CityOS is an edge runtime that enforces a three-tier privacy API for urban sensors: local raw data, differentially private single-location stats, and cross-location aggregates with per-user budgets enforced on devices.
PAMAP2 Physical Activity Monitoring
4 Pith papers cite this work, alongside 42 external citations. Polarity classification is still indexing.
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This survey provides the first comprehensive overview of deep multimodal learning methods designed to remain robust when some input modalities are absent.
Controlled large-scale pretraining on 18.2M hours of wearables shows self-supervised motion models beat scratch training, with triaxial fidelity, data diversity, and task-matched windows mattering more than model size alone.
MSTN claims SOTA across time-series tasks with early temporal pooling, but internal inconsistencies in parameter counts, benchmark tables, and implausible error reductions undermine the central claims.
citing papers explorer
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CityOS: Privacy Architecture for Urban Sensing
CityOS is an edge runtime that enforces a three-tier privacy API for urban sensors: local raw data, differentially private single-location stats, and cross-location aggregates with per-user budgets enforced on devices.
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Deep Multimodal Learning with Missing Modality: A Survey
This survey provides the first comprehensive overview of deep multimodal learning methods designed to remain robust when some input modalities are absent.
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Inertia-1: An Open Exploration of Wearable Motion Foundation Models
Controlled large-scale pretraining on 18.2M hours of wearables shows self-supervised motion models beat scratch training, with triaxial fidelity, data diversity, and task-matched windows mattering more than model size alone.
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MSTN: A Lightweight and Fast Model for General TimeSeries Analysis
MSTN claims SOTA across time-series tasks with early temporal pooling, but internal inconsistencies in parameter counts, benchmark tables, and implausible error reductions undermine the central claims.