Search Results (50)

The poetry of distant country with different culture and language is always distinctive and fascinating. Chinese and Romanian belong to Sinitic languages of the Sino-Tibetan language family and Romance languages of the Indo-European language family, which have relatively different syntax and general imagery of literature. Therefore, in this study, we make an attempt that was rarely involved in previous poetry generation research, using modern Chinese as the carrier, and generating modern poetry with Romanian style based on pre-trained model and direct preference optimization. Using a 5-point grading system, human evaluators awarded scores ranging from 3.21 to 3.83 across seven evaluation perspectives for the generated poems, achieving 76.2% to 91.6% of the comparable scores for the Chinese translations of authentic Romanian poems. The coincidence of the 30th to the 50th most frequently occurring poetic images in both generated poems and Romanian poems can reach 58.0–63.3%. Human evaluation and comparative statistical results on poetic imagery show that direct preference optimization is of great help in improving the degree of stylization, and the model can successfully create Chinese modern poems with Romanian style. Full article

Background: The role of molecular imaging in urothelial cancer is less defined than other cancers, and its utility remains controversial due to limitations such as high urinary tracer excretion, complicating primary tumour assessment in the bladder and upper urinary tract. This review explores the current landscape of PET imaging in the clinical management of urothelial cancer, with a special emphasis on potential future advancements including emerging novel non-¹⁸F FDG PET agents, PET radiopharmaceuticals, and PET-MRI applications. Methods: We conducted a comprehensive literature search in the PubMed database, using keywords such as “PET”, “PET-CT”, “PET-MRI”, “FDG PET”, “Urothelial Cancer”, and “Theranostics”. Studies were screened for relevance, focusing on imaging modalities and advances in PET tracers for urothelial carcinoma. Non-English language, off-topic papers, and case reports were excluded, resulting in 80 articles being selected for discussion. Results: ¹⁸F FDG PET-CT has demonstrated superior sensitivity over conventional imaging, such as contrast-enhanced CT and MRI, for detecting lymph node metastasis and distant disease. Despite these advantages, FDG PET-CT is limited for T-staging of primary urothelial tumours due to high urinary excretion of the tracer. Emerging evidence supports the role of PETC-CT in assessing response to neoadjuvant chemotherapy and in identifying recurrence, with a high diagnostic accuracy reported in several studies. Novel PET tracers, such as ⁶⁸Ga-labelled FAPI, have shown promising results in targeting cancer-associated fibroblasts, providing higher tumour-to-background ratios and detecting lesions missed by traditional imaging. Antibody-based PET tracers, like those targeting Nectin-4, CAIX, and uPAR, are under investigation for their diagnostic and theranostic potential, and initial studies indicate that these agents may offer advantages over conventional imaging and FDG PET. Conclusions: Molecular imaging is a rapidly evolving field in urothelial cancer, offering improved diagnostic and prognostic capabilities. While ¹⁸F FDG PET-CT has shown utility in staging, further prospective research is needed to establish and refine standardised protocols and validate new tracers. Advances in theranostics and precision imaging may revolutionise urothelial cancer management, enhancing the ability to tailor treatments and improve patient outcomes. Full article

Educational dialogue classification is a critical task for analyzing classroom interactions and fostering effective teaching strategies. However, the scarcity of annotated data and the high cost of manual labeling pose significant challenges, especially in low-resource educational contexts. This article presents the EduDCM framework for the first time, offering an original approach to addressing these challenges. EduDCM innovatively integrates distant supervision with the capabilities of Large Language Models (LLMs) to automate the construction of high-quality educational dialogue classification datasets. EduDCM reduces the noise typically associated with distant supervision by leveraging LLMs for context-aware label generation and incorporating heuristic alignment techniques. To validate the framework, we constructed the EduTalk dataset, encompassing diverse classroom dialogues labeled with pedagogical categories. Extensive experiments on EduTalk and publicly available datasets, combined with expert evaluations, confirm the superior quality of EduDCM-generated datasets. Models trained on EduDCM data achieved a performance comparable to that of manually annotated datasets. Expert evaluations using a 5-point Likert scale show that EduDCM outperforms Template-Based Generation and Few-Shot GPT in terms of annotation accuracy, category coverage, and consistency. These findings emphasize EduDCM’s novelty and its effectiveness in generating high-quality, scalable datasets for low-resource educational NLP tasks, thus reducing manual annotation efforts. Full article

The improvements in collecting and processing data using machine learning algorithms have increased the interest in data mining. This trend has led to the development of real-life decision support systems (DSSs) in diverse areas such as biomedical informatics, fraud detection, natural language processing, face recognition, autonomous vehicles, image processing, and each part of the real production environment. The imbalanced datasets in some of these studies, which result in low performance measures, have highlighted the need for additional efforts to address this issue. The proposed method (HOUM) is used to address the issue of imbalanced datasets for classification problems in this study. The aim of the model is to prevent the overfitting problem caused by oversampling and valuable data loss caused by undersampling in imbalanced data and obtain successful classification results. The HOUM is a hybrid approach that tackles imbalanced class distribution challenges, refines datasets, and improves model robustness. In the first step, majority-class data points that are distant from the decision boundary obtained via SVM are reduced. If the data are not balanced, SLS is employed to augment the minority-class data. This loop continues until the dataset becomes balanced. The main contribution of the proposed method is reproducing informative minority data using SLS and diminishing non-informative majority data using the SVM before applying classification techniques. Firstly, the efficiency of the proposed method, the HOUM, is verified by comparison with the SMOTE, SMOTEENN, and SMOTETomek techniques using eight datasets. Then, the results of the W-SIMO and RusAda algorithms, which were developed for imbalanced datasets, are compared with those of the HOUM. The strength of the HOUM is revealed through this comparison. The proposed HOUM algorithm utilizes a real dataset obtained from a project endorsed by The Scientific and Technical Research Council of Turkey. The collected data include quality control and processing parameters of yarn data. The aim of this project is to prevent yarn breakage errors during the weaving process on looms. This study introduces a decision support system (DSS) designed to prevent yarn breakage during fabric weaving. The high performance of the algorithm may encourage producers to manage yarn flow and enhance the HOUM’s efficiency as a DSS. Full article

This paper explores the challenges of measuring and classifying the East African Asian population in Aotearoa New Zealand. As a particularly diverse country, New Zealand has a significant and varied population of immigrants from South Asia, including India, Pakistan and Bangladesh, along with immigrants of South Asian origin, from Fiji, Southeast Asia, the Caribbean and East Africa. New Zealand’s system of ethnic classification relies on self-identification, with a broad definition of ethnicity encompassing heritage, ancestry, culture, language and feelings of belonging. However, the collection of this information at a granularity that enables detailed analysis is constrained for the South Asian population, regardless of origin or identification. People are typically presented with the choice of selecting “Indian” ethnicity as a tick box, or providing ethnicities under “Other” as write-in descriptors, which in turn are coded to a limited set of categories within the classification being used. This practice potentially conceals a diversity of ethnicities, which can only partially be hinted at by responses to questions relating to religions, languages and birthplaces, especially for second or third-generation descendants of migrants. Ethnic classification at the highest level, moreover, includes East African Indians as Asian, rather than African, reflecting diasporic heritage as a shorthand for ancestry and overlooking the relevance of layers of identity associated with the double diaspora. Drawing on Peter J. Aspinall’s work on collective terminology in ethnic data collection and categorizing the “Asian” ethnic group in the UK, this paper looks at the overlaps and disconnects between heritage, ethnicity and national belonging in classifying less clearcut identities. We explore the strengths and limitations of New Zealand’s self-identification approach to ethnic identity, and query what exactly is being asked of groups on the margins: when self-identification does not match external perception, are we looking for geographic, cultural, or genetic origins? A focus on the East African Asian population in Aotearoa highlights the complexity of identity for diasporic groups with distant ancestral links with India, as lived experience of cultural connection extends far beyond the bounds of ethnicity, language and even ancestry. Full article

Objectives: Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has gained a primary role in prostate cancer (PCa) imaging, overcoming conventional imaging and prostate-specific antigen (PSA) serum levels, and has recently emerged as a promising technique for monitoring therapy response in metastatic castration-resistant prostate cancer (mCRPC) patients treated with novel hormonal therapy, taxanes, and radioligand therapy (RLT). In this review, we aim to provide an overview of the most relevant aspects under study and future prospects related to the prognostic role of PSMA PET/CT in mCRPC. Methods: A systematic literature search was performed in the following databases: MEDLINE, PubMed, and EMBASE databases. The study focused exclusively on English-language studies, excluding papers not pertinent to the topic. Results: PSMA PET imaging offers a higher sensitivity and specificity than conventional imaging and provides accurate staging and efficient diagnosis of distant metastases. The data presented herein highlight the usefulness of PET in risk stratification, with a prognostic potential that can have a significant impact on clinical practice. Several prospective trials are ongoing and will shortly provide more evidence supporting the prognostic potential of PET PSMA data in this clinical scenario. Conclusions: Current evidence proves the prognostic role of PSMA PET/CT in different settings, with raising relevance also in the context of mCRPC. Full article

Background and objectives: Children living in an area distant from or associated with barriers to travelling to health service centres usually experience health and well-being disparities. This is a survey of child health and well-being on Bulon-Don Island, located 22 kms. from the southern mainland of Thailand, to gather essential background data before activating responses from local service provider agencies. Methods: Demographic data, physical and crude psychological health, harm to health, and living conditions of Bulon-Don children aged 1–14 years were studied and compared with the results of the corresponding national child health survey. Descriptive statistics were used for the statistical analysis of significance (p < 0.05). Results: A total of 21 male and 41 female children (N = 62) participated in the survey after obtaining consents from parents or care providers. The islanders are Indigenous people who use their own languages and have traditional beliefs. Comparing with the children of the national survey, most children aged <5 years were found to have significantly lower height and weight according to their age (p = 0.044 and p = 0.043, respectively), whereas those aged >5 years had a similar nutritional status. In addition, there is a lack of facilities for healthy living. However, the mean total psychological and ethical standards scores were significantly higher in the 1–5 and 6–9-year-old children. Conclusions: Disparity of socio-political status, cultural beliefs and practices, socioeconomic basis, and geographic distance from the mainland were the social determinants and barriers of low health service accessibility for the islander children. Comprehensive child health and well-being evaluation in an enclave of isolation like this is mandatory before an integrated intervention carried out by the local healthcare and living facilities providers is implemented. Full article

Multilingual large language models (MLLMs) have demonstrated remarkable performance across a wide range of cross-lingual Natural Language Processing (NLP) tasks. The emergence of MLLMs made it possible to achieve knowledge transfer from high-resource to low-resource languages. Several MLLMs have been released for cross-lingual transfer tasks. However, no systematic evaluation comparing all models for Arabic cross-lingual Named-Entity Recognition (NER) is available. This paper presents a benchmark evaluation to empirically investigate the performance of the state-of-the-art multilingual large language models for Arabic cross-lingual NER. Furthermore, we investigated the performance of different MLLMs adaptation methods to better model the Arabic language. An error analysis of the different adaptation methods is presented. Our experimental results indicate that GigaBERT outperforms other models for Arabic cross-lingual NER, while language-adaptive pre-training (LAPT) proves to be the most effective adaptation method across all datasets. Our findings highlight the importance of incorporating language-specific knowledge to enhance the performance in distant language pairs like English and Arabic. Full article

Background and Objectives: Retrieve data from the National Cancer Database (NCDB) to examine information on the epidemiological prevalence, treatment strategies, and survival outcomes of pediatric vertebral, sacral and pelvic osteosarcomas. Methods: We reviewed NCDB data from 2008 to 2018, concentrating on vertebral, sacral, and pelvic osteosarcomas in children 0 to 21 years. Our analysis involved logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. Results: The study population included 207 patients. For vertebral osteosarcomas, 62.5% of patients were female, and 78.1% were white. Regional lymph node involvement predicted 80 times higher mortality hazard (p = 0.021). Distant metastasis predicted 25 times higher mortality hazard (p = 0.027). For sacral and pelvic osteosarcomas, 58.3% of patients were male, and 72% were white. Patients with residual tumor were 4 times more likely to have prolonged LOS (p = 0.031). No residual tumor (HR = 0.53, p = 0.03) and radiotherapy receipt (HR = 0.46, p = 0.034) were associated with lower mortality hazards. Distant metastasis predicted 3 times higher mortality hazard (p < 0.001). Hispanic ethnicity was linked to lower resection odds (OR = 0.342, p = 0.043), possibly due to language barriers affecting patient understanding and care decisions. Conclusions: In conclusion, our examination of NCDB offers a thorough exploration of demographics, treatment patterns, and results, highlighting the importance of personalized approaches to enhance patient outcomes. Full article

The purpose of this study was to analyze the cross-linguistic influence of previously learned languages and working memory capacities on the vocabulary performance of two different typological languages. The objectives of this study were (1) to compare the working memory capacities of bilingual adults in relation to the vocabulary performance of two different languages never learned by the participants, and (2) to analyze to what extent the typology of previously learned languages influences working memory capacities in relation to the vocabulary performance of French and Nahuatl. A group of 43 Mexican Spanish college students participated in this experimental study. The participants completed a series of working memory tasks in Nahuatl and French. The results showed that working memory capacities were lower in Nahuatl than in French. Thus, a correlation was found between their first and second language and vocabulary performance in French. We can consider the influence of previously learned languages as a significant factor in vocabulary acquisition in accordance with the participants’ working memory capacities. Full article

Natural language processing for languages with limited resources is hampered by a lack of data. Using English as a hub language for such languages, cross-lingual sentiment analysis has been developed. The sheer quantity of English language resources raises questions about its status as the primary resource. This research aims to examine the impact on sentiment analysis of adding data from same-family versus distant-family languages. We analyze the performance using low-resource and high-resource data from the same language family (Slavic), investigate the effect of using a distant-family language (English) and report the results for both settings. Quantitative experiments using multi-task learning demonstrate that adding a large quantity of data from related and distant-family languages is advantageous for cross-lingual sentiment transfer. Full article

Online conversation communities have become an influential source of consumer recommendations in recent years. We propose a set of meaningful user segments which emerge from user embedding representations, based exclusively on comments’ text input. Data were collected from three popular recommendation communities in Reddit, covering the domains of book and movie suggestions. We utilized two neural language model methods to produce user embeddings, namely Doc2Vec and Sentence-BERT. Embedding interpretation issues were addressed by examining latent factors’ associations with behavioral, sentiment, and linguistic variables, acquired using the VADER, LIWC, and LFTK libraries in Python. User clusters were identified, having different levels of engagement and linguistic characteristics. The latent features of both approaches were strongly correlated with several user behavioral and linguistic indicators. Both approaches managed to capture significant variability in writing styles and quality, such as length, readability, use of function words, and complexity. However, the Doc2Vec features better described users by varying level of contribution, while S-BERT-based features were more closely adapted to users’ varying emotional engagement. Prominent segments revealed prolific users with formal, intuitive, emotionally distant, and highly analytical styles, as well as users who were less elaborate, less consistent, but more emotionally connected. The observed patterns were largely similar across communities. Full article

This paper contextualizes Okudzhava’s song “And We to the Doorman” (AWD), initially marginal in the Soviet poetic mainstream. It explores its shifts in tone, irregular rhythms, colloquial language, and semi-criminal undertones. AWD’s structure, with uneven stanzas and no clear refrain, reveals underlying symmetry and recurring themes. The meter is predominantly iambic but varies. Unconventional verse endings and various rhyme schemes, including distant chains, characterize its prosody. The narrative touches on social cohesion and class conflict. The style reflects a challenging attitude toward privilege, employing rhetorical devices and indirect threats. The melody aligns with thematic elements, featuring repetitive patterns and a spoken quality. Semantically, AWD presents an ambiguous message on class struggle and moral issues. In sum, this analysis uncovers Okudzhava’s song’s formal complexities, thematic nuances, and stylistic innovations. Full article

Investigating public attitudes on social media is crucial for opinion mining systems. Stance detection aims to predict the attitude towards a specific target expressed in a text. However, effective neural stance detectors require substantial training data, which are challenging to curate due to the dynamic nature of social media. Moreover, deep neural networks (DNNs) lack explainability, rendering them unsuitable for scenarios requiring explanations. We propose a distantly supervised explainable stance detection framework (DS-ESD), comprising an instruction-based chain-of-thought (CoT) method, a generative network, and a transformer-based stance predictor. The CoT method employs prompt templates to extract stance detection explanations from a very large language model (VLLM). The generative network learns the input-explanation mapping, and a transformer-based stance classifier is trained with VLLM-annotated stance labels, implementing distant supervision. We propose a label rectification strategy to mitigate the impact of erroneous labels. Experiments on three benchmark datasets showed that our model outperformed the compared methods, validating its efficacy in stance detection tasks. This research contributes to the advancement of explainable stance detection frameworks, leveraging distant supervision and label rectification strategies to enhance performance and interpretability. Full article

In their quest to find a suitable tone of voice in an informalizing society, many companies are confronted with the choice of using T or V pronouns in their B2C communications. This paper presents an experimental study addressing the question of whether the recipients of these messages actually notice the difference between being addressed with a T form, which carries social meanings of informality and proximity, or a more distant V form, and to what extent the presence of additional informal linguistic features influences the salience of a pronoun switch. We furthermore investigate to what extent the professional socialization of participants impacts on the noticing of pronoun use. In a case study for Belgian Dutch, participants (N = 279) were presented with two versions of an information letter that they were asked to read quickly. The texts were manipulated for the use of T/V pronouns, as well as, depending on the condition, a number of additional informal linguistic features (i.e., informal punctuation, intensifiers, and English lexical items). Participants were not warned in advance about the changes between the two versions of the stimulus text. In a salience test following the presentation of the two text versions, less than 10% of participants noticed a switch in T/V form regardless of the presence of additional informal features. Similarly low rates of noticing were found for the other informal features, except for English loanwords. No differences were found depending on whether participants had a language-related professional background (e.g., language teachers, journalists, editors). We argue that the lack of noticing T/V pronouns may be due to the specifics of the Belgian Dutch system of pronominal address that has an additional highly salient colloquial pronoun of address which may obscure the difference in social meaning between the standard T and V pronouns. The discussion critically evaluates the implications of the study for the use of T/V pronouns in professional communication, musing on the complex relationship between noticing and evaluating. Full article