On using conditional rotations and randomized heuristics for self-organizing ternary search tries
Bibliographic record
Abstract
A Ternary Search Trie (TST) is a highly efficient dynamic dictionary structure applicable for strings and textual data. The strings are accessed based on a set of access probabilities and are to be arranged using a TST. We consider the scenario where the probabilities are not known a priori, and is time-invariant. Our aim is to adaptively restructure the TST so as to yield the best access or retrieval time. Unlike the case of lists and binary search trees, where numerous methods have been proposed, in the case of the TST, currently, the number of reported adaptive schemes are few. In this paper, we consider various self-organizing schemes that were applied to Binary Search Trees, and apply them to TSTs. Three new schemes, which are the splaying, the conditional rotation and the randomization heuristics, have been proposed, tested and comparatively presented. The results demonstrate that the conditional rotation heuristic is the best when compared to other heuristics that are considered in the paper. As far as we know, this paper reports the first search-based adaptive strategies for TSTs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".