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Optimally Sorting Evolving Data.

, , , , and . ICALP, volume 107 of LIPIcs, page 81:1-81:13. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2018)

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Windows into Geometric Events: Data Structures for Time-Windowed Querying of Temporal Point Sets., , , , and . CCCG, Carleton University, Ottawa, Canada, (2014)Superpatterns and Universal Point Sets., , , and . J. Graph Algorithms Appl., 18 (2): 177-209 (2014)Small Superpatterns for Dominance Drawing., , and . ANALCO, page 92-103. SIAM, (2014)Scheduling Autonomous Vehicle Platoons Through an Unregulated Intersection., , , and . ATMOS, volume 54 of OASIcs, page 5:1-5:14. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2016)Graph Drawing Contest Report., , , and . GD, volume 11282 of Lecture Notes in Computer Science, page 609-617. Springer, (2018)Quadratic Time Algorithms Appear to be Optimal for Sorting Evolving Data., , , , and . CoRR, (2018)ERGMs are Hard., , and . CoRR, (2014)Parallel Equivalence Class Sorting: Algorithms, Lower Bounds, and Distribution-Based Analysis., , and . SPAA, page 265-274. ACM, (2016)The Online House Numbering Problem: Min-Max Online List Labeling., , , and . ESA, volume 87 of LIPIcs, page 33:1-33:15. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2017)Track Layouts, Layered Path Decompositions, and Leveled Planarity., , , , and . Algorithmica, 81 (4): 1561-1583 (2019)