Activity-centric streaming of virtual environments and games to mobile devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As mobile devices still have limited battery, processing power, memory, and display size, compared to their PC counterparts, they cannot yet execute Virtual Environment (VE) and Online Gaming applications with the same fidelity and quality as their PC counterpart. In response, we have recently witnessed some research with the goal of real-time delivery of VE and multiplayer game content specifically to fit mobile devices' limitations. In this paper, we present a novel approach to tackle the streaming of 3D objects for such environments. Our goal is to improve the real-time response and interactivity of networked VEs and games through an efficient context-aware 3D object selection and prioritization scheme, before streaming those objects over the network. In our architecture, we take advantage of game context for culling and streaming only the most relevant objects in each frame of gameplay and stream them from the server to a client. Our evaluations show that this technique not only leads to better performance in general, but also increases gameplay experience by helping the player to achieve a higher score in comparison with traditional approaches.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it