University of Glasgow

Cardiac motion tracking from cine magnetic resonance previously either lacked anatomical correspondence across subjects or relied on a slow, error-prone manual atlas fit. CardioDrift generalises coherent point drift to jointly register the left-ventricular endocardium, epicardium and right-ventricular atlas surfaces, and estimates per-slice translations inside the same probabilistic model to correct slice misalignment, fitting and propagating the atlas automatically from short-axis segmentation contours alone. On 1,301 UK Biobank subjects and the M&M dataset, its rigid fitting and non-rigid propagation lead every distance and overlap metric against the strongest baseline.

Cardiac motion tracking from cine magnetic resonance previously either lacked anatomical correspondence across subjects or relied on a slow, error-prone manual atlas fit. CardioDrift generalises coherent point drift to jointly register the left-ventricular endocardium, epicardium and right-ventricular atlas surfaces, and estimates per-slice translations inside the same probabilistic model to correct slice misalignment, fitting and propagating the atlas automatically from short-axis segmentation contours alone. On 1,301 UK Biobank subjects and the M&M dataset, its rigid fitting and non-rigid propagation lead every distance and overlap metric against the strongest baseline.

Molecular data in lung adenocarcinoma are high-dimensional relative to the number of observed events, so concatenating them into a survival model invites overfitting and unstable feature selection. CAISL writes predicted risk as a clinical Cox risk plus a sparse molecular correction, and penalizes how much that correction varies across resampled training cohorts. On 470 TCGA-LUAD patients with 119 observed deaths, mean C-index across five independent cross-validation runs was 0.7246 versus 0.6970 for clinical Cox, a paired gain of 0.0276 (95% CI 0.005–0.050); this is internal validation and still needs an independent cohort.

Molecular data in lung adenocarcinoma are high-dimensional relative to the number of observed events, so concatenating them into a survival model invites overfitting and unstable feature selection. CAISL writes predicted risk as a clinical Cox risk plus a sparse molecular correction, and penalizes how much that correction varies across resampled training cohorts. On 470 TCGA-LUAD patients with 119 observed deaths, mean C-index across five independent cross-validation runs was 0.7246 versus 0.6970 for clinical Cox, a paired gain of 0.0276 (95% CI 0.005–0.050); this is internal validation and still needs an independent cohort.

Judgment prediction has mostly reasoned over case facts, yet real proceedings are shaped by exchanges among judge, petitioner and respondent. ArguGenCourt assigns three independent GPT-4o agents these roles and lets them converse in rounds, seeding prompts with courtroom utterances retrieved from the factually closest precedent document. On criminal petitioner arguments, local precedent retrieval in the multi-agent setting reaches a Legal-sim of 0.8921 against 0.8288 for fact-based conversation under the same retrieval; feeding the generated arguments into judgment prediction gives 0.55 accuracy versus 0.48 for zero-shot.

Judgment prediction has mostly reasoned over case facts, yet real proceedings are shaped by exchanges among judge, petitioner and respondent. ArguGenCourt assigns three independent GPT-4o agents these roles and lets them converse in rounds, seeding prompts with courtroom utterances retrieved from the factually closest precedent document. On criminal petitioner arguments, local precedent retrieval in the multi-agent setting reaches a Legal-sim of 0.8921 against 0.8288 for fact-based conversation under the same retrieval; feeding the generated arguments into judgment prediction gives 0.55 accuracy versus 0.48 for zero-shot.

The work introduces Flow-of-Thought (FoT), a framework that trains coordinate-, time-, and action-conditioned spatial U-Nets with flow matching on rotation orbits and cumulative shortest-path prefixes, then freezes the learned dynamics; at inference, the source and its horizontal reflection serve as competing hypotheses selected by foreground-weighted reconstruction energy. On locked tests FoT reaches 100.0% on Tetris and 99.0% on colored shapes, with maze endpoint IoU 97.5% and 100% goal reach; under frozen transfer to 133 BLINK Multi-view public validation pairs, the orbit-trained flow reaches 72.2% versus 63.9% for its endpoint-only control.

The work introduces Flow-of-Thought (FoT), a framework that trains coordinate-, time-, and action-conditioned spatial U-Nets with flow matching on rotation orbits and cumulative shortest-path prefixes, then freezes the learned dynamics; at inference, the source and its horizontal reflection serve as competing hypotheses selected by foreground-weighted reconstruction energy. On locked tests FoT reaches 100.0% on Tetris and 99.0% on colored shapes, with maze endpoint IoU 97.5% and 100% goal reach; under frozen transfer to 133 BLINK Multi-view public validation pairs, the orbit-trained flow reaches 72.2% versus 63.9% for its endpoint-only control.