Complex systems in the real world often can be modeled as network structures,and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of ...Complex systems in the real world often can be modeled as network structures,and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks.Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes.However,many real-world networks consist of multiple types of nodes and edges,and there may be rich semantic information on nodes and edges.The methods for single-layer networks cannot effectively tackle multi-layer information,multi-relationship information,and attribute information.This paper proposes a community discovery algorithm based on multi-relationship embedding.The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder.The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network(GCN)to obtain the final node embedding matrix.This strategy allows capturing of rich structural and attributes information in multi-relational networks.Experiments were conducted on different datasets with baselines,and the results show that the proposed algorithm obtains significant performance improvement in community discovery,node clustering,and similarity search tasks,and compared to the baseline with the best performance,the proposed algorithm achieves an average improvement of 3.1%on Macro-F1 and 4.7%on Micro-F1,which proves the effectiveness of the proposed algorithm.展开更多
堆煤是输送机常见故障之一,为了保障煤矿工业生产的安全,需要对煤矿井下输送机的堆煤情况进行检测。然而现有的检测方法存在容易误触、检测可靠性较差等缺点,针对这些问题提出一种基于Transformer统一多尺度时序卷积(unified multi-scal...堆煤是输送机常见故障之一,为了保障煤矿工业生产的安全,需要对煤矿井下输送机的堆煤情况进行检测。然而现有的检测方法存在容易误触、检测可靠性较差等缺点,针对这些问题提出一种基于Transformer统一多尺度时序卷积(unified multi-scale temporal ConvTransformer,UnMS-TCT)网络用于输送机堆煤检测。首先融合RGB帧和光流帧提取的特征,使网络更全面地建模时空关系;然后在时序编码器中,将动态位置嵌入(dynamic position embedding,DPE),多头关系聚合器(multi-head relation aggregator,MHRA)以及多层感知机(multilayer perceptron,MLP)组成的全局模块,交叉注意力(cross-attention,CA)组成的局部模块,以交替方式形成全局-局部关系模块,增强多尺度下获取全局和局部时间关系的能力;其次利用残差全局-局部融合(residual global and local fusion,ResGLFus)模块融合多尺度特征,有效地提高融合过程的稳定性,最终实现高精度堆煤预测。实验结果表明:该方法能够实现对输送机堆煤的检测,mAP达到98.17%。展开更多
随着药物种类增多和临床用药复杂性提升,药物相互作用可能性增加,了解药物相互作用对优化治疗方案、提高疗效和降低不良反应意义重大.前人在DDI(Drug-Drug Interaction)数据集进行关系抽取时,存在特征抽取不全、依赖数据集、正负样本数...随着药物种类增多和临床用药复杂性提升,药物相互作用可能性增加,了解药物相互作用对优化治疗方案、提高疗效和降低不良反应意义重大.前人在DDI(Drug-Drug Interaction)数据集进行关系抽取时,存在特征抽取不全、依赖数据集、正负样本数量不平衡等问题,导致模型训练性能差、准确率低.基于此,提出一种基于多模态融合特征的神经网络模型对药物实体关系抽取进行研究.该模型将文本描述、分子结构特征及药物描述等信息进行融合,首先从输入数据中抽取药物实体间的潜在关联,随后基于深度学习方法对提取的关系进行语义分类,最终利用分类器预测药物实体间的相互作用类型,并输出结构化的关系分类结果.实验显示,此模型在DDI数据集上关系抽取的效果良好,F1值达84.48%,比基于深度学习融合的BiLSTM(Bidirectional Long Short Term Memory)模型的F1值提高约1%.研究表明,该模型能更高效准确地挖掘药物的相互作用关系,为临床用药提供科学合理的指导,从而提升药物治疗效果与安全性.展开更多
针对目前方法大多未能充分利用跨度语义信息和局部上下文隐含信息等问题,提出基于跨度和多层次特征融合的实体关系联合抽取模型。该模型首先将文本输入到预训练语言模型(Bidirectional Encoder Representations from Transformer,BERT)...针对目前方法大多未能充分利用跨度语义信息和局部上下文隐含信息等问题,提出基于跨度和多层次特征融合的实体关系联合抽取模型。该模型首先将文本输入到预训练语言模型(Bidirectional Encoder Representations from Transformer,BERT)转换为词向量后,将其与通过图卷积获得的句法依赖信息进行融合,形成更丰富的文本特征;然后通过多头注意力层对文本特征进行加权处理,以此抑制噪声特征的干扰,并促进特征之间的交互,随后根据跨度将文本信息分割成跨度序列进行实体识别;最后使用双向门控循环单元提取局部上下文隐含信息,将与实体类型信息融合到候选实体跨度对并使用sigmoid函数进行关系分类。实验表明,该模型在SciERC数据集和CoNLL04数据集上取得良好的提升效果。展开更多
基金This work was supported by the Key Technologies Research and Development Program of Liaoning Province in China under Grant 2021JH1/10400079the Fundamental Research Funds for the Central Universities under Grant 2217002.
文摘Complex systems in the real world often can be modeled as network structures,and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks.Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes.However,many real-world networks consist of multiple types of nodes and edges,and there may be rich semantic information on nodes and edges.The methods for single-layer networks cannot effectively tackle multi-layer information,multi-relationship information,and attribute information.This paper proposes a community discovery algorithm based on multi-relationship embedding.The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder.The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network(GCN)to obtain the final node embedding matrix.This strategy allows capturing of rich structural and attributes information in multi-relational networks.Experiments were conducted on different datasets with baselines,and the results show that the proposed algorithm obtains significant performance improvement in community discovery,node clustering,and similarity search tasks,and compared to the baseline with the best performance,the proposed algorithm achieves an average improvement of 3.1%on Macro-F1 and 4.7%on Micro-F1,which proves the effectiveness of the proposed algorithm.
文摘堆煤是输送机常见故障之一,为了保障煤矿工业生产的安全,需要对煤矿井下输送机的堆煤情况进行检测。然而现有的检测方法存在容易误触、检测可靠性较差等缺点,针对这些问题提出一种基于Transformer统一多尺度时序卷积(unified multi-scale temporal ConvTransformer,UnMS-TCT)网络用于输送机堆煤检测。首先融合RGB帧和光流帧提取的特征,使网络更全面地建模时空关系;然后在时序编码器中,将动态位置嵌入(dynamic position embedding,DPE),多头关系聚合器(multi-head relation aggregator,MHRA)以及多层感知机(multilayer perceptron,MLP)组成的全局模块,交叉注意力(cross-attention,CA)组成的局部模块,以交替方式形成全局-局部关系模块,增强多尺度下获取全局和局部时间关系的能力;其次利用残差全局-局部融合(residual global and local fusion,ResGLFus)模块融合多尺度特征,有效地提高融合过程的稳定性,最终实现高精度堆煤预测。实验结果表明:该方法能够实现对输送机堆煤的检测,mAP达到98.17%。
文摘随着药物种类增多和临床用药复杂性提升,药物相互作用可能性增加,了解药物相互作用对优化治疗方案、提高疗效和降低不良反应意义重大.前人在DDI(Drug-Drug Interaction)数据集进行关系抽取时,存在特征抽取不全、依赖数据集、正负样本数量不平衡等问题,导致模型训练性能差、准确率低.基于此,提出一种基于多模态融合特征的神经网络模型对药物实体关系抽取进行研究.该模型将文本描述、分子结构特征及药物描述等信息进行融合,首先从输入数据中抽取药物实体间的潜在关联,随后基于深度学习方法对提取的关系进行语义分类,最终利用分类器预测药物实体间的相互作用类型,并输出结构化的关系分类结果.实验显示,此模型在DDI数据集上关系抽取的效果良好,F1值达84.48%,比基于深度学习融合的BiLSTM(Bidirectional Long Short Term Memory)模型的F1值提高约1%.研究表明,该模型能更高效准确地挖掘药物的相互作用关系,为临床用药提供科学合理的指导,从而提升药物治疗效果与安全性.
基金山东省泰山学者工程专项(tsqn202312127)国家自然科学基金项目“过渡金属-掺氮碳纳米片同源催化微藻两级热解机理及产物定向调控机制”(52206291)+2 种基金山东省自然科学基金项目“金属-掺氮碳纳米片同源催化微藻两级热解产物定向调控机制研究”(ZR2021QE051)中央高校基本科研业务费专项(22CX06030A,20CX06028A)The China Scholarship Council(202406450055)。
文摘针对目前方法大多未能充分利用跨度语义信息和局部上下文隐含信息等问题,提出基于跨度和多层次特征融合的实体关系联合抽取模型。该模型首先将文本输入到预训练语言模型(Bidirectional Encoder Representations from Transformer,BERT)转换为词向量后,将其与通过图卷积获得的句法依赖信息进行融合,形成更丰富的文本特征;然后通过多头注意力层对文本特征进行加权处理,以此抑制噪声特征的干扰,并促进特征之间的交互,随后根据跨度将文本信息分割成跨度序列进行实体识别;最后使用双向门控循环单元提取局部上下文隐含信息,将与实体类型信息融合到候选实体跨度对并使用sigmoid函数进行关系分类。实验表明,该模型在SciERC数据集和CoNLL04数据集上取得良好的提升效果。
