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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.054 |
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; both teacher heads agree on what is shown here.
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".