Kaixiang Yang

Decision Boundary Drift: A Security, Privacy, and Trust Risk of Continual Learning for Agentic AI in Edge Networks

Continual learning (CL) is a key paradigm that enables intelligent agents to operate autonomously in edge networks over the long term. However, continuous model updates can lead to catastrophic forgetting and representation instability in edge deployment scenarios, which may further induce Decision Boundary Drift (DBD)...

ProtoPSR: Prototype-guided Pairwise Similarity Regularization for Robust Spatial Multi-Omics Integration

ProtoPSR is proposed, an unsupervised framework for robust spatial multi-omics domain discovery that stabilizes both local clustering structure and global pairwise relations and demonstrates strong robustness under modality-specific feature corruption.

FB-TDF1: A Fuzzy-Boundary, Timeliness- and Dispersion-Weighted Evaluation Metric for Anomaly Detection in Satellite Remote Sensing

F fuzzy-boundary timeliness and dispersion-weighted F-score (FB-TDF1), a new evaluation metric that jointly accounts for i) detection timeliness, ii) boundary uncertainty in expert annotations, iii) the dispersion characteristics of false positives, and iv) event-level missed-anomaly risk through an event-aware recall...

FedGRAIL: Trustworthy and Efficient Federated Class-Incremental Learning for Long-Tailed IoT Edge Intelligence