A patch-based TDA approach for CT volumes outperforms cubical complex persistent homology and radiomic features in classification accuracy while reducing computation time.
International Business Machines Company
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Oriented per-voxel height images plus map-informed dual-resolution sampling yield robust real-time LiDAR-inertial odometry where standard LIO diverges.
SparseSAM achieves 2x faster inference and 2.8x memory reduction in SAM with only 0.004 mIoU loss at 0.4 density via Stripe-Sort Attention and Residual-Consistency MLP.
citing papers explorer
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A Novel Patch-Based TDA Approach for Computed Tomography Imaging
A patch-based TDA approach for CT volumes outperforms cubical complex persistent homology and radiomic features in classification accuracy while reducing computation time.
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BIEVR-LIO: Robust LiDAR-Inertial Odometry through Bump-Image-Enhanced Voxel Maps
Oriented per-voxel height images plus map-informed dual-resolution sampling yield robust real-time LiDAR-inertial odometry where standard LIO diverges.
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SparseSAM: Structured Sparsification of Activations in Segment Anything Models
SparseSAM achieves 2x faster inference and 2.8x memory reduction in SAM with only 0.004 mIoU loss at 0.4 density via Stripe-Sort Attention and Residual-Consistency MLP.