HDCluster: High-Degree Graph Clustering for Robust Analysis of Single Molecule Localization Microscopy
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
Clustering is a fundamental task in data analysis: grouping similar objects together into distinguishable subsets. Here, we introduce HDCluster, a novel high-degree graph-based clustering algorithm designed to effectively and rapidly handle various real-world clustering applications, particularly in the context of super-resolution single molecule localization microscopy (SMLM). HDCluster efficiently handles datasets with large and variable numbers of clusters, without requiring prior knowledge of the cluster count, relying on only one parameter. The high speed and efficiency of HDCluster allow it to handle large SMLM datasets with millions of localizations. A comprehensive quantitative comparison against state-of-the-art clustering methods using simulated, public, and real-world datasets demonstrates that HDCluster outperforms other clustering algorithms in terms of time efficiency and clustering performance measures, such as ARI and AMI. HDCluster is particularly robust to noise, making it a promising and effective tool for various clustering tasks in big-data settings, such as SMLM.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 it