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Record W1694286979 · doi:10.1109/pacrim.1997.620353

On the relation between disjunctive decomposition and ROBDD variable ordering

2002· article· en· W1694286979 on OpenAlexaff
Elena Dubrova, D. Michael Miller, J.C. Muzio

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBinary decision diagramBoolean functionRelation (database)Variable (mathematics)DecompositionSet (abstract data type)Function (biology)MathematicsBinary relationComputer scienceAlgorithmDiscrete mathematicsPartially ordered setComponent (thermodynamics)Theoretical computer scienceCombinatoricsData miningProgramming language

Abstract

fetched live from OpenAlex

The relation between disjunctive decomposition of functions and a variable ordering minimizing the size of a ROBDD (Reduced Ordered Binary Decision Diagram) is considered. We show that a best ordering for a function with a disjunctive decomposition cannot always be directly determined from the best orderings for the component functions. We also demonstrate that keeping the variables from a bound set of the function adjacent does not always guarantee obtaining the ROBDD for the function with a minimal number of nodes.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.900
Threshold uncertainty score0.164

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.274
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations3
Published2002
Admission routes1
Has abstractyes

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