Documenting and developing the current German Lexicon

As an important language tool, literature in business English defines rights and obligations in business activities from the perspective of literature translation. This article discusses business English from the perspective of literature translation, which should not only preserve the characteristics of literature, but also ensure the smooth and correct language. In order to improve the accuracy of the automatic translation of business English literature and optimize the design of the teaching platform for business English literature translation, a design method of the teaching platform for business English literature translation based on the decision tree logistic model is proposed. The platform design consists of two modules: automatic translation algorithm design and software development of the platform. Using the decision tree logistics model to analyze the semantic features of business English translation and context feature matching and adaptive semantic variable optimization method to analyze automation lexical features of business English translation and to extract the correlation between vocabulary and vocabulary characteristics, in the context of a specific business translation difference correction, the accuracy of English translation is improved. The software design of the platform is carried out under the decision tree logistics model. The platform construction is mainly divided into vocabulary database module, English information processing module, network interface module, and human-computer interaction interface module. B/S framework protocol is used for integrated development and the design of translation platform. According to the characteristics of the data business application and the particularity of data security risk monitoring, from business English requirement analysis, the study of business English translation behavior monitoring ability and analysis in the process of abnormal behavior monitoring techniques and methods, including data access, data processing, experience in engine, and model engine puts forward the future research direction. The platform test results show that this method has good accuracy and strong automatic translation ability in business English literature translation.

This research investigates (1) types of culture-specific items (CSIs), (2) translation techniques applied to CSIs, and (3) translation ideologies employed by the translators of Eka Kurniawan's novel Cantik Itu Luka into its English (Beauty is a Wound) and Russian (Красота – это горе) translations by Annie Tucker and Marina Izvekova, respectively. Using a mixed-methods approach, the study identifies 110 CSIs based on Newmark's classification and analyzes translation techniques through Vinay and Darbelnet’s framework and Venuti’s ideologies of foreignization and domestication. The findings reveal that the culture-specific items (CSIs) identified in the novel fall into several categories: material culture, social culture, organizations, customs, ideas, activities, procedures, concepts, ecology, and gestures and habits. In translating these CSIs, the English version predominantly employed borrowing (39%), amplification (19%), and generalization (13%) techniques. Meanwhile, the Russian translation primarily utilized literal translation (35%), borrowing (28%), and amplification (16%). The English translation's preference for borrowing and amplification techniques aligns with a foreignization ideology, introducing Indonesian culture to English-speaking audiences with minimal alteration. Conversely, the Russian translation leans toward domestication, favoring literal translation (35%) and borrowing (28%) for cultural adaptation.

Surrounded by structured data, such as medical data, financial data, knowledge bases, etc., data-to-text generation has become an important natural language processing task that can help people better understand the meaning of those data by providing them with user-friendly text. Existing methods for data-to-text generation show promising results in tackling two major challenges: content planning and surface realization, which transform structured data into fluent text. However, they lack an iterative refinement process for generating text, which can enable the model to perfect the text step-by-step while accepting control over the process. In this paper, we explore enhancing data-to-text generation with an iterative refinement process via diffusion. We have four main contributions: (1) we use the diffusion model to improve the prefix tuning for data-to-text generation; (2) we propose a look-ahead guiding loss to supervise the iterative refinement process for better text generation; (3) we extract content plans from reference text and propose a planning-then-writing pipeline to give the model content planning ability; and (4) we conducted experiments on three data-to-text generation datasets and both automatic evaluation criteria (BLEU, NIST, METEOR, ROUGEL, CIDEr, TER, MoverScore, BLEURT, and BERTScore) and human evaluation criteria (Quality and Naturalness) show the effectiveness of our model. Our model can improve the competitive prefix tuning method by 2.19% in terms of a widely-used automatic evaluation criterion BLEU (BiLingual Evaluation Understudy) on WebNLG dataset with GPT-2 Large as the pretrained language model backbone. Human evaluation criteria also show that our model can improve the quality and naturalness of the generated text across all three datasets.

With the change and development of the times and the deepening of economic globalization, people in various countries and regions have also established closer communication and exchange. It is undeniable that literature plays an important and irreplaceable role in the process of cultural exchange in various regions. With the close communication between China and the west, the translation of British and American literature is playing an increasingly important role. Therefore, it can be called a bridge of cultural communication between China and the West. In the process of translating English and American literature, context culture has a great influence on the content or form of its translation. Based on the above reasons, it is necessary to analyze the author, translator and reader of the original works in the process of translating British and American literature, the different contextual cultures of the three, and how this will affect the translation of literature, so that more translators can translate more high-quality articles, and lay a good foundation for the cultural exchange between China and the West. Based on this, this article uses big data technology to help analyze the influence of cultural background on the translation of English and American literature.

Tao Hua Yuan Ji, as a representative prose of ancient Jiangxi literature, its English translation has attracted much attention from Chinese academia. There are single translation version research and comparative studies of multiple versions all with different perspectives, but little attention is paid to the version of American Sinologist Hightower. The rich expressions of four-character structures is one of the noticeable features of the original text of Tao Hua Yuan Ji, but at present, there lacks study of its English translation, especially in-depth cognitive analysis. Therefore, starting from the Embodied-Cognitive Linguistics, guided by its kernel principle “Reality-Cognition-language”, this paper explores the choices of Hightower’s translation of four-character structures from the three levels of language, cognition and reality, and analyzes the translation-induced cognitive processes and their pragmatic effects when the rendition takes place on different levels. It is found that among the 33 four-character structures in the original text, Hightower chose 23 on the level of language, mainly using complete literal translation and the method of combining literal and liberal translation, and 10 on the levels of cognition and reality, adopting complete liberal translation and contextual liberal translation. Hightower’s three-level choices of the translation of the four-character structures in Tao Hua Yuan Ji reflects the cognitive bases of embodied-cognition, namely universality, distinctness and deficiency.

Pragmatic markers, either primary or secondary, contribute to the specificity of languages and are sensitive on being translated. This study traces the use of well, the commonest pragmatic marker in the English discourse, in a corpus of translated Arabic novels. The study, too, addresses the influence of the translators’ phonological awareness on their word choices, in the same corpus. Adjacent consonants, consonant-starting and quarter-syllabic words are studied in four groups: free writing of native authors as a control group (G1), literary translations by native English translators (G2), literary English translations by Arabic translators (G3) as well as literary English translations by joint effort (native-speaking and non-native-speaking translators) [G4]. The findings are statistically compared using one-way ANOVA test. Results show a statistically significant difference in the use of the pragmatic marker well and in the use of the three phonological patterns among the four groups. The findings are interpreted and implications are offered for the pragmatic gap and linguistic competence between native-speaking and non-native-speaking translators.

Related entity finding (REF) is a promising application, which aims to return a list of related entities given a query that describes the source entity, the specific type of target entities, and the relation between the source entity and target entities. One typical entity ranking strategy is to rank the candidate entities based on the co-occurrence between the candidate entities and the given query. However, such a strategy is inadequate to rank entities properly especially for those related but unpopular entities. In this paper, we propose a bipartite graph based entity ranking method, which leverage the Co-List relationship between candidate entities (i.e., entities co-occurring in the same structured/unstructured lists) to help improve the entity ranking. Specifically, we first estimate the initial relevance scores for the candidate entities based on a generative probabilistic model. We then construct a bipartite graph based on Co-List relation between candidate entities, and apply an iterative refinement process analogous to heat diffusion on the graph to propagate the relevance scores over entities. Finally the candidate entities are ranked according to their refined scores. We further develop an optimization framework for the iterative refinement process in our ranking method. Experimental results on the data collection from the TREC 2010 Entity Track show the effectiveness of our proposed method.

Employing early model-based safety evaluation to iteratively derive E/E architecture design

Problematizing the long-standing practice of and negative perspectives regarding indirect translation in literature, this paper aims to discuss its general characteristics and cultural background as well as its creative potential in poetry translation, based on the textual analysis of Pablo Neruda’s Veinte poemas de amor y una canción desesperada [Twenty Poems of Love and a Song of Despair]1 which has been translated into Korean via an English translation. The paper points out that in Korea the practice of indirect translation has been generally related to the reception of major canonical literatures, and that the languages of political or cultural hegemony of the time such as Japanese and English have been the middle language. An analysis of the English and Korean translations of Neruda’s poetry collection indicates that, while it is inevitable that indirect translation has produced unintentional and sometimes unnecessary textual modifications, its translation strategy of literal translation as well as the translator’s in-depth knowledge of the poet and poet’s poetic world, despite the translator’s lack of proficiency in Spanish, has rather contributed to creating the expansion and diversification of new poetic dimension of the poem, which has been a byproduct of indirect translation.

<sec><p indent="0mm">AlphaFold2 (AF2), the first deep learning-based protein structure prediction model to achieve near-experimental accuracy, has revolutionized the field of structural biology. With its ability to predict protein structures at unprecedented levels of accuracy, AF2 has become an essential tool in structural biology. In parallel, the deep learning methodologies derived from AF2 have made significant advancements in a range of downstream applications, such as efficient sampling of protein conformational ensembles, protein sequence design, and ligand generation. These applications are underpinned by an accurate description of the protein-free energy landscape, which is critical for modeling protein behavior in various biological contexts. </sec><sec> Despite these advancements, challenges remain, particularly in the area of multi-conformation sampling. Traditional methods such as molecular dynamics (MD) and Monte Carlo (MC) simulations, which are based on Boltzmann statistics, can provide detailed insights into conformational distributions. However, the application of these methods is hindered by their high computational cost and limited efficiency, especially for large or complex systems. In contrast, existing generative deep learning models can rapidly generate protein conformations, but often struggle to incorporate biophysical constraints, and are limited in the power of predicting the accurate energy minima, avoiding steric clashes, or understanding physics such as electrostatic interactions. This lack of control over the physical space makes it difficult to generate conformations that are both physically realistic and biologically relevant. </sec><sec> Furthermore, the robustness of the initial protein structure is crucial for downstream applications, including protein-protein interaction predictions, molecular docking, and virtual screening, as well as reliable and efficient MD simulations. However, conventional structure repair tools often fail to accurately model missing regions, such as loops or partially resolved regions, which may lead to unrealistic geometries or energetically unfavorable conformations. </sec><sec> To address these limitations, we present FoldCopilot, an innovative suite based on AF2. FoldCopilot uses both target sequences and multiple sequence alignments (MSAs) as input prompts for the Evoformer module, leveraging a homology-based sequence perturbation mechanism that generates diverse local conformations without the need for retraining. This method allows for the efficient generation of protein conformations and at the same time maintains high fidelity with known structural templates and evolutionary constraints. In addition, FoldCopilot integrates structural templates and initial reference frames into the iterative refinement process, enabling the directed evolution and control over generated protein structures. This iterative process ensures that the generated conformations not only adhere to biophysical principles, such as realistic energy states and steric compatibility but also meet specific functional requirements dictated by the target application. </sec><sec> Our experimental results demonstrate that FoldCopilot excels in key tasks such as protein structure repair and localized multi-conformation generation. The method significantly outperforms traditional structure repair techniques, yielding more accurate and biologically relevant models for regions of missing data. Additionally, FoldCopilot shows a high capability to generate diverse conformations, making it an ideal tool for protein engineering, protein-ligand interaction predictions, and other structure-based drug design applications. </sec><sec> In summary, FoldCopilot bridges the gap between high-fidelity conformational sampling and the ability to control conformational space, offering a powerful approach to facilitate protein-molecule interaction predictions, structural modeling, and molecular dynamics simulations. </sec>

ObjectivesArtificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.Materials and MethodsWe analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle that consolidates these themes into a more comprehensive lifecycle. We then considered the potential benefits and harms of AI through this lifecycle to identify ethical questions that can arise at each step and to identify where conflicts and errors could arise in ethical analysis. We illustrated the approach in 3 case studies that highlight how different ethical dilemmas arise at different points in the lifecycle.Results, Discussion, and ConclusionThrough case studies, we show how a systematic lifecycle-informed approach to the ethical analysis of AI enables mapping of the effects of AI onto different steps to guide deliberations on benefits and harms. The lifecycle-informed approach has broad applicability to different stakeholders and can facilitate communication on ethical issues for patients, healthcare professionals, research participants, and other stakeholders.

People speak different languages across the globe and belong to different cultures, but they still need to communicate. People of discrete cultures think differently and use various concepts and expressions across languages. Such differences appear in literature and are expressed through culture-specific items (CSIs). This study analyzes the CSIs in Al Shehhi’s أنا و العم سام /?na: wa l؟am sa:m/ (2016) and its English translation, ‘Uncle Sam &amp; Myself’ (2017). The book was translated by Renaissance Translation and Businessmen Services. The analysis provides a comparison between the CSIs in the ST (Arabic) and their TT (English) translations. The analysis was theoretically tethered to Newmark’s (1988) classification of CSIs and the translation procedures he postulated. Throughout the analysis, it has been found that some CSIs are inaccurately translated due to the cultural differences between Arabic and English, the use of specific local Emirati expressions in the ST, and the misuse of some translation techniques, as represented in the overuse of invalid literal translations.

Proofs of progress properties often require fairness assumptions. Directly incorporating global fairness assumptions in a compositional method is difficult, given the local flavor of such reasoning. We present a fully automated local reasoning algorithm which handles fairness assumptions through a process of iterative refinement. Refinement strengthens local proofs by the addition of auxiliary shared variables which expose internal process state; it is needed as local reasoning is inherently incomplete. Experiments demonstrate that the new algorithm shows significant improvement over standard model checking.

The visually annoying artifacts resulting form block matching algorithm (BMA), blocky artifacts, become noticeable in applications for low bit rates. Warping prediction (WP) based schemes can remove the blocky artifacts of BMA successfully, but they also produce severe prediction errors around the boundaries of moving objects. Since the errors around the boundaries of objects are visually sensitive, they may sometimes look more annoying than blocky artifacts. The lack of ability of modeling motion discontinuities is the major reason of the errors from WP. Motion discontinuities usually exist in practical video sequences, so that it is required to develop a more reliable motion estimation and usually exist in practical video sequences, so that it is required to develop a more reliable motion estimation and compensation scheme for low bit rate applications. In this paper, we propose a new WP scheme, named region constrained warping prediction (RCWP), which places motion discontinuities according to the segmentation results. In RCWP, there is mutual dependency between estimated motion field and segmentation mask. Because of the mutual dependency, an iterative refinement process is also introduced. Experimental results have shown that the proposed algorithm can provide much better subjective and objective performance than the BMA and the conventional warping prediction.

We present an automatic method for the synthesis of certificates for components in embedded real-time systems. A certificate is a small homomorphic abstraction that can transparently replace the component during model checking: if the verification with the certificate succeeds, then the component is guaranteed to be correct; if the verification with the certificate fails, then the component itself must be erroneous. We give a direct construction, based on a forward and backward reachability analysis of the timed system, and an iterative refinement process, which produces a series of successively smaller certificates. In our experiments, model checking the certificate is several orders of magnitude faster than model checking the original system.