Identification of Linear Time Varying Systems using Basis Pursuit
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
System identification involves creating mathematical models of systems using measurements of their inputs and outputs. Linear time-varying systems form an important sub-class of models that require the use of specialized system identification techniques. One such approach involves expanding the time-varying parameters onto a set of temporal basis functions and then estimating the resulting expansion coefficients. This, however, requires the estimation of a large number of parameters and often results in extreme noise sensitivity. In this paper a novel algorithm for identifying time-varying systems is presented. It combines a temporal expansion with a term selection step that uses the "Least Absolute Shrinkage and Selection Operator", or Lasso. The Lasso term selection technique constructs a model structure with a nearly minimal number of non-zero terms, and hence with relatively low estimation variances. The algorithm is demonstrated by using it to detect changes in the dynamic stiffness of the human elbow immediately following the onset of a broadband perturbation.
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