Variability-Aware Latency Amelioration in Distributed Environments
Bibliographic record
Abstract
Application designers of collaborative distributed virtual environments must account for the influence of the network connection and its detrimental effects on user performance. Based upon analysis and classification of existing latency compensation techniques, this paper introduces a novel approach to latency amelioration in the form of a two-tier predictor-estimator framework. The technique is variability-aware due to its proactive sender-side prediction of a pose a variable time into the future. The prediction interval required is estimated based on current and past network delay characteristics. This latency estimate is subsequently used by a Kalman filter-based predictor to replace the measurement event with a predicted pose that matches the event's arrival time at the receiving workstation. The compensation technique was evaluated in a simulation through an offline playback of real head motion data and network delay traces collected under a variety of real network conditions. The experimental results indicate that the variability-aware approach significantly outperforms a state-of-the-art one, which assumes a constant system delay
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".