Bibliographic record
Abstract
When the real world and the digital world meet, they create a shared virtual space called the metaverse, involving multiple virtual communities in which users interact with one another in a highly immersive and interactive fashion. Due to the pressing need for scalability when handling millions of users in the metaverse regardless of the applications and services provided, it is imperative to deploy multiple servers across geographically distributed datacenters around the world. In this paper, we envision, design, and implement a new rendezvous service between a large number of users and a collection of geographically distributed servers, taking into account the latencies and pair-wise network bandwidth between the users and the servers, as well as bandwidth and processing capacity constraints on the servers themselves when handling the users. Our new rendezvous service is designed with simplicity and efficiency as its primary objective, and uses a revised design of the Deferred Acceptance algorithm to guarantee a stable matching between the users in the metaverse and its servers. As a case study, our rendezvous service has been implemented in the context of a federated learning application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".