Rui Man

The increasing frequency of extreme weather events has resulted in a rapid growth of unstructured meteorological texts, posing challenges for accurate disaster-related information extraction. Existing relation extraction models that integrate semantic and syntactic information via Graph Convolutional Networks (GCNs) often suffer from noise amplification and information redundancy. To address these limitations, we propose a domain-specific relation extraction framework that integrates Bidirectional Encoder Representations from Transformers (BERT) with an Adaptive Denoising Graph Network. A dual-tuning architecture is designed to jointly model semantic and syntactic features. Specifically, a Self-Attention Graph Convolutional Network (SA-GCN) dynamically prunes irrelevant dependency structures, while a Residual Shrinkage Network (RS-Net) performs fine-grained feature denoising through sample-adaptive threshold learning. Experiments on the public DUIE 2.0 dataset and a newly constructed Agricultural Meteorological Disaster dataset demonstrate that the proposed model achieves F1 scores of 91.51% and 92.84%, respectively, outperforming state-of-the-art methods. Ablation studies further confirm the effectiveness of adaptive denoising in handling complex linguistic structures in disaster reports. The proposed approach enhances the robustness and accuracy of meteorological relation extraction and provides technical support for knowledge graph construction and intelligent disaster early warning systems.

Genomic prediction (GP) plays a pivotal role in expediting genetic gains for crop breeding. Recently, deep learning models has attracted growing attention in this field due to its superior performance over traditional models. However, most advanced models rely on simplified numerical representations of genomic variants and treat individuals in isolation, failing to capture the full complexity of genetic variation and the genetic relatedness among individuals. In response, we propose RAGP, a deep learning model driven by a retrieval-augmented mechanism composed of two synergistic components: 1) an embedding-based retrieval module that identifies a group of nearest neighbors highly relevant to the target sample as references, employs a gene-specific discriminator to refine the representation space and capture informative genetic structures; 2) an augmentation module that integrates the retrieved references to enhance the target representation. The enriched representation is then propagated through a regression network for final prediction. This retrieval-guided strategy enables more informed and fine-grained utilization of genomic data and strengthens the model’s capacity to capture individual-level genetic relationships. Extensive experiments reveal that RAGP achieves consistently superior predictive performance compared to both conventional methods and state-of-the-art deep learning models. By incorporating retrieval-augmented mechanisms, RAGP achieves substantial improvements in predictive accuracy and offers a promising direction for advancing genomic selection in crop breeding. Our code is available on https://github.com/l00907l/RAGP.