On the synthesis of MVL functions using input and output phase assignments
Bibliographic record
Abstract
In this paper, a number of decomposition based mapping techniques are proposed. In these techniques, the synthesis problem is formulated as a mapping from an input matrix to an output matrix. The minimization is obtained by constructing a matching-count matrix. The entries of the matching-count matrix MC/sub i,j/ represent the number of entry matches between the input variable number i in the input matrix (X) and the output function number j in the output matrix (Y). It then selects those input-output pairings that give the maximum matching count, thus maximizing the number of switching operations which can be eliminated in the realization of MVL functions. The proposed techniques are classified as: output-phase with complement, input-phase with and without complement. Numerical results are presented to show that the proposed techniques result in significant reduction in the number of switching operators required for the implementation of 5000 randomly generated r-valued functions (for r=3, 4, and 5).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".