ReTAMamba adds reliability decay modeling and chronological weaving to Mamba for irregular clinical time series and reports 7.5-10% relative AUPRC gains on MIMIC-IV, eICU, and PhysioNet 2012.
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eDySec is a deep learning-based framework that detects malicious PyPI packages through dynamic analysis, halving feature dimensionality, reducing false positives by 82%, false negatives by 79%, and boosting accuracy by 3% with near-perfect stability.
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ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction
ReTAMamba adds reliability decay modeling and chronological weaving to Mamba for irregular clinical time series and reports 7.5-10% relative AUPRC gains on MIMIC-IV, eICU, and PhysioNet 2012.
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eDySec: A Deep Learning-based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystem
eDySec is a deep learning-based framework that detects malicious PyPI packages through dynamic analysis, halving feature dimensionality, reducing false positives by 82%, false negatives by 79%, and boosting accuracy by 3% with near-perfect stability.