WeldMamba achieves 74.63% mIoU for 500 ms lookahead segmentation of keyhole, wire, and molten pool using spatiotemporal state space modeling conditioned on welding signals and physics-based losses on a 43-sequence dataset.
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A systematic mapping study of 87 papers derives an architecture-based taxonomy for Workflow as a Service brokers and identifies future research directions.
The OSS Challenge provides benchmarks showing spatiotemporal video models excel at open suturing skill classification and OSATS scoring but struggle with keypoint tracking under occlusion.
TRUST-TAEA extends two-archive evolutionary algorithms with trustworthiness-guided coordination and variable-grouping for improved convergence, diversity, and stability on LSMOPs with 500-5000 variables.
A vision-based system uses deep neural networks for pixel-level risk assessment and risk-map algorithms to identify stable safe landing zones for UAV emergency descents in dynamic urban settings.
citing papers explorer
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Physics-Guided Spatiotemporal State Space Modeling for Lookahead Molten Pool Segmentation in Laser Wire-Feed Welding
WeldMamba achieves 74.63% mIoU for 500 ms lookahead segmentation of keyhole, wire, and molten pool using spatiotemporal state space modeling conditioned on welding signals and physics-based losses on a 43-sequence dataset.
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Workflow as a Service Broker in Cloud Environment: A Systematic Mapping Study
A systematic mapping study of 87 papers derives an architecture-based taxonomy for Workflow as a Service brokers and identifies future research directions.
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TRUST-TAEA: A trustworthiness-guided two-archive evolutionary algorithm with variable-grouping sparse search for large-scale multi-objective optimization
TRUST-TAEA extends two-archive evolutionary algorithms with trustworthiness-guided coordination and variable-grouping for improved convergence, diversity, and stability on LSMOPs with 500-5000 variables.
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Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments
A vision-based system uses deep neural networks for pixel-level risk assessment and risk-map algorithms to identify stable safe landing zones for UAV emergency descents in dynamic urban settings.