Fast Compressed Self-indexes with Deterministic Linear-Time Construction
Bibliographic record
Abstract
We introduce a compressed suffix array representation that, on a text T of length n over an alphabet of size \(\sigma \) , can be built in O ( n ) deterministic time, within \(O(n\log \sigma )\) bits of working space, and counts the number of occurrences of any pattern P in T in time \(O(|P| + \log \log _w \sigma )\) on a RAM machine of \(w=\Omega (\log n)\) -bit words. This time is almost optimal for large alphabets ( \(\log \sigma =\Theta (\log n)\) ), and it outperforms all the other compressed indexes that can be built in linear deterministic time, as well as some others. The only faster indexes can be built in linear time only in expectation, or require \(\Theta (n\log n)\) bits. For smaller alphabets, where \(\log \sigma = o(\log n)\) , we show how, by using space proportional to a compressed representation of the text, we can build in linear time an index that counts in time \(O(|P|/\log _\sigma n + \log _\sigma ^\epsilon n)\) for any constant \(\epsilon >0\) . This is almost RAM-optimal in the typical case where \(w=\Theta (\log n)\) .
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".