Nondeterministic State Complexity of Site-Directed Operations
Bibliographic record
Abstract
In this thesis, we consider the nondeterministic state complexity of PCR-inspired (polymerase chain reaction) operations. Site-directed operations are used to formally describe the behavior of certain DNA (deoxyribonucleic acid) editing methods which need to identify a subsequence in a host DNA strand prior to editing. These operations can be considered as language operations acting to match patterns between two sets of strings. The site-directed insertion and deletion operations, insert or delete into a host string based on a directing string. The directing string must have a non-empty outfix that matches a substring in the host before operating. Prefix and suffix directed insertion are similar to site-directed insertion except, instead of matching a non-empty outfix, a non-empty prefix or suffix is matched before insertion. We consider the nondeterministic state complexity of site-directed insertion and deletion. Constructing a nondeterministic finite automaton (NFA) for the operation provides an upper bound for the state complexity of the operation. Our construction improves the earlier upper bound in the literature. Existing literature did not give lower bounds for the nondeterministic state complexity of site-directed insertion and deletion. Using the fooling set method we establish lower bounds that are fairly close to the upper bound, albeit the lower bound is not tight. The other operations considered are those that identify subsets of a language. The prefix, suffix, infix and outfix operations check to see if a word from another language is contained as a prefix, suffix, infix or outfix of the target word. We introduce NFA constructions which apply these operations over regular languages, and establish bounds for the prefix, suffix, infix and outfix operations.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".