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Record W314096538 · doi:10.1109/ccc.2010.14

Simple Affine Extractors Using Dimension Expansion

2010· article· en· W314096538 on OpenAlexaff
Matt DeVos, Ariel Gabizon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCoding theory and cryptography
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOmegaAffine transformationDimension (graph theory)CombinatoricsConstant (computer programming)MathematicsAffine combinationAffine spaceField (mathematics)Simple (philosophy)Type (biology)Discrete mathematicsPhysicsPure mathematicsQuantum mechanicsComputer science

Abstract

fetched live from OpenAlex

Let F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> be the field of q elements. An (n, k)-affine extractor is a mapping D : F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> → {0,1} such that for any k-dimensional affine subspace X ⊆ F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , D(x) is an almost unbiased bit when x is chosen uniformly from X. Loosely speaking, the problem of explicitly constructing affine extractors gets harder as q gets smaller and easier as k gets larger. This is reflected in previous results: When q is 'large enough', specifically q = Ω(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), Gabizon and Raz construct affine extractors for any k ≥ 1. In the 'hardest case', i.e. when q = 2, Bourgain constructs affine extractors for k ≥ δn for any constant (and even slightly subconstant) δ > 0. Our main result is the following: Fix any k ≥ 2 and let d = 5n/k. Then whenever q > 2 · d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and p = char(F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> ) > d, we give an explicit (n, k)-affine extractor. For example, when k = δn for constant δ > 0, we get an extractor for a field of constant size Ω((1/δ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). We also get weaker results for fields of arbitrary characteristic (but can still work with a constant field size when k = δn for constant δ > 0). Thus our result may be viewed as a 'field-size/dimension' tradeoff for affine extractors. For a wide range of k this gives a new result, but even for large k where we do not improve (or even match) the previous result of, we believe that our construction and proof have the advantage of being very simple: Assume n is prime and d is odd, and fix any non-trivial linear map T : F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> → F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> . Define QR : F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> → {0,1} by QR(x) = 1 if and only if x is a quadratic residue. Then, the function D : F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> → {0,1} defined by D(x) = <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">△</sup> QR(T(x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> )) is an (n, k)-affine extractor. Our proof uses a result of Heur, Leung and Xiang giving a lower bound on the dimension of products of subspaces.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.284

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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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