Proceedings of the 2016 annual ACM Conference on SIGSIM Principles of Advanced Discrete Simulation
Bibliographic record
Abstract
Welcome to the annual ACM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS), the flagship conference for ACM's Special Interest Group on Simulation and Modeling (SIGSIM). The conference dates back to the annual PADS conference which began in 1985. Originally focusing exclusively on parallel and distributed simulation technologies and applications, the conference has since broadened its scope to encompass all aspects of modeling and simulation, including parallel and distributed execution, focusing on topics at the intersection of modeling and simulation and computer science. We are pleased to hold this year's conference in Banff, Alberta Canada. Banff provides a wonderful setting for the conference and offers many opportunities for outdoor activities and recreation. We hope you will enjoy the conference, the town of Banff, and the surrounding mountains. We wish to thank the many individuals who help make this year's conference possible. First, we thank the program committee members and additional external referees who provided timely reviews of the paper submissions, and participated in discussions concerning paper selection. In addition, we recognize the other members of the organizing committee for their contributions to the conference, including Jason Liu (Proceedings Chair), Philip Wilsey (Ph.D. Colloquium Chair), Dong (Kevin) Jin (Publicity Chair), and Holly Rush (Registration Chair and administrative support). Osman Balci did a terrific job with the web site, providing updates as soon as they were submitted. We thank the ACM Special Interest Group on Simulation both for their continued support, and for providing fellowships to students to help enable them to attend the conference. Finally, we wish to express special thanks to our two keynote speakers, Frederica Darema and David Jefferson for sharing their thoughts and insights with conference attendees. We have an excellent program to offer our attendees this year. New this year is a special interest in data-driven simulation with several sessions dedicated to this emerging topic. All papers submitted to the conference were rigorously reviewed with all papers receiving 3 referee reports. We thank the program committee and additional referees for their diligent efforts to provide timely, critical reviews and feedback to the authors. This process resulted in a total of 20 submissions accepted as full papers and 6 papers accepted as short papers. We expect everyone will appreciate the high-quality papers and presentations in the conference this year. This year's candidates for the best paper award are listed below. As is customary, the winning paper will be announced at the conference itself. Selected papers from the conference will also be invited to be extended to create a special issue of the ACM Transactions on Modeling and Computer Simulation (TOMACS) journal. DSSnet: A Smart Grid Modeling Platform Combining Electrical Power Distribution System Simulation and Software Defined Networking Emulation by Christopher Hannon, Jiaqi Yan and Dong Jin Modeling a Million-Node Slim Fly Network using Parallel Discrete Event Simulation by Noah Wolfe, Misbah Mubarak, Christopher D. Carothers, Philip Carns, and Robert Ross Automated Memoization for Parameter Studies Implemented in Impure Languages by Mirko Stoffers, Daniel Schemmel, Oscar Soria Dustmann and Klaus Wehrle We congratulate the authors of the paper winning the best paper award for the 2015 conference. Well done! FatTreeSim: Modeling a Large-scale Fat-Tree Network for HPC Systems and Data Centers Using Parallel and Discrete Event Simulation by Ning Liu, Adnan Haider, Xian-He Sun and Dong Jin. This year's Ph.D. Colloquium included eight students who prepared brief presentations as well as posters concerning their research. The Ph.D. Colloquium speaker was Dong (Kevin) Jin whose keynote presentation was entitled Early Career Experiences. We thank Kevin for his presentation as well as the students for their participation. The meeting this year will include many interesting presentations as well as excellent opportunities for networking. We hope you enjoy the 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.101 | 0.036 |
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".