A Corpus-based and Eye-Tracking Study on the Audience Reception and Processing of English-Chinese Swearwords Produced by Amateur (Fansubbing) and Professional (Prosubbing) Subtitling

Technological advances continue to enable the creation of more and more audiovisual (AV) products, i.e., films, TV shows, podcasts, etc., which are widely disseminated online and accessible to millions of diverse users across the globe. The translation of these AV products remains a significant challenge, resulting in increasing numbers of individuals and groups becoming online volunteers to translate foreign audiovisual products into their domestic markets, i.e., fansubbing – a contraction of ‘fan’ and ‘subtitling’. However, when translating culturally-sensitive information, particularly swearwords, there is reported convergence and divergence between fan-produced subtitles and professional/official subtitles produced by government-owned companies. This dissertation aims to explore the nature and impact of these similarities and differences. To do so, initially a corpus-based translation approach was employed to identify translation patterns of swearwords by these two groups. A corpus of 549,349 words in original English subtitle scripts was collated and aligned with 528,889 professionally and 543,522 non-professionally translated Chinese words from a diverse sample of 57 recent English films. The results showed mostly convergent practices, but fansubbing appeared to adopt a more vulgarising approach when rendering swearwords (55%) than prosubbing (46%). Informed by this corpus information, the study then employed an eye-tracking approach to investigate how audiences receive and cognitively process translated swearwords in films. An eye-tracking experiment collected data from 150 participants who were allocated into one of the five subtitling groups to watch four representative film clips: four different groups for translation strategies of swearwords ranging from low to high profanity, and one control group which saw only the original English same-language subtitles. Established measures of Total Fixation Count, Total Fixation Duration, Mean Fixation Duration and Time to First Fixation were analysed using a series of one-way ANOVAs. In general, these eye-tracking results showed no significant differences between the swearword translation strategies in terms of processing and reception. Pre- and post-task questionnaires were also employed to collect demographic information and qualitative feedback, in addition to the receptive measurement of immersion, satisfaction, enjoyment, comprehension and offensiveness. Generally, there were no significant differences among these measurements across all clips. Further, participants reported functional awareness of swearwords, which helped them to understand the observed characters’ emotions and feelings. As a result, they reported an expectation for a more authentic translation of swearwords that closely reflected the real-life usage of the target audience. The dissertation concludes with a presentation of the empirical and methodological contributions of the research, followed by a critical reflection on its limitations and the identification of future avenues of study

Sudowoodo: a Chinese Lyric Imitation System with Source Lyrics

Lyrics generation is a well-known application in natural language generation research, with several previous studies focusing on generating accurate lyrics using precise control such as keywords, rhymes, etc. However, lyrics imitation, which involves writing new lyrics by imitating the style and content of the source lyrics, remains a challenging task due to the lack of a parallel corpus. In this paper, we introduce \textbf{\textit{Sudowoodo}}, a Chinese lyrics imitation system that can generate new lyrics based on the text of source lyrics. To address the issue of lacking a parallel training corpus for lyrics imitation, we propose a novel framework to construct a parallel corpus based on a keyword-based lyrics model from source lyrics. Then the pairs \textit{(new lyrics, source lyrics)} are used to train the lyrics imitation model. During the inference process, we utilize a post-processing module to filter and rank the generated lyrics, selecting the highest-quality ones. We incorporated audio information and aligned the lyrics with the audio to form the songs as a bonus. The human evaluation results show that our framework can perform better lyric imitation. Meanwhile, the \textit{Sudowoodo} system and demo video of the system is available at \href{https://Sudowoodo.apps-hp.danlu.netease.com/}{Sudowoodo} and \href{https://youtu.be/u5BBT_j1L5M}{https://youtu.be/u5BBT\_j1L5M}.Comment: 7 pages,3 figures, submit to emnlp 2023 demo trac

Effects of thermal stratification and mixing on the vertical distribution of dissolved oxygen in aquaculture ponds

Thermal stratification and mixing in aquaculture ponds can seriously affect the vertical distribution of dissolved oxygen (DO) and result in a lack of DO in ponds. Therefore, revealing the vertical distribution of DO in aquaculture ponds influenced by thermal stratification and mixing can provide a theoretical basis for aquaculture water management. Here, we explore the impacts of thermal stratification and mixing on the vertical distribution of DO in two aquaculture ponds under different weather conditions. The results showed that thermal stratification mainly occurs during the daytime, and mixing occurs at nighttime. Water thermal stratification appears 4 h after sunrise, while mixing occurs 1 h after nightfall. When the Richardson index is less than 0.25, the mixing direction is unstable. In the daytime, the vertical distribution of DO, chlorophyll a (Chl-a), and phytoplankton abundance varied with thermal stratification and mixing. The concentration of DO gradually dropped with increasing water depth during the nighttime. The concentration of DO was lowest in the early morning and peaked in the afternoon. Multiple regression analysis demonstrated that water temperature (WT), Chl-a, and phytoplankton abundance provided the best model for the vertical distribution of DO. Based on our results, DO regulation can provide important insights for aquaculture pond management

Observation of nonrelativistic plaid-like spin splitting in a noncoplanar antiferromagnet

Spatial, momentum and energy separation of electronic spins in condensed matter systems guides the development of novel devices where spin-polarized current is generated and manipulated. Recent attention on a set of previously overlooked symmetry operations in magnetic materials leads to the emergence of a new type of spin splitting besides the well-studied Zeeman, Rashba and Dresselhaus effects, enabling giant and momentum dependent spin polarization of energy bands on selected antiferromagnets independent of relativistic spin-orbit interaction. Despite the ever-growing theoretical predictions, the direct spectroscopic proof of such spin splitting is still lacking. Here, we provide solid spectroscopic and computational evidence for the existence of such materials. In the noncoplanar antiferromagnet MnTe$_2$, the in-plane components of spin are found to be antisymmetric about the high-symmetry planes of the Brillouin zone, comprising a plaid-like spin texture in the antiferromagnetic ground state. Such an unconventional spin pattern, further found to diminish at the high-temperature paramagnetic state, stems from the intrinsic antiferromagnetic order instead of the relativistic spin-orbit coupling. Our finding demonstrates a new type of spin-momentum locking with a nonrelativistic origin, placing antiferromagnetic spintronics on a firm basis and paving the way for studying exotic quantum phenomena in related materials.Comment: Version 2, 30 pages, 4 main figures and 8 supporting figure

Distinct EH domains of the endocytic TPLATE complex confer lipid and protein binding

Clathrin-mediated endocytosis (CME) is the gatekeeper of the plasma membrane. In contrast to animals and yeasts, CME in plants depends on the TPLATE complex (TPC), an evolutionary ancient adaptor complex. However, the mechanistic contribution of the individual TPC subunits to plant CME remains elusive. In this study, we used a multidisciplinary approach to elucidate the structural and functional roles of the evolutionary conserved N-terminal Eps15 homology (EH) domains of the TPC subunit AtEH1/Pan1. By integrating high-resolution structural information obtained by X-ray crystallography and NMR spectroscopy with all-atom molecular dynamics simulations, we provide structural insight into the function of both EH domains. Both domains bind phosphatidic acid with a different strength, and only the second domain binds phosphatidylinositol 4,5-bisphosphate. Unbiased peptidome profiling by mass-spectrometry revealed that the first EH domain preferentially interacts with the double N-terminal NPF motif of a previously unidentified TPC interactor, the integral membrane protein Secretory Carrier Membrane Protein 5 (SCAMP5). Furthermore, we show that AtEH/Pan1 proteins control the internalization of SCAMP5 via this double NPF peptide interaction motif. Collectively, our structural and functional studies reveal distinct but complementary roles of the EH domains of AtEH/Pan1 in plant CME and connect the internalization of SCAMP5 to the TPLATE complex. AtEH/Pan1 proteins contain two N-terminal Eps15 homology (EH) domains and are subunits of the endocytic TPLATE complex present in plants. Here, the authors combine X-ray crystallography, NMR and MD simulations with biochemical and in planta analysis to characterize the two AtEH1/Pan1 EH domains and reveal their structural differences and complementary functional roles

Deep Personalized Medical Recommendations Based on the Integration of Rating Features and Review Sentiment Analysis

To comply with the rapid development of big data in mobile services, an increasing number of websites have begun to provide users with recommendation decisions in various areas, like shopping, tourism, food, and medical treatment. However, there are still some challenges in the field of medical recommendation systems, such as the lack of personalized medical recommendations and the problem of data sparseness, which seriously restricts the effectiveness of such recommendations. In this paper, we propose a personalized medical recommendation method based on a convolutional neural network that integrates revised ratings and review text, called revised rating and review based on a convolutional neural network (RR&R-CNN). First, the review text is divided into user and doctor datasets, and BERT vectorized representations are performed on them. Moreover, the original rating features are revised by adding the sentiment analysis values of the review text. Then, the vectorized review text and the revised rating features are spliced together and input into the convolutional neural network to extract the deep nonlinear feature vectors of both users and doctors. Finally, we use a factorization machine for feature interaction. We conduct comparison experiments based on a Yelp dataset in the “Health & Medical” category. The experimental results confirm the conclusion that RR&R-CNN has a better effect compared to a traditional method

Application of Rough Ant Colony Algorithm in Adolescent Psychology

With the rapid development of big data, big data research in the security protection industry has been increasingly regarded as a hot spot. This article mainly aims at solving the problem of predicting the tendency of juvenile delinquency based on the experimental data of juvenile blindly following psychological crime. To solve this problem, this paper proposes a rough ant colony classification algorithm, referred to as RoughAC, which first uses the concept of upper and lower approximate sets in rough sets to determine the degree of membership. In addition, in the ant colony algorithm, we use the membership value to update the pheromone. Experiments show that the algorithm can not only solve the premature convergence problem caused by stagnation near the local optimal solution but also solve the continuous domain and combinatorial optimization problems and achieve better classification results. Moreover, the algorithm has a good effect on predicting classification and can provide guidance for predicting the tendency of juvenile delinquency