Simple and semi-dynamic structures for cache-oblivious planar orthogonal range searching
Bibliographic record
Abstract
In this paper, we develop improved cache-oblivious data structures for two- and three-sided planar orthogonal range searching. Our main result is an optimal static structure for two-sided range searching that uses linear space and supports queries in O(logBN + T/B) memory transfers, where B is the block size of any level in a multi-level memory hierarchy and T is the number of reported points. Our structure is the first linear-space cache-oblivious structure for a planar range searching problem with the optimal O(logBN + T/B) query bound. The structure is very simple, and we believe it to be of practical interest.We also show that our two-sided range search structure can be constructed cache-obliviously in O(N logBN) memory transfers. Using the logarithmic method and fractional cascading, this leads to a semi-dynamic linear-space structure that supports two-sided range queries in O(log2 N + T/B) memory transfers and insertions in O(log2N ⋅ logB N) memory transfers amortized. This structure is the first (semi-)dynamic structure for any planar range searching problem with a query bound that is logarithmic in the number of elements in the structure and linear in the output size.Finally, using a simple standard construction, we also obtain a static O(N log2 N)-space structure for three-sided range searching that supports queries in the optimal bound of O(logB N + T/B) memory transfers. These bounds match the bounds of the best previously known structure for this problem; but our structure is much simpler, simple enough, we believe, to be of practical interest.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".