{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T09:58:40Z","timestamp":1740131920101,"version":"3.37.3"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput."],"published-print":{"date-parts":[[2023,1,1]]},"DOI":"10.1109\/tc.2022.3204225","type":"journal-article","created":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T20:09:58Z","timestamp":1662408598000},"page":"97-110","source":"Crossref","is-referenced-by-count":1,"title":["Network Calculus With Flow Prolongation \u2013 A Feedforward FIFO Analysis Enabled by ML"],"prefix":"10.1109","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6522-4385","authenticated-orcid":false,"given":"Fabien","family":"Geyer","sequence":"first","affiliation":[{"name":"Technical University of Munich, Munich, Germany"}]},{"given":"Alexander","family":"Scheffler","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Ruhr University Bochum, Bochum, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0483-5107","authenticated-orcid":false,"given":"Steffen","family":"Bondorf","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Ruhr University Bochum, Bochum, Germany"}]}],"member":"263","reference":[{"key":"ref39","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"gilmer","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2021.12.015"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3004964"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3468890"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICOIN48656.2020.9016603"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3043600"},{"key":"ref36","first-page":"44","article-title":"The DiscoDNC v2: A comprehensive tool for deterministic network calculus","author":"bondorf","year":"2014","journal-title":"Proc 8th Int Conf Perform Eval Methodol Tools"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.1998.665083"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1002\/9781119440284"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC50000.2020.9219693"},{"key":"ref40","article-title":"Gated graph sequence neural networks","author":"li","year":"2016","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2020.3025806"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-16558-0_15"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2000.858698"},{"article-title":"Service curves in network calculus: Dos and don’ts","year":"2009","author":"bouillard","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2013.40"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2017.8056981"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/RTCSA.2019.8864582"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2003.12.004"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2014.2332071"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.peva.2018.12.003"},{"journal-title":"Network Calculus A Theory of Deterministic Queuing Systems for the Internet","year":"2001","author":"le boudec","key":"ref4"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-85172-9_8"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-04086-x"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s11241-012-9153-1"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3000405"},{"key":"ref5","first-page":"1","article-title":"Improving performance bounds in feed-forward networks by paying multiplexing only once","author":"schmitt","year":"2008","journal-title":"Proc 14th GI\/ITG Conf Meas Modelling Eval Comput Commun Syst"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3388831.3388848"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.4108\/ICST.VALUETOOLS2008.4388"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2017.7996996"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/RTAS52030.2021.00021"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737496"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.peva.2021.102250"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"16:1","DOI":"10.1145\/3084453","article-title":"Quality and cost of deterministic network calculus – design and evaluation of an accurate and fast analysis","volume":"1","author":"bondorf","year":"2017","journal-title":"Proc ACM Meas Anal Comput Syst"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3000907"},{"key":"ref42","article-title":"Fast graph representation learning with PyTorch Geometric","author":"fey","year":"2019","journal-title":"Proc ICLR Workshop Representation Learn Graphs Manifolds"},{"article-title":"Relational inductive biases, deep learning, and graph networks","year":"2018","author":"battaglia","key":"ref23"},{"key":"ref41","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Proc 33rd Int Conf Neural Inf Process Syst"},{"key":"ref44","article-title":"Improving policy gradient by exploring under-appreciated rewards","author":"nachum","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref26","article-title":"Learning a SAT solver from single-bit supervision","author":"selsam","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref25","first-page":"537","article-title":"Combinatorial optimization with graph convolutional networks and guided tree search","author":"li","year":"2018","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"}],"container-title":["IEEE Transactions on Computers"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/12\/9984045\/09875957.pdf?arnumber=9875957","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T21:12:39Z","timestamp":1673298759000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9875957\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,1]]},"references-count":45,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tc.2022.3204225","relation":{},"ISSN":["0018-9340","1557-9956","2326-3814"],"issn-type":[{"type":"print","value":"0018-9340"},{"type":"electronic","value":"1557-9956"},{"type":"electronic","value":"2326-3814"}],"subject":[],"published":{"date-parts":[[2023,1,1]]}}}