RPCL is a training-only method that raises mean Pair F1 by 2.58-2.83 points on three MECPE datasets by enforcing larger gold-negative confidence gaps and prediction stability under context corruption.
Low-Complexity Samples Versus Symbols-Based Neural Network Receiver for Channel Equalization
2 Pith papers cite this work. Polarity classification is still indexing.
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Experimental demonstration of self-coherent 32 Gbaud QAM reception over fiber distances using ROSS photonic accelerator and direct detection, with reported power savings potential.
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Learning Robust Pair Confidence for Multimodal Emotion-Cause Pair Extraction
RPCL is a training-only method that raises mean Pair F1 by 2.58-2.83 points on three MECPE datasets by enforcing larger gold-negative confidence gaps and prediction stability under context corruption.
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Experimental Analysis of a Self-Coherent M-QAM Receiver by Means of Recurrent Optical Spectrum Slicing and Direct Detection
Experimental demonstration of self-coherent 32 Gbaud QAM reception over fiber distances using ROSS photonic accelerator and direct detection, with reported power savings potential.