Bibliographic record
Abstract
Imagine you are playing a videogame in which you impersonate a wizard who needs to create a potion in order to enchant your enemies. Through a desktop haptic probe, shaped as a baton, you are able to stir and feel the magical fluid inside a bowl. As you follow the potion recipe, you feel how the fluid changes its viscosity, density, velocity and other properties. Various hapto-visual user interfaces enable users to interact in three-dimensions with the digital world and receive realistic kinesthetic and tactile cues in a computer-generated environment. So far solid or deformable objects have been experimented for haptic-tactile feedback. In this paper we innovate by devising techniques that enable the haptical rendering of shape-less objects, such as fluids. Focusing on the real-time performance to enhance the user's experience, the system imitates the physical forces generated by the real-time fluid animation, stirring movements and fluid changes. We achieved real-time 3D fluid and overcame the challenges that arise during the integration of both haptics and graphics workspaces, the free-view visualization of 3D fluid volume, and the rendering of haptic forces. These fluid interaction techniques with haptic feedback have wide possible applications including game development and haptic communities.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".