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arXiv 2024年

作者： Liao, Xincheng Duan, Junwen Huang, Yixi Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Hunan Changsha China

Unified information extraction (UIE) aims to extract diverse structured information from unstructured text. While large language models (LLMs) have shown promise for UIE, they require significant computational resources and often struggle to generalize to unseen tasks. We propose RUIE (Retrieval-based Unified Information Extraction), a framework that leverages in-context learning for efficient task generalization. RUIE introduces a novel demonstration selection mechanism combining LLM preferences with a keyword-enhanced reward model, and employs a bi-encoder retriever trained through contrastive learning and knowledge distillation. As the first trainable retrieval framework for UIE, RUIE serves as a universal plugin for various LLMs. Experimental results on eight held-out datasets demonstrate RUIE’s effectiveness, with average F1-score improvements of 19.22 and 3.22 compared to instruction-tuning methods and other retrievers, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Data mining

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The safety detection for double tapered roller bearing based on deep learning 11th

The safety detection for double tapered roller bearing based...

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11th International Conference on Security, Privacy and Anonymity in Computation, Communication, and Storage, SpaCCS 2018

作者： Tao, Jie Zhang, Shaobo Yang, Dalian School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment Hunan University of Science and Technology Xiangtan411201 China

ISBN: (纸本)9783030053444

The double tapered roller bearing is widely used in mechanical equipment, due to its complex structure, traditional safety detection is difficult to recognize early weak fault. In order to solve this problem, a deep learning method for safety detection of roller bearing is put forward. In experiment, vibration signals of bearing are firstly separated into a series of intrinsic mode functions by empirical mode decomposition, then we extracted the transient energy to construct eigenvectors. In pattern recognition, deep learning method is used to generate safety detector by unsupervised study. There are three states rolling bearings in experiments, as normal, inner fault and outer fault. The results show that the proposed method is more stable and accurately to identify bearing faults, and the classification accuracy is 98%. © Springer Nature Switzerland AG 2018.

关键词： Empirical mode decomposition

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FedMMR: Multi-Modal Federated Learning via Missing Modality Reconstruction

FedMMR: Multi-Modal Federated Learning via Missing Modality ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Shu Wang Zhe Qu Yuan Liu Shichao Kan Yixiong Liang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Federated Learning (FL) presents a decentralized learning approach for privacy preservation. While many research focuses on uni-modal FL, a more intricate version, multimodal FL, uncovers fundamental attributes. Clients might only gather specific modalities, incurring missing modalities in multi-modal FL. The missing modality problem incurs modality heterogeneity among clients and loses inter-modal connection within one client. To address these issues, we introduce a novel multi-modal FL method called Federated Missing Modality Reconstruction (FedMMR), which tackles these dual challenges through two distinct facets. First, we devise a cross-modal reconstruction policy for synthesizing absent modalities from other modalities. This aligns the data feature spaces across clients, thereby alleviating bias in resultant local models. Subsequently, we steer local models to maintain an inter-modal awareness of both existing and reconstructed modalities, recognizing their potential as complementary components. Comprehensive results show that FedMMR outperforms existing FL baselines.

关键词： Weight measurement Privacy Federated learning Predictive models Data models

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Improving Medical Image Denoising via a Lightweight Plug-and-play Module

Improving Medical Image Denoising via a Lightweight Plug-and...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Lei Ma Hulin Kuang Jin Liu Chengchao Shen Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

Medical image denoising, as a part of medical image processing, is significant for the assessment and diagnosis of diseases. To improve the medical image denoising performance of existing deep learning methods, we propose a lightweight plug-and-play module (LP 2 M) with low complexity, which can be plugged before current image denoising methods. Specifically, the proposed LP 2 M contains three stacked Convolutional Neural Network (CNN) based blocks: a image receptor block, an adaptive receptive field selection block and a high-low frequency processing block. The image receptor block can perceive color or grayscale images and perform preliminary processing. The adaptive receptive field selection block includes two parallel paths with different receptive fields (i.e., convolution kernel sizes) and the adaptive weighting operation, which can process multi-scale information in the image. The high-low frequency processing block consists of a low frequency pathway using convolutional layers with large kernel sizes, and a high frequency pathway using covolutional layers with small kernel sizes, which can process the low and high frequency components in images. Extensive validation experiments are performed on five state-of-the-art denoising methods on multiple medical image datasets for three different medical image denoising tasks (X-ray image denoising, magnetic resonance image denoising and dermoscopic image denoising). Experimental results show that our proposed LP 2 M can effectively improve the results of these five state-of-the-art methods for three denoising tasks with only increasing 0.996K parameters and 63.112M FLOPs, and it can provide potential direction for improving image denoising.

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Domain-specific Knowledge Guided Self-supervised Learning for Pathological Image Segmentation

Domain-specific Knowledge Guided Self-supervised Learning fo...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Chenhui Wang Hulin Kuang Jin Liu Junjian Li Hailin Yue Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

Self-supervised learning provides a possible solution to extract effective visual representations from unlabeled pathological images. However, most of the existing methods either do not effectively utilize domain-specific information or are designed and optimized for image classification, resulting in these pre-trained models that may not be optimal for pathological image segmentation. In this paper, we propose DKSL: Domain-specific Knowledge guided Self-supervised Learning, which uses image reconstruction tasks to aid contrastive learning and exploits single-dye stained pathological images after stain separation as domain-specific knowledge to guide the model. Our method provides a novel way to exploit the domain-specific knowledge of pathological images. In contrastive learning, we add single-dye stained images as an expansion of the original positive samples to the contrastive learning process to preserve more global semantic information. In image reconstruction, the model is forced to focus on local image details relevant to downstream tasks by reconstructing single-dye stained images from the representation extracted by the encoder of contrastive learning. Finally, the encoder and decoder from the pre-training stage are fine-tuned by the downstream segmentation task. Fine-tuning experimental results demonstrate that DKSL outperforms state-of-the-art methods with Dices of 90.50% and 79.68% on two publicly available datasets, GlaS and MoNuSeg, respectively.

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Local-Global Spatio-Temporal Encoding with Adaptive Brain Region Signal Segmentation for Mild Cognitive Impairment Detection

Local-Global Spatio-Temporal Encoding with Adaptive Brain Re...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Jie Wang Luohuang Wu Hulin Kuang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) has been widely used in Mild Cognitive Impairment (MCI) detection. However, most existing methods rely on fixed sliding windows to capture the temporal variations in brain connectivity and fail to model temporal features effectively. To address these limitations, we propose a spatio-temporal graph Transformer framework. First, we segment rs-fMRI data into distinct segments using the multivariate Gaussian distribution of brain region signals and remove redundant segments based on cumulative Jensen-Shannon divergence. Next, we extract features for each brain region and time point within segments, using these temporal features to guide spatial feature aggregation. A position encoder is designed to leverage temporal features across segments to capture both local and global spatio-temporal information. Finally, we employ a population graph framework as the classifier, with spatio-temporal features and demographic data of each subject forming the edge weights, to output the detection results. Our method achieves 92.80%, 81.89%, and 82.80% accuracy in MCI detection, early MCI detection, and late MCI detection tasks on the ADNI3 and ADNI2 datasets, respectively, outperforming existing methods.

关键词： Image segmentation Accuracy Image edge detection Gaussian distribution Functional magnetic resonance imaging Feature extraction Transformers Brain modeling Encoding bioinformatics

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Global Contrastive Learning with High-Quality Data in Large Vision-Language Models for Pathological Question Answering

Global Contrastive Learning with High-Quality Data in Large ...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Hulin Kuang Suoni Liu Haojie Zhang Zhe Qu Shichao Kan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Pathological question answering (PQA) is vital in computational pathology, as it involves interpreting pathological images and answering questions posed by humans. This interaction offers an effective means of engaging with users and enhancing the understanding of pathology-related information. Recent methods developed using large vision-language models (LVLMs), such as QUILT-LLAVA, have made significant progress in advancing PQA. However, existing models, such as those using the QUILT-1M dataset, neglect the quality of the training set during the fine-tuning stage, leading to sub-optimal performance. We recognize that high-quality training data can significantly enhance model performance. Therefore, we design a model-based data filtering strategy to remove images with obvious impurities from the instruction fine-tuning dataset. The filtered high-quality images are then used to fine-tune the model. Additionally, most existing vision-language alignment strategies focus primarily on aligning local features through next-word prediction, leading to a relatively homogeneous granularity in inter-modal alignment. To address this issue, we propose a global-wise alignment module, which introduces global-level contrastive learning during the pretraining stage to establish multi-granularity alignment between pathological images and language descriptions. Based on the above two processes, we design our method, named Global Contrastive Learning with High-Quality Data (GCL-HQD) for pathological question answering in LVLMs. Extensive experiments on two types of experimental settings demonstrate the effectiveness of the GCL-HQD method.

关键词： Training Pathology Filtering Impurities Pipelines Training data Contrastive learning Chatbots Question answering (information retrieval) Data models

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Identification of Disease-Associated Genes Based on Differential Intron Retention

Identification of Disease-Associated Genes Based on Differen...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Zhenpeng Wu Jiantao Zheng Hong-Dong Li Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Differential analysis of gene-level expression is a commonly used approach for identifying disease-associated genes. Recently, intron retention (IR) has been shown to be associated with complex diseases such as cancers. IR provides value that is complementary to traditional gene-level expression. However, a systematic method to exploit IR for identifying disease-associated genes remains largely unexplored. We developed a pipeline to identify the disease-associated gene based on differential intron retention (IRDAG), which integrates IR events detected by two methods, IRFinder and iREAD. We applied it to Alzheimer's disease (AD). We found that many of the differential genes detected based on IR were not able to be discovered by the traditional gene-level differential expression method, suggesting that our method is complementary to traditional methods. We showed that the differential genes identified with IRDAG were functionally related to AD based on the analysis of protein-protein interaction networks and brain-specific functional gene networks. Being complementary to the existing method, IRDAG provides a new and generic approach for identifying the disease-associated gene.

关键词： Diseases Pipelines Proteins Splicing Databases Testing bioinformatics

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Predicting of microbe-drug associations via a pre-completion-based label propagation algorithm

Predicting of microbe-drug associations via a pre-completion...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Haochen Zhao Guihua Duan Botu Yang Suning Li Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Identifying microbe-drug associations is important to systematically understand a drug’s mechanism of action in the therapeutic application. Since identifying microbe-drug associations is expensive and time-consuming via biological experiments, in this study, we propose a Pre-completion-based label Propagation (PLP) method (called PLPMDA) to predict microbe-drug associations based on the multi-type similarities. To obtain richer information of drugs and microbes, we calculate drug chemical structure similarity, drug Anatomical Therapeutic Chemical (ATC) code similarity, microbe functional similarity, microbe sequence similarity and Gaussian Interaction Profile (GIP) kernel similarities of microbes and drugs, and then introduce a non-linear similarity fusion method. Comparing baseline methods, our advantage lies in performing an effective pre-completion step on the initial association matrix from the drug-related and microbe-related information and does not rely on the known drug-microbe associations, which can accelerate the design and discovery of the new drugs. The computational experiment results demonstrate that our proposed approach PLPMDA achieves significantly higher performance than the comparative methods in de novo and cross-validation experiments.

关键词： Drugs Codes Biological system modeling Predictive models Prediction algorithms Kernel Biological information theory

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Hybrid malware detection approach with feedback-directed machine learning

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science China(Information sciences) 2020年第3期63卷 240-242页

作者： Zhetao LI Wenlin LI Fuyuan LIN Yi SUN Min YANG Yuan ZHANG Zhibo WANG Hunan Provincial Key Lab of Internet of Things and Information Security Xiangtan University The College of Information Engineering Xiangtan University Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science Fudan University Shanghai Institute of Intelligent Electronics & Systems Shanghai Institute for Advanced Communication and Data Science School of Cyber Science and Engineering Wuhan University

Dear editor,In recent years, the number of mobile malware has increased at an alarming rate. According to a report from G DATA [1], there are approximately 9000 new Android malware instances each *** malicious applica... 详细信息

关键词： Hybrid malware detection approach with feedback-directed machine learning

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