A Progressive Multidimensional Particle Swarm Optimizer for magnetic core placement in Dipole Field Navigation
Bibliographic record
Abstract
This paper tackles the inverse problem of finding optimal configurations of magnetic gradient sources in Dipole Field Navigation (DFN), a magnetic navigation method proposed recently for the direct targeting of drugs. In DFN, a limited number of these gradient sources, called the cores, must be positioned properly around a patient in a Magnetic Resonance Imaging scanner to induce the required directional forces on the navigated therapeutic carriers. To overcome some limitations of the previous approach for solving this problem, here we propose a novel and conceptually simple multidimensional variant of the well-known Particle Swarm Optimization (PSO) algorithm. This variant, called Progressive Multidimensional PSO (PMD-PSO), enables a tradeoff between the quality and the complexity of the solutions by progressively increasing the number of dimensions in the search space. We apply this algorithm to the core placement problem using an improved fitness function for the evaluation of a core configuration given a vascular path towards a target. Experiments on simulated vasculatures show that, while the approach can effectively solve this inverse problem, PMD-PSO exhibits better performances for DFN compared with two other multidimensional PSO variants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".