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Becker, Brett","first_name":"Brett","last_name":"A. Becker"}],"title":"ITiCSE 2021 recap","status":"public","year":"2021","intvolume":"        53","date_updated":"2022-01-06T06:57:06Z","date_created":"2021-10-05T14:18:48Z","department":[{"_id":"67"}],"type":"journal_article","citation":{"bibtex":"@article{Schulte_A. Becker_2021, title={ITiCSE 2021 recap}, volume={53}, DOI={<a href=\"https://doi.org/10.1145/3483403.3483405\">10.1145/3483403.3483405</a>}, number={3}, journal={ACM SIGCSE Bull.}, author={Schulte, Carsten and A. Becker, Brett}, year={2021}, pages={3–4} }","ama":"Schulte C, A. Becker B. ITiCSE 2021 recap. <i>ACM SIGCSE Bull</i>. 2021;53(3):3-4. doi:<a href=\"https://doi.org/10.1145/3483403.3483405\">10.1145/3483403.3483405</a>","mla":"Schulte, Carsten, and Brett A. Becker. “ITiCSE 2021 Recap.” <i>ACM SIGCSE Bull.</i>, vol. 53, no. 3, 2021, pp. 3–4, doi:<a href=\"https://doi.org/10.1145/3483403.3483405\">10.1145/3483403.3483405</a>.","short":"C. Schulte, B. A. Becker, ACM SIGCSE Bull. 53 (2021) 3–4.","chicago":"Schulte, Carsten, and Brett A. Becker. “ITiCSE 2021 Recap.” <i>ACM SIGCSE Bull.</i> 53, no. 3 (2021): 3–4. <a href=\"https://doi.org/10.1145/3483403.3483405\">https://doi.org/10.1145/3483403.3483405</a>.","ieee":"C. Schulte and B. A. Becker, “ITiCSE 2021 recap,” <i>ACM SIGCSE Bull.</i>, vol. 53, no. 3, pp. 3–4, 2021, doi: <a href=\"https://doi.org/10.1145/3483403.3483405\">10.1145/3483403.3483405</a>.","apa":"Schulte, C., &#38; A. Becker, B. (2021). ITiCSE 2021 recap. <i>ACM SIGCSE Bull.</i>, <i>53</i>(3), 3–4. <a href=\"https://doi.org/10.1145/3483403.3483405\">https://doi.org/10.1145/3483403.3483405</a>"},"publication":"ACM SIGCSE Bull.","issue":"3"},{"date_created":"2020-10-16T12:39:27Z","department":[{"_id":"600"}],"type":"conference","citation":{"ieee":"G. Skitalinskaya, J. Klaff, and H. Wachsmuth, “Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale,” in <i>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics</i>, 2021, pp. 1718–1729.","apa":"Skitalinskaya, G., Klaff, J., &#38; Wachsmuth, H. (2021). Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale. In <i>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics</i> (pp. 1718–1729).","short":"G. Skitalinskaya, J. Klaff, H. Wachsmuth, in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 2021, pp. 1718–1729.","chicago":"Skitalinskaya, Gabriella, Jonas Klaff, and Henning Wachsmuth. “Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale.” In <i>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics</i>, 1718–29, 2021.","mla":"Skitalinskaya, Gabriella, et al. “Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale.” <i>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics</i>, 2021, pp. 1718–29.","bibtex":"@inproceedings{Skitalinskaya_Klaff_Wachsmuth_2021, title={Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale}, booktitle={Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics}, author={Skitalinskaya, Gabriella and Klaff, Jonas and Wachsmuth, Henning}, year={2021}, pages={1718–1729} }","ama":"Skitalinskaya G, Klaff J, Wachsmuth H. Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale. In: <i>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics</i>. ; 2021:1718-1729."},"publication":"Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics","language":[{"iso":"eng"}],"_id":"20115","page":"1718-1729","main_file_link":[{"url":"https://www.aclweb.org/anthology/2021.eacl-main.147/"}],"user_id":"82920","author":[{"full_name":"Skitalinskaya, Gabriella","first_name":"Gabriella","last_name":"Skitalinskaya"},{"full_name":"Klaff, Jonas","last_name":"Klaff","first_name":"Jonas"},{"id":"3900","full_name":"Wachsmuth, Henning","first_name":"Henning","last_name":"Wachsmuth"}],"status":"public","year":"2021","title":"Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale","date_updated":"2022-01-06T06:54:19Z"},{"user_id":"63288","ddc":["000"],"_id":"20125","publisher":"IEEE Computer Society","status":"public","conference":{"location":"Las Vegas, USA","start_date":"2021-01-09","name":"2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)","end_date":"2021-01-12"},"project":[{"name":"SFB 901 - Project Area C","_id":"4"},{"_id":"16","name":"SFB 901 - Subproject C4"},{"name":"SFB 901","_id":"1"}],"citation":{"ieee":"A. Hasnain and H. Karl, “Learning Flow Scheduling,” in <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>, Las Vegas, USA.","apa":"Hasnain, A., &#38; Karl, H. (n.d.). Learning Flow Scheduling. In <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>. Las Vegas, USA: IEEE Computer Society. <a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>","mla":"Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>, IEEE Computer Society, doi:<a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>.","bibtex":"@inproceedings{Hasnain_Karl, title={Learning Flow Scheduling}, DOI={<a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>}, booktitle={2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)}, publisher={IEEE Computer Society}, author={Hasnain, Asif and Karl, Holger} }","chicago":"Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” In <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>. IEEE Computer Society, n.d. <a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>.","short":"A. Hasnain, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC), IEEE Computer Society, n.d.","ama":"Hasnain A, Karl H. Learning Flow Scheduling. In: <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>. IEEE Computer Society. doi:<a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>"},"doi":"https://doi.org/10.1109/CCNC49032.2021.9369514","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9369514"}],"language":[{"iso":"eng"}],"publication_status":"accepted","date_updated":"2022-01-06T06:54:20Z","year":"2021","title":"Learning Flow Scheduling","author":[{"id":"63288","full_name":"Hasnain, Asif","last_name":"Hasnain","first_name":"Asif"},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger","id":"126"}],"keyword":["Flow scheduling","Deadlines","Reinforcement learning"],"type":"conference","department":[{"_id":"75"}],"date_created":"2020-10-19T14:27:17Z","abstract":[{"lang":"eng","text":"Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load."}],"publication":"2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)"},{"file_date_updated":"2021-07-20T16:19:56Z","citation":{"apa":"Gottschalk, S., Kirchhoff, J., &#38; Engels, G. (2021). Extending Business Model Development Tools with Consolidated Expert Knowledge . In B. Shishkov (Ed.), <i>Business Modeling and Software Design</i>. Sofia. <a href=\"https://doi.org/10.1007/978-3-030-79976-2_1\">https://doi.org/10.1007/978-3-030-79976-2_1</a>","ieee":"S. Gottschalk, J. Kirchhoff, and G. Engels, “Extending Business Model Development Tools with Consolidated Expert Knowledge ,” in <i>Business Modeling and Software Design</i>, Sofia, 2021.","short":"S. Gottschalk, J. Kirchhoff, G. Engels, in: B. Shishkov (Ed.), Business Modeling and Software Design, 2021.","chicago":"Gottschalk, Sebastian, Jonas Kirchhoff, and Gregor Engels. “Extending Business Model Development Tools with Consolidated Expert Knowledge .” In <i>Business Modeling and Software Design</i>, edited by Boris Shishkov, 2021. <a href=\"https://doi.org/10.1007/978-3-030-79976-2_1\">https://doi.org/10.1007/978-3-030-79976-2_1</a>.","mla":"Gottschalk, Sebastian, et al. “Extending Business Model Development Tools with Consolidated Expert Knowledge .” <i>Business Modeling and Software Design</i>, edited by Boris Shishkov, 2021, doi:<a href=\"https://doi.org/10.1007/978-3-030-79976-2_1\">10.1007/978-3-030-79976-2_1</a>.","ama":"Gottschalk S, Kirchhoff J, Engels G. Extending Business Model Development Tools with Consolidated Expert Knowledge . In: Shishkov B, ed. <i>Business Modeling and Software Design</i>. ; 2021. doi:<a href=\"https://doi.org/10.1007/978-3-030-79976-2_1\">10.1007/978-3-030-79976-2_1</a>","bibtex":"@inproceedings{Gottschalk_Kirchhoff_Engels_2021, title={Extending Business Model Development Tools with Consolidated Expert Knowledge }, DOI={<a href=\"https://doi.org/10.1007/978-3-030-79976-2_1\">10.1007/978-3-030-79976-2_1</a>}, booktitle={Business Modeling and Software Design}, author={Gottschalk, Sebastian and Kirchhoff, Jonas and Engels, Gregor}, editor={Shishkov, BorisEditor}, year={2021} }"},"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area C","_id":"4"},{"_id":"17","name":"SFB 901 - Subproject C5"}],"oa":"1","status":"public","conference":{"name":"International Symposium on Business Modeling and Software Design","start_date":"2021-07-05","location":"Sofia","end_date":"2021-07-07"},"has_accepted_license":"1","_id":"20244","user_id":"47208","ddc":["000"],"editor":[{"full_name":"Shishkov, Boris","last_name":"Shishkov","first_name":"Boris"}],"publication":"Business Modeling and Software Design","file":[{"creator":"sego","date_created":"2021-07-20T16:17:55Z","file_size":1101782,"access_level":"open_access","file_name":"BMSD21.pdf","date_updated":"2021-07-20T16:19:56Z","relation":"main_file","content_type":"application/pdf","file_id":"22768"}],"date_created":"2020-11-02T13:27:29Z","type":"conference","department":[{"_id":"66"},{"_id":"534"}],"title":"Extending Business Model Development Tools with Consolidated Expert Knowledge ","year":"2021","author":[{"id":"47208","full_name":"Gottschalk, Sebastian","first_name":"Sebastian","last_name":"Gottschalk"},{"id":"39928","last_name":"Kirchhoff","first_name":"Jonas","full_name":"Kirchhoff, Jonas"},{"id":"107","full_name":"Engels, Gregor","first_name":"Gregor","last_name":"Engels"}],"date_updated":"2022-01-06T06:54:25Z","language":[{"iso":"eng"}],"doi":"10.1007/978-3-030-79976-2_1"},{"date_updated":"2022-01-06T06:57:51Z","publication_status":"published","intvolume":"        15","article_type":"original","year":"2021","title":"The Strong Scaling Advantage of FPGAs in HPC for N-body Simulations","author":[{"first_name":"Johannes","last_name":"Menzel","full_name":"Menzel, Johannes"},{"id":"16153","orcid":"0000-0001-5728-9982","first_name":"Christian","last_name":"Plessl","full_name":"Plessl, Christian"},{"first_name":"Tobias","last_name":"Kenter","full_name":"Kenter, Tobias","id":"3145"}],"publication_identifier":{"issn":["1936-7406","1936-7414"]},"doi":"10.1145/3491235","main_file_link":[{"open_access":"1","url":"https://dl.acm.org/doi/10.1145/3491235"}],"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"N-body methods are one of the essential algorithmic building blocks of high-performance and parallel computing. Previous research has shown promising performance for implementing n-body simulations with pairwise force calculations on FPGAs. However, to avoid challenges with accumulation and memory access patterns, the presented designs calculate each pair of forces twice, along with both force sums of the involved particles. Also, they require large problem instances with hundreds of thousands of particles to reach their respective peak performance, limiting the applicability for strong scaling scenarios. This work addresses both issues by presenting a novel FPGA design that uses each calculated force twice and overlaps data transfers and computations in a way that allows to reach peak performance even for small problem instances, outperforming previous single precision results even in double precision, and scaling linearly over multiple interconnected FPGAs. For a comparison across architectures, we provide an equally optimized CPU reference, which for large problems actually achieves higher peak performance per device, however, given the strong scaling advantages of the FPGA design, in parallel setups with few thousand particles per device, the FPGA platform achieves highest performance and power efficiency."}],"publication":"ACM Transactions on Reconfigurable Technology and Systems","issue":"1","type":"journal_article","department":[{"_id":"27"},{"_id":"518"}],"date_created":"2021-11-30T10:00:31Z","status":"public","user_id":"3145","volume":15,"page":"1-30","_id":"28099","quality_controlled":"1","citation":{"bibtex":"@article{Menzel_Plessl_Kenter_2021, title={The Strong Scaling Advantage of FPGAs in HPC for N-body Simulations}, volume={15}, DOI={<a href=\"https://doi.org/10.1145/3491235\">10.1145/3491235</a>}, number={1}, journal={ACM Transactions on Reconfigurable Technology and Systems}, author={Menzel, Johannes and Plessl, Christian and Kenter, Tobias}, year={2021}, pages={1–30} }","ama":"Menzel J, Plessl C, Kenter T. The Strong Scaling Advantage of FPGAs in HPC for N-body Simulations. <i>ACM Transactions on Reconfigurable Technology and Systems</i>. 2021;15(1):1-30. doi:<a href=\"https://doi.org/10.1145/3491235\">10.1145/3491235</a>","mla":"Menzel, Johannes, et al. “The Strong Scaling Advantage of FPGAs in HPC for N-Body Simulations.” <i>ACM Transactions on Reconfigurable Technology and Systems</i>, vol. 15, no. 1, 2021, pp. 1–30, doi:<a href=\"https://doi.org/10.1145/3491235\">10.1145/3491235</a>.","chicago":"Menzel, Johannes, Christian Plessl, and Tobias Kenter. “The Strong Scaling Advantage of FPGAs in HPC for N-Body Simulations.” <i>ACM Transactions on Reconfigurable Technology and Systems</i> 15, no. 1 (2021): 1–30. <a href=\"https://doi.org/10.1145/3491235\">https://doi.org/10.1145/3491235</a>.","short":"J. Menzel, C. Plessl, T. Kenter, ACM Transactions on Reconfigurable Technology and Systems 15 (2021) 1–30.","ieee":"J. Menzel, C. Plessl, and T. Kenter, “The Strong Scaling Advantage of FPGAs in HPC for N-body Simulations,” <i>ACM Transactions on Reconfigurable Technology and Systems</i>, vol. 15, no. 1, pp. 1–30, 2021, doi: <a href=\"https://doi.org/10.1145/3491235\">10.1145/3491235</a>.","apa":"Menzel, J., Plessl, C., &#38; Kenter, T. (2021). The Strong Scaling Advantage of FPGAs in HPC for N-body Simulations. <i>ACM Transactions on Reconfigurable Technology and Systems</i>, <i>15</i>(1), 1–30. <a href=\"https://doi.org/10.1145/3491235\">https://doi.org/10.1145/3491235</a>"},"oa":"1"}]