The denoising problem of impure chaotic signals is addressed in this paper. A method based on sparse representation is proposed, in which the random frame dictionary is generated by a chaotic random search algorithm. ...The denoising problem of impure chaotic signals is addressed in this paper. A method based on sparse representation is proposed, in which the random frame dictionary is generated by a chaotic random search algorithm. The numerical simulation shows the proposed algorithm outperforms those recently reported alternative denoising methods.展开更多
A new definition of the alternative coherent-mode representation of a random planar source with the a priori unknown statistical properties is proposed. This definition is based on the measurements of the source cross...A new definition of the alternative coherent-mode representation of a random planar source with the a priori unknown statistical properties is proposed. This definition is based on the measurements of the source cross-spectral density followed by the optimal approximation of the obtained results in the chosen basis of modal functions. The proposed definition is illustrated by the results of numerical simulation.展开更多
针对中文文本检错纠错研究任务,提出了基于知识增强的自然语言表示模型(enhanced representation through knowledge integration, ERNIE)与序列标注结合的中文文本检错纠错模型。该模型由检错和纠错两部分组成,检错阶段ERNIE使用全局...针对中文文本检错纠错研究任务,提出了基于知识增强的自然语言表示模型(enhanced representation through knowledge integration, ERNIE)与序列标注结合的中文文本检错纠错模型。该模型由检错和纠错两部分组成,检错阶段ERNIE使用全局注意力机制进行词向量编码输入到BiLSTM-CRF序列标注模型中,双向长短期记忆网络(bi-directional long short-term memory, BiLSTM)提取上下文的信息进行拼接生成双向的词向量,再通过条件随机场(conditional random field, CRF)计算联合概率增加对邻近词标签的依赖性优化整个序列,从而解决标注偏置等问题给出的错误标注。纠错阶段根据检错模型输出的结果采用不同策略分类纠错,将标注为错字、缺字的错误使用ERNIE掩码语言模型和混淆集匹配进行预测,对多字、乱序错误直接纠正。实验结果表明,引入序列标注根据错误类型进行分类纠错有效提升了纠错率,在SIGHAN数据集上测试F1达到了81.8%。展开更多
A effective approximate scheme which is combined by cluster with the discrelized path-integral representation (DPIR) is used in the study on the random-bond Ising model in a transverse field (RTIM). The critical therm...A effective approximate scheme which is combined by cluster with the discrelized path-integral representation (DPIR) is used in the study on the random-bond Ising model in a transverse field (RTIM). The critical thermodynamical properties, such as the critical temperature, the critical transverse field, the average magnetization ,the susceptibility and the special heat atc.. are calculated, And some results have been improved.展开更多
Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning models.However,any metapath...Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning models.However,any metapaths consisting of multiple,simple metarelations must be driven by domain experts.These sensitive,expensive,and limited metapaths severely reduce the flexibility and scalability of the existing models.A metapath-free,scalable representation learning model,called Metarelation2vec,is proposed for HNs with biased joint learning of all metarelations in a bid to address this problem.Specifically,a metarelation-aware,biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given metapaths.Thereafter,grouped nodes by the type,a common and shallow skip-gram model is used to separately learn structural proximity for each node type.Next,grouped links by the type,a novel and shallow model is used to separately learn the semantic proximity for each link type.Finally,supervised by the cooperation probabilities of all meta-words,the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs,ensuring the accuracy and scalability of the models.Extensive experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.展开更多
针对油气领域知识图谱构建过程中命名实体识别使用传统方法存在实体特征信息提取不准确、识别效率低的问题,提出了一种基于BERT-BiLSTM-CRF模型的命名实体识别研究方法。该方法首先利用BERT(bidirectional encoder representations from...针对油气领域知识图谱构建过程中命名实体识别使用传统方法存在实体特征信息提取不准确、识别效率低的问题,提出了一种基于BERT-BiLSTM-CRF模型的命名实体识别研究方法。该方法首先利用BERT(bidirectional encoder representations from transformers)预训练模型得到输入序列语义的词向量;然后将训练后的词向量输入双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)模型进一步获取上下文特征;最后根据条件随机场(conditional random fields,CRF)的标注规则和序列解码能力输出最大概率序列标注结果,构建油气领域命名实体识别模型框架。将BERT-BiLSTM-CRF模型与其他2种命名实体识别模型(BiLSTM-CRF、BiLSTM-Attention-CRF)在包括3万多条文本语料数据、4类实体的自建数据集上进行了对比实验。实验结果表明,BERT-BiLSTM-CRF模型的准确率(P)、召回率(R)和F_(1)值分别达到91.3%、94.5%和92.9%,实体识别效果优于其他2种模型。展开更多
Binary digit representation of partial sums for random variables has been investigated, and a good upper bound of moments of maximum partial sums for random variables has been reduced by using this representation. As ...Binary digit representation of partial sums for random variables has been investigated, and a good upper bound of moments of maximum partial sums for random variables has been reduced by using this representation. As an applications, stability and strong law of large numbers have been discussed. Many known classical results have been refined.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No. 60872123)the Joint Fund of the National Natural Science Foundation and the Guangdong Provincial Natural Science Foundation (Grant No. U0835001)by the Doctorate Foundation of South China University of Technology,China
文摘The denoising problem of impure chaotic signals is addressed in this paper. A method based on sparse representation is proposed, in which the random frame dictionary is generated by a chaotic random search algorithm. The numerical simulation shows the proposed algorithm outperforms those recently reported alternative denoising methods.
文摘A new definition of the alternative coherent-mode representation of a random planar source with the a priori unknown statistical properties is proposed. This definition is based on the measurements of the source cross-spectral density followed by the optimal approximation of the obtained results in the chosen basis of modal functions. The proposed definition is illustrated by the results of numerical simulation.
文摘针对中文文本检错纠错研究任务,提出了基于知识增强的自然语言表示模型(enhanced representation through knowledge integration, ERNIE)与序列标注结合的中文文本检错纠错模型。该模型由检错和纠错两部分组成,检错阶段ERNIE使用全局注意力机制进行词向量编码输入到BiLSTM-CRF序列标注模型中,双向长短期记忆网络(bi-directional long short-term memory, BiLSTM)提取上下文的信息进行拼接生成双向的词向量,再通过条件随机场(conditional random field, CRF)计算联合概率增加对邻近词标签的依赖性优化整个序列,从而解决标注偏置等问题给出的错误标注。纠错阶段根据检错模型输出的结果采用不同策略分类纠错,将标注为错字、缺字的错误使用ERNIE掩码语言模型和混淆集匹配进行预测,对多字、乱序错误直接纠正。实验结果表明,引入序列标注根据错误类型进行分类纠错有效提升了纠错率,在SIGHAN数据集上测试F1达到了81.8%。
文摘A effective approximate scheme which is combined by cluster with the discrelized path-integral representation (DPIR) is used in the study on the random-bond Ising model in a transverse field (RTIM). The critical thermodynamical properties, such as the critical temperature, the critical transverse field, the average magnetization ,the susceptibility and the special heat atc.. are calculated, And some results have been improved.
基金supported by the National Key Research and Development Program(No.2019YFE0105300)the National Natural Science Foundation of China(No.62103143)+2 种基金the Hunan Province Key Research and Development Program(No.2022WK2006)the Special Project for the Construction of Innovative Provinces in Hunan(Nos.2020TP2018 and 2019GK4030)the Scientific Research Fund of Hunan Provincial Education Department(No.22B0471).
文摘Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning models.However,any metapaths consisting of multiple,simple metarelations must be driven by domain experts.These sensitive,expensive,and limited metapaths severely reduce the flexibility and scalability of the existing models.A metapath-free,scalable representation learning model,called Metarelation2vec,is proposed for HNs with biased joint learning of all metarelations in a bid to address this problem.Specifically,a metarelation-aware,biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given metapaths.Thereafter,grouped nodes by the type,a common and shallow skip-gram model is used to separately learn structural proximity for each node type.Next,grouped links by the type,a novel and shallow model is used to separately learn the semantic proximity for each link type.Finally,supervised by the cooperation probabilities of all meta-words,the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs,ensuring the accuracy and scalability of the models.Extensive experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.
文摘针对油气领域知识图谱构建过程中命名实体识别使用传统方法存在实体特征信息提取不准确、识别效率低的问题,提出了一种基于BERT-BiLSTM-CRF模型的命名实体识别研究方法。该方法首先利用BERT(bidirectional encoder representations from transformers)预训练模型得到输入序列语义的词向量;然后将训练后的词向量输入双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)模型进一步获取上下文特征;最后根据条件随机场(conditional random fields,CRF)的标注规则和序列解码能力输出最大概率序列标注结果,构建油气领域命名实体识别模型框架。将BERT-BiLSTM-CRF模型与其他2种命名实体识别模型(BiLSTM-CRF、BiLSTM-Attention-CRF)在包括3万多条文本语料数据、4类实体的自建数据集上进行了对比实验。实验结果表明,BERT-BiLSTM-CRF模型的准确率(P)、召回率(R)和F_(1)值分别达到91.3%、94.5%和92.9%,实体识别效果优于其他2种模型。
文摘Binary digit representation of partial sums for random variables has been investigated, and a good upper bound of moments of maximum partial sums for random variables has been reduced by using this representation. As an applications, stability and strong law of large numbers have been discussed. Many known classical results have been refined.
