Bibliographic record
Abstract
Broadcasting is a fundamental information dissemination problem, wherein a message is sent from one vertex, the originator, to all other vertices in a graph. In k -broadcasting, an informed vertex can sends the message to at most k uninformed neighbors in each time unit. This thesis presents several algorithms to perform efficient k -broadcasting. The algorithm KBT generates the optimal k -broadcast scheme in trees, while the algorithm KBC finds the k -broadcast center of a given tree. This thesis presents an efficient heuristic for k -broadcasting. The heuristic has a low time complexity and generates fast k -broadcast schemes in many network topologies. A k -broadcast graph G is a graph on n vertices where the k -broadcast time of G is [Special characters omitted.] log k +1 n [Special characters omitted.] . B k (n) stands for the minimum possible number of edges in a k -broadcast graph on n vertices. A k -broadcast graph on n vertices with B k (n) edges is a minimum k -broadcast graph, which is denoted by k -mbg. This thesis presents several new k -mbg's and an improved lower bound on B k (n)
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".