UNICS pre-trains on a pseudocode dataset for cross-lingual logic then applies multi-task transfer learning with hard-positive mining and dynamic hard-negative sampling to reach claimed SOTA on multilingual code-search benchmarks.
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Color2Struct is a deep learning framework for inverse design of structural colors that improves on tandem networks by 65% in color difference and 48% in short-wave near-infrared reflectivity through sampling bias correction, adaptive loss weighting, and physics-guided inference.
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UNICS: Multilingual Code Search via Unified Pseudocode and Contrastive Transfer Learning
UNICS pre-trains on a pseudocode dataset for cross-lingual logic then applies multi-task transfer learning with hard-positive mining and dynamic hard-negative sampling to reach claimed SOTA on multilingual code-search benchmarks.
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Color2Struct: efficient and accurate deep-learning inverse design of structural color with controllable inference
Color2Struct is a deep learning framework for inverse design of structural colors that improves on tandem networks by 65% in color difference and 48% in short-wave near-infrared reflectivity through sampling bias correction, adaptive loss weighting, and physics-guided inference.