Best Paper Awards
Bibliographic record
Abstract
The DATE 2012 Best Paper is: "COMPOSITIONAL SYSTEM-LEVEL DESIGN EXPLORATION WITH PLANNING OF HIGH-LEVEL SYNTHESIS" by Hung-Yi Liu, Michele Petracca, and Luca P. Carloni, Columbia University, USA. The DATE 2013 best papers are: (1) "AVICA: AN ACCESS-TIME VARIATION INSENSITIVE L1 CACHE ARCHITECTURE" by Seokin Hong and Soontea Kim - Korea Advanced Institute of Science and Technology, South Korea; (2) "SCC THERMAL MODEL IDENTIFICATION VIA ADVANCED BIAS-COMPENSATED LEAST-SQUARES" by Roberto Diversi, Andrea Bartolini, Andrea Tilli, Francesco Beneventi and Luca Benini - University of Bologna, Italy; (3) "HANDLING DISCONTINUOUS EFFECTS IN MODELING SPATIAL CORRELATION OF WAFER-LEVEL ANALOG/RF TESTS" by Ke Huang and Yiorgos Makris - University of Texas at Dallas, USA and Nathan Kupp - Yale University, USA and John Carulli - Texas Instruments, USA; and (4) "FIFO CACHE ANALYSIS FOR WCET ESTIMATION: A QUANTITATIVE APPROACH" by Nan Guan, Xinping Yang and Wang Yi - Uppsala University, Sweeden and Mingsong Lv - Northeastern University, Canada.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.616 | 0.511 |
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".