Extreme-Dynamic-Range Sensing: Real-Time Adaptation to Extreme Signals
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
The new concept of coupled dynamic dynamic-range (D<;super>2<;/super>R) compositing operates by assembling sensor information, such as images or audio, from multiple "strong" and "weak" samplings or sensor snapshots, whose sensitivities drift and change over time, as lighting conditions or sound conditions change over time in their amplitude-domain properties. The authors introduce a feedback-control method to automatically adjust multiple exposure settings for compositing to increase the dynamic range of a sensory process such as video capture. The method uses a cost function to express uncertainty in the measurements from each sensor, along with salience detection, which are then fed into a dynamic control system. The system responds in real time to changing ambient conditions and sensor motion, asymptotically tracking the sensor controls to minimize uncertainty to capture an extremely high dynamic range for compositing.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.002 | 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 it