An Improved Bound for an Extension of Fine and Wilf’s Theorem and Its Optimality
Bibliographic record
Abstract
Considering two DNA molecules which are Watson-Crick (WK) complementary to each other “equivalent” with respect to the information they encode enables us to extend the classical notions of repetition, period, and power. WK-complementarity has been modelled mathematically by an antimorphic involution θ, i.e., a function θ such that θ(xy) = θ(y)θ(x) for any x, y ∞ Σ*, and θ 2 is the identity. The WK-complementarity being thus modelled, any word which is a repetition of u and θ(u) such as uu, uθ(u)u, and uθ(u)θ(u)θ(u) can be regarded repetitive in this sense, and hence, called a θ-power of u. Taking the notion of θ-power into account, the Fine and Wilf’s theorem was extended as “given an antimorphic involution θ and words u, v, if a θ-power of u and a θ-power of v have a common prefix of length at least b(|u|, |v|) = 2|u| + |v| – gcd(|u|, |v|), then u and v are θ-powers of a same word.” In this paper, we obtain an improved bound b′(|u|, |v|) = b(|u|, |v|) – [gcd(|u|, |v|)/2]. Then we show all the cases when this bound is optimal by providing all the pairs of words (u, v) such that they are not θ-powers of a same word, but one can construct a θ-power of u and a θ-power of v whose maximal common prefix is of length equal to b′(|u|, |v|) − 1. Furthermore, we characterize such words in terms of Sturmian words.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".