Searching BWT against Pattern Matching Machine to Find Multiple String Matches
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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