A Pipelining Implementation for Parsing X-ray Diffraction Source Data and Removing the Background Noise
Bibliographic record
Abstract
Synchrotrons can be used to generate X-rays in order to probe materials at the atomic level. One approach is to use X-ray diffraction (XRD) to do this. The data from an XRD experiment consists of a sequence of digital image files which for a single scan could consist of hundreds or even thousands of digital images. Existing analysis software processes these images individually sequentially and is usually used after the experiment is completed. The results from an XRD detector can be thought of as a sequence of images, generated during the scan by the X-ray beam. If these images could be analyzed in near real-time, the results could be sent to the researcher running the experiment and used to improve the overall experimental process and results. In this paper, we report on a stream processing application to remove background from XRD images using a pipelining implementation. We describe our implementation techniques of using IBM Infosphere Streams for parsing XRD source data and removing the background. We present experimental results showing the super-linear speedup attained over a purely sequential version of the algorithm on a quad-core machine. These results demonstrate the potential of making good use of multi-cores for high-performance stream processing of XRD images.
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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 | 0.000 |
| Open science | 0.000 | 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 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".