Multiple choice tries and distributed hash tables
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this article we consider tries built from n strings such that each string can be chosen from a pool of k strings, each of them generated by a discrete i.i.d. source. Three cases are considered: k = 2, k is large but fixed, and k ˜ c log n . The goal in each case is to obtain tries as balanced as possible. Various parameters such as height and fill‐up level are analyzed. It is shown that for two‐choice tries a 50% reduction in height is achieved when compared with ordinary tries. In a greedy online construction when the string that minimizes the depth of insertion for every pair is inserted, the height is only reduced by 25 % . To further reduce the height by another 25 % , we design a more refined online algorithm. The total computation time of the algorithm is O ( n log n ). Furthermore, when we choose the best among k ≥ 2 strings, then for large but fixed k the height is asymptotically equal to the typical depth in a trie. Finally, we show that further improvement can be achieved if the number of choices for each string is proportional to log n . In this case highly balanced trees can be constructed by a simple greedy algorithm for which the difference between the height and the fill‐up level is bounded by a constant with high probability. This, in turn, has implications for distributed hash tables, leading to a randomized ID management algorithm in peer‐to‐peer networks such that, with high probability, the ratio between the maximum and the minimum load of a processor is O(1). © 2008 Wiley Periodicals, Inc. Random Struct. Alg., 2009
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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