A distributed latency-aware architecture for massively multi-user virtual environments
Bibliographic record
Abstract
Massively multi-user virtual environments (MMVE) incorporate computer graphics, sound and haptics to simulate the experience of real-time interaction among multiple users in a shared three-dimensional virtual world. Such applications therefore deal with the distribution of updates among their users to provide them with a common sense of time and place while interacting in the virtual environment. This paper introduces a new distributed architecture for message exchange in MMVE applications. We propose the use of Hilbert space filling curve, due to its good locality preserving characteristics, as a mechanism for indexing users' three dimensional locations to a one dimension. We also propose our novel routing strategy based on this architecture, which is executed among the points that are mapped over the Hilbert curve. Such hierarchical routing requires only few entries in the routing tables while it is guaranteed to converge in few steps. The routing procedure is based on guiding the packets to their destinations through traversing the Hilbert curve in tree format. A three dimensional Hilbert curve of order K can be described with a tree of K levels. In making a forwarding decision, a node finds the best neighbour that moves the message closer to its destination over this tree. This avoids the complexity of locating the points over the Hilbert curve as the mapping is actually performed throughout the routing process. The performance evaluation results show that the propose architecture can efficiently handle update exchange among MMVE users.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".