{"date_updated":"2023-08-28T12:24:57Z","project":[{"_id":"1","name":"SFB 901: SFB 901","grant_number":"160364472"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"_id":"12","name":"SFB 901 - B4: SFB 901 - Subproject B4"}],"date_created":"2023-05-24T07:55:24Z","language":[{"iso":"eng"}],"user_id":"477","citation":{"mla":"Dongol, Brijesh, et al. “Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement.” 33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland, edited by Bartek Klin et al., vol. 243, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, p. 31:1–31:23, doi:10.4230/LIPIcs.CONCUR.2022.31.","chicago":"Dongol, Brijesh, Gerhard Schellhorn, and Heike Wehrheim. “Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement.” In 33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland, edited by Bartek Klin, Slawomir Lasota, and Anca Muscholl, 243:31:1–31:23. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. https://doi.org/10.4230/LIPIcs.CONCUR.2022.31.","apa":"Dongol, B., Schellhorn, G., & Wehrheim, H. (2022). Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement. In B. Klin, S. Lasota, & A. Muscholl (Eds.), 33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland (Vol. 243, p. 31:1–31:23). Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.CONCUR.2022.31","ieee":"B. Dongol, G. Schellhorn, and H. Wehrheim, “Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement,” in 33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland, 2022, vol. 243, p. 31:1–31:23, doi: 10.4230/LIPIcs.CONCUR.2022.31.","short":"B. Dongol, G. Schellhorn, H. Wehrheim, in: B. Klin, S. Lasota, A. Muscholl (Eds.), 33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, p. 31:1–31:23.","bibtex":"@inproceedings{Dongol_Schellhorn_Wehrheim_2022, series={LIPIcs}, title={Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement}, volume={243}, DOI={10.4230/LIPIcs.CONCUR.2022.31}, booktitle={33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland}, publisher={Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, author={Dongol, Brijesh and Schellhorn, Gerhard and Wehrheim, Heike}, editor={Klin, Bartek and Lasota, Slawomir and Muscholl, Anca}, year={2022}, pages={31:1–31:23}, collection={LIPIcs} }","ama":"Dongol B, Schellhorn G, Wehrheim H. Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement. In: Klin B, Lasota S, Muscholl A, eds. 33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland. Vol 243. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2022:31:1–31:23. doi:10.4230/LIPIcs.CONCUR.2022.31"},"author":[{"last_name":"Dongol","full_name":"Dongol, Brijesh","first_name":"Brijesh"},{"first_name":"Gerhard","last_name":"Schellhorn","full_name":"Schellhorn, Gerhard"},{"last_name":"Wehrheim","id":"573","full_name":"Wehrheim, Heike","first_name":"Heike"}],"volume":243,"publication":"33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland","department":[{"_id":"77"}],"year":"2022","page":"31:1–31:23","type":"conference","_id":"45248","intvolume":" 243","editor":[{"full_name":"Klin, Bartek","last_name":"Klin","first_name":"Bartek"},{"last_name":"Lasota","full_name":"Lasota, Slawomir","first_name":"Slawomir"},{"last_name":"Muscholl","full_name":"Muscholl, Anca","first_name":"Anca"}],"doi":"10.4230/LIPIcs.CONCUR.2022.31","status":"public","series_title":"LIPIcs","title":"Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement","publisher":"Schloss Dagstuhl - Leibniz-Zentrum für Informatik"}