Proposes Topological Resilience Index (TRI) via persistent homology to quantify resilience of deep learning OFDM receivers to channel shifts, claiming superior warning lead and BER reduction in simulations across ITU-R transitions.
A theory of learning from different domains
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
DASM is a new optimizer integrating domain-supervised contrastive learning and sharpness-aware minimization with adaptive gap modulation to boost generalization and robustness in multi-domain voice stream steganalysis.
AMDD achieves 99.7% balanced accuracy and 99.8% AUC on FakeAVCeleb by using cross-modal forensic fingerprint consistency loss to align generator-specific artifacts across modalities while also reporting 95.9% attribution accuracy.
citing papers explorer
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Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology
Proposes Topological Resilience Index (TRI) via persistent homology to quantify resilience of deep learning OFDM receivers to channel shifts, claiming superior warning lead and BER reduction in simulations across ITU-R transitions.
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DASM: Domain-Aware Sharpness Minimization for Multi-Domain Voice Stream Steganalysis
DASM is a new optimizer integrating domain-supervised contrastive learning and sharpness-aware minimization with adaptive gap modulation to boost generalization and robustness in multi-domain voice stream steganalysis.
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Attribution-Guided Multimodal Deepfake Detection via Cross-Modal Forensic Fingerprints
AMDD achieves 99.7% balanced accuracy and 99.8% AUC on FakeAVCeleb by using cross-modal forensic fingerprint consistency loss to align generator-specific artifacts across modalities while also reporting 95.9% attribution accuracy.