Bibliographic record
Abstract
The 38th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing (PODC 2019) was held on July 29-August 2, 2019 at the Double Tree Hilton hotel in Toronto, Canada. With three keynotes, over 40 accepted papers, over 20 accepted brief announcements, two workshops, and roughly 150 attendants, PODC 2019 constituted a composition of fascinating improvements in many areas of distributed and parallel computing. On the evening of the 31st of July, the conference banquet was held on a cruise over beautiful Lake Ontario and included the best papers award ceremony. The best paper award went to Yi-Jun Chang, and Thatchaphol Saranurak for their work titled, \Improved Distributed Expander Decomposition and Nearly Optimal Triangle Enumeration" [19]; two best student paper awards were given to Yi-Jun Chang, Manuela Fischer, and Yufan Zheng for their work titled, \The Complexity of (Δ + 1) Coloring in Congested Clique, Massively Parallel Computation, and Centralized Local Computation" [17] which was co-authored with Mohsen Gha ari, and Jara Uitto, and to Michal Dory and Dean Leitersdorf for the work on \Fast Approximate Shortest Paths in the Congested Clique" [16] which was co-authored with Keren Censor-Hillel, and Janne H. Korhonen. Congratulations to all the awardees and a special congratulations to Yi-Jun Chang for having received both awards! This review would be incomplete without mentioning perhaps one of the most notable results in the eld in recent years which was uploaded to the online archive just days before the conference gathered, and which was not presented at PODC 2019 but a ected many works presented at the conference: the work titled, ¶olylogarithmic-Time Deterministic Network Decomposition and Distributed Derandomization" [48] by Vaclav Rozhon, and Mohsen Gha ari. Throughout the entire conference there was talk regarding the implications of this work, culminating with perhaps one of the more memorable moments of PODC 2019, where Mohsen described this result during his talk on a di erent paper which he co-authored with Fabian Kuhn - Mohsen quoted from his and Kuhn's paper [31]: \we provide results that are in some sense the strongest that one can achieve, barring a major breakthrough", and on the next slide was that major breakthrough - a picture of Vaclav Rozhon along with the rst page of [48]. Congratulations to the authors on this work! We hope that this review will give readers the opportunity to experience some of PODC 2019 and potentially attend the conference in future years. Thank you to the organizers and authors for a captivating and though-provoking conference!
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.336 | 0.340 |
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".