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Record W2403413752

Fourier Concentration from Shrinkage.

2013· article· en· W2403413752 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

VenueElectronic colloquium on computational complexity · 2013
Typearticle
Languageen
FieldComputer Science
TopicComputability, Logic, AI Algorithms
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsExponentCombinatoricsDegree (music)Fourier seriesFourier transformMathematicsConnection (principal bundle)Function (biology)ShrinkagePhysicsMathematical analysisStatisticsGeometry
DOInot available

Abstract

fetched live from OpenAlex

For a class $${\mathcal{F}}$$F of formulas (general de Morgan or read-once de Morgan), the shrinkage exponent$${\Gamma_{\mathcal{F}}}$$ΓF is the parameter measuring the reduction in size of a formula $${F\in\mathcal{F}}$$FźF after $${F}$$F is hit with a random restriction. A Boolean function $${f\colon \{0,1\}^n\to\{1,-1\}}$$f:{0,1}nź{1,-1} is Fourier-concentrated if, when viewed in the Fourier basis, $${f}$$f has most of its total mass on low-degree coefficients. We show a direct connection between the two notions by proving that shrinkage implies Fourier concentration: For a shrinkage exponent $${\Gamma_{\mathcal{F}}}$$ΓF, a formula $${F\in\mathcal{F}}$$FźF of size $${s}$$s will have most of its Fourier mass on the coefficients of degree up to about $${s^{1/\Gamma_{\mathcal{F}}}}$$s1/ΓF. More precisely, for a Boolean function $${f\colon\{0,1\}^n\to\{1,-1\}}$$f:{0,1}nź{1,-1} computable by a formula of (large enough) size $${s}$$s and for any parameter $${r > 0}$$r>0, $$\sum_{A\subseteq [n]\; :\; |A|\geq s^{1/\Gamma} \cdot r} \hat{f}(A)^2\leq s\cdot{\mathscr{polylog}}(s)\cdot exp\left(-\frac{r^{\frac{\Gamma}{\Gamma-1}}}{s^{o(1)}} \right),$$źA⊆[n]:|A|źs1/Γ·rf^(A)2źs·polylog(s)·exp-rΓΓ-1so(1),where $${\Gamma}$$Γ is the shrinkage exponent for the corresponding class of formulas: $${\Gamma=2}$$Γ=2 for de Morgan formulas, and $${\Gamma=1/\log_2(\sqrt{5}-1)\approx 3.27}$$Γ=1/log2(5-1)ź3.27 for read-once de Morgan formulas. This Fourier concentration result is optimal, to within the $${o(1)}$$o(1) term in the exponent of $${s}$$s. As a standard application of these Fourier concentration results, we get that subquadratic-size de Morgan formulas have negligible correlation with parity. We also show the tight $${\Theta(s^{1/\Gamma})}$$ź(s1/Γ) bound on the average sensitivity of read-once formulas of size $${s}$$s, which mirrors the known tight bound $${\Theta(\sqrt{s})}$$ź(s) on the average sensitivity of general de Morgan $${s}$$s-size formulas.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.709
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.021
GPT teacher head0.249
Teacher spread0.228 · 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