ALGORITMO DIVISÃO UTILIZANDO A META-HEURÍSTICA SIMULATED ANNEALING APLICADO NA OTIMIZAÇÃO DE CIRCUITOS REVERSÍVEIS
Bibliographic record
Abstract
Neste trabalho foram implementados dois algoritmos adaptados para otimiza¸c˜ao de circuitos revers´iveis, tendo como custo a quantidade de portas no circuito. O algoritmo Simulated Annealing utilizado como m´etodo de otimiza¸c˜ao, em que junto as regras de reescrita permite a altera¸c˜ao do tamanho do circuito sem alterar a respectiva funcionalidade e o algoritmo Divis˜ao, desenvolvido para resolver a inefiˆencia do Simulated Annealing para circuitos com mais de 50 portas, tendo como objetivo evitar o crescimento indesej´avel do circuito nas itera¸c˜oes iniciais. A funcionalidade do m´etodo Divis˜ao ´e aplicar o m´etodo de otimiza¸c˜ao em pequenas partes do circuito por vez. Para avalia¸c˜ao do desempenho do algoritmo Divis˜ao, utilizou-se a heur´istica Dynamic Template como compara¸c˜ao. De acordo com os resultados, obteve-se maior redu¸c˜ao de custo em 15 circuitos, mesmo custo em 25 circuitos e piores custos em apenas 3 circuitos.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".