SOFT TISSUE DEFORMATION WITH NEURAL DYNAMICS FOR SURGERY SIMULATION
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
Soft tissue deformation is of great importance to virtual-reality-based-surgery simulation. This paper presents a new neural-dynamics-based methodology for simulation of soft tissue deformation from the perspective of energy propagation. A novel neural network is established to propagate the energy generated by an external force among mass points of a soft tissue. The stability of the proposed neural network system is proved by using the Lyapunov stability theory. A potential-based method is presented to derive the internal forces from the natural energy distribution established by the neural dynamics. Integration with a haptic device has been achieved for interactive deformation simulation with force feedback. The proposed methodology not only accommodates isotropic, anisotropic and inhomogeneous materials by simple modification of the control coefficients, but it also accepts large-range deformations.
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