Time-Aware Non-Uniform Rational Basis Spline (T-NURBS)
Bibliographic record
Abstract
Explicitly adopting time as a parameter, the defi-nition of non-uniform rational basis spline profile known short as NURBS is revisited and updated. This results in another NURBS definition that is aware of time, and not only the geometry. Also, it can jointly exist as-is at the CAD-CAM side, and at the motion numeric controller side without resorting to segmentation, curve fitting, and interpolation techniques usually used when extracting motion information from the standard geometric NURBS profiles. This gives rise to the notion of “what you see is what you get” when the proposed NURBS definition is used. First, working at the jerk signal level and by using quadratic polynomials with time as the independent variable, quintic polynomials are obtained at the position level and are smoothly glued together to form the needed basis functions that facilitate introducing time-aware splines. Similarly, the trigonometric sine function is used to define another set of time-aware basis functions. Second, and as with standard NURBS, the herein-defined time-aware splines are extended and put into the rational polynomial form such that the proposed time-aware NURBS structure is revealed. Despite being normalized, the time signature used to define the basis functions persists once velocity, acceleration, and jerk profiles are obtained. At the level of the coefficient, these kinematical quantities are neatly written using vector notation that- with the aid of a developed algorithm- reduces the computation burden at the motion numeric controller side during real-time execution. This results in a smooth motion with reduced feed rate variation while adhering to any imposed kinematical constraints. The usefulness, and simplicity of the proposed approach is mainly demonstrated through numeric simulation where the proposed concept of “what you see is what you get” is verified.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".