A Comparative Study of English and Chinese Film Title Translation——from the Perspective of “Four Values”

With the deepening of cultural exchanges between China and western countries, interaction and dissemination of film works have become a common trend. A film title is the “eye” of a film; therefore, concerning the differences between English and Chinese languages, film title translation should not only convey necessary information about the films to the corresponding audience in the target language, but also arouse the interest of the audience to achieve a satisfactory box office. Based on the theories of functional equivalence and communicative translation, and especially “four values” serving as the evaluation standard of film title translation, the thesis makes a comparative analysis of English and Chinese in film title translation and evaluates it with evidence from the successful experience of C-E (Chinese to English) and E-C (English to Chinese) translation from 2016 to 2019 in order to provide references for C-E film title translation under three translating techniques, thus promoting the value of title translation and the entrance of Chinese films to international markets

A permeability model for the hydraulic fracture filled with proppant packs under combined effect of compaction and embedment

The authors acknowledge the financial support from Science Foundation of China University of Petroleum, Beijing (No. 2462014YJRC060 and No.2462014YJRC059)Peer reviewedPostprin

Utility-maximization Resource Allocation for Device-to-Device Communication Underlaying Cellular Networks

Device-to-device(D2D) underlaying communication brings great benefits to the cellular networks from the improvement of coverage and spectral efficiency at the expense of complicated transceiver design. With frequency spectrum sharing mode, the D2D user generates interference to the existing cellular networks either in downlink or uplink. Thus the resource allocation for D2D pairs should be designed properly in order to reduce possible interference, in particular for uplink. In this paper, we introduce a novel bandwidth allocation scheme to maximize the utilities of both D2D users and cellular users. Since the allocation problem is strongly NP-hard, we apply a relaxation to the association indicators. We propose a low-complexity distributed algorithm and prove the convergence in a static environment. The numerical result shows that the proposed scheme can significant improve the performance in terms of utilities.The performance of D2D communications depends on D2D user locations, the number of D2D users and QoS(Quality of Service) parameters

Distribution Shift Matters for Knowledge Distillation with Webly Collected Images

Knowledge distillation aims to learn a lightweight student network from a pre-trained teacher network. In practice, existing knowledge distillation methods are usually infeasible when the original training data is unavailable due to some privacy issues and data management considerations. Therefore, data-free knowledge distillation approaches proposed to collect training instances from the Internet. However, most of them have ignored the common distribution shift between the instances from original training data and webly collected data, affecting the reliability of the trained student network. To solve this problem, we propose a novel method dubbed ``Knowledge Distillation between Different Distributions" (KD$^{3}$), which consists of three components. Specifically, we first dynamically select useful training instances from the webly collected data according to the combined predictions of teacher network and student network. Subsequently, we align both the weighted features and classifier parameters of the two networks for knowledge memorization. Meanwhile, we also build a new contrastive learning block called MixDistribution to generate perturbed data with a new distribution for instance alignment, so that the student network can further learn a distribution-invariant representation. Intensive experiments on various benchmark datasets demonstrate that our proposed KD$^{3}$ can outperform the state-of-the-art data-free knowledge distillation approaches