Bibliographic record
Abstract
Let Fqbe the field of q elements. An (n, k)-affine extractor is a mapping D : Fqn→ {0,1} such that for any k-dimensional affine subspace X ⊆ Fqn, 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(n2), 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 · d2and p = char(Fq) > 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)2). 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 : Fqn→ Fq. Define QR : Fq→ {0,1} by QR(x) = 1 if and only if x is a quadratic residue. Then, the function D : Fqn→ {0,1} defined by D(x) =△QR(T(xd)) 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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".