Fusing of binary correlated data with unknown statistics
Bibliographic record
Abstract
We address the problem of distributed target detection with correlated observations, i.e., where local detectors transmit their binary decisions to a fusion center but these decisions are correlated in an unknown manner. We propose three Separating Function Estimation Tests (SFETs) and a Generalized Likelihood Ratio Test (GLRT) to fuse the binary data. SFETs convert the detection problem into a problem of estimating a separating function that is positive under the alternative (to the null) hypothesis. Detection decisions are achieved by comparing the estimate of the Separating Function (SF) with a threshold, where the threshold is set to satisfy a probability of false alarm constraint. The SFETs are derived based on the asymptotically optimal SF (AOSF) theorem (SFET1), the Euclidean distance (SFET2) and Kullback-Leibler (K-L) divergence (SFET3) of the probability mass function (pmf) of the observations under each hypothesis. Since the correlations are unknown, we formulate a linear optimization program to estimate the pmf. The simulation results show that the probability of detection of the SFETs using the AOSF and the Euclidean distance is greater than the GLRT and the SFET using K-L divergence. Interestingly, when the observations are independent SFET1and SFET2provide optimal performance for the problem.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".