模余代数和Morita-Takeuchi关系

H是Hopf代数,C是H-模余代数。首先利用余积分的概念,诱导C的右H-余模结构,并构造了Smash余积余代数C×H,使C×H作为余代数同构于C H。然后,由C的右H-余模结构诱导C的左H0-模结构,令 C=C/KerεH0C,则C×H与 C有Morita-Takeuchi关系。     

基于双解码路径DD-UNet的脑肿瘤图像分割算法

针对医学图像中病灶区域尺度不一、边界模糊和周围组织强度不均匀所导致的分割精度降低问题,提出了一种基于双解码器的脑肿瘤图像分割模型。为了增强特征的表征力,提出了高阶微分残差模块并使用不同空洞率的扩张卷积用于提取特征编码,提高了网络模型的分割性能;引入上下文语义信息感知模块(multi scale dilation, MSD),从不同的目标尺度中提取更多的精细信息,提高了对结构细节信息的捕获能力,同时减少了编解码器之间的特征差异;在空间解码路径中使用选择性聚合空间注意力模块(spatial aggregation attention module, SAAM),增加了对有效空间特征的权重比例,减少了无效的特征干扰。在脑肿瘤数据集上进行了实验验证,实验结果表明,所提算法的Dice系数、平均交并比、敏感性、特异性、准确率等指标分别为：93.35%、90.71%、91.15%、99.94%、96.75%。     

基于多尺度特征选择性融合的遥感图像检测算法北大核心CSCD

遥感图像的检测在监察自然环境、军事、国土安全等方面具有极其广阔的应用前景,而遥感图像具有背景复杂、目标面积小、特征提取困难等缺点,进行检测时容易产生小目标漏检问题。本文提出一种基于多尺度特征选择性融合的遥感图像检测算法。所提算法采用改进的Resnet50作为主干网络,将Resnet50第一个卷积替换成动态卷积,并将其ConvBlock模块中的卷积替换成金字塔卷积,提高特征提取能力。同时,为了避免遗漏底层信息,在动态卷积层后加入所提有效空间通道注意力机制模块。最后,选取基于上下文信息的不同尺度特征进行融合,提高了模型对目标物体的定位能力。实验结果表明,本文算法在保证速度的同时提高了对遥感图像的检测精度,在遥感图像公开数据集RSOD和NWPUVHR-10上平均精度均值(mean average precision,mAP)分别达到91.88%和90.23%,检测速度达到33 FPS。     

基于卡口上下文和深度置信网络的车辆轨迹预测模型研究

针对车辆轨迹预测中节点序列的时序特性和实际路网中的空间关联性,该文提出一种基于深度置信网络和SoftMax (DBN-SoftMax)轨迹预测方法.首先,考虑到轨迹在节点集合中的强稀疏性和一般特征学习方法对新特征的泛化能力不足,该文利用深度置信网络(DBN)较强的无监督特征学习能力,达到提取轨迹局部空间特性的目的;然后,针对轨迹的时序特性,该文采用逻辑回归的预测思路,用当前轨迹集在路网特征空间中的线性组合来预测轨迹;最后,结合自然语言处理领域中的词嵌入的思想,基于实际轨迹中节点存在的上下文关系,运用节点的向量集表征了节点间的交通时空关系.实验结果表明该模型不仅能够有效地提取轨迹特征,并且在拓扑结构复杂的路网中也能得到较好的预测结果.     

Forecasting accuracy of behavioural models for participation in the arts

This paper assesses the forecasting performance of count data models applied to arts attendance. We estimate participation models for two artistic activities that differ in their degree of popularity – museums and jazz concerts – with data derived from the 2002 release of the Survey of Public Participation in the Arts for the United States. We estimate a finite mixture model – a zero-inflated negative binomial model – that allows us to distinguish between “true” non-attendants and “goers” and their respective behaviour regarding participation in the arts. We evaluate the predictive (in-sample) and forecasting (out-of-sample) accuracy of the estimated model using bootstrapping techniques to compute the Brier score. Overall, the results indicate the model performs well in terms of forecasting. Finally, we draw certain policy implications from the model’s forecasting capacity, thereby allowing the identification of target populations.     

Generating video animation from single still image in social media based on intelligent computing

Bringing a single still image into reality is a challenging topic in computer animation because the driven and structural information in single still image is inadequate. In this paper, we present an image animating method for enhancing single still image in social media with virtual realistic and animated motions without prior information. We imitate the interaction between the active objects in an image and their neighboring passive objects. The existing actions in the image and the virtual specified force are employed to animate the active objects. Observing that the change between two subsequent motions of the active objects derives a motion tendency, we can calculate a virtual driving force based on the motion tendency. By virtue of the virtual driving force, the stochastic motion texture is used to animate the passive objects. Finally, the convolutional neural network is employed to optimize the virtual motion animations. In this way, the proposed method produces visually natural results while guaranteeing motion harmony between active objects and passive objects. To demonstrate the applicability and rationality of virtual animation driving force, our method generates several animations from still images in Social Media.     

Expanding conceptions of utility: middle school students’ perspectives on the usefulness of mathematics

This research explores how adolescents conceptualize the usefulness of mathematics. Integrating sociocultural theory with the study of utility value, this study uses open-ended survey items and interview tasks to examine conceptions of usefulness among a group of predominantly Latinx middle school students. Findings reveal that students primarily conceptualized the usefulness of mathematics in two ways. First, students considered the applicability of mathematics content, focusing on applications of mathematics in everyday life and future jobs/careers. Second, students considered the usefulness of features of the learning experience, such as the form of interaction and structure of the activity. Both conceptions are compared to existing conceptions of usefulness in the literature, and implications for classroom practice and future research are discussed.     

Culture,technologies and democracy: A cross-national analysis of political development

Communication technologies have been hailed as having a potential to promote democracy and freedom, and this paper aims to examine these claims in a comparative, international context. Our analysis focuses on the mediating role of horizontal communication networks (i.e., telephone, mobile telephone and the Internet) in the relationship between culture and political development. Using cultural value indicators of the World Values Survey and the measures of communication technology development, this study tests the mediating path from culture to communication technologies and to political development. The results suggest that cultural values have a role in shaping the structural characteristics of horizontal networks such as accessibility and decentralization, and that these features of horizontal networks are positively related to institutional and effective democracy as well as to economic competitiveness of nations.     

基于宽频调谐特征和谱残差分析的显著性目标检测

根据人眼视觉系统的基本特点,提出一种基于宽频调谐特征和谱残差分析的显著性目标检测算法。该方法通过在上下文信息中提取图像的宽频调谐特征,运用线性叠加融合宽频调谐特征子图得到初步特征图,然后利用多尺度方法构建多分辨率显著图谱子序列,最后利用谱残差分析融合位置特征对显著性子图进行操作运算得到最终的显著图。基于自然图像的显著性目标检测实验结果证明,该方法具有较好的实用性和较强的稳定性,能够获取较为精确的检测结果。     

Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain

We make the case for developing a Computational Comparative Neuroprimatology to inform the analysis of the function and evolution of the human brain.First, we update the mirror system hypothesis on the evolution of the language-ready brain by (i) modeling action and action recognition and opportunistic scheduling of macaque brains to hypothesize the nature of the last common ancestor of macaque and human (LCA-m); and then we (ii) introduce dynamic brain modeling to show how apes could acquire gesture through ontogenetic ritualization, hypothesizing the nature of evolution from LCA-m to the last common ancestor of chimpanzee and human (LCA-c). We then (iii) hypothesize the role of imitation, pantomime, protosign and protospeech in biological and cultural evolution from LCA-c to Homo sapiens with a language-ready brain.Second, we suggest how cultural evolution in Homo sapiens led from protolanguages to full languages with grammar and compositional semantics.Third, we assess the similarities and differences between the dorsal and ventral streams in audition and vision as the basis for presenting and comparing two models of language processing in the human brain: A model of (i) the auditory dorsal and ventral streams in sentence comprehension; and (ii) the visual dorsal and ventral streams in defining “what language is about” in both production and perception of utterances related to visual scenes provide the basis for (iii) a first step towards a synthesis and a look at challenges for further research.