SUB-OBJECT PRUNING ALGORITHM: A NOVEL PRUNING STRATEGY FOR DISTANCE DETERMINATION IN COMPLEX DYNAMIC ENVIRONMENTS
Bibliographic record
Abstract
Computing the minimum distance between objects is known to be a complex problem particularly in compact dynamic environments. Determining the minimum distance between complex objects has been solved by many different authors. Some methods rely on computational geometry techniques, while others rely on numerical optimization techniques. Most algorithms can only deal with convex objects and thus the concave objects need to be partitioned into smaller purely-convex sub-objects. This usually results in increased run times as all pair-wise sub-object combinations need to be tested. In this paper, a two-stage distance determination method is proposed to perform precise and fast distance calculations for concave objects. In the first stage, a pruning strategy is used to obtain the closest pair of sub-objects. In the second stage, the set of closest features is used in a local optimization method to find the exact distance. Numerical results for different complex objects showing the proposed algorithm's capabilities are included. The preliminary implementation of the proposed algorithm has proven to be robust and computationally efficient.
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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 | 0.000 |
| Open science | 0.001 | 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 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".