Bibliographic record
Abstract
Preface The 19th International Symposium on Scientific Computing, Computer Arithmetic and Verified Numerical Computation (SCAN) was originally planned to be organized by the Institute of Informatics of the University of Szeged (SZTE) in Szeged, Hungary, in the year 2020. Due to the pandemic situation, the Scientific Committee of SCAN decided to postpone the meeting to September 13–15, 2021 and to have it in a fully online format.The members of the Scientific Committee were the following representatives of the topics of the conference: G. Alefeld (Karlsruhe, Germany), A. Bauer (Ljubljana, Slovenia), J. B. van den Berg (Amsterdam, the Netherlands), G.F. Corliss (Milwaukee, USA), T. Csendes (Szeged, Hungary), R.B. Kearfott (Lafayette, USA), V. Kreinovich (El Paso, USA), J.-P. Lessard (Montreal, Canada), W. Luther (Duisburg, Germany), S. Markov (Sofia, Bulgaria), G. Mayer (Rostock, Germany), J.-M. Muller (Lyon, France), M. Nakao (Tokyo, Japan), T. Ogita (Tokyo, Japan), S. Oishi (Tokyo, Japan), K. Ozaki (Tokyo, Japan), M. Plum (Karlsruhe, Germany), A. Rauh (Brest, France), N. Revol (Lyon, France), J. Rohn (Prague, Czech Republic), S. Rump (Hamburg, Germany/Tokyo, Japan), S. Shary (Novosibirsk, Russia), W. Tucker (Uppsala, Sweden), W. Walter (Dresden, Germany), J. Wolff von Gudenberg (Würzburg, Germany), and N. Yamamoto (Tokyo, Japan). The members of the Organizing Committee were: Balázs Bánhelyi, Tibor Csendes, Boglárka G.-Tóth, Viktor Homolya, Tamás Vinkó, and Dániel Zombori. During SCAN, more than 50 participants were present and 48 talks in several fields of reliable computation and its applications were given, and organized in 18 thematic sessions. The plenary speakers were Fabienne Jézéquel (Sorbonne University, France), Marko Lange (Hamburg University of Technology, Germany), J.D. Mireles James (Florida Atlantic University, USA), together with the Moore Prize winners Marko Lange and Siegfried Rump (Waseda University, Japan).The open-access scientific journal Acta Cybernetica offered to publish paper versions of selected presentations after a careful peer review process. Altogether, 7 papers were accepted for publication in the present special issue of Acta Cybernetica. The full program of the conference, the collection of all abstracts, and further information can be found at https://www.inf.u-szeged.hu/scan2020/. Andreas Rauh, Balázs Bánhelyi Guest Editors
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.558 | 0.426 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".