Contrastive Analysis of Chinese and English Love Proverbs from the Perspective of Ontological Metaphor

With the advancement of technology, the communication among diverse cultures has grown rapidly, which brings more opportunities to people in all countries who can have mutual understandings and then fall in an international love. Nevertheless, as an abstract conception, love in different countries has metaphorical expressions basically. Therefore, the development of international love could be influenced by misunderstanding caused by cultural differences. The paper makes a contrastive analysis of Chinese and English love proverbs from the perspective of ontological metaphor. The result proves that it has further the cultural, economic and environmental reasons, as for the abstract conception of ‘love’, that the similarities and difference of Chinese and English nations’ cognition, thus enriching recognitions and comprehensions about “love” between these two nations and eliminating communication misunderstandings from cultural difference to a certain extent

A Case Study on Ren Rongrong’s Translation of Charlotte’s Web from the Perspective of Translational Poetics

Andre Lefevere believes that translators must adapt the translation to the requirements of the times in the translation of literary works, so the language of the translation is inevitably manipulated by the dominant poetics. Children’s literature is a work that is instructive to children and can arouse children’s interest in reading, whose language requires vitality and attractiveness, therefore the linguistic level of children’s literature translation is bound to be manipulated by translational poetics. In the light of Levefere’s poetics of translation, the paper attempts to analyze Ren Rongrong’s translation of Charlotte’s Web and finds out that the translated version’s poetics have actually changed, including lexicon, syntax and rhetoric, to restore the characteristic language form of the original text. And it concludes amplification, literal translation and those translation methods which applied in it, with the desire for providing an innovative theoretical direction for the study of translation of children’s literature

Automatic Counting of Wheat Spikes from Wheat Growth Images

This study aims to develop an automated screening system that can estimate the number of wheat spikes (i.e. ears) from a given wheat plant image acquired after the flowering stage. The platform can be used to assist the dynamic estimation of wheat yield potential as well as grain yield based on wheat images captured by the CropQuant platform. Our proposed system framework comprises three main stages. Firstly, it transforms the wheat plant raw image data using colour index of vegetation extraction (CIVE) and then segments wheat ear regions from the image to reduce the influence of the background signals. Secondly, it detects wheat ears using Gabor filter banks and K-means clustering algorithm. Finally, it estimates the number of wheat spikes within extracted wheat spike region through a regression method. The framework is tested with a real-world dataset of wheat growth images equally distributed from flowering to ripening stages. The estimations of the wheat ears were benchmarked against the ground truth produced in this study by human manual counting. Our automatic counting system achieved an average accuracy of 90.7% with a standard deviation of 0.055, at a much faster speed than human experts and hence the system has a potential to be improved for agricultural applications on wheat growth studies in the future

Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding

Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contribute to the theoretical understanding of SSCL and uncover its connection to the classic data visualization method, stochastic neighbor embedding (SNE), whose goal is to preserve pairwise distances. From the perspective of preserving neighboring information, SSCL can be viewed as a special case of SNE with the input space pairwise similarities specified by data augmentation. The established correspondence facilitates deeper theoretical understanding of learned features of SSCL, as well as methodological guidelines for practical improvement. Specifically, through the lens of SNE, we provide novel analysis on domain-agnostic augmentations, implicit bias and robustness of learned features. To illustrate the practical advantage, we demonstrate that the modifications from SNE to $t$-SNE can also be adopted in the SSCL setting, achieving significant improvement in both in-distribution and out-of-distribution generalization.Comment: Accepted by ICLR 202