Use Of Transputers In A 3-d Positron Emission Tomograph
Bibliographic record
Abstract
The use of a VME-based transputer network as a parallel processing engine for positron volume imaging is dis- cussed. We find that the speedups of parallel networks depend on two major factors: the ratio of computation to communica- tion for a task, and the size of the task, and we give a simple model to explore the limits to speedups. Through actual imple- mentation we show that real-time PVI data acquisition can be achieved with about 20 transputer nodes, and we estimate that 3-D image reconstruction can be achieved within 10 min using 200 nodes. Larger images and a larger number of histograms can readily be accommodated using the same parallel algo- rithms as our model places no limits to the size of the images. The versatility and scalability of transputers makes them very suitable for use in PVI tomographs in that the same transputers can be used for speeding up data acquisition, image reconstruc- tion, and display.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".