Rolling sphere problems on spaces of constant curvature
Bibliographic record
Abstract
Abstract The rolling sphere problem on Euclidean space consists of determining the path of minimal length traced by the point of contact of the oriented unit sphere $\mathbb{S}^{n}$ as it rolls on $\mathbb{E}^{n}$ without slipping between two points of $\mathbb{E}^{n}\times SO_{n+1}(\mathbb{R})$ . This problem is extended to situations in which an oriented sphere $\mathbb{S}_{\rho}^{n}$ of radius ρ rolls on a stationary sphere $\mathbb{S}_{\sigma}^{n}$ and to the hyperbolic analogue in which the spheres $\mathbb{S}_{\rho}^{n}$ and $\mathbb{S}_{\sigma}^{n}$ are replaced by the hyperboloids $\mathbb{H}_{\rho}^{n}$ and $\mathbb{H}_{\sigma}^{n}$ respectively. The notion of “rolling” is defined in an isometric sense: the length of the path traced by the point of contact is measured by the Riemannian metric of the stationary manifold, and the orientation of the rolling object is measured by a matrix in its isometry group. These rolling problems are formulated as left invariant optimal control problems on Lie groups whose Hamiltonian extremal equations reveal two remarkable facts: on the level of Lie algebras the extremal equations of all these rolling problems are governed by a single set of equations, and the projections onto the stationary manifold of the extremal equations havingI4=0, whereI4is an integral of motion, coincide with the elastic curves on this manifold. The paper then outlines some explicit solutions based on the use of symmetries and the corresponding integrals of motion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".