Computational efficient multidimensional singular spectrum analysis
Why this work is in the frame
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Bibliographic record
Abstract
We present a computational efficient multi-dimensional Singular Spectrum Analysis method for the recovery and de-noising of multi-dimensional seismic data. Compared to the other implementations of Singular Spectrum Analysis method, the proposed algorithm does not require building multi-level block Hankel trajectory matrices. The key is to replace the singular value decomposition of a multi-level block Hankel matrix by the randomized QR decomposition. We also present a new strategy in which anti-diagonal averaging of the multi-level block Hankel matrix is efficiently computed via convolution. The new algorithm significantly decreases the computational cost and memory requirement of Singular Spectrum Analysis data recovery. We test the effectiveness of the method through reconstructing a small patch of a real data set acquired at the Western Canadian Sedimentary Basin. Presentation Date: Wednesday, September 27, 2017 Start Time: 4:20 PM Location: 360A Presentation Type: ORAL
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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 | 0.000 | 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) | 0.001 | 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