On interacting with physics-based models of graphical objects
Bibliographic record
Abstract
Enhancing graphical objects whose behaviors are governed by the laws of physics is an important requirement in modeling virtual physical environments. In such environments, the user can interact with graphical objects and is able to either feel the simulated reaction forces through a physical computer interface such as a force feedback mouse or through such interactions, objects behave in a natural way. One of the key requirements for such interaction is determination of the type of contact between the user controlled object and the objects representing the environment. This paper presents an approach for reconstructing the contact configuration between two objects. This is accomplished through usage of the time history of the motion of the approaching objects for inverse trajectory mapping of polygonal representation. In the case of deformable objects and through usage of mass-spring-damper system this paper also presents a special global filter that can map the local deformation of an object to the adjacent vertices of polygonal mesh. In addition to offering a fast computational framework, the proposed method also offers more realistic representation of the deformation. The results of this paper are shown through detailed examples and comparison analysis using different computational platforms.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".