Min-cost multicast networks in Euclidean space
Bibliographic record
Abstract
Space information flow is a new field of research recently proposed by Li and Wu [1], [2]. It studies the transmission of information in a geometric space, where information flows can be routed along any trajectories, and can be encoded wherever they meet. The goal is to satisfy given end-to-end unicast/multicast throughput demands, while minimizing a natural bandwidth-distance sum-product (network volume). Space information flow models the design of a blueprint for a minimum-cost network. We study the multicast version of the space information flow problem, in Euclidean spaces. We present a simple example that demonstrates the design of an information network is indeed different from that of a transportation network. We discuss properties of optimal multicast network embedding, prove that network coding does not make a difference in the basic case of 1-to-2 multicast, and prove upper-bounds on the number of relay nodes required in an optimal acyclic multicast network.
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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".