Encrypted network traffic from smartphones reveals distinct longitudinal behavioral patterns for sleep, stress, and loneliness through transformer representations and sparse autoencoders that capture within-person changes better than standard features.
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FinLangNet applies dual-granularity prompting in a sequential model to heterogeneous financial data, reporting 6.3 pp KS improvement and 9.9% bad debt reduction in real-world deployment.
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From Packets to Patterns: Interpreting Encrypted Network Traffic as Longitudinal Behavioral Signals
Encrypted network traffic from smartphones reveals distinct longitudinal behavioral patterns for sleep, stress, and loneliness through transformer representations and sparse autoencoders that capture within-person changes better than standard features.
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A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting
FinLangNet applies dual-granularity prompting in a sequential model to heterogeneous financial data, reporting 6.3 pp KS improvement and 9.9% bad debt reduction in real-world deployment.