Searching BWT against Pattern Matching Machine to Find Multiple String Matches
Bibliographic record
Abstract
In this paper, we discuss an indexing method for solving the multiple string pattern matching problem, by which we are given a set of short strings R = {r1, ..., rl} and required to locate all substrings of a target string s such that each of them matches an rj in R. The main idea is to construct a pattern matching machine A and transform the reverse of s to a BWT-array as an index, denoted as BWT(s̅), and search A against it. During the process, the failure function of A is used to decrease the subranges of BWT(s̅) to be searched at each step. In addition, we change a single-character checking against BWT(s̅) to a multiple-character checking, by which multiple searches of BWT(s̅) are reduced to a single scanning. In this way, high efficiency can be achieved. Extensive experiments have been conducted, which shows that our method works better than almost all the existing methods for this problem.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".