Destination-driven shortest path tree algorithms
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
Shortest Path Tree (SPT) is the most widely used type of tree for multicast provisioning due to its simplicity and low per-destination cost. An SPT minimizes the accumulated cost, individually, from the source of a group to each destination of the group. However, SPTs have not considered the overall resource utilization in their constructions. This work aims at building cost-effective SPTs by enhancing link sharing between destinations of a group. We achieve this goal by introducing destination-driven characteristic into SPT constructions. Specifically, each destination is connected with the source via a shortest path. When equal cost multiple paths are available, priority is given to the one biasing through a destination among all such routes. We accordingly present the design of an algorithm building destination-driven SPTs. To achieve further improved performance in resource utilization, we also present an algorithm, which is designed to further enhance link sharing among the destinations of a group while meeting a maximum path length constraint for each destination. Simulation results are used to demonstrate the high performance of the proposed algorithms.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it