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Bibliographic record
Abstract
In this paper, a robust linear programming beamformer (RLPB) is proposed for non-Gaussian signals in the presence of steering vector uncertainties. Unlike most of the existing beamforming techniques based on the minimum variance criterion, the proposed RLPB minimizes the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -norm of the output to exploit the non-Gaussianity. We make use of a new definition of the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm (1 ≤ p ≤ ∞) of a complex-valued vector, which is based on the lp-modulus of complex numbers. To achieve robustness against steering vector mismatch, the proposed method constrains the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -modulus of the response of any steering vector within a specified uncertainty set to exceed unity. The uncertainty set is modeled as a rhombus, which differs from the spherical or ellipsoidal uncertainty region widely adopted in the literature. The resulting optimization problem is cast as a linear programming and hence can be solved efficiently. The proposed RLPB is computationally simpler than its robust counterparts requiring solution to a second-order cone programming. We also address the issue of appropriately choosing the uncertainty region size. Simulation results demonstrate the superiority of the proposed RLPB over several state-of-the-art robust beamformers and show that its performance can approach the optimal performance bounds.
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 | 0.001 |
| 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