A multi-channel masked discrete diffusion model generates semantic mobility skeletons faster than two-stage diffusion baselines while strongly matching temporal length and interval distributions.
Exploring automatic diagnosis of covid-19 from crowdsourced respiratory sound data
3 Pith papers cite this work, alongside 134 external citations. Polarity classification is still indexing.
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A new framework evaluates utility of synthetic mobility trajectories while a membership inference attack reveals privacy vulnerabilities in generative models thought to be safe.
ConvNet with MFCC features and data augmentation classifies cough sounds for COVID-19 with 87.07 AUC on the DiCOVA 2021 blind test, outperforming the baseline by 23%.
citing papers explorer
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MobiDiff: Semantic-Aware Multi-Channel Discrete Diffusion for Human Mobility Data Generation
A multi-channel masked discrete diffusion model generates semantic mobility skeletons faster than two-stage diffusion baselines while strongly matching temporal length and interval distributions.
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A Dual Perspective on Synthetic Trajectory Generators: Utility Framework and Privacy Vulnerabilities
A new framework evaluates utility of synthetic mobility trajectories while a membership inference attack reveals privacy vulnerabilities in generative models thought to be safe.
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COVID-19 Diagnosis from Cough Acoustics using ConvNets and Data Augmentation
ConvNet with MFCC features and data augmentation classifies cough sounds for COVID-19 with 87.07 AUC on the DiCOVA 2021 blind test, outperforming the baseline by 23%.