Concordia University

Individual-level trajectory generators can produce plausible single traces, yet once many individuals are aggregated their displacement distribution often misses the heavy tail of real data. ScaleTraj splits next-location prediction into two steps: a Scale Selection Head decides which spatial scale a move belongs to, and a Scale-Aware Cell Selection Head predicts the destination conditioned on that scale. On GeoLife and Tencent Beijing it lowers both the Scale Fidelity Profile error and the deviations of the fitted truncated power-law parameters relative to the compared model-driven and data-driven baselines.

Individual-level trajectory generators can produce plausible single traces, yet once many individuals are aggregated their displacement distribution often misses the heavy tail of real data. ScaleTraj splits next-location prediction into two steps: a Scale Selection Head decides which spatial scale a move belongs to, and a Scale-Aware Cell Selection Head predicts the destination conditioned on that scale. On GeoLife and Tencent Beijing it lowers both the Scale Fidelity Profile error and the deviations of the fitted truncated power-law parameters relative to the compared model-driven and data-driven baselines.

SCCGBench replaces text-similarity scoring for service-call code with virtual-execution hits, and on 612 test samples the strongest model lands the correct host, endpoint, and HTTP method together only 3.76% of the time.

SCCGBench replaces text-similarity scoring for service-call code with virtual-execution hits, and on 612 test samples the strongest model lands the correct host, endpoint, and HTTP method together only 3.76% of the time.

VesselSim proposes a two-stage framework for universal 3D blood vessel segmentation: a stochastic, geometry-driven vascular simulation (recursive branching, curvature-controlled growth, collision-aware topology) with domain-randomized intensity synthesis generates 16,500 anatomically plausible 3D angiographic volumes, a 3D U-Net is trained solely on this synthetic data, and a test-time adaptation strategy via a self-supervised mask reconstruction decoder adapts at inference, achieving zero-shot performance competitive with state-of-the-art vascular segmentation foundation models on multiple real datasets spanning MR and CT and several anatomical regions including the brain and kidneys, without real annotated data during training.

VesselSim proposes a two-stage framework for universal 3D blood vessel segmentation: a stochastic, geometry-driven vascular simulation (recursive branching, curvature-controlled growth, collision-aware topology) with domain-randomized intensity synthesis generates 16,500 anatomically plausible 3D angiographic volumes, a 3D U-Net is trained solely on this synthetic data, and a test-time adaptation strategy via a self-supervised mask reconstruction decoder adapts at inference, achieving zero-shot performance competitive with state-of-the-art vascular segmentation foundation models on multiple real datasets spanning MR and CT and several anatomical regions including the brain and kidneys, without real annotated data during training.