A Visibility-Driven Approach for Zone Management in Simulations
Bibliographic record
Abstract
Massively multi-user simulations aim to support a large number of users while keeping the communication among the parties synchronous and highly interactive. In this paper, we present a collaborative virtual architecture that supports a large number of users by dividing the virtual environment into multiple adjacent hexagonal regions in order to manage the interest of the entities. A master node, called a hybrid node, constructs a Peer-to-Peer (P2P) overlay network to connect and manage nodes that lie in its region. Messaging is done at the application layer rather than the network layer, and a node-joining algorithm is proposed to reflect the underlying network physical topology onto the data distribution pathways among the end hosts to enhance the system performance. In addition, the introduction of a buffer zone between adjacent zones reduces the number of connections and disconnections that occur when a node frequently moves at the boundary of the two zones and provides more resilience to the system. We also attempt to shift the messaging among parties in one region from a zone-based method to a visibility-driven method to refine their interest by enabling message filtering. The effectiveness of this collaboration architecture is tested through a prototype implementation and a high level application.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".