Automated strength-interval curve generation using actors
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Strength-interval (SI) curves are used by physiologists to quantify the response of excitable tissue as a function of the strength and timing of an electrical stimulus. In the context of cardiac electrophysiology, SI curves characterize the refractoriness of cardiac tissue as a function of inter-stimulus interval length. Although conventionally collected experimentally, this type of information can now more conveniently be obtained through computational simulation. Nevertheless, the computational generation of SI curves can be labor-intensive and time-consuming due to its iterative nature, the number and size of computations required, and the amount of manual researcher intervention involved. The objective of this study is to use the Actor Model of concurrent computation to automate the process of SI curve generation, relieving much of the burden from the researcher while maximizing the use of available computational resources. METHODS: The C++ Actor Framework is used to create an automated tool for controlling the openCARP simulation platform. An SI curve is generated for the bidomain model of electrophysiology through the use of sophisticated parallelization techniques, e.g., dynamic information passing between parallel simulations, facilitated by the use of actors. Computational resource management is optimized by the dynamic monitoring, assessment, and reallocation based on each actor's current simulation state in relation to all other actors. RESULTS: A bidomain SI curve with 31 data points that takes 27.5 h to compute conventionally using 80 CPU cores is now generated in 15.4 h. This is over 40% faster than using conventional parallel programming techniques with MPI. Furthermore, it requires no researcher intervention, which can add significantly to the time to solution. CONCLUSION: Novel parallelization techniques enabled via the Actor Model significantly improve the efficiency of computational SI curve generation, both from the viewpoints of computation and labor intensiveness. This improvement in efficiency has implications for future studies involving cardiac refractory tissue, along with other types of excitable tissue, including the rapid generation of both general and patient-specific SI curves and the use of these curves for design and in silico testing of new therapeutic tools such as personalized pacemakers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".