A Performance Evaluation of a Hybrid Multicast Transport Protocol for a Distributed Collaborative Virtual Simulation of a Brain Tumor Tele-Surgery Class of Applications
Bibliographic record
Abstract
Haptic interfaces have been designed for brain surgery simulation may prove to be especially useful for training surgeons to conduct minimally invasive procedures and remote surgery using tele-operators. However, it well known that collaborative, haptic, audio and visual environments (C-HAVE) suffer from setbacks due to network delay, scalability, reliability and synchronization problem when the users are geographically distributed. In this paper, we focus upon a Brain Tumor Tele-Surgery application that is based on closely coupled and highly synchronized haptic tasks that require a high-level of coordination among the participants. We considered four main protocols: the synchronous collaboration transport protocol (SCTP), the selective reliable transmission protocol (SRTP), the reliable multicast transport protocol (RMTP) and the scalable reliable multicast (SRM) and presented a hybrid protocol that is able to satisfy all the CVE and C-HAVE requirements and discuss its implementation using brain tumor tele-surgery application as a case study.
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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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".