Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Bibliographic record
Abstract
papers in this volume were presented at the 44th Annual ACM Symposium on Theory of Computing (STOC 2012), held in New York, NY, May 20-22, 2012. Symposium was sponsored by the ACM Special Interest Group on Algorithms and Computation Theory (SIGACT). The program committee met on January 27-28, 2012 in Toronto, Ontario, Canada, and selected 90 papers from 303 detailed abstracts submitted. One pair of papers was merged into a single talk, and one paper was withdrawn, resulting in a total of 88 talks presented at the conference, and 89 papers in this proceedings. submissions were not formally refereed, and many of them represent reports of continuing research. It is expected that most of them will appear in a more polished and complete form in scientific journals. In addition to the regular program, there were several other avenues for research presentations. program committee invited Michael Kearns to present a tutorial in the morning of May 19, 2012, before the main conference, on the topic of Algorithmic Trading and Computational Finance. In the afternoon of May 19, 2012, STOC hosted four workshops, in the areas of: Computational Sustainability, Algorithms for Distributed and Streaming Data, Algorithms for Memory-Sensitive Computing, and the Unique Games Conjecture and Related Advances. Lastly, many additional papers were presented during a poster session held on the evening of May 20, 2012. From the many outstanding candidates, the STOC program committee selected the following two papers as recipients of the Best Paper Award: Linear vs. Semidefinite Extended Formulations: Exponential Separation and Strong Lower Bounds by Samuel Fiorini, Serge Massar, Sebastian Pokutta, Hans Raj and Ronald de Wolf, and The Cell Probe Complexity of Dynamic Range Counting, by Kasper Green Larsen. latter of these two papers is also the recipient of the Danny Lewin Best Student Paper Award.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.146 | 0.057 |
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".