Bibliographic record
Abstract
This paper presents two new techniques with time-modulated (i.e., time expansion or compression) discrete sequences. One for generating such sequences and the other for estimating the time delay between these sequences. The Time-Varying Discrete Fourier Transform (TVDFT) is introduced to generate time-modulated discrete signals. For time delay estimation, two time-modulated received signals are cross-correlated in the frequency domain, via TVDFT. Compensation of their time-modulation effect is realized by searching for perfect matching between them. A new optimization technique is specifically developed for maximizing the cross-correlation function. The optimization vector has as elements the time delay and a preassinged number of time-modulation coefficients. This optimization technique is a modification of the N-STEP Newton method and guarantees convergence to the local maximum. At high Signal-to-Noise Ratio (SNR), the scheme attains the Cramer-Rao Lower Bound (CRLB) in variance. Simulation results are given to demonstrate the effectiveness of the scheme and to confirm the validity of the development.
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.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".