SMAC detects shape deformations and color anomalies in 4D point clouds using Laplace-Beltrami spectral properties without registration or mesh reconstruction.
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5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
A spectral framework for nonlinear DR uses spectral bases plus cross-entropy optimization to create multi-scale embeddings that preserve both global manifold geometry and local neighborhoods while supporting graph-frequency analysis.
NOMAD delivers an MPI-based distributed implementation of graph embedding models achieving 10-100x median speedups over multi-threaded baselines and 35-76x over prior distributed systems on large clusters.
Earth embeddings from satellite images predict neighborhood-level urban indicators with higher accuracy for built-environment outcomes than for behavior-driven ones, showing city-specific variation but year-to-year stability.
The MPEX AI Digital Twins project reports that its two phase-I AI milestones for hot-spot control and damage assessment are on track for June 2026 demonstration.
citing papers explorer
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Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach
SMAC detects shape deformations and color anomalies in 4D point clouds using Laplace-Beltrami spectral properties without registration or mesh reconstruction.
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A Spectral Framework for Multi-Scale Nonlinear Dimensionality Reduction
A spectral framework for nonlinear DR uses spectral bases plus cross-entropy optimization to create multi-scale embeddings that preserve both global manifold geometry and local neighborhoods while supporting graph-frequency analysis.
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NOMAD: Generating Embeddings for Massive Distributed Graphs
NOMAD delivers an MPI-based distributed implementation of graph embedding models achieving 10-100x median speedups over multi-threaded baselines and 35-76x over prior distributed systems on large clusters.
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Earth Embeddings Reveal Diverse Urban Signals from Space
Earth embeddings from satellite images predict neighborhood-level urban indicators with higher accuracy for built-environment outcomes than for behavior-driven ones, showing city-specific variation but year-to-year stability.
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MPEX AI Digital Twins Milestone Report
The MPEX AI Digital Twins project reports that its two phase-I AI milestones for hot-spot control and damage assessment are on track for June 2026 demonstration.