The Discriminative Power of Shape an Empirical Study in Time Series Matching
Why this work is in the frame
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Bibliographic record
Abstract
Shape provides significant discriminating power in time series matching of visual or geometric data as required in many important applications in graphics and vision. The well established dynamic time warping (DTW) algorithm and its variants do this matching by determining a non-linear time mapping to minimise euclidean distances between corresponding time-warped points. However the shape of curves is not considered. In this paper, we present a new shape-aware algorithm which uses time and shape correspondence (TSC) at increasing levels of detail to define a similarity measure with an norm to aggregate the results, making it robust to noise and missing data. The norm is implicitly regularised using a shape-based error. Through extensive experiments we empirically show that our algorithm outperforms existing state of the art algorithms, works more effectively with high dimensional data, and handles noise and missing data better. We demonstrate its versatile applicability and comparative performance using a large in-house created gait data base, an action data base from Microsoft, exercise action data from a local company, a large public time series data base from University of California, Riverside and hand movement in quaternion stream data format.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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