Fast circular dictionary-matching algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Circular string matching is a problem which naturally arises in many contexts. It consists in finding all occurrences of the rotations of a pattern of length m in a text of length n . There exist optimal worst- and average-case algorithms for circular string matching. Here, we present a suboptimal average-case algorithm for circular string matching requiring time $\mathcal{O}$ ( n ) and space $\mathcal{O}$ ( m ). The importance of our contribution is underlined by the fact that the proposed algorithm can be easily adapted to deal with circular dictionary matching. In particular, we show how the circular dictionary-matching problem can be solved in average-case time $\mathcal{O}$ ( n + M ) and space $\mathcal{O}$ ( M ), where M is the total length of the dictionary patterns, assuming that the shortest pattern is sufficiently long. Moreover, the presented average-case algorithms and other worst-case approaches were also implemented. Experimental results, using real and synthetic data, demonstrate that the implementation of the presented algorithms can accelerate the computations by more than a factor of two compared to the corresponding implementation of other approaches.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it