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Record W2078477494 · doi:10.1109/newcas.2014.6933979

A hybrid arithmetic transform for precision analysis of floating-point polynomial specifications

2014· article· en· W2078477494 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNumerical Methods and Algorithms
Canadian institutionsMcGill University
Fundersnot available
KeywordsAffine arithmeticInterval arithmeticSaturation arithmeticPolynomialFloating pointAlgorithmAffine transformationArithmeticArbitrary-precision arithmeticMathematicsComputer science

Abstract

fetched live from OpenAlex

Precision analysis of floating-point polynomial data-flow-graphs in terms of the error measure Maximum Mismatch (MM) is a challenging verification problem in computer arithmetic and embedded systems. In this paper by pairing the spectral technique Arithmetic Transform (AT) with Interval Arithmetic (IA), we introduce a static analysis to compute an overestimation of MM for floating-point polynomial specifications. The proposed analysis is applicable to fixed-point designs as well. We compare our solution with Affine Arithmetic (AA), the Gappa tool, which uses IA, as well as simulation-based methods, on a set of polynomial benchmarks. Experiments show that our analysis results in much lower overestimations of MM compared to previous work.

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.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.972
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.292
Teacher spread0.257 · 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

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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