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The Computational Nature of Language Learning and Evolution
The introduction of a mathematical and computational framework within which to analyze the interplay between language learning and language evolution.
The nature of the interplay between language learning and the evolution of a language over generational time is subtle. We can observe the learning of language by children and marvel at the phenomenon of language acquisition; the evolution of a language, however, is not so directly experienced. Language learning by children is robust and reliable, but it cannot be perfect or languages would never change—and English, for example, would not have evolved from the language of the Anglo-Saxon Chronicles. In this book Partha Niyogi introduces a framework for analyzing the precise nature of the relationship between learning by the individual and evolution of the population.
Learning is the mechanism by which language is transferred from old speakers to new. Niyogi shows that the evolution of language over time will depend upon the learning procedure—that different learning algorithms may have different evolutionary consequences. He finds that the dynamics of language evolution are typically nonlinear, with bifurcations that can be seen as the natural explanatory construct for the dramatic patterns of change observed in historical linguistics. Niyogi investigates the roles of natural selection, communicative efficiency, and learning in the origin and evolution of language—in particular, whether natural selection is necessary for the emergence of shared languages.
Over the years, historical linguists have postulated several accounts of documented language change. Additionally, biologists have postulated accounts of the evolution of communication systems in the animal world. This book creates a mathematical and computational framework within which to embed those accounts, offering a research tool to aid analysis in an area in which data is often sparse and speculation often plentiful.
eBook
Using Interactive Virtual Reality Tools in an Advanced Chinese Language Class: a Case Study
This case study explored college students’ use of interactive virtual reality tools (Google Cardboard and Expeditions) for learning Chinese as a foreign language. Specifically, the purpose of the study was to probe into students’ perceived benefits and challenges of using VR tools for Chinese language and culture learning. Twelve students were paired and role-played as virtual tour guides for six locations throughout a semester. Every two weeks, each dyad studied a particular Chinese tourist attraction or location and presented orally in Chinese as virtual tour guides by using the VR tools. Data collection included class observations of all presentations by each dyad, 24 reflections (two per participant, after the first and fifth presentations), and individual follow-up interviews. The study indicated that the real-life view VR tools offered an authentic context for Chinese language learning, sparked interest in the virtually presented locales, and encouraged students to further explore the target culture.
Journal Article
Language in Scotland
The chapters in this volume take as their focus aspects of three of the languages of Scotland: Scots, Scottish English, and Scottish Gaelic. They present linguistic research which has been made possible by new and developing corpora of these languages: this encompasses work on lexis and lexicogrammar, semantics, pragmatics, orthography, and punctuation. Throughout the volume, the findings of analysis are accompanied by discussion of the methodologies adopted, including issues of corpus design and representativeness, search possibilities, and the complementarity and interoperability of linguistic resources. Together, the chapters present the forefront of the research which is currently being directed towards the linguistics of the languages of Scotland, and point to an exciting future for research driven by ever more refined corpora and related language resources.
eBook
Exploring drivers of modern Hanfu purchase in digital commerce: A mixed-methods perspective
The rapid growth of the modern Hanfu market, driven by digital commerce and social media, has increased the interest in understanding consumer purchase intentions. This study investigates the key factors influencing online purchase intentions for modern Hanfu by integrating text mining, grounded theory, and quantitative analyses. A dataset of 5,992 consumer reviews from major Chinese e-commerce platforms was analyzed using natural language processing techniques to extract critical themes. Additionally, a structured survey (n = 344) was conducted to develop and validate a measurement scale for assessing the key determinants of purchase intention. The findings reveal that co-design has the strongest influence on purchase intention (β = 0.252), followed by website attractiveness (β = 0.235) and product quality (β = 0.160). Despite product quality being a dominant theme in consumer reviews, its impact on the quantitative model is moderate (β = 0.160). Social media engagement and cultural identity emerge as significant but less frequently discussed factors, while merchant service has the weakest effect (β = 0.145). This study contributes to the literature by integrating cultural identity and co-design into purchase intention models, offering theoretical insights into heritage-driven fashion consumption. Future research should explore demographic variations, cross-cultural comparisons, and longitudinal trends to further refine our understanding of modern Hanfu purchase behavior. These results suggest that the top priority of modern Hanfu brands in their marketing management activities is to encourage and promote consumer engagement. Optimizing website design, improving product quality, considering social media strategies, tapping into the essence of ethnic culture, and enhancing customer service are also important.
Journal Article
The ROCEEH Out of Africa Database (ROAD): A large-scale research database serves as an indispensable tool for human evolutionary studies
Large scale databases are critical for helping scientists decipher long-term patterns in human evolution. This paper describes the conception and development of such a research database and illustrates how big data can be harnessed to formulate new ideas about the past. The Role of Culture in Early Expansions of Humans (ROCEEH) is a transdisciplinary research center whose aim is to study the origins of culture and the multifaceted aspects of human expansions across Africa and Eurasia over the last three million years. To support its research, the ROCEEH team developed an online tool named the ROCEEH Out of Africa Database (ROAD) and implemented its web-based applications. ROAD integrates geographical data as well as archaeological, paleoanthropological, paleontological and paleobotanical content within a robust chronological framework. In fact, a unique feature of ROAD is its ability to dynamically link scientific data both spatially and temporally, thereby allowing its reuse in ways that were not originally conceived. The data stem from published sources spanning the last 150 years, including those generated by the research team. Descriptions of these data rely on the development of a standardized vocabulary and profit from online explanations of each table and attribute. By synthesizing legacy data, ROAD facilitates the reuse of heritage data in novel ways. Database queries yield structured information in a variety of interoperable formats. By visualizing data on maps, users can explore this vast dataset and develop their own theories. By downloading data, users can conduct further quantitative analyses, for example with Geographic Information Systems, modeling programs and artificial intelligence. In this paper, we demonstrate the innovative nature of ROAD and show how it helps scientists studying human evolution to access datasets from different fields, thereby connecting the social and natural sciences. Because it permits the reuse of “old” data in new ways, ROAD is now an indispensable tool for researchers of human evolution and paleogeography.
Journal Article
Dataset creation and benchmarking for Kashmiri news snippet classification using fine-tuned transformer and LLM models in a low resource setting
Kashmiri language, recognized as one of the low-resource languages, has rich cultural heritage but remains underexplored in NLP due to lack of resources and datasets. The proposed research addresses this gap by creating a dataset of 15,036 news snippets for the task of Kashmiri news snippets classification, created through the translation of English news snippets into Kashmiri using the Microsoft Bing translation tool. These snippets are manually refined to ensure domain specificity, covering ten categories: Medical, Politics, Sports, Tourism, Education, Art and Craft, Environment, Entertainment, Technology, and Culture. Various machine learning, deep learning, transformer-models, and LLMs are explored for text classification. Among the models experimented for classification, fine-tuned ParsBERT-Uncased emerged as the best-performing transformer model, achieving an F1 score of 0.98. This work not only contributes a valuable dataset for Kashmiri but also identifies effective methodologies for accurate news snippet classification in the Kashmiri language. This research developed an essential dataset, which to our best belief, is the first attempt at creating a manually labelled corpus for the Kashmiri language and also devised an architecture using the best combination of embeddings, algorithms, and transformer-models for accurate text classification. It contributes significantly to the field of NLP for this underrepresented language.
Journal Article
Biology-driven insights into the power of single-cell foundation models
Background
Single-cell foundation models (scFMs) have emerged as powerful tools for integrating heterogeneous datasets and exploring biological systems. Despite high expectations, their ability to extract unique biological insights beyond standard methods and their advantages over traditional approaches in specific tasks remain unclear.
Results
Here, we present a comprehensive benchmark study of six scFMs against well-established baselines under realistic conditions, encompassing two gene-level and four cell-level tasks. Pre-clinical batch integration and cell type annotation are evaluated across five datasets with diverse biological conditions, while clinically relevant tasks, such as cancer cell identification and drug sensitivity prediction, are assessed across seven cancer types and four drugs. Model performance is evaluated using 12 metrics spanning unsupervised, supervised, and knowledge-based approaches, including scGraph-OntoRWR, a novel metric designed to uncover intrinsic knowledge encoded by scFMs. We provide holistic rankings from dataset-specific to general performance to guide model selection. Our findings reveal that scFMs are robust and versatile tools for diverse applications while simpler machine learning models are more adept at efficiently adapting to specific datasets, particularly under resource constraints. Notably, no single scFM consistently outperforms others across all tasks, emphasizing the need for tailored model selection based on factors such as dataset size, task complexity, biological interpretability, and computational resources.
Conclusions
This benchmark introduces novel evaluation perspectives, identifying the strengths and limitations of current scFMs, and paves the way for their effective application in biological and clinical research, including cell atlas construction, tumor microenvironment studies, and treatment decision-making.
Journal Article
Translation, Cross-Cultural Adaptation, and Validation of Measurement Instruments: A Practical Guideline for Novice Researchers
Cross-cultural validation of self-reported measurement instruments for research is a long and complex process, which involves specific risks of bias that could affect the research process and results. Furthermore, it requires researchers to have a wide range of technical knowledge about the translation, adaptation and pre-test aspects, their purposes and options, about the different psychometric properties, and the required evidence for their assessment and knowledge about the quantitative data processing and analysis using statistical software. This article aimed: 1) identify all guidelines and recommendations for translation, cross-cultural adaptation, and validation within the healthcare sciences; 2) describe the methodological approaches established in these guidelines for conducting translation, adaptation, and cross-cultural validation; and 3) provide a practical guideline featuring various methodological options for novice researchers involved in translating, adapting, and validating measurement instruments. Forty-two guidelines on translation, adaptation, or cross-cultural validation of measurement instruments were obtained from \"CINAHL with Full Text\" (via EBSCO) and \"MEDLINE with Full Text\". A content analysis was conducted to identify the similarities and differences in the methodological approaches recommended. Bases on these similarities and differences, we proposed an eight-step guideline that includes: a) forward translation; 2) synthesis of translations; 3) back translation; 4) harmonization; 5) pre-testing; 6) field testing; 7) psychometric validation, and 8) analysis of psychometric properties. It is a practical guideline because it provides extensive and comprehensive information on the methodological approaches available to researchers. This is the first methodological literature review carried out in the healthcare sciences regarding the methodological approaches recommended by existing guidelines.
Journal Article
Harnessing the deep learning power of foundation models in single-cell omics
Foundation models hold great promise for analyzing single-cell omics data, yet various challenges remain that require further advancements. In this Comment, we discuss the progress, limitations and best practices in applying foundation models to interrogate data and improve downstream tasks in single-cell omics.This Comment discusses the progress, limitations and best practices in applying foundation models to single-cell omics data.
Journal Article