On Designing Adaptive Data Structures with Adaptive Data "Sub"-Structures
Bibliographic record
Abstract
Data structures are key pillars for optimizing computational efficiency, as they contribute in no small measure to enhancing the "speed" in the accessing and subsequent processing of data.The need for enhancing speed is critical for almost all applications and domains, and this is most relevant when real-time or near real-time efficiency is desired.This thesis proposes the use of "Adaptive" Data-Structures (ADSs) that invoke reinforcement learning schemes from the theory of Learning Automata (LA).These operate in conjunction with select re-organization rules to update themselves as they receive queries from the Environment of interaction.The result of such a process is the subsequent minimization of the cost associated with query accesses.The Environments under consideration are those that exhibit a so-called "locality of reference", and are referred to as Non-stationary Environments (NSEs).A hierarchy of data "sub"-structures is used to design Singly-Linked Lists (SLLs) First and foremost, I give thanks to the Lord, my God, for He is good, and His mercies endure forever.Let all the earth praise the Name of the Lord God and His Son Jesus Christ, who is blessed forever and ever.I am particularly grateful to my Supervisor Prof. B. John Oommen.He has been a rock and a support to me.He taught me all that I know about the field of learning automata, and by extension, the broader body of reinforcement learning.Prof. Oommen was my mentor and received me as his son.He was an example of what it means to live in holiness and righteousness.I am privileged to sit and learn under him.I would like to offer special thanks to my parents
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".