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This paper presents an interpretable network-based framework for representing idiomatic expressions across eight languages using binary conceptual features. Community detection reveals that idioms cluster by conceptual schema rather than language, and the framework improves downstream idiom detection and cross-lingual transfer over embedding-based baselines.
Proposes RGNet, a neural network architecture based on renormalization group theory for hierarchical coarse-graining of feature space to address class imbalance and noise in fault diagnosis. Experimental results on the AI4I dataset show RGNet provides interpretable and competitive performance.