语篇连贯理论对大学英语阅读教学的启示

阏读是英语综合应用能力的重要组成部分.语篇连贯理论对于大学英语阅读教学有重要指导作用.根据大学英语阅读教学要求,重点介绍整体连贯、线性连贯和衔接等重要连贯理论知识,并进行举例分析,阐明连贯理论有关知识对阅读教学的积极作用.     

图式理论对第二语言阅读的指导作用

图式理论研究发现,第二语言阅读受到三种图式类型的影响,即内容图式、外形图式和语言图式.内容图式指读者的文化背景和认识世界的知识层次;外形图式常常被认为是语篇图式,包括语篇的组织形式和修辞结构,其知识涉及到不同的语篇类型和风格,也涉及到如何理解不同语言的语篇类型以及其中对不同语篇组织、语言结构、词汇、语法的不同处理方法;语言图式包括认识词语及其在句子中组合方式所需要的解码特征.通过阐述三种图式类型,论证了图式理论对第二语言阅读的指导作用.     

地应力测量非弹性应变恢复法在某金属矿的应用

为获得某金属矿深部岩体地应力分布特征,采用非弹性应变恢复法,选取该矿同一勘探线附近距地表埋深615 m、665 m、715 m三个水平进行现场地应力测量。通过分析3个水平各测点地应力测量结果,得到该矿区地应力分布规律。该矿区最大主应力和最小主应力均为近水平主应力,中间主应力为近垂向的应力;3个水平的最大水平主应力方向基本一致,方位角为101°～130°。主应力的大小随深度呈线性增长。近水平应力大于近垂向应力,表明该矿浅部地壳应力占主导地位的是水平构造应力。两个相邻样品的非弹性应变恢复法地应力测量值平均差系数最大为8.33%,表明其结果具有较好的一致性,验证了非弹性应变恢复法的可靠性。     

基于改进的深度学习网络的SAR图像瞬时海岸线自动提取算法

针对目前合成孔径雷达(synthetic aperture radar, SAR)在对大尺度瞬时海岸线提取方面的图像解译过程中, 仍然存在精度低与自动化水平差的问题, 提出一种基于深度学习网络的瞬时海岸线自动提取算法。首先, 将SAR图像进行Lee滤波增强来抑制相干斑。其次, 通过升级残差网络为主干网络,分4级提取海水目标的特征。然后, 将4级特征经过全局卷积网络、密集连接网络和解码器网络配合,充分提取目标的本质特征, 并通过上采样产生海水分割结果。最后, 利用Sobel算子分离出海岸线并和原SAR图像融合以便清晰查看结果。通过与全卷积网络与细化网络的海岸线提取实验结果进行对比, 证明所提算法对海岸线的提取更加准确, 能够减少虚警和漏警, 具有更好的性能。     

基于CWD和残差收缩网络的调制方式识别方法

针对低信噪比时莱斯信道下特征提取准确性难以保证、识别准确率偏低等问题, 提出一种基于Choi-Williams分布(Choi-Williams distribution, CWD)和深度残差收缩网络(deep residual shrinkage network, DRSN)的通信辐射源信号调制方式识别方法。利用CWD将时域复信号转换为二维时频矩阵, 对深度残差网络添加软阈值化得到DRSN, 将时频矩阵样本用于对DRSN的训练, 最终构建不同信噪比下的调制方式识别网络。仿真实验表明, 基于RadioML2016.10a数据集, 利用部分先验信息的情况下, 该分类识别方法具有较高的识别准确率和噪声鲁棒性。在0 dB时, 对11类信号的总体识别准确率达到了89.95%;在2 dB以上时, 总体识别准确率均超过91%, 优于其他深度学习识别方法。     

基于神经网络的图像描述方法研究

自动识别和描述图像的内容是人工智能中一个重要的研究方向,它涉及计算机视觉和自然语言处理技术。针对这一难题,提出了一种由深层神经网络模型生成自然语言句子来描述图像内容的方法。该方法提出的模型由卷积神经网络(Convolution Neural Network,CNN)和循环神经网络(Recurrent Neural Network,RNN)组成,其中,CNN用来提取输入图像的特征生成固定长度的特征向量,该特征向量初始化RNN来生成句子。在MSCOCO图像描述数据集上的实验结果表明了该模型所生成句子的语法准确性和语义准确性,且优于先前的基线模型。     

Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods

Financial distress prediction (FDP) has been widely considered as a promising approach to reducing financial losses. While financial information comprises the traditional factors involved in FDP, nonfinancial factors have also been examined in recent studies. In light of this, the purpose of this study is to explore the integrated factors and multiple models that can improve the predictive performance of FDP models. This study proposes an FDP framework to reveal the financial distress features of listed Chinese companies, incorporating financial, management, and textual factors, and evaluating the prediction performance of multiple models in different time spans. To develop this framework, this study employs the wrapper-based feature selection method to extract valuable features, and then constructs multiple single classifiers, ensemble classifiers, and deep learning models in order to predict financial distress. The experiment results indicate that management and textual factors can supplement traditional financial factors in FDP, especially textual ones. This study also discovers that integrated factors collected 4 years prior to the predicted benchmark year enable a more accurate prediction, and the ensemble classifiers and deep learning models developed can achieve satisfactory FDP performance. This study makes a novel contribution as it expands the predictive factors of financial distress and provides new findings that can have important implications for providing early warning signals of financial risk.     

基于深度残差适配网络的通信辐射源个体识别

为解决通信辐射源识别中传统的人工特征提取方法鲁棒性不足和深度学习方法需要大量带标签目标域数据的问题,提出一种基于深度残差适配网络的通信辐射源个体识别方法.应用深度学习技术实现从源域到目标域上的迁移识别,只需要将带标签的源域数据和无标签的目标域数据进行训练.原始通信辐射源信号经过预处理后输入网络训练,将源域和目标域的分布...     

Phylogenetic analysis of extreme morphologies: deep-sea holasteroid echinoids

Because of their ‘bizarre’ features, some echinoids can be considered laboratories in which to analyse the origin of extreme morphologies. The holasteroid family Urechinidae Duncan, 1889 is such a group. It is composed of 18 previously named living species in three genera: Urechinus A. Agassiz, 1879, Plexechinus A. Agassiz, 1898, and Pilematechinus A. Agassiz, 1904. With Stereopneustes, the most closely related living taxon, and the calymnid-plus-pourtalesiid clade as outgroups, a phylogenetic analysis on 35 binary characters is produced. We focus on two major aspects of morphology: test features (apical system, basicoronals, interambulacrum 5, test shape, ambulacra) and external appendages (pedicellariae, spines, sphaeridia). New data on plate architecture, morphology, and appendages are illustrated, and the position of the fossil, Chelonechinus, is discussed. We show that the Urechinidae is paraphyletic: the genus Plexechinus shares more recent common ancestry with the calymnids and pourtalesiids than with other urechinids. In order to retain the well-circumscribed clade, Pilematechinus A. Agassiz, 1904, the phylogenetic classification also recognizes two additional genera: Cystechinus A. Agassiz, 1879, and Antrechinus new genus. A key to the species is provided. The production of extreme morphologies, such as internal parental care, is explored by mapping test size, the occurrence of fascioles, and periproct position onto the phylogeny. By adding plates early in ontogeny, Pilematechinus develops radically different plate architectures from another genus of large forms, Cystechinus. In contrast, the latter gets large by exaggerating allometric trends seen in other holasteroids. Paedomorphosis and miniaturization have evolved independently in Antrechinus and some Plexechinus, but always by truncation of allometric trajectories.