Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.

Recommendation Method for Contrastive Enhancement of Neighborhood Information

Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.

Region-Aware Fashion Contrastive Learning for Unified Attribute Recognition and Composed Retrieval

Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing methods cannot recognize newly added attributes and may fail to capture region-level visual features.To address the aforementioned issues,a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed.This model aligned cropped and segmented images with category and multiple fine-grained attribute texts,achieving the matching of fashion region and corresponding texts through contrastive learning.Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes,and to further improve the accuracy of retrieval,an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced,specifically designed for composed image retrieval.This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism,it realized the fusion and selection of image features and attribute text features.Experimental results show that the proposed model achieves a mean precision of 0.6633 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39.18 for composed image retrieval task,satisfying user needs for freely searching for clothing through images and texts.

A Contrastive Analysis of English and Chinese Idioms About Color Words & Their Cultural Connotations

There are many idioms related to color words in English and Chinese.The use of color words in idioms adds beauty and vividness to the language.Due to the cultural differences,“color idioms”have gained different cultural connotations with the development of English and Chinese languages.It is of great significance to accurately understand and grasp the meanings and differences of color-related idioms in Chinese and English.This paper intends to analyze and expound the cultural connotations of English and Chinese idioms related to several widely used basic color words with the aim of helping English learners know and use the idioms about color words better.

Contrastive Analysis:A Phonetic Case

This paper is to make a comparison on Chinese and English sounds under contrastive analysis with the consideration of that Chinese teachers of English would be better able to foresee their students' difficulty and consequently be better able to do their teaching plan in practice.

A Contrastive Study of Western and Chinese Culture

The successful cross-culture communication lies in not only good master of foreign language,but also profound understanding about the cultural differences between the mother language and the target language.Moreover,the neat management over this knowledge in the real cross-culture communication is also necessary.So in this paper,the contrast between Chinese and English language together with culture is presented.We should not only see the differences between them,but also find the similarities.

Contrastive thematic analysis in research articles

Based on the theory of thematic complexity, a contrastive analysis on 2 sections selected from research articles-method and discussion, was conducted. The differences and similarities between the 2 sections in simple theme, multiple theme, textual theme, interpersonal theme, topical theme and thematic progression were analyzed and concluded. The author believes that the causes of the differences and similarities are the different genre features of the 2 sections and the academic features of the materials selected.

Contrastive Analysis of Conversational Implicature between English and Chinese

Implicature is a universal feature of human communication.In conversations,it is common to see examples of something unsaid but communicated.After introducing the theory of conversational impilcature and its four maxims,this paper presents English and Chinese examples to compare their conversational implicature,and finds conversational implicature exists in both Chinese and English conversation.

A Study of E-C and C-E Translation in the Light of Lexical Contrastive Analysis and Cultural Differences

The essay briefly illustrates E-C and C-E Translation strategies in the light of lexical similarity and differences of Lexical Contrastive Analysis, and explores lexical translation skills under the theory of Cultural Differences.

A Great Work on the Leap-Forward Development of Linguistics——A Book Review on Contrastive Linguistics

Contrastive Linguistics is a book on linguistics by Professor Ke Ping.It defined the discipline property of contrastive linguistics.The categories and the theory system of contrastive linguistics get deepened,for Professor Ke brings forward the term tertium contrast and divide the inferiority into extra-linguistic subsystem and intra-linguistic subsystem.The research methods of contrastive linguistics get enriched through putting forward three kinds of positive quantitative contrastive research methods.In addition,it cites various authorities and develops the new through critical assimilation of the old.And the theory mentioned in this book is tightly integrated with practice.So the book is rather valuable and Suitable for a variety of readers with different purposes.

A Look at Contrastive Linguistics--Differences and Similarities between English Attributes and Chinese Attributes

Contrastive linguistics is a branch of linguistics which mainly involves contrast or comparison,and it can leave us some useful insights into our problems,especially for translation work.This paper discusses similarities and differences between English attributes and Chinese attributes from the perspective of the location,the composition,and the function,for the purpose of presenting a sound version for the original text.

A Contrastive Analysis of Two Chinese Versions of Women in Love

There are many differences between English and Chinese in terms of grammar,form,vocabulary,tense and so on.This paper mainly discusses the differences of form,voice,and vocabulary of the two languages based on the original work and the two Chinese versions of Women in Love by applying a contrastive method.It is not a paper discussing translation methods but analyzing the linguistically differences of English and Chinese.The aim of this paper is to study the differences of the two languages in the aspect of contrastive linguistics in order to help the students to have a clear concept of what is language learning in their mind and how to learn English in an effective way.And at the same time,this paper shows the teachers some applicable means of English teaching.Therefore,the study of the differences between English and Chinese benefits both the students and the teachers.

Contrastive Rhetoric and Inter-language Theory

A learner’s stages of L2 development are connected by his or her L1 and culture.It is,accordingly,of paramount importance to understand the second language learners’culture and learning process and better assist them through this process in the way of teaching them English.Similarly,inter-language theory(IL)and contrastive rhetoric are affected by factors,such as learner’s L1,learning experiences,and culture.This paper talks about these two theory’s characteristics,constructs,and importance,so language instructors may better understand the L2 learning phenomena and think out better methods to help language learners improve their language skills.

A Brief Analysis of Contrastive Analysis Hypothesis

This paper makes a brief analysis on the contrastive analysis hypothesis, and it is hoped to be helpful for the English teachers.

Recognition of Similar Weather Scenarios in Terminal Area Based on Contrastive Learning

In order to improve the recognition accuracy of similar weather scenarios(SWSs)in terminal area,a recognition model for SWS based on contrastive learning(SWS-CL)is proposed.Firstly,a data augmentation method is designed to improve the number and quality of weather scenarios samples according to the characteristics of convective weather images.Secondly,in the pre-trained recognition model of SWS-CL,a loss function is formulated to minimize the distance between the anchor and positive samples,and maximize the distance between the anchor and the negative samples in the latent space.Finally,the pre-trained SWS-CL model is fine-tuned with labeled samples to improve the recognition accuracy of SWS.The comparative experiments on the weather images of Guangzhou terminal area show that the proposed data augmentation method can effectively improve the quality of weather image dataset,and the proposed SWS-CL model can achieve satisfactory recognition accuracy.It is also verified that the fine-tuned SWS-CL model has obvious advantages in datasets with sparse labels.

The contrastive study of cultural connotations of color words in English and Chinese——Take“red”and“红色”for Example

Color words, as a part of language, has different cultural connotations in different culture, which usually express strong cultural characteristics. This thesis explores the similarities and differences between the color word"red"and"红色". Further comprehension of color words in different cultural background can help us to communicate with others successfully.

The Organizational Patterns of English and Chinese Texts:A Contrastive Study

Thispapercontrastivelystudies,basedonthetheoreticframeworkof'topicanalysis'byvanDijkandsomeothers,theunderlyingarrangementpatternsoftopicsofEnglishandChinesetexts.TheoverallobjectiveistoexaminethediscoursepatternsofarepresentativesampleofEnglishandCh...

Contrastive Learning for Blind Super-Resolution via A Distortion-Specific Network

Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real degradation is not consistent with the assumption.To deal with real-world scenarios,existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme.However,degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors.In this paper,we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples,respectively.Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space.Furthermore,instead of estimating the degradation,we extract global statistical prior information to capture the character of the distortion.Considering the coupling between the degradation and the low-resolution image,we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions.We term our distortion-specific network with contrastive regularization as CRDNet.The extensive experiments on synthetic and realworld scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.

Contrastive analysis and crashworthiness optimization of two composite thin-walled structures

For the safety protection of passengers when train crashes occur, special structures are crucially needed as a kind of indispensable energy absorbing device. With the help of the structures, crash kinetic-energy can be completely absorbed or dissipated for the aim of safety. Two composite structures(circumscribed circle structure and inscribed circle structure) were constructed. In addition, comparison and optimization of the crashworthy characteristic of the two structures were carried out based on the method of explicit finite element analysis(FEA) and Kriging surrogate model. According to the result of Kriging surrogate model, conclusions can be safely drawn that the specific energy absorption(SEA) and ratio of specific energy absorption to initial peak force(REAF) of circumscribed circle structure are lager than those of inscribed circle structure under the same design parameters. In other words, circumscribed circle structure has better performances with higher energy-absorbing ability and lower initial peak force. Besides, error analysis was adopted and the result of which indicates that the Kriging surrogate model has high nonlinear fitting precision. What is more, the SEA and REAF optimum values of the two structures have been obtained through analysis, and the crushing results have been illustrated when the two structures reach optimum SEA and REAF.

Solving Geometry Problems via Feature Learning and Contrastive Learning of Multimodal Data

This paper presents an end-to-end deep learning method to solve geometry problems via feature learning and contrastive learning of multimodal data.A key challenge in solving geometry problems using deep learning is to automatically adapt to the task of understanding single-modal and multimodal problems.Existing methods either focus on single-modal ormultimodal problems,and they cannot fit each other.A general geometry problem solver shouldobviouslybe able toprocess variousmodalproblems at the same time.Inthispaper,a shared feature-learning model of multimodal data is adopted to learn the unified feature representation of text and image,which can solve the heterogeneity issue between multimodal geometry problems.A contrastive learning model of multimodal data enhances the semantic relevance betweenmultimodal features and maps them into a unified semantic space,which can effectively adapt to both single-modal and multimodal downstream tasks.Based on the feature extraction and fusion of multimodal data,a proposed geometry problem solver uses relation extraction,theorem reasoning,and problem solving to present solutions in a readable way.Experimental results show the effectiveness of the method.