The nonlocal low-rank (NLR) optimization has shown promise for generalized multispectral filter array (MSFA) demosaicing. However, it faces challenges in balancing efficiency and accuracy. To tackle these challenges, we report here the multi-channel global low-rank optimization technique, achieving efficient high-fidelity MSFA demosaicing. Inspired by the cross-band correlations of natural multispectral images, we introduce the multi-channel matching and low-rank strategies that jointly optimize image patches of all channels, exhibiting higher efficiency and accuracy than existing approaches. Furthermore, we present global structural matching (GSM) which performs structure-aware multi-channel matching across the entire multispectral image. GSM extracts structurally important patches and efficiently searches their similar patches via parallel correlation, providing an order-of-magnitude improvement in efficiency. By combining the aforementioned techniques, we have achieved superior performance over the state-of-the-art NLR demosaicing technique, leading to up to 3.9 dB peak signal-to-noise ratio (PSNR) gain and over a 150-fold increase in computational speed. Experiments validated that the technique outperforms existing methods in reconstructing fine textures and details and exhibits superior robustness to noise.

Chinese abbreviation prediction is essential for various natural language processing tasks, including query comprehension, entity linking, and information retrieval.Existing approaches rely on sequence tagging for abbreviation prediction.However, these approaches fail to guarantee the predicted abbreviations preserve their respective meanings and only consider context information related to the entity itself.This paper proposes a novel Chinese abbreviation prediction approach using multi-feature fusion and global context.The approach initially generates multiple candidate abbreviations using a Chinese pretrained unbalanced Transformer generative model.It then selects high-quality candidates through a multi-feature optimization stage, and finally evaluates their quality within the global contextual information.Experimental results indicate that our approach surpasses other comparable approaches in abbreviation prediction.

Event detection is a fundamental task in information extraction, aiming to identify trigger words from given texts and categorize them into distinct event types.Existing approaches for event detection predominantly rely on annotating trigger words to classify events, which can lead to semantic recognition errors due to the oversimplified semantics of these trigger words.To tackle this challenge, we propose a trigger word expansionbased approach for robust contextual event detection.Specifically, we propose a framework comprising a trigger word extractor within a model for trigger word expansion classifier.This enables event detection to be conducted through trigger word expansion, enriching the semantics of trigger words.Additionally, we leverage the GPT model to generate contexts for the expanded trigger words, thereby enhancing the contextual understanding of trigger words.Extensive experiments conducted on the standard MAVEN benchmark dataset showcase the superior performance of our approach compared to state-of-the-art methods.This confirms the effectiveness of our proposed approach over existing approaches that rely on trigger word annotation.