Path and Ancestor Queries over Trees with Multidimensional Weight Vectors
Bibliographic record
Abstract
We consider an ordinal tree T on n nodes, with each node assigned a d-dimensional weight vector w in {1,2,...,n}^d, where d in N is a constant. We study path queries as generalizations of well-known {orthogonal range queries}, with one of the dimensions being tree topology rather than a linear order. Since in our definitions d only represents the number of dimensions of the weight vector without taking the tree topology into account, a path query in a tree with d-dimensional weight vectors generalize the corresponding (d+1)-dimensional orthogonal range query. We solve {ancestor dominance reporting} problem as a direct generalization of dominance reporting problem, in time O(lg^{d-1}{n}+k) and space of O(n lg^{d-2}n) words, where k is the size of the output, for d >= 2. We also achieve a tradeoff of O(n lg^{d-2+epsilon}{n}) words of space, with query time of O((lg^{d-1} n)/(lg lg n)^{d-2}+k), for the same problem, when d >= 3. We solve {path successor problem} in O(n lg^{d-1}{n}) words of space and time O(lg^{d-1+epsilon}{n}) for d >= 1 and an arbitrary constant epsilon > 0. We propose a solution to {path counting problem}, with O(n(lg{n}/lg lg{n})^{d-1}) words of space and O((lg{n}/lg lg{n})^{d}) query time, for d >= 1. Finally, we solve {path reporting problem} in O(n lg^{d-1+epsilon}{n}) words of space and O((lg^{d-1}{n})/(lg lg{n})^{d-2}+k) query time, for d >= 2. These results match or nearly match the best tradeoffs of the respective range queries. We are also the first to solve path successor even for d = 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".