A 1-D U-Net anonymizes ECG signals by exploiting near-orthogonal privacy and utility gradients, driving re-identification to chance while preserving diagnostic AUROC.
On the theory of filter amplifiers
3 Pith papers cite this work. Polarity classification is still indexing.
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FDN uses spectral decomposition, asymmetric heads for deterministic and probabilistic wrench components, and frequency-aware filtering to forecast high-frequency wrench from proprioception, outperforming baselines on hydraulic manipulator grinding data after pretraining and transfer.
An inertial navigation system for bikes fuses mixture-of-experts learning with pedal-to-wheel mechanical constraints to reduce drift, reporting at least 12% accuracy gain and sub-0.5 m/s wheel-speed error on real DiDi ride data.
citing papers explorer
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REAN: Reconstruction-aware ECG Anonymization Based on Privacy--Utility Orthogonality
A 1-D U-Net anonymizes ECG signals by exploiting near-orthogonal privacy and utility gradients, driving re-identification to chance while preserving diagnostic AUROC.
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Frequency-aware Decomposition Learning for Sensorless Wrench Forecasting on a Vibration-rich Hydraulic Manipulator
FDN uses spectral decomposition, asymmetric heads for deterministic and probabilistic wrench components, and frequency-aware filtering to forecast high-frequency wrench from proprioception, outperforming baselines on hydraulic manipulator grinding data after pretraining and transfer.
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Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments
An inertial navigation system for bikes fuses mixture-of-experts learning with pedal-to-wheel mechanical constraints to reduce drift, reporting at least 12% accuracy gain and sub-0.5 m/s wheel-speed error on real DiDi ride data.