Bibliographic record
Abstract
Signal processing methods have been changed substantially over the last several decades. Traditional sampling theorem of Shannon-Nyquist states that the sampling rate must be at least twice the maximum frequency presented in the signal; however, sampling at the Nyquist rate is inefficient because the signals of interest contain only a small number of significant frequencies relative to the band limit, although the locations of the frequencies may not be known a priori. Recently, compressive sensing (CS) has made a paradigmatic step in the way information is presented, stored, transmitted and recovered, by which we can acquire and reconstruct sparse signals from sub-Nyquist incoherent measurements. Three key ingredients of successfully implementing compressed sampling technique are sparsible/compressible probed signal, reliable hardware design, and low-cost computational algorithm. In this paper, we focused on two aspects about the robust sampling of sparsible/compressible signal, in particular, the design of compressed sampling hardware and the robust reconstruction via sparse Bayesian analysis. Primary results showed the high performance of proposed strategies.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".