《诗观》闺秀别卷的文学史料价值——清初女诗人考略之一

《诗观》是清初人选清初诗歌中文学史料价值最为丰富的一种。《诗观》闺秀别卷对女性诗人的重视也是空前的,是后人了解清初女性诗人事迹的第一手资料。从王正、王端淑、吴绡及范姝4位女诗人事迹可以看出《诗观》闺秀别卷关于女诗人姓氏、字号、里籍、身世、家庭关系、诗歌创作、交游等记载,这些记载既丰富了清初女性诗人的史料、影响到后世选本,而且其重视程度在那个女性诗人被轻忽的时代尤其难能可贵。

A method for workpiece surface small-defect detection based on CutMix and YOLOv3

Surface small defects are often missed and incorrectly detected due to their small quantity and unapparent visual features.A method named CSYOLOv3,which is based on CutMix and YOLOv3,is proposed to solve such a problem.First,a four-image CutMix method is used to increase the small-defect quantity,and the process is dynamically adjusted based on the beta distribution.Then,the classic YOLOv3 is improved to detect small defects accurately.The shallow and large feature maps are split,and several of them are merged with the feature maps of the predicted branch to preserve the shallow features.The loss function of YOLOv3 is optimized and weighted to improve the attention to small defects.Finally,this method is used to detect 512×512 pixel images under RTX 2060Ti GPU,which can reach the speed of 14.09 frame/s,and the mAP is 71.80%,which is 5%-10%higher than that of other methods.For small defects below 64×64 pixels,the mAP of the method reaches 64.15%,which is 14%higher than that of YOLOv3-GIoU.The surface defects of the workpiece can be effectively detected by the proposed method,and the performance in detecting small defects is significantly improved.

Multimodal emotion recognition based on deep neural network

In order to increase the accuracy rate of emotion recognition in voiceand video,the mixed convolutional neural network(CNN)and recurrent neural network(RNN)ae used to encode and integrate the two information sources.For the audio signals,several frequency bands as well as some energy functions are extacted as low-level features by using a sophisticated audio technique,and then they are encoded w it a one-dimensional(I D)convolutional neural network to abstact high-level features.Finally,tiese are fed into a recurrent neural network for te sake of capturing dynamic tone changes in a temporal dimensionality.As a contrast,a two-dimensional(2D)convolutional neural network and a similar RNN are used to capture dynamic facial appearance changes of temporal sequences.The method was used in te Chinese Natral Audio-'Visual Emotion Database in te Chinese Conference on Pattern Recognition(CCPR)in2016.Experimental results demonstrate that te classification average precision of the proposed metiod is41.15%,which is increased by16.62%compaed with te baseline algorithm offered by the CCPR in2016.It is proved ta t te proposed method has higher accuracy in te identification of emotional information.

Can color vision variation explain sex differences in invertebrate foraging by capuchin monkeys?

Invertebrates are the main source of protein for many small-to-medium sized monkeys. Prey vary in size, mobility, degree of protective coveting, and use of the forest, i.e. canopy height, and whether they are exposed or embed themselves in substrates. Sex-differentiation in foraging patterns is well documented for some monkey species and recent studies find that color vision phenotype can also affect invertebrate foraging. Since vision phenotype is polymorphic and sex-linked in most New World monkeys - males have dichromatic vision and females have either dichromatic or trichromatic vision - this raises the possibility that sex differences are linked to visual ecology. We tested predicted sex differences for invertebrate foraging in white-faced capuchins Cebus capucinus and conducted 12 months of study on four free-ranging groups between January 2007 and September 2008. We found both sex and color vision effects. Sex： Males spent more time foraging for invertebrates on the ground. Females spent more time consuming embedded, colonial invertebrates, ate relatively more ＂soft＂ sedentary invertebrates, and devoted more of their activity budget to invertebrate foraging. Color Vision： Dichromatic monkeys had a higher capture efficiency of ex- posed invertebrates and spent less time visually foraging. Trichromats ate relatively more ＂hard＂ sedentary invertebrates. We con- elude that some variation in invertebrate foraging reflects differences between the sexes that may be due to disparities in size, strength, reproductive demands or niche preferences. However, other intraspecific variation in invertebrate foraging that might be mistakenly attributed to sex differences actually reflects differences in color vision [Current Zoology 56 （3）： 300-312, 2010].

Influence of image data set noise on classification with a convolutional network

To evaluate the influence of data set noise, the network in network(NIN) model is introduced and the negative effects of different types and proportions of noise on deep convolutional models are studied. Different types and proportions of data noise are added to two reference data sets, Cifar-10 and Cifar-100. Then, this data containing noise is used to train deep convolutional models and classify the validation data set. The experimental results show that the noise in the data set has obvious adverse effects on deep convolutional network classification models. The adverse effects of random noise are small, but the cross-category noise among categories can significantly reduce the recognition ability of the model. Therefore, a solution is proposed to improve the quality of the data sets that are mixed into a single noise category. The model trained with a data set containing noise is used to evaluate the current training data and reclassify the categories of the anomalies to form a new data set. Repeating the above steps can greatly reduce the noise ratio, so the influence of cross-category noise can be effectively avoided.

A Survey on College English Learning Burnou

Based on burnout theories and published researches in this field, a survey is done on non-majors＇ college English learning burnout through the methods of questionnaire and interview. A considerable degree of burnout is found among freshmen, sophomores, and juniors, regardless of gender, though there are gender differences in different aspects of learning burnout. Insights are drawn from the research data involving such aspects as learner autonomy, teaching style, curriculum design, rapport, modernized facilities, employment awareness, and so on, with an aim to suggest some strategies to overcome learning burnout and improve students＇ language competence. Future research might extend the present study to English majors and attempt a comparison in between.

Multi-modal face parts fusion based on Gabor feature for face recognition

A novel face recognition method, which is a fusion of muhi-modal face parts based on Gabor feature （MMP-GF）, is proposed in this paper. Firstly, the bare face image detached from the normalized image was convolved with a family of Gabor kernels, and then according to the face structure and the key-points locations, the calculated Gabor images were divided into five parts： Gabor face, Gabor eyebrow, Gabor eye, Gabor nose and Gabor mouth. After that multi-modal Gabor features were spatially partitioned into non-overlapping regions and the averages of regions were concatenated to be a low dimension feature vector, whose dimension was further reduced by principal component analysis （PCA）. In the decision level fusion, match results respectively calculated based on the five parts were combined according to linear discriminant analysis （LDA） and a normalized matching algorithm was used to improve the performance. Experiments on FERET database show that the proposed MMP-GF method achieves good robustness to the expression and age variations.

Establishing and validating a spotted tongue recognition and extraction model based on multiscale convolutional neural network

Objective In tongue diagnosis,the location,color,and distribution of spots can be used to speculate on the viscera and severity of the heat evil.This work focuses on the image analysis method of artificial intelligence(AI)to study the spotted tongue recognition of traditional Chinese medicine(TCM).Methods A model of spotted tongue recognition and extraction is designed,which is based on the principle of image deep learning and instance segmentation.This model includes multiscale feature map generation,region proposal searching,and target region recognition.Firstly,deep convolution network is used to build multiscale low-and high-abstraction feature maps after which,target candidate box generation algorithm and selection strategy are used to select high-quality target candidate regions.Finally,classification network is used for classifying target regions and calculating target region pixels.As a result,the region segmentation of spotted tongue is obtained.Under non-standard illumination conditions,various tongue images were taken by mobile phones,and experiments were conducted.Results The spotted tongue recognition achieved an area under curve(AUC)of 92.40%,an accuracy of 84.30%with a sensitivity of 88.20%,a specificity of 94.19%,a recall of 88.20%,a regional pixel accuracy index pixel accuracy(PA)of 73.00%,a mean pixel accuracy(m PA)of73.00%,an intersection over union(Io U)of 60.00%,and a mean intersection over union(mIo U)of 56.00%.Conclusion The results of the study verify that the model is suitable for the application of the TCM tongue diagnosis system.Spotted tongue recognition via multiscale convolutional neural network(CNN)would help to improve spot classification and the accurate extraction of pixels of spot area as well as provide a practical method for intelligent tongue diagnosis of TCM.

Research on the Gender and Age Differences of Middle School Students＇ Shame Sense

In order to ensure the existence of the gender and age differences of middle school students＇ shame, 1258 Shanghai middle school students are surveyed with the help of the Middle school students＇ shame measurement scale with the combination of delaminating and random sampling. The results of questionnaire show that there are significant gender differences in middle school students＇ shame and the average scores of male students＇ shame are higher than those of female students. Besides, there does exist significant age differences in middle school students＇ shame, but it is not simple linear relationship between the feeling of shame and ages. The feeling of shame reaches maximum at the age of 14 and intervention should be given at this age, which young students feel sensitive most. It is suggested that collective remission of the shame should be offered to middle school students with the methods such as physical exercises, persuasion etc.

A Study of College Students＇ Autonomous English Listening Ability

Based on the data got from questionnaire in which 75 college students from Heze University participated, the present study aims to explore the current situation of college students＇ autonomous listening ability, its gender difference and the correlation between autonomous listening ability and CET-4 listening achievement. The results indicate that the autonomous listening ability for college students is at the medium level with a great individual difference and there＇s a vast space to be promoted. Moreover, there＇s no significant gender difference in autonomous listening ability for college students and there＇s significant positive correlation between autonomous listening ability and CET-4 Listening achievement, especially the dimension of Application of Strategies. Thus, a conclusion can be drawn that in English listening teaching, teachers should change their teaching model and strengthen the application of network teaching; in English listening learning, English learners should promote their ability of using strategies in English listening in order to improve their autonomous learning ability.

Bridge damage identification based on convolutional autoencoders and extreme gradient boosting trees

To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.

翻译的选词和语言环境

关于做好翻译需要注意哪些方面的问题,我们的老前辈严复先生提出过信、达、雅的条件,这几乎是众所周知的。天下事往往是无独有偶。英国有一位作牧师的著名翻译家名叫罗纳尔·诺克斯,是耶苏教“圣经”的翻译者。他在所著题为“翻译工作者的实践”(Trials of a Translator)

RECOGNITION AND CLASSIFICATION FOR O(n)-EQUIVARIANT BIFURCATIONS WITH O(n)-CODIMENSION LESS THAN 5

Bifurcation problems equivariant under the standard action of the orthogonal group O(n) up to O(n)-codimension 4 are classified into 19 classes. For each class the normal form and one universal unfolding are calculated and the recognition problem is solved.