Models regimes in temporal graphs as geodesic trajectories and detects changes as drifts from estimated geodesics, outperforming baselines on synthetic data and showing better alignment with external events on COVID mobility data.
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Tenenbaum, Vin de Silva, and John C
13 Pith papers cite this work. Polarity classification is still indexing.
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An exact algebraic identity plus low-rank SVD and Haar-measure null-space approximation reduce per-point mean curvature cost from O(m^4) to O(k^2 m + k m p^2) with 50-300x speedups and negligible accuracy loss.
A graph-based technique splits ambiguous instances into multiple points in DR projections to reduce partial neighborhood embedding and reveal hidden memberships.
SMAC detects shape deformations and color anomalies in 4D point clouds using Laplace-Beltrami spectral properties without registration or mesh reconstruction.
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.
MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.
CODA enables continuous online adaptation for HAR sensing by cache-based selective assimilation of informative instances and adaptive temporal retention to forget obsolete data under non-stationary drift.
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.
A joint optimization of neural manifold learning and active-learning-guided Gaussian process regression in latent space outperforms random sampling on synthetic data for complex functions.
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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Geodesics of Dynamic Graphs for Regime Change Detection
Models regimes in temporal graphs as geodesic trajectories and detects changes as drifts from estimated geodesics, outperforming baselines on synthetic data and showing better alignment with external events on COVID mobility data.
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Efficient Mean Curvature Computation on High-Dimensional Data Manifolds
An exact algebraic identity plus low-rank SVD and Haar-measure null-space approximation reduce per-point mean curvature cost from O(m^4) to O(k^2 m + k m p^2) with 50-300x speedups and negligible accuracy loss.
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When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting
A graph-based technique splits ambiguous instances into multiple points in DR projections to reduce partial neighborhood embedding and reveal hidden memberships.
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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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MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.
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CODA: A Continuous Online Evolve Framework for Deploying HAR Sensing Systems
CODA enables continuous online adaptation for HAR sensing by cache-based selective assimilation of informative instances and adaptive temporal retention to forget obsolete data under non-stationary drift.
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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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Active Learning for Manifold Gaussian Process Regression
A joint optimization of neural manifold learning and active-learning-guided Gaussian process regression in latent space outperforms random sampling on synthetic data for complex functions.
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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.
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