifurcation theory - Symmetry - Monoid representation theory language: - iso: eng publication: Chaos, Solitons & Fractals publication_identifier: issn: - 0960-0779 publication_status: published publisher: Elsevier BV status: public title: Homogeneous coupled cell systems with high-dimensional internal dynamics type: journal_article user_id: '97359' volume: 208 year: '2026' ... --- _id: '63498' author: - first_name: Wilhelm full_name: Kirchgässner, Wilhelm last_name: Kirchgässner - first_name: Nikolas full_name: Förster, Nikolas last_name: Förster - first_name: Till full_name: Piepenbrock, Till last_name: Piepenbrock - first_name: Oliver full_name: Schweins, Oliver last_name: Schweins - first_name: Oliver full_name: Wallscheid, Oliver last_name: Wallscheid citation: ama: 'Kirchgässner W, Förster N, Piepenbrock T, Schweins O, Wallscheid O. HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. IEEE Transactions on Power Electronics. 2025;40(2):3326-3335. doi:10.1109/TPEL.2024.3488174' apa: 'Kirchgässner, W., Förster, N., Piepenbrock, T., Schweins, O., & Wallscheid, O. (2025). HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. IEEE Transactions on Power Electronics, 40(2), 3326–3335. https://doi.org/10.1109/TPEL.2024.3488174' bibtex: '@article{Kirchgässner_Förster_Piepenbrock_Schweins_Wallscheid_2025, title={HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores}, volume={40}, DOI={10.1109/TPEL.2024.3488174}, number={2}, journal={IEEE Transactions on Power Electronics}, author={Kirchgässner, Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid, Oliver}, year={2025}, pages={3326–3335} }' chicago: 'Kirchgässner, Wilhelm, Nikolas Förster, Till Piepenbrock, Oliver Schweins, and Oliver Wallscheid. “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores.” IEEE Transactions on Power Electronics 40, no. 2 (2025): 3326–35. https://doi.org/10.1109/TPEL.2024.3488174.' ieee: 'W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, and O. Wallscheid, “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores,” IEEE Transactions on Power Electronics, vol. 40, no. 2, pp. 3326–3335, 2025, doi: 10.1109/TPEL.2024.3488174.' mla: 'Kirchgässner, Wilhelm, et al. “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores.” IEEE Transactions on Power Electronics, vol. 40, no. 2, 2025, pp. 3326–35, doi:10.1109/TPEL.2024.3488174.' short: W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, O. Wallscheid, IEEE Transactions on Power Electronics 40 (2025) 3326–3335. date_created: 2026-01-06T08:07:13Z date_updated: 2026-01-06T08:08:01Z department: - _id: '52' doi: 10.1109/TPEL.2024.3488174 intvolume: ' 40' issue: '2' keyword: - Mathematical models - Estimation - Data models - Convolutional neural networks - Accuracy - Magnetic hysteresis - Magnetic cores - Temperature measurement - Magnetic domains - Temperature distribution - Convolutional neural network (CNN) - machine learning (ML) - magnetics page: 3326-3335 publication: IEEE Transactions on Power Electronics status: public title: 'HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores' type: journal_article user_id: '83383' volume: 40 year: '2025' ... --- _id: '59171' abstract: - lang: eng text: To model dynamical systems on networks with higher-order (non-pairwise) interactions, we recently introduced a new class of ordinary differential equations (ODEs) on hypernetworks. Here, we consider one-parameter synchrony breaking bifurcations in such ODEs. We call a synchrony breaking steady-state branch ‘reluctant’ if it is tangent to a synchrony space, but does not lie inside it. We prove that reluctant synchrony breaking is ubiquitous in hypernetwork systems, by constructing a large class of examples that support it. We also give an explicit formula for the order of tangency to the synchrony space of a reluctant steady-state branch. author: - first_name: Sören full_name: von der Gracht, Sören id: '97359' last_name: von der Gracht orcid: 0000-0002-8054-2058 - first_name: Eddie full_name: Nijholt, Eddie last_name: Nijholt - first_name: Bob full_name: Rink, Bob last_name: Rink citation: ama: 'von der Gracht S, Nijholt E, Rink B. Higher-order interactions lead to ‘reluctant’ synchrony breaking. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2024;480(2301). doi:10.1098/rspa.2023.0945' apa: 'von der Gracht, S., Nijholt, E., & Rink, B. (2024). Higher-order interactions lead to ‘reluctant’ synchrony breaking. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 480(2301). https://doi.org/10.1098/rspa.2023.0945' bibtex: '@article{von der Gracht_Nijholt_Rink_2024, title={Higher-order interactions lead to ‘reluctant’ synchrony breaking}, volume={480}, DOI={10.1098/rspa.2023.0945}, number={2301}, journal={Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences}, publisher={The Royal Society}, author={von der Gracht, Sören and Nijholt, Eddie and Rink, Bob}, year={2024} }' chicago: 'Gracht, Sören von der, Eddie Nijholt, and Bob Rink. “Higher-Order Interactions Lead to ‘Reluctant’ Synchrony Breaking.” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 480, no. 2301 (2024). https://doi.org/10.1098/rspa.2023.0945.' ieee: 'S. von der Gracht, E. Nijholt, and B. Rink, “Higher-order interactions lead to ‘reluctant’ synchrony breaking,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 480, no. 2301, 2024, doi: 10.1098/rspa.2023.0945.' mla: 'von der Gracht, Sören, et al. “Higher-Order Interactions Lead to ‘Reluctant’ Synchrony Breaking.” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 480, no. 2301, The Royal Society, 2024, doi:10.1098/rspa.2023.0945.' short: 'S. von der Gracht, E. Nijholt, B. Rink, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 480 (2024).' date_created: 2025-03-27T10:15:06Z date_updated: 2025-03-27T10:19:56Z ddc: - '510' department: - _id: '101' doi: 10.1098/rspa.2023.0945 file: - access_level: open_access content_type: application/pdf creator: svdg date_created: 2025-03-27T10:16:20Z date_updated: 2025-03-27T10:19:48Z file_id: '59172' file_name: higher-order-interactions-lead-to-reluctant-synchrony-breaking.pdf file_size: 820435 relation: main_file file_date_updated: 2025-03-27T10:19:48Z has_accepted_license: '1' intvolume: ' 480' issue: '2301' keyword: - higher-order interactions - synchrony breaking - network dynamics - coupled cell systems language: - iso: eng oa: '1' publication: 'Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences' publication_identifier: issn: - 1364-5021 - 1471-2946 publication_status: published publisher: The Royal Society status: public title: Higher-order interactions lead to ‘reluctant’ synchrony breaking type: journal_article user_id: '97359' volume: 480 year: '2024' ... --- _id: '49109' abstract: - lang: eng text: "We propose a diarization system, that estimates “who spoke when” based on spatial information, to be used as a front-end of a meeting transcription system running on the signals gathered from an acoustic sensor network (ASN). Although the\r\nspatial distribution of the microphones is advantageous, exploiting the spatial diversity for diarization and signal enhancement is challenging, because the microphones’ positions are typically unknown, and the recorded signals are initially unsynchronized in general. Here, we approach these issues by first blindly synchronizing the signals and then estimating time differences of arrival (TDOAs). The TDOA information is exploited to estimate the speakers’ activity, even in the presence of multiple speakers being simultaneously active. This speaker activity information serves as a guide for a spatial mixture model, on which basis the individual speaker’s signals are extracted via beamforming. Finally, the extracted signals are forwarded to a speech recognizer. Additionally, a novel initialization scheme for spatial mixture models based on the TDOA estimates is proposed. Experiments conducted on real recordings from the LibriWASN data set have shown that our proposed system is advantageous compared to a system using a spatial mixture model, which does not make use\r\nof external diarization information." author: - first_name: Tobias full_name: Gburrek, Tobias id: '44006' last_name: Gburrek - first_name: Joerg full_name: Schmalenstroeer, Joerg id: '460' last_name: Schmalenstroeer - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Gburrek T, Schmalenstroeer J, Haeb-Umbach R. Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks. In: Proc. Asilomar Conference on Signals, Systems, and Computers. ; 2023.' apa: Gburrek, T., Schmalenstroeer, J., & Haeb-Umbach, R. (2023). Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks. Proc. Asilomar Conference on Signals, Systems, and Computers. 57th Asilomar Conference on Signals, Systems, and Computers. bibtex: '@inproceedings{Gburrek_Schmalenstroeer_Haeb-Umbach_2023, title={Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks}, booktitle={Proc. Asilomar Conference on Signals, Systems, and Computers}, author={Gburrek, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2023} }' chicago: Gburrek, Tobias, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks.” In Proc. Asilomar Conference on Signals, Systems, and Computers, 2023. ieee: T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks,” presented at the 57th Asilomar Conference on Signals, Systems, and Computers, 2023. mla: Gburrek, Tobias, et al. “Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks.” Proc. Asilomar Conference on Signals, Systems, and Computers, 2023. short: 'T. Gburrek, J. Schmalenstroeer, R. Haeb-Umbach, in: Proc. Asilomar Conference on Signals, Systems, and Computers, 2023.' conference: end_date: 2023-11-01 name: 57th Asilomar Conference on Signals, Systems, and Computers start_date: 2023-10-31 date_created: 2023-11-22T07:52:29Z date_updated: 2023-11-22T07:58:49Z ddc: - '004' department: - _id: '54' file: - access_level: open_access content_type: application/pdf creator: schmalen date_created: 2023-11-22T07:51:18Z date_updated: 2023-11-22T07:58:49Z file_id: '49110' file_name: asilomar.pdf file_size: 212317 relation: main_file file_date_updated: 2023-11-22T07:58:49Z has_accepted_license: '1' keyword: - Diarization - time difference of arrival - ad-hoc acoustic sensor network - meeting transcription language: - iso: eng oa: '1' publication: Proc. Asilomar Conference on Signals, Systems, and Computers quality_controlled: '1' status: public title: Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks type: conference user_id: '460' year: '2023' ... --- _id: '33734' abstract: - lang: eng text: '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: - first_name: N'Dah Jean full_name: KOUAGOU, N'Dah Jean id: '87189' last_name: KOUAGOU - first_name: Stefan full_name: Heindorf, Stefan id: '11871' last_name: Heindorf orcid: 0000-0002-4525-6865 - first_name: Caglar full_name: Demir, Caglar id: '43817' last_name: Demir - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille id: '65716' last_name: Ngonga Ngomo citation: ama: 'KOUAGOU NJ, Heindorf S, Demir C, Ngonga Ngomo A-C. Neural Class Expression Synthesis. In: Pesquita C, Jimenez-Ruiz E, McCusker J, et al., eds. The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023). Vol 13870. Springer International Publishing; 2023:209-226. doi:https://doi.org/10.1007/978-3-031-33455-9_13' apa: KOUAGOU, N. J., Heindorf, S., Demir, C., & Ngonga Ngomo, A.-C. (2023). Neural Class Expression Synthesis. In C. Pesquita, E. Jimenez-Ruiz, J. McCusker, D. Faria, M. Dragoni, A. Dimou, R. Troncy, & S. Hertling (Eds.), The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023) (Vol. 13870, pp. 209–226). Springer International Publishing. https://doi.org/10.1007/978-3-031-33455-9_13 bibtex: '@inproceedings{KOUAGOU_Heindorf_Demir_Ngonga Ngomo_2023, title={Neural Class Expression Synthesis}, volume={13870}, DOI={https://doi.org/10.1007/978-3-031-33455-9_13}, booktitle={The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)}, publisher={Springer International Publishing}, author={KOUAGOU, N’Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, 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}, year={2023}, pages={209–226} }' chicago: KOUAGOU, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Axel-Cyrille Ngonga Ngomo. “Neural Class Expression Synthesis.” In The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), edited by Catia Pesquita, Ernesto Jimenez-Ruiz, Jamie McCusker, Daniel Faria, Mauro Dragoni, Anastasia Dimou, Raphael Troncy, and Sven Hertling, 13870:209–26. Springer International Publishing, 2023. https://doi.org/10.1007/978-3-031-33455-9_13. ieee: 'N. J. KOUAGOU, S. Heindorf, C. Demir, and A.-C. Ngonga Ngomo, “Neural Class Expression Synthesis,” in The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), Hersonissos, Crete, Greece, 2023, vol. 13870, pp. 209–226, doi: https://doi.org/10.1007/978-3-031-33455-9_13.' mla: KOUAGOU, N’Dah Jean, et al. “Neural Class Expression Synthesis.” The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), edited by Catia Pesquita et al., vol. 13870, Springer International Publishing, 2023, pp. 209–26, doi:https://doi.org/10.1007/978-3-031-33455-9_13. short: 'N.J. KOUAGOU, S. Heindorf, C. Demir, A.-C. Ngonga Ngomo, in: C. Pesquita, E. Jimenez-Ruiz, J. McCusker, D. Faria, M. Dragoni, A. Dimou, R. Troncy, S. Hertling (Eds.), The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), Springer International Publishing, 2023, pp. 209–226.' conference: end_date: 2023-06-01 location: Hersonissos, Crete, Greece name: 20th Extended Semantic Web Conference start_date: 2023-05-28 date_created: 2022-10-15T19:20:11Z date_updated: 2023-07-02T18:10:02Z department: - _id: '574' - _id: '760' doi: https://doi.org/10.1007/978-3-031-33455-9_13 editor: - first_name: Catia full_name: Pesquita, Catia last_name: Pesquita - first_name: Ernesto full_name: Jimenez-Ruiz, Ernesto last_name: Jimenez-Ruiz - first_name: Jamie full_name: McCusker, Jamie last_name: McCusker - first_name: Daniel full_name: Faria, Daniel last_name: Faria - first_name: Mauro full_name: Dragoni, Mauro last_name: Dragoni - first_name: Anastasia full_name: Dimou, Anastasia last_name: Dimou - first_name: Raphael full_name: Troncy, Raphael last_name: Troncy - first_name: Sven full_name: Hertling, Sven last_name: Hertling external_id: unknown: - https://link.springer.com/chapter/10.1007/978-3-031-33455-9_13 intvolume: ' 13870' keyword: - Neural network - Concept learning - Description logics language: - iso: eng main_file_link: - open_access: '1' url: https://2023.eswc-conferences.org/wp-content/uploads/2023/05/paper_Kouagou_2023_Neural.pdf oa: '1' page: 209 - 226 project: - _id: '410' name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale' - _id: '407' grant_number: '101070305' name: 'ENEXA: Efficient Explainable Learning on Knowledge Graphs' - _id: '285' grant_number: NW21-059D name: 'SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems' publication: The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023) publication_identifier: unknown: - 978-3-031-33455-9 publication_status: published publisher: Springer International Publishing status: public title: Neural Class Expression Synthesis type: conference user_id: '11871' volume: 13870 year: '2023' ... --- _id: '56477' abstract: - lang: eng text: We describe a prototype of a Clinical Decision Support System (CDSS) that provides (counterfactual) explanations to support accurate medical diagnosis. The prototype is based on an inherently interpretable Bayesian network (BN). Our research aims to investigate which explanations are most useful for medical experts and whether co-constructing explanations can foster trust and acceptance of CDSS. author: - first_name: Felix full_name: Liedeker, Felix id: '93275' last_name: Liedeker - first_name: Philipp full_name: Cimiano, Philipp last_name: Cimiano citation: ama: 'Liedeker F, Cimiano P. A Prototype of an Interactive Clinical Decision Support System with Counterfactual Explanations. In: ; 2023.' apa: Liedeker, F., & Cimiano, P. (2023). A Prototype of an Interactive Clinical Decision Support System with Counterfactual Explanations. xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Lissabon. bibtex: '@inproceedings{Liedeker_Cimiano_2023, title={A Prototype of an Interactive Clinical Decision Support System with Counterfactual Explanations}, author={Liedeker, Felix and Cimiano, Philipp}, year={2023} }' chicago: Liedeker, Felix, and Philipp Cimiano. “A Prototype of an Interactive Clinical Decision Support System with Counterfactual Explanations,” 2023. ieee: F. Liedeker and P. Cimiano, “A Prototype of an Interactive Clinical Decision Support System with Counterfactual Explanations,” presented at the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Lissabon, 2023. mla: Liedeker, Felix, and Philipp Cimiano. A Prototype of an Interactive Clinical Decision Support System with Counterfactual Explanations. 2023. short: 'F. Liedeker, P. Cimiano, in: 2023.' conference: end_date: 2023-07-28 location: Lissabon name: xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023) start_date: 2023-07-26 date_created: 2024-10-09T14:50:09Z date_updated: 2024-10-09T15:04:53Z department: - _id: '660' keyword: - Explainable AI - Clinical decision support - Bayesian network - Counterfactual explanations language: - iso: eng project: - _id: '128' name: 'TRR 318 - C5: TRR 318 - Subproject C5' status: public title: A Prototype of an Interactive Clinical Decision Support System with Counterfactual Explanations type: conference user_id: '93275' year: '2023' ... --- _id: '30236' abstract: - lang: eng text: "Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive\r\nresults. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field.\r\n\r\nTo this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of\r\nwireless mobile networks." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Stefan full_name: Werner, Stefan last_name: Werner - first_name: Ramin full_name: Khalili, Ramin last_name: Khalili - first_name: Artur full_name: Hecker, Artur last_name: Hecker - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Schneider SB, Werner S, Khalili R, Hecker A, Karl H. mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks. In: IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE; 2022.' apa: 'Schneider, S. B., Werner, S., Khalili, R., Hecker, A., & Karl, H. (2022). mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks. IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest.' bibtex: '@inproceedings{Schneider_Werner_Khalili_Hecker_Karl_2022, title={mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks}, booktitle={IEEE/IFIP Network Operations and Management Symposium (NOMS)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl, Holger}, year={2022} }' chicago: 'Schneider, Stefan Balthasar, Stefan Werner, Ramin Khalili, Artur Hecker, and Holger Karl. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.” In IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE, 2022.' ieee: 'S. B. Schneider, S. Werner, R. Khalili, A. Hecker, and H. Karl, “mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks,” presented at the IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest, 2022.' mla: 'Schneider, Stefan Balthasar, et al. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.” IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.' short: 'S.B. Schneider, S. Werner, R. Khalili, A. Hecker, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.' conference: end_date: 2022-04-29 location: Budapest name: IEEE/IFIP Network Operations and Management Symposium (NOMS) start_date: 2022-04-25 date_created: 2022-03-10T18:28:14Z date_updated: 2022-03-10T18:28:19Z ddc: - '004' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2022-03-10T18:25:41Z date_updated: 2022-03-10T18:25:41Z file_id: '30237' file_name: author_version.pdf file_size: 223412 relation: main_file file_date_updated: 2022-03-10T18:25:41Z has_accepted_license: '1' keyword: - wireless mobile networks - network management - continuous control - cognitive networks - autonomous coordination - reinforcement learning - gym environment - simulation - open source language: - iso: eng oa: '1' project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' publication: IEEE/IFIP Network Operations and Management Symposium (NOMS) publisher: IEEE quality_controlled: '1' status: public title: 'mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks' type: conference user_id: '35343' year: '2022' ... --- _id: '34140' abstract: - lang: eng text: In this paper, machine learning techniques will be used to classify different PCB layouts given their electromagnetic frequency spectra. These spectra result from a simulated near-field measurement of electric field strengths at different locations. Measured values consist of real and imaginary parts (amplitude and phase) in X, Y and Z directions. Training data was obtained in the time domain by varying transmission line geometries (size, distance and signaling). It was then transformed into the frequency domain and used as deep neural network input. Principal component analysis was applied to reduce the sample dimension. The results show that classifying different designs is possible with high accuracy based on synthetic data. Future work comprises measurements of real, custom-made PCB with varying parameters to adapt the simulation model and also test the neural network. Finally, the trained model could be used to give hints about the error’s cause when overshooting EMC limits. author: - first_name: Jad full_name: Maalouly, Jad last_name: Maalouly - first_name: Dennis full_name: Hemker, Dennis last_name: Hemker - first_name: Christian full_name: Hedayat, Christian last_name: Hedayat - first_name: Christian full_name: Rückert, Christian last_name: Rückert - first_name: Ivan full_name: Kaufmann, Ivan last_name: Kaufmann - first_name: Marcel full_name: Olbrich, Marcel last_name: Olbrich - first_name: Sven full_name: Lange, Sven id: '38240' last_name: Lange - first_name: Harald full_name: Mathis, Harald last_name: Mathis citation: ama: 'Maalouly J, Hemker D, Hedayat C, et al. AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development. In: 2022 Kleinheubach Conference. IEEE; 2022.' apa: Maalouly, J., Hemker, D., Hedayat, C., Rückert, C., Kaufmann, I., Olbrich, M., Lange, S., & Mathis, H. (2022). AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development. 2022 Kleinheubach Conference. 2022 Kleinheubach Conference, Miltenberg, Germany. bibtex: '@inproceedings{Maalouly_Hemker_Hedayat_Rückert_Kaufmann_Olbrich_Lange_Mathis_2022, place={Miltenberg, Germany}, title={AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development}, booktitle={2022 Kleinheubach Conference}, publisher={IEEE}, author={Maalouly, Jad and Hemker, Dennis and Hedayat, Christian and Rückert, Christian and Kaufmann, Ivan and Olbrich, Marcel and Lange, Sven and Mathis, Harald}, year={2022} }' chicago: 'Maalouly, Jad, Dennis Hemker, Christian Hedayat, Christian Rückert, Ivan Kaufmann, Marcel Olbrich, Sven Lange, and Harald Mathis. “AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development.” In 2022 Kleinheubach Conference. Miltenberg, Germany: IEEE, 2022.' ieee: J. Maalouly et al., “AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development,” presented at the 2022 Kleinheubach Conference, Miltenberg, Germany, 2022. mla: Maalouly, Jad, et al. “AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development.” 2022 Kleinheubach Conference, IEEE, 2022. short: 'J. Maalouly, D. Hemker, C. Hedayat, C. Rückert, I. Kaufmann, M. Olbrich, S. Lange, H. Mathis, in: 2022 Kleinheubach Conference, IEEE, Miltenberg, Germany, 2022.' conference: end_date: 2022-09-29 location: Miltenberg, Germany name: 2022 Kleinheubach Conference start_date: 2022-09-27 date_created: 2022-11-24T14:21:17Z date_updated: 2022-11-24T14:21:34Z department: - _id: '59' - _id: '485' keyword: - emc - pcb - electronic system development - machine learning - neural network language: - iso: eng main_file_link: - url: https://ieeexplore.ieee.org/document/9954484 place: Miltenberg, Germany project: - _id: '52' name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing' publication: 2022 Kleinheubach Conference publication_identifier: eisbn: - 978-3-948571-07-8 publication_status: published publisher: IEEE status: public title: AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development type: conference user_id: '38240' year: '2022' ... --- _id: '29220' abstract: - lang: eng text: "Modern services often comprise several components, such as chained virtual network functions, microservices, or\r\nmachine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them. \r\nTo overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination. \r\n\r\nInstead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios." author: - first_name: Stefan full_name: Werner, Stefan last_name: Werner - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Werner S, Schneider SB, Karl H. Use What You Know: Network and Service Coordination Beyond Certainty. In: IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE; 2022.' apa: 'Werner, S., Schneider, S. B., & Karl, H. (2022). Use What You Know: Network and Service Coordination Beyond Certainty. IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest.' bibtex: '@inproceedings{Werner_Schneider_Karl_2022, title={Use What You Know: Network and Service Coordination Beyond Certainty}, booktitle={IEEE/IFIP Network Operations and Management Symposium (NOMS)}, publisher={IEEE}, author={Werner, Stefan and Schneider, Stefan Balthasar and Karl, Holger}, year={2022} }' chicago: 'Werner, Stefan, Stefan Balthasar Schneider, and Holger Karl. “Use What You Know: Network and Service Coordination Beyond Certainty.” In IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE, 2022.' ieee: 'S. Werner, S. B. Schneider, and H. Karl, “Use What You Know: Network and Service Coordination Beyond Certainty,” presented at the IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest, 2022.' mla: 'Werner, Stefan, et al. “Use What You Know: Network and Service Coordination Beyond Certainty.” IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.' short: 'S. Werner, S.B. Schneider, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.' conference: end_date: 2022-04-29 location: Budapest name: IEEE/IFIP Network Operations and Management Symposium (NOMS) start_date: 2022-04-25 date_created: 2022-01-11T08:43:26Z date_updated: 2022-01-11T08:44:04Z ddc: - '004' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2022-01-11T08:39:57Z date_updated: 2022-01-11T08:39:57Z file_id: '29222' file_name: author_version.pdf file_size: 528653 relation: main_file file_date_updated: 2022-01-11T08:39:57Z has_accepted_license: '1' keyword: - network management - service management - AI - Monte Carlo Tree Search - model-based - QoS language: - iso: eng oa: '1' project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' publication: IEEE/IFIP Network Operations and Management Symposium (NOMS) publisher: IEEE quality_controlled: '1' status: public title: 'Use What You Know: Network and Service Coordination Beyond Certainty' type: conference user_id: '35343' year: '2022' ... --- _id: '35620' abstract: - lang: eng text: Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows to extend the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this paper, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post-hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks (CNNs) for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density. article_type: original author: - first_name: Jan-Peter full_name: Kucklick, Jan-Peter id: '77066' last_name: Kucklick - first_name: Oliver full_name: Müller, Oliver id: '72849' last_name: Müller citation: ama: 'Kucklick J-P, Müller O. Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal. ACM Transactions on Management Information Systems. Published online 2022. doi:10.1145/3567430' apa: 'Kucklick, J.-P., & Müller, O. (2022). Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal. ACM Transactions on Management Information Systems. https://doi.org/10.1145/3567430' bibtex: '@article{Kucklick_Müller_2022, title={Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal}, DOI={10.1145/3567430}, journal={ACM Transactions on Management Information Systems}, publisher={Association for Computing Machinery (ACM)}, author={Kucklick, Jan-Peter and Müller, Oliver}, year={2022} }' chicago: 'Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate Appraisal.” ACM Transactions on Management Information Systems, 2022. https://doi.org/10.1145/3567430.' ieee: 'J.-P. Kucklick and O. Müller, “Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal,” ACM Transactions on Management Information Systems, 2022, doi: 10.1145/3567430.' mla: 'Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate Appraisal.” ACM Transactions on Management Information Systems, Association for Computing Machinery (ACM), 2022, doi:10.1145/3567430.' short: J.-P. Kucklick, O. Müller, ACM Transactions on Management Information Systems (2022). date_created: 2023-01-10T05:16:02Z date_updated: 2023-01-10T05:20:18Z department: - _id: '195' - _id: '196' doi: 10.1145/3567430 keyword: - Interpretability - Convolutional Neural Network - Accuracy-Interpretability Trade-Of - Real Estate Appraisal - Hedonic Pricing - Grad-Ram language: - iso: eng main_file_link: - url: https://dl.acm.org/doi/pdf/10.1145/3567430 publication: ACM Transactions on Management Information Systems publication_identifier: issn: - 2158-656X - 2158-6578 publication_status: published publisher: Association for Computing Machinery (ACM) status: public title: 'Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal' type: journal_article user_id: '77066' year: '2022' ... --- _id: '51343' abstract: - lang: eng text: This paper presents preliminary work on the formalization of three prominent cognitive biases in the diagnostic reasoning process over epileptic seizures, psychogenic seizures and syncopes. Diagnostic reasoning is understood as iterative exploration of medical evidence. This exploration is represented as a partially observable Markov decision process where the state (i.e., the correct diagnosis) is uncertain. Observation likelihoods and belief updates are computed using a Bayesian network which defines the interrelation between medical risk factors, diagnoses and potential findings. The decision problem is solved via partially observable upper confidence bounds for trees in Monte-Carlo planning. We compute a biased diagnostic exploration policy by altering the generated state transition, observation and reward during look ahead simulations. The resulting diagnostic policies reproduce reasoning errors which have only been described informally in the medical literature. We plan to use this formal representation in the future to inversely detect and classify biased reasoning in actual diagnostic trajectories obtained from physicians. author: - first_name: Dominik full_name: Battefeld, Dominik id: '91864' last_name: Battefeld orcid: 0000-0002-5480-0594 - first_name: Stefan full_name: Kopp, Stefan last_name: Kopp citation: ama: 'Battefeld D, Kopp S. Formalizing cognitive biases in medical diagnostic reasoning. In: Proceedings of the 8th Workshop on Formal and Cognitive Reasoning. ; 2022.' apa: Battefeld, D., & Kopp, S. (2022). Formalizing cognitive biases in medical diagnostic reasoning. Proceedings of the 8th Workshop on Formal and Cognitive Reasoning. 8th Workshop on Formal and Cognitive Reasoning (FCR), Trier. bibtex: '@inproceedings{Battefeld_Kopp_2022, title={Formalizing cognitive biases in medical diagnostic reasoning}, booktitle={Proceedings of the 8th Workshop on Formal and Cognitive Reasoning}, author={Battefeld, Dominik and Kopp, Stefan}, year={2022} }' chicago: Battefeld, Dominik, and Stefan Kopp. “Formalizing Cognitive Biases in Medical Diagnostic Reasoning.” In Proceedings of the 8th Workshop on Formal and Cognitive Reasoning, 2022. ieee: D. Battefeld and S. Kopp, “Formalizing cognitive biases in medical diagnostic reasoning,” presented at the 8th Workshop on Formal and Cognitive Reasoning (FCR), Trier, 2022. mla: Battefeld, Dominik, and Stefan Kopp. “Formalizing Cognitive Biases in Medical Diagnostic Reasoning.” Proceedings of the 8th Workshop on Formal and Cognitive Reasoning, 2022. short: 'D. Battefeld, S. Kopp, in: Proceedings of the 8th Workshop on Formal and Cognitive Reasoning, 2022.' conference: end_date: 2022-09-23 location: Trier name: 8th Workshop on Formal and Cognitive Reasoning (FCR) start_date: '2022-09-19 ' date_created: 2024-02-14T09:06:04Z date_updated: 2024-10-31T10:00:01Z ddc: - '000' department: - _id: '660' file: - access_level: closed content_type: application/pdf creator: doba2 date_created: 2024-10-31T09:59:46Z date_updated: 2024-10-31T09:59:46Z file_id: '56846' file_name: paper8.pdf file_size: 261528 relation: main_file success: 1 file_date_updated: 2024-10-31T09:59:46Z has_accepted_license: '1' keyword: - Diagnostic reasoning - Cognitive bias - Cognitive model - POMDP - Bayesian network - Epilepsy - CDSS language: - iso: eng project: - _id: '128' name: 'TRR 318 - C5: TRR 318 - Subproject C5' publication: Proceedings of the 8th Workshop on Formal and Cognitive Reasoning quality_controlled: '1' status: public title: Formalizing cognitive biases in medical diagnostic reasoning type: conference user_id: '91864' year: '2022' ... --- _id: '25281' abstract: - lang: eng text: "Wireless Acoustic Sensor Networks (WASNs) have a wide range of audio signal processing applications. Due to the spatial diversity of the microphone and their relative position to the acoustic source, not all microphones are equally useful for subsequent audio signal processing tasks, nor do they all have the same wireless data transmission rates. Hence, a central task in WASNs is to balance a microphone’s estimated acoustic utility against its transmission delay, selecting a best-possible subset of microphones to record audio signals.\r\n\r\nIn this work, we use reinforcement learning to decide if a microphone should be used or switched off to maximize the acoustic quality at low transmission delays, while minimizing switching frequency. In experiments with moving sources in a simulated acoustic environment, our method outperforms naive baseline comparisons" author: - first_name: Haitham full_name: Afifi, Haitham id: '65718' last_name: Afifi - first_name: Michael full_name: Guenther, Michael last_name: Guenther - first_name: Andreas full_name: Brendel, Andreas last_name: Brendel - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl - first_name: Walter full_name: Kellermann, Walter last_name: Kellermann citation: ama: 'Afifi H, Guenther M, Brendel A, Karl H, Kellermann W. Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities. In: 14. ITG Conference on Speech Communication (ITG 2021). ; 2021.' apa: Afifi, H., Guenther, M., Brendel, A., Karl, H., & Kellermann, W. (2021). Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities. 14. ITG Conference on Speech Communication (ITG 2021). bibtex: '@inproceedings{Afifi_Guenther_Brendel_Karl_Kellermann_2021, title={Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities}, booktitle={14. ITG Conference on Speech Communication (ITG 2021)}, author={Afifi, Haitham and Guenther, Michael and Brendel, Andreas and Karl, Holger and Kellermann, Walter}, year={2021} }' chicago: Afifi, Haitham, Michael Guenther, Andreas Brendel, Holger Karl, and Walter Kellermann. “Reinforcement Learning-Based Microphone Selection in Wireless Acoustic Sensor Networks Considering Network and Acoustic Utilities.” In 14. ITG Conference on Speech Communication (ITG 2021), 2021. ieee: H. Afifi, M. Guenther, A. Brendel, H. Karl, and W. Kellermann, “Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities,” 2021. mla: Afifi, Haitham, et al. “Reinforcement Learning-Based Microphone Selection in Wireless Acoustic Sensor Networks Considering Network and Acoustic Utilities.” 14. ITG Conference on Speech Communication (ITG 2021), 2021. short: 'H. Afifi, M. Guenther, A. Brendel, H. Karl, W. Kellermann, in: 14. ITG Conference on Speech Communication (ITG 2021), 2021.' date_created: 2021-10-04T10:59:50Z date_updated: 2022-01-06T06:56:59Z ddc: - '620' file: - access_level: closed content_type: application/pdf creator: hafifi date_created: 2021-10-04T10:58:07Z date_updated: 2021-10-04T10:58:07Z file_id: '25282' file_name: ITG_2021_paper_26 (3).pdf file_size: 283616 relation: main_file success: 1 file_date_updated: 2021-10-04T10:58:07Z has_accepted_license: '1' keyword: - microphone utility - microphone selection - wireless acoustic sensor network - network delay - reinforcement learning language: - iso: eng project: - _id: '27' name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung über funkbasierte Sensornetzwerke publication: 14. ITG Conference on Speech Communication (ITG 2021) status: public title: Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities type: conference user_id: '65718' year: '2021' ... --- _id: '21543' abstract: - lang: eng text: "Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks.\r\n\r\nTo address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms)." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Haydar full_name: Qarawlus, Haydar last_name: Qarawlus - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Schneider SB, Qarawlus H, Karl H. Distributed Online Service Coordination Using Deep Reinforcement Learning. In: IEEE International Conference on Distributed Computing Systems (ICDCS). IEEE; 2021.' apa: 'Schneider, S. B., Qarawlus, H., & Karl, H. (2021). Distributed Online Service Coordination Using Deep Reinforcement Learning. In IEEE International Conference on Distributed Computing Systems (ICDCS). Washington, DC, USA: IEEE.' bibtex: '@inproceedings{Schneider_Qarawlus_Karl_2021, title={Distributed Online Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International Conference on Distributed Computing Systems (ICDCS)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}, year={2021} }' chicago: Schneider, Stefan Balthasar, Haydar Qarawlus, and Holger Karl. “Distributed Online Service Coordination Using Deep Reinforcement Learning.” In IEEE International Conference on Distributed Computing Systems (ICDCS). IEEE, 2021. ieee: S. B. Schneider, H. Qarawlus, and H. Karl, “Distributed Online Service Coordination Using Deep Reinforcement Learning,” in IEEE International Conference on Distributed Computing Systems (ICDCS), Washington, DC, USA, 2021. mla: Schneider, Stefan Balthasar, et al. “Distributed Online Service Coordination Using Deep Reinforcement Learning.” IEEE International Conference on Distributed Computing Systems (ICDCS), IEEE, 2021. short: 'S.B. Schneider, H. Qarawlus, H. Karl, in: IEEE International Conference on Distributed Computing Systems (ICDCS), IEEE, 2021.' conference: location: Washington, DC, USA name: IEEE International Conference on Distributed Computing Systems (ICDCS) date_created: 2021-03-18T17:15:47Z date_updated: 2022-01-06T06:55:04Z ddc: - '000' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2021-03-18T17:12:56Z date_updated: 2021-03-18T17:12:56Z file_id: '21544' file_name: public_author_version.pdf file_size: 606321 relation: main_file title: Distributed Online Service Coordination Using Deep Reinforcement Learning file_date_updated: 2021-03-18T17:12:56Z has_accepted_license: '1' keyword: - network management - service management - coordination - reinforcement learning - distributed language: - iso: eng oa: '1' project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: IEEE International Conference on Distributed Computing Systems (ICDCS) publisher: IEEE related_material: link: - relation: software url: https://github.com/ RealVNF/distributed-drl-coordination status: public title: Distributed Online Service Coordination Using Deep Reinforcement Learning type: conference user_id: '35343' year: '2021' ... --- _id: '20693' abstract: - lang: eng text: "In practical, large-scale networks, services are requested\r\nby users across the globe, e.g., for video streaming.\r\nServices consist of multiple interconnected components such as\r\nmicroservices in a service mesh. Coordinating these services\r\nrequires scaling them according to continuously changing user\r\ndemand, deploying instances at the edge close to their users,\r\nand routing traffic efficiently between users and connected instances.\r\nNetwork and service coordination is commonly addressed\r\nthrough centralized approaches, where a single coordinator\r\nknows everything and coordinates the entire network globally.\r\nWhile such centralized approaches can reach global optima, they\r\ndo not scale to large, realistic networks. In contrast, distributed\r\napproaches scale well, but sacrifice solution quality due to their\r\nlimited scope of knowledge and coordination decisions.\r\n\r\nTo this end, we propose a hierarchical coordination approach\r\nthat combines the good solution quality of centralized approaches\r\nwith the scalability of distributed approaches. In doing so, we divide\r\nthe network into multiple hierarchical domains and optimize\r\ncoordination in a top-down manner. We compare our hierarchical\r\nwith a centralized approach in an extensive evaluation on a real-world\r\nnetwork topology. Our results indicate that hierarchical\r\ncoordination can find close-to-optimal solutions in a fraction of\r\nthe runtime of centralized approaches." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Mirko full_name: Jürgens, Mirko last_name: Jürgens - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Schneider SB, Jürgens M, Karl H. Divide and Conquer: Hierarchical Network and Service Coordination. In: IFIP/IEEE International Symposium on Integrated Network Management (IM). IFIP/IEEE; 2021.' apa: 'Schneider, S. B., Jürgens, M., & Karl, H. (2021). Divide and Conquer: Hierarchical Network and Service Coordination. In IFIP/IEEE International Symposium on Integrated Network Management (IM). Bordeaux, France: IFIP/IEEE.' bibtex: '@inproceedings{Schneider_Jürgens_Karl_2021, title={Divide and Conquer: Hierarchical Network and Service Coordination}, booktitle={IFIP/IEEE International Symposium on Integrated Network Management (IM)}, publisher={IFIP/IEEE}, author={Schneider, Stefan Balthasar and Jürgens, Mirko and Karl, Holger}, year={2021} }' chicago: 'Schneider, Stefan Balthasar, Mirko Jürgens, and Holger Karl. “Divide and Conquer: Hierarchical Network and Service Coordination.” In IFIP/IEEE International Symposium on Integrated Network Management (IM). IFIP/IEEE, 2021.' ieee: 'S. B. Schneider, M. Jürgens, and H. Karl, “Divide and Conquer: Hierarchical Network and Service Coordination,” in IFIP/IEEE International Symposium on Integrated Network Management (IM), Bordeaux, France, 2021.' mla: 'Schneider, Stefan Balthasar, et al. “Divide and Conquer: Hierarchical Network and Service Coordination.” IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP/IEEE, 2021.' short: 'S.B. Schneider, M. Jürgens, H. Karl, in: IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP/IEEE, 2021.' conference: location: Bordeaux, France name: IFIP/IEEE International Symposium on Integrated Network Management (IM) date_created: 2020-12-11T08:39:47Z date_updated: 2022-01-06T06:54:32Z ddc: - '006' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2020-12-11T08:37:37Z date_updated: 2020-12-11T08:37:37Z file_id: '20694' file_name: preprint_with_header.pdf file_size: 7979772 relation: main_file title: 'Divide and Conquer: Hierarchical Network and Service Coordination' file_date_updated: 2020-12-11T08:37:37Z has_accepted_license: '1' keyword: - network management - service management - coordination - hierarchical - scalability - nfv language: - iso: eng oa: '1' project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: IFIP/IEEE International Symposium on Integrated Network Management (IM) publisher: IFIP/IEEE quality_controlled: '1' status: public title: 'Divide and Conquer: Hierarchical Network and Service Coordination' type: conference user_id: '35343' year: '2021' ... --- _id: '21808' abstract: - lang: eng text: "Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge).\r\n\r\nWe propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available." article_type: original author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Ramin full_name: Khalili, Ramin last_name: Khalili - first_name: Adnan full_name: Manzoor, Adnan last_name: Manzoor - first_name: Haydar full_name: Qarawlus, Haydar last_name: Qarawlus - first_name: Rafael full_name: Schellenberg, Rafael last_name: Schellenberg - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl - first_name: Artur full_name: Hecker, Artur last_name: Hecker citation: ama: Schneider SB, Khalili R, Manzoor A, et al. Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. Transactions on Network and Service Management. 2021. doi:10.1109/TNSM.2021.3076503 apa: Schneider, S. B., Khalili, R., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., & Hecker, A. (2021). Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. Transactions on Network and Service Management. https://doi.org/10.1109/TNSM.2021.3076503 bibtex: '@article{Schneider_Khalili_Manzoor_Qarawlus_Schellenberg_Karl_Hecker_2021, title={Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}, DOI={10.1109/TNSM.2021.3076503}, journal={Transactions on Network and Service Management}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}, year={2021} }' chicago: Schneider, Stefan Balthasar, Ramin Khalili, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, and Artur Hecker. “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning.” Transactions on Network and Service Management, 2021. https://doi.org/10.1109/TNSM.2021.3076503. ieee: S. B. Schneider et al., “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning,” Transactions on Network and Service Management, 2021. mla: Schneider, Stefan Balthasar, et al. “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning.” Transactions on Network and Service Management, IEEE, 2021, doi:10.1109/TNSM.2021.3076503. short: S.B. Schneider, R. Khalili, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, A. Hecker, Transactions on Network and Service Management (2021). date_created: 2021-04-27T08:04:16Z date_updated: 2022-01-06T06:55:15Z ddc: - '000' department: - _id: '75' doi: 10.1109/TNSM.2021.3076503 file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2021-04-27T08:01:26Z date_updated: 2021-04-27T08:01:26Z description: Author version of the accepted paper file_id: '21809' file_name: ris-accepted-version.pdf file_size: 4172270 relation: main_file file_date_updated: 2021-04-27T08:01:26Z has_accepted_license: '1' keyword: - network management - service management - coordination - reinforcement learning - self-learning - self-adaptation - multi-objective language: - iso: eng oa: '1' project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: Transactions on Network and Service Management publisher: IEEE status: public title: Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning type: journal_article user_id: '35343' year: '2021' ... --- _id: '19607' abstract: - lang: eng text: "Modern services consist of modular, interconnected\r\ncomponents, e.g., microservices forming a service mesh. To\r\ndynamically adjust to ever-changing service demands, service\r\ncomponents have to be instantiated on nodes across the network.\r\nIncoming flows requesting a service then need to be routed\r\nthrough the deployed instances while considering node and link\r\ncapacities. Ultimately, the goal is to maximize the successfully\r\nserved flows and Quality of Service (QoS) through online service\r\ncoordination. Current approaches for service coordination are\r\nusually centralized, assuming up-to-date global knowledge and\r\nmaking global decisions for all nodes in the network. Such global\r\nknowledge and centralized decisions are not realistic in practical\r\nlarge-scale networks.\r\n\r\nTo solve this problem, we propose two algorithms for fully\r\ndistributed service coordination. The proposed algorithms can be\r\nexecuted individually at each node in parallel and require only\r\nvery limited global knowledge. We compare and evaluate both\r\nalgorithms with a state-of-the-art centralized approach in extensive\r\nsimulations on a large-scale, real-world network topology.\r\nOur results indicate that the two algorithms can compete with\r\ncentralized approaches in terms of solution quality but require\r\nless global knowledge and are magnitudes faster (more than\r\n100x)." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Lars Dietrich full_name: Klenner, Lars Dietrich last_name: Klenner - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Schneider SB, Klenner LD, Karl H. Every Node for Itself: Fully Distributed Service Coordination. In: IEEE International Conference on Network and Service Management (CNSM). IEEE; 2020.' apa: 'Schneider, S. B., Klenner, L. D., & Karl, H. (2020). Every Node for Itself: Fully Distributed Service Coordination. In IEEE International Conference on Network and Service Management (CNSM). IEEE.' bibtex: '@inproceedings{Schneider_Klenner_Karl_2020, title={Every Node for Itself: Fully Distributed Service Coordination}, booktitle={IEEE International Conference on Network and Service Management (CNSM)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Klenner, Lars Dietrich and Karl, Holger}, year={2020} }' chicago: 'Schneider, Stefan Balthasar, Lars Dietrich Klenner, and Holger Karl. “Every Node for Itself: Fully Distributed Service Coordination.” In IEEE International Conference on Network and Service Management (CNSM). IEEE, 2020.' ieee: 'S. B. Schneider, L. D. Klenner, and H. Karl, “Every Node for Itself: Fully Distributed Service Coordination,” in IEEE International Conference on Network and Service Management (CNSM), 2020.' mla: 'Schneider, Stefan Balthasar, et al. “Every Node for Itself: Fully Distributed Service Coordination.” IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020.' short: 'S.B. Schneider, L.D. Klenner, H. Karl, in: IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020.' date_created: 2020-09-22T06:23:40Z date_updated: 2022-01-06T06:54:08Z ddc: - '006' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2020-09-22T06:25:57Z date_updated: 2020-09-22T06:36:25Z file_id: '19608' file_name: ris_with_copyright.pdf file_size: 500948 relation: main_file file_date_updated: 2020-09-22T06:36:25Z has_accepted_license: '1' keyword: - distributed management - service coordination - network coordination - nfv - softwarization - orchestration language: - iso: eng oa: '1' project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: IEEE International Conference on Network and Service Management (CNSM) publisher: IEEE status: public title: 'Every Node for Itself: Fully Distributed Service Coordination' type: conference user_id: '35343' year: '2020' ... --- _id: '19609' abstract: - lang: eng text: "Modern services comprise interconnected components,\r\ne.g., microservices in a service mesh, that can scale and\r\nrun on multiple nodes across the network on demand. To process\r\nincoming traffic, service components have to be instantiated and\r\ntraffic assigned to these instances, taking capacities and changing\r\ndemands into account. This challenge is usually solved with\r\ncustom approaches designed by experts. While this typically\r\nworks well for the considered scenario, the models often rely\r\non unrealistic assumptions or on knowledge that is not available\r\nin practice (e.g., a priori knowledge).\r\n\r\nWe propose a novel deep reinforcement learning approach that\r\nlearns how to best coordinate services and is geared towards\r\nrealistic assumptions. It interacts with the network and relies on\r\navailable, possibly delayed monitoring information. Rather than\r\ndefining a complex model or an algorithm how to achieve an\r\nobjective, our model-free approach adapts to various objectives\r\nand traffic patterns. An agent is trained offline without expert\r\nknowledge and then applied online with minimal overhead. Compared\r\nto a state-of-the-art heuristic, it significantly improves flow\r\nthroughput and overall network utility on real-world network\r\ntopologies and traffic traces. It also learns to optimize different\r\nobjectives, generalizes to scenarios with unseen, stochastic traffic\r\npatterns, and scales to large real-world networks." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Adnan full_name: Manzoor, Adnan last_name: Manzoor - first_name: Haydar full_name: Qarawlus, Haydar last_name: Qarawlus - first_name: Rafael full_name: Schellenberg, Rafael last_name: Schellenberg - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl - first_name: Ramin full_name: Khalili, Ramin last_name: Khalili - first_name: Artur full_name: Hecker, Artur last_name: Hecker citation: ama: 'Schneider SB, Manzoor A, Qarawlus H, et al. Self-Driving Network and Service Coordination Using Deep Reinforcement Learning. In: IEEE International Conference on Network and Service Management (CNSM). IEEE; 2020.' apa: Schneider, S. B., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., Khalili, R., & Hecker, A. (2020). Self-Driving Network and Service Coordination Using Deep Reinforcement Learning. In IEEE International Conference on Network and Service Management (CNSM). IEEE. bibtex: '@inproceedings{Schneider_Manzoor_Qarawlus_Schellenberg_Karl_Khalili_Hecker_2020, title={Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International Conference on Network and Service Management (CNSM)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}, year={2020} }' chicago: Schneider, Stefan Balthasar, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, Ramin Khalili, and Artur Hecker. “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning.” In IEEE International Conference on Network and Service Management (CNSM). IEEE, 2020. ieee: S. B. Schneider et al., “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning,” in IEEE International Conference on Network and Service Management (CNSM), 2020. mla: Schneider, Stefan Balthasar, et al. “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning.” IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020. short: 'S.B. Schneider, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, R. Khalili, A. Hecker, in: IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020.' date_created: 2020-09-22T06:28:22Z date_updated: 2022-01-06T06:54:08Z ddc: - '006' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2020-09-22T06:29:16Z date_updated: 2020-09-22T06:36:00Z file_id: '19610' file_name: ris_with_copyright.pdf file_size: 642999 relation: main_file file_date_updated: 2020-09-22T06:36:00Z has_accepted_license: '1' keyword: - self-driving networks - self-learning - network coordination - service coordination - reinforcement learning - deep learning - nfv language: - iso: eng oa: '1' project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: IEEE International Conference on Network and Service Management (CNSM) publisher: IEEE status: public title: Self-Driving Network and Service Coordination Using Deep Reinforcement Learning type: conference user_id: '35343' year: '2020' ... --- _id: '16218' abstract: - lang: eng text: "Despite recent progress in orchestration of Virtual Network Functions (VNFs) and in multi-technology SDN connectivity, the automated provisioning of end-to-end network services composed of virtual functions deployed across distributed compute locations remains an open challenge. This problem is especially relevant to support the deployment of future 5G networks, comprising virtual access and core network functions connected through a potentially multi-domain transport network.\r\nIn this paper, we present and demonstrate the 5GOS, a lightweight end-to-end orchestration framework that enables the\r\nautomated provisioning of virtual radio access network services. Using an experimental multi-domain testbed we demonstrate that the 5GOS can provision multi-domain virtual Wi-Fi and LTE services in less than three minutes." author: - first_name: Daniel full_name: Camps-Mur, Daniel last_name: Camps-Mur - first_name: Ferran full_name: ' Canellas, Ferran' last_name: ' Canellas' - first_name: Azahar full_name: Machwe, Azahar last_name: Machwe - first_name: Jorge full_name: Paracuellos, Jorge last_name: Paracuellos - first_name: Kostas full_name: Choumas, Kostas last_name: Choumas - first_name: Dimitris full_name: Giatsios, Dimitris last_name: Giatsios - first_name: Thanasis full_name: Korakis, Thanasis last_name: Korakis - first_name: Hadi full_name: Razzaghi Kouchaksaraei, Hadi id: '60845' last_name: Razzaghi Kouchaksaraei citation: ama: 'Camps-Mur D, Canellas F, Machwe A, et al. 5GOS: Demonstrating multi-domain orchestration of end-to-end virtual RAN services. In: The 6th IEEE International Conference on Network Softwarization (IEEE NetSoft 2020).' apa: 'Camps-Mur, D., Canellas, F., Machwe, A., Paracuellos, J., Choumas, K., Giatsios, D., … Razzaghi Kouchaksaraei, H. (n.d.). 5GOS: Demonstrating multi-domain orchestration of end-to-end virtual RAN services. In the 6th IEEE International Conference on Network Softwarization (IEEE NetSoft 2020). Ghent, Belgium.' bibtex: '@inproceedings{Camps-Mur_ Canellas_Machwe_Paracuellos_Choumas_Giatsios_Korakis_Razzaghi Kouchaksaraei, title={5GOS: Demonstrating multi-domain orchestration of end-to-end virtual RAN services}, booktitle={the 6th IEEE International Conference on Network Softwarization (IEEE NetSoft 2020)}, author={Camps-Mur, Daniel and Canellas, Ferran and Machwe, Azahar and Paracuellos, Jorge and Choumas, Kostas and Giatsios, Dimitris and Korakis, Thanasis and Razzaghi Kouchaksaraei, Hadi} }' chicago: 'Camps-Mur, Daniel, Ferran Canellas, Azahar Machwe, Jorge Paracuellos, Kostas Choumas, Dimitris Giatsios, Thanasis Korakis, and Hadi Razzaghi Kouchaksaraei. “5GOS: Demonstrating Multi-Domain Orchestration of End-to-End Virtual RAN Services.” In The 6th IEEE International Conference on Network Softwarization (IEEE NetSoft 2020), n.d.' ieee: 'D. Camps-Mur et al., “5GOS: Demonstrating multi-domain orchestration of end-to-end virtual RAN services,” in the 6th IEEE International Conference on Network Softwarization (IEEE NetSoft 2020), Ghent, Belgium.' mla: 'Camps-Mur, Daniel, et al. “5GOS: Demonstrating Multi-Domain Orchestration of End-to-End Virtual RAN Services.” The 6th IEEE International Conference on Network Softwarization (IEEE NetSoft 2020).' short: 'D. Camps-Mur, F. Canellas, A. Machwe, J. Paracuellos, K. Choumas, D. Giatsios, T. Korakis, H. Razzaghi Kouchaksaraei, in: The 6th IEEE International Conference on Network Softwarization (IEEE NetSoft 2020), n.d.' conference: end_date: 2020-07-3 location: Ghent, Belgium name: IEEE Conference on Network Softwarization (NetSoft) start_date: 2020-06-29 date_created: 2020-03-03T11:49:41Z date_updated: 2022-01-06T06:52:45Z keyword: - Orchestration - multi-domain - cellular network virtualization - SDN - 5G language: - iso: eng project: - _id: '23' grant_number: '762057' name: 5G Programmable Infrastructure Converging disaggregated neTwork and compUte Resources publication: the 6th IEEE International Conference on Network Softwarization (IEEE NetSoft 2020) publication_status: accepted status: public title: '5GOS: Demonstrating multi-domain orchestration of end-to-end virtual RAN services' type: conference user_id: '60845' year: '2020' ... --- _id: '23568' abstract: - lang: eng text: "We study the structure of power networks in consideration of local protests against certain\r\npower lines (’not-in-my-backyard’). An application of a network formation game is used to\r\ndetermine whether or not such protests arise. We examine the existence of stable networks and\r\ntheir characteristics, when no player wants to make an alteration. Stability within this game is\r\nonly reached if each player is sufficiently connected to a power source but is not linked to more\r\nplayers than necessary. In addition we introduce an algorithm that creates a stable network." author: - first_name: Lukas full_name: Block, Lukas id: '22527' last_name: Block citation: ama: Block L. Network Formation with NIMBY Constraints.; 2020. apa: Block, L. (2020). Network formation with NIMBY constraints. bibtex: '@book{Block_2020, title={Network formation with NIMBY constraints}, author={Block, Lukas}, year={2020} }' chicago: Block, Lukas. Network Formation with NIMBY Constraints, 2020. ieee: L. Block, Network formation with NIMBY constraints. 2020. mla: Block, Lukas. Network Formation with NIMBY Constraints. 2020. short: L. Block, Network Formation with NIMBY Constraints, 2020. date_created: 2021-08-30T10:05:18Z date_updated: 2022-02-07T20:08:54Z ddc: - '330' file: - access_level: closed content_type: application/pdf creator: lblock date_created: 2021-08-30T10:01:53Z date_updated: 2021-08-30T10:01:53Z file_id: '23569' file_name: Network formation with NIMBY constraints.pdf file_size: 169285 relation: main_file success: 1 file_date_updated: 2021-08-30T10:01:53Z has_accepted_license: '1' jel: - D85 - H54 - L52 keyword: - Network formation - NIMBY - Power networks - Nash stability language: - iso: eng main_file_link: - open_access: '1' oa: '1' status: public title: Network formation with NIMBY constraints type: working_paper user_id: '22527' year: '2020' ... --- _id: '20504' abstract: - lang: eng text: 'In recent years time domain speech separation has excelled over frequency domain separation in single channel scenarios and noise-free environments. In this paper we dissect the gains of the time-domain audio separation network (TasNet) approach by gradually replacing components of an utterance-level permutation invariant training (u-PIT) based separation system in the frequency domain until the TasNet system is reached, thus blending components of frequency domain approaches with those of time domain approaches. Some of the intermediate variants achieve comparable signal-to-distortion ratio (SDR) gains to TasNet, but retain the advantage of frequency domain processing: compatibility with classic signal processing tools such as frequency-domain beamforming and the human interpretability of the masks. Furthermore, we show that the scale invariant signal-to-distortion ratio (si-SDR) criterion used as loss function in TasNet is related to a logarithmic mean square error criterion and that it is this criterion which contributes most reliable to the performance advantage of TasNet. Finally, we critically assess which gains in a noise-free single channel environment generalize to more realistic reverberant conditions.' author: - first_name: Jens full_name: Heitkaemper, Jens id: '27643' last_name: Heitkaemper - first_name: Darius full_name: Jakobeit, Darius last_name: Jakobeit - first_name: Christoph full_name: Boeddeker, Christoph id: '40767' last_name: Boeddeker - first_name: Lukas full_name: Drude, Lukas last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Heitkaemper J, Jakobeit D, Boeddeker C, Drude L, Haeb-Umbach R. Demystifying TasNet: A Dissecting Approach. In: ICASSP 2020 Virtual Barcelona Spain. ; 2020.' apa: 'Heitkaemper, J., Jakobeit, D., Boeddeker, C., Drude, L., & Haeb-Umbach, R. (2020). Demystifying TasNet: A Dissecting Approach. ICASSP 2020 Virtual Barcelona Spain.' bibtex: '@inproceedings{Heitkaemper_Jakobeit_Boeddeker_Drude_Haeb-Umbach_2020, title={Demystifying TasNet: A Dissecting Approach}, booktitle={ICASSP 2020 Virtual Barcelona Spain}, author={Heitkaemper, Jens and Jakobeit, Darius and Boeddeker, Christoph and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2020} }' chicago: 'Heitkaemper, Jens, Darius Jakobeit, Christoph Boeddeker, Lukas Drude, and Reinhold Haeb-Umbach. “Demystifying TasNet: A Dissecting Approach.” In ICASSP 2020 Virtual Barcelona Spain, 2020.' ieee: 'J. Heitkaemper, D. Jakobeit, C. Boeddeker, L. Drude, and R. Haeb-Umbach, “Demystifying TasNet: A Dissecting Approach,” 2020.' mla: 'Heitkaemper, Jens, et al. “Demystifying TasNet: A Dissecting Approach.” ICASSP 2020 Virtual Barcelona Spain, 2020.' short: 'J. Heitkaemper, D. Jakobeit, C. Boeddeker, L. Drude, R. Haeb-Umbach, in: ICASSP 2020 Virtual Barcelona Spain, 2020.' date_created: 2020-11-25T14:56:53Z date_updated: 2022-01-13T08:47:32Z ddc: - '000' department: - _id: '54' file: - access_level: closed content_type: application/pdf creator: jensheit date_created: 2020-12-11T12:36:37Z date_updated: 2020-12-11T12:36:37Z file_id: '20699' file_name: ms.pdf file_size: 3871374 relation: main_file success: 1 file_date_updated: 2020-12-11T12:36:37Z has_accepted_license: '1' keyword: - voice activity detection - speech activity detection - neural network - statistical speech processing language: - iso: eng project: - _id: '52' name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing' publication: ICASSP 2020 Virtual Barcelona Spain quality_controlled: '1' status: public title: 'Demystifying TasNet: A Dissecting Approach' type: conference user_id: '40767' year: '2020' ...