Foundations Of Language Research Articles

Young sanctuary-living chimpanzees produce more communicative expressions with artificial objects than with natural objects

In humans, interactions with objects are often embedded in communicative exchanges. Objects offer unique affordances to explore, carry functions and hold cultural relevance, which can shape children’s interactions and communication. Research indicates that the use of artificial objects, such as certain toys, helps promote pre-linguistic communication, consequently impacting language development. Given that chimpanzees use objects extensively compared to other great apes, and considering the differences between chimpanzees and bonobos in intrinsic motivation for tool use and the extended developmental period during which they learn to use objects, it is reasonable to expect that objects may influence chimpanzees’ communication. Here, we examined interactions of 31 immature sanctuary-living chimpanzees with non-novel artificial and natural objects and tested their vocal and facial expressions, applying methods previously designed for children. Our results showed an increase in these expressions associated with artificial objects. These findings provide the first empirical evidence that chimpanzee communicative expressions may be influenced by inherent properties of objects, potentially promoting varied communication, comparable to the impact distinctive objects have on pre-linguistic children. By exploring this connection between object-centric interactions and communication, this study reveals deep phylogenetic roots where objects may have shaped great ape communication and possibly evolutionary foundations of language.

Literacy Promotion: An Essential Component of Primary Care Pediatric Practice: Policy Statement.

Reading together often with infants and young children strengthens their relationships with parents/caregivers at a critical time in child development, stimulating brain circuitry and early attachment. A positive parenting practice, shared reading helps build the foundation for healthy social-emotional, cognitive, language, and literacy development, setting the stage for school readiness and providing enduring benefits across the life course. Pediatric physicians and advanced care providers have a unique opportunity to encourage parents/caregivers to establish routines and enjoy conversations around books and stories with their children beginning in infancy. Research has demonstrated that parents read and children learn when pediatricians offer literacy promotion as a practical and evidence-based primary prevention strategy in primary care practice to support early brain and child development. This supports families with a strengths-based approach, shaping a child's life trajectory and helping mitigate stress and adverse experiences. The American Academy of Pediatrics (AAP) recommends that pediatricians encourage shared reading beginning at birth and continuing at least through kindergarten as a strategy for supporting parents/caregivers, enhancing foundational relationships, promoting positive language-rich interactions, and helping families create a nurturing and stimulating home environment. The integration of literacy promotion into pediatric resident education is crucial to achieve that goal and thus is also essential. The AAP supports advocacy toward establishing public and private funding for diverse high-quality, developmentally appropriate children's books in the languages preferred by the family to be provided at pediatric health supervision visits to all children, but especially to children living in underresourced communities. This statement is supported by multiple AAP policies and implementation resources, including the accompanying "Literacy Promotion: An Essential Component of Primary Care Pediatric Practice: Technical Report."

Impact of the curriculum system on the quality of stomatology graduates: a multivariable analysis

BackgroundThe curriculum system is a central component in achieving the goals and specifications of talent training schemes. However, problems and difficulties exist in curriculum provision due to a lack of curriculum system design logic. This study aimed to investigate the correlation between the university curriculum system and graduate quality and to reveal the design logic of the curriculum system.MethodsA total of 699 stomatology graduates from a university in Zhejiang were selected as research subjects from 2015 to 2022. The students’ curriculum system and graduate quality data were collected and classified. The graduate quality information contained (1) the National Board Dental Examination (NBDE) pass rate and score, (2) the postgraduate entrance rate and destination, and (3) the employment rate and work institution. Regression analysis was also conducted to assess the correlation between the curriculum system and graduate quality.ResultsRegression analysis revealed significant associations between general education, specialization, and stomatology X courses and NBDE score, postgraduate entrance rate and destination, and work institution. All courses except public elective courses had significant impacts on NBDE score. General basic, medical English, and employment guidance courses significantly influenced the postgraduate entrance rate and destination. Restricted elective and public elective courses had significant effects on employment rate and work institution.ConclusionsIncreasing the quality of specialized and stomatology X courses in the curriculum system is beneficial for deepening graduates’ expertise and enhancing their education. Moreover, English courses are suggested to be offered in the early stage to lay a better language foundation.

Event-Oriented State Alignment Network for Weakly Supervised Temporal Language Grounding.

Weakly supervised temporal language grounding (TLG) aims to locate events in untrimmed videos based on natural language queries without temporal annotations, necessitating a deep understanding of semantic context across both video and text modalities. Existing methods often focus on simple correlations between query phrases and isolated video segments, neglecting the event-oriented semantic coherence and consistency required for accurate temporal grounding. This can lead to misleading results due to partial frame correlations. To address these limitations, we propose the Event-oriented State Alignment Network (ESAN), which constructs "start-event-end" semantic state sets for both textual and video data. ESAN employs relative entropy for cross-modal alignment through knowledge distillation from pre-trained large models, thereby enhancing semantic coherence within each modality and ensuring consistency across modalities. Our approach leverages vision-language models to extract static frame semantics and large language models to capture dynamic semantic changes, facilitating a more comprehensive understanding of events. Experiments conducted on two benchmark datasets demonstrate that ESAN significantly outperforms existing methods. By reducing false high correlations and improving the overall performance, our method effectively addresses the challenges posed by previous approaches. These advancements highlight the potential of ESAN to improve the precision and reliability of temporal language grounding tasks.

Grokking the Sequent Calculus (Functional Pearl)

The sequent calculus is a proof system which was designed as a more symmetric alternative to natural deduction. The 𝜆𝜇𝜇-calculus is a term assignment system for the sequent calculus and a great foundation for compiler intermediate languages due to its first-class representation of evaluation contexts. Unfortunately, only experts of the sequent calculus can appreciate its beauty. To remedy this, we present the first introduction to the 𝜆𝜇𝜇-calculus which is not directed at type theorists or logicians but at compiler hackers and programming-language enthusiasts. We do this by writing a compiler from a small but interesting surface language to the 𝜆𝜇𝜇-calculus as a compiler intermediate language.

Enhancing the vision–language foundation model with key semantic knowledge-emphasized report refinement

Recently, vision–language representation learning has made remarkable advancements in building up medical foundation models, holding immense potential for transforming the landscape of clinical research and medical care. The underlying hypothesis is that the rich knowledge embedded in radiology reports can effectively assist and guide the learning process, reducing the need for additional labels. However, these reports tend to be complex and sometimes even consist of redundant descriptions that make the representation learning too challenging to capture the key semantic information. This paper develops a novel iterative vision–language representation learning framework by proposing a key semantic knowledge-emphasized report refinement method. Particularly, raw radiology reports are refined to highlight the key information according to a constructed clinical dictionary and two model-optimized knowledge-enhancement metrics. The iterative framework is designed to progressively learn, starting from gaining a general understanding of the patient’s condition based on raw reports and gradually refines and extracts critical information essential to the fine-grained analysis tasks. The effectiveness of the proposed framework is validated on various downstream medical image analysis tasks, including disease classification, region-of-interest segmentation, and phrase grounding. Our framework surpasses seven state-of-the-art methods in both fine-tuning and zero-shot settings, demonstrating its encouraging potential for different clinical applications.

Linguistic Harmonization: Dissecting Alfiyah Ibn Malik with Chomsky's Transformational Generative Theory

This study is motivated by the need to update and deepen the understanding of Alfiyah Ibnu Malik through a modern linguistic perspective, given the complexity of the 1,002 stanzas that are difficult for students with a weak language foundation to comprehend, and the existing gap in the literature regarding the application of Transformational Generative Theory (TGT) in Arabic grammar, which can help elucidate language structure and enhance understanding and interest in learning. This research aims to examine Alfiyah Ibnu Malik using the modern linguistic approach of Transformational Generative Theory (TGT). The research method applied is library research with a descriptive qualitative approach. The findings indicate that Transformational Generative Theory has specific characteristics in linguistic studies, including deep and surface structures, the concepts of competence and performance, as well as specific and general transformations. Additionally, TGT includes a universal grammar that can be applied to Alfiyah Ibnu Malik. The application of TGT to Alfiyah Ibnu Malik reveals that specific transformations occur in various aspects of Arabic grammar, including optional and obligatory deletion, optional substitution, optional replacement, optional expansion, optional addition, and optional and obligatory permutation.

A Logical Approach to Type Soundness

Type soundness, which asserts that “well-typed programs cannot go wrong”, is widely viewed as the canonical theorem one must prove to establish that a type system is doing its job. It is commonly proved using the so-called syntactic approach (aka progress and preservation ), which has had a huge impact on the study and teaching of programming language foundations. Unfortunately, syntactic type soundness is a rather weak theorem. It only applies to programs that are well-typed in their entirety, and thus tells us nothing about the many programs written in “safe” languages that make use of “unsafe” language features. Even worse, it tells us nothing about whether type systems achieve one of their main goals: enforcement of data abstraction. One can easily define a language that enjoys syntactic type soundness and yet fails to support even the most basic modular reasoning principles for abstraction mechanisms like closures, objects, and abstract data types. Given these concerns, we argue that programming languages researchers should no longer be satisfied with proving syntactic type soundness, and should instead start proving semantic type soundness , a more useful theorem which captures more accurately what type systems are actually good for. Semantic type soundness is an old idea—Milner’s original account of type soundness from 1978 was semantic—but it fell out of favor in the 1990s due to limitations and complexities of denotational models. In the succeeding decades, thanks to a series of technical advances—notably, step-indexed Kripke logical relations constructed over operational semantics, and higher-order concurrent separation logic as consolidated in the Iris framework in Coq—we can now build (machine-checked) semantic soundness proofs at a much higher level of abstraction than was previously possible. The resulting “logical” approach to semantic type soundness has already been employed to great effect in a number of recent papers, but those papers typically (a) concern advanced problem scenarios that complicate the presentation, (b) assume significant prior knowledge of the reader, and (c) suppress many details of the proofs. Here, we aim to provide a gentler, more pedagogically motivated introduction to logical type soundness, targeted at a broader audience that may or may not be familiar with logical relations and Iris. As a bonus, we also show how logical type soundness proofs can easily be generalized to establish an even stronger relational property— representation independence —for realistic type systems.

On the structural properties of paradoxes: the distinction between formal language and natural language that comes with the use of the liar paradox

This study delves into the intriguing realm of paradoxes that have long fascinated philosophers and logicians throughout history. It begins by discussing the nature and purpose of paradoxes, ranging from their role in entertainment to their capacity to reveal flaws within logical systems. This work emphasizes the challenge paradoxes pose to the completeness of systems and the subsequent development of axiomatic systems that aim to eliminate paradoxes. Rather than providing definitive solutions to paradoxes, the primary aim of this study is to defend the idea that systems containing paradoxes can coexist with completeness. The focus is on categorizing paradoxes, with special attention given to the group known as liar paradoxes, including Russell’s famous variation. The text demonstrates how these paradoxes are intrinsically linked to the principle of non-contradiction and argues that the truth and contradiction of propositions are both the cause and consequence of these paradoxes, presenting a dilemma within the system. The work introduces the concept of Aristotelian Sets (A-Sets) and Empty Sets as potential solutions to these paradoxes. It explores the idea that these sets, when carefully defined, can provide a meaningful representation of individual substances and predicates without violating the principle of non-contradiction. By proposing the inclusion of non-existents within a naive set theory and introducing A-Sets, this work seeks to contribute to the ongoing discourse surrounding paradoxes and their resolution. Ultimately, this study offers a fresh perspective on the handling of paradoxes, emphasizing the importance of reevaluating the foundations of formal and natural languages in the pursuit of a more comprehensive understanding of logic and philosophy.

The Speculative Journey—or, What Does It Mean to be a Traveller?

This paper explores the pervasive use of journey and traveller metaphors in everyday language, and their applications in philosophical discourse. While these metaphors offer rich insights into abstract concepts such as love and philosophy, they also introduce ambiguities that can impede effective communication. By dissecting the nuances of these metaphorical figures, the paper aims to clarify their meanings and enhance their explanatory power. Divided into three main sections, the paper first discusses different types of metaphors and their general structure, providing a foundation for understanding metaphorical language. The second section delves into the specific metaphor of journey and traveller, analyzing its various connotations and implications with insights from scholars such as Dariusz Kubok and Steven Shaviro. Through this analysis, the distinctions between a traveller and a tourist are explored to deepen our understanding of metaphorical frameworks in philosophical discourse. Finally, the paper examines a specific journey strategy, focusing on Object-Oriented Ontology as an exemplification of a philosophical journey. Building upon prior conclusions, this last section elucidates the meaning and implications of this journey strategy, seeking to shed light on the multifaceted nature of philosophical discourse.

Merlin: A Vision Language Foundation Model for 3D Computed Tomography.

Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current shortage of both general and specialized radiologists, there is a large impetus to use artificial intelligence to alleviate the burden of interpreting these complex imaging studies while simultaneously using the images to extract novel physiological insights. Prior state-of-the-art approaches for automated medical image interpretation leverage vision language models (VLMs) that utilize both the image and the corresponding textual radiology reports. However, current medical VLMs are generally limited to 2D images and short reports. To overcome these shortcomings for abdominal CT interpretation, we introduce Merlin - a 3D VLM that leverages both structured electronic health records (EHR) and unstructured radiology reports for pretraining without requiring additional manual annotations. We train Merlin using a high-quality clinical dataset of paired CT scans (6+ million images from 15,331 CTs), EHR diagnosis codes (1.8+ million codes), and radiology reports (6+ million tokens) for training. We comprehensively evaluate Merlin on 6 task types and 752 individual tasks. The non-adapted (off-the-shelf) tasks include zero-shot findings classification (31 findings), phenotype classification (692 phenotypes), and zero-shot cross-modal retrieval (image to findings and image to impressions), while model adapted tasks include 5-year chronic disease prediction (6 diseases), radiology report generation, and 3D semantic segmentation (20 organs). We perform internal validation on a test set of 5,137 CTs, and external validation on 7,000 clinical CTs and on two public CT datasets (VerSe, TotalSegmentator). Beyond these clinically-relevant evaluations, we assess the efficacy of various network architectures and training strategies to depict that Merlin has favorable performance to existing task-specific baselines. We derive data scaling laws to empirically assess training data needs for requisite downstream task performance. Furthermore, unlike conventional VLMs that require hundreds of GPUs for training, we perform all training on a single GPU. This computationally efficient design can help democratize foundation model training, especially for health systems with compute constraints. We plan to release our trained models, code, and dataset, pending manual removal of all protected health information.

Large language foundation models encode clinical radiation oncology domain knowledge: Performance on the American College of Radiology Standardized Examination.

e13585 Background: The integration of Large Language Models (LLMs) into healthcare and medical education will represent a significant paradigm shift, offering transformative potential in how medical knowledge is accessed and assimilated. These models, however, have not yet been systematically trained, tested, or validated on complex medical information such as sub-specialty medical examinations. This study explores the performance of seven major LLMs in clinical radiation oncology using residency in-training exams. Methods: In this study, the 2021 American College of Radiology (ACR) Radiation Oncology In-Training Exam (TXIT) was used to evaluate the performance of various LLMs, including OpenAI's GPT-3.5-turbo, GPT-4, GPT-4-turbo, three Meta's Llama-2 models (7 billion, 13 billion, and 70 billion parameter), and Google's PaLM-2-text-bison. The ACR provided the publicly available national scoring for this exam. The exam comprised 298 questions across 13 domains, including clinical radiation oncology (195 questions, 65.4%). The exam was processed through each LLM via an application programming interface. LLM-generated answers were analyzed by clinical disease sites and compared to Radiation Oncology trainee performance and stratified by Post-Graduate Year (PGY) 2-5. Results: LLMs showed varied performance in the overall clinical radiation oncology domain, with OpenAI's GPT-4-turbo having the best performance with 68.0% correct answers, GPT-4 61.0%, GPT-3.5-turbo 48.0%, PaLM-2-text-bison 40.0%, and then the three Llama-2 models (70b 37.0%, 13b 38%, 7b 26%). GPT-4-turbo performed superiorly to lower-level (PGY2 51.6%, PGY3 61.6%) and comparably to upper-level radiation oncology trainees (PGY4 64.1%, PGY5 68.3%). Notably, GPT-4-turbo demonstrated 7.0% improvement over its predecessor GPT-4. LLMs scored the lowest in the gastrointestinal, genitourinary, and gynecology domains and highest in the bone and soft tissue, central nervous system and eye, and head, neck, and skin domains. Conclusions: GPT-4-turbo demonstrates clinical accuracy comparable to upper-level and superior to lower-level trainees in nearly all clinical domains. Conversely, Llama2 foundation models demonstrate overall worse performance than Level 1 (PGY2) trainees. Score discrepancies across disease site domains may be due to data availability, complexity of medical conditions, quality and quantity of training datasets, and interdisciplinary data inputs. Future research will need to evaluate the performance of models that are fine-tune trained in clinical oncology. This study also underscores the need for rigorous validation of LLM-generated information against established medical literature and expert consensus, necessitating expert oversight in their application in medical education and practice.

Infant vocal category exploration as a foundation for speech development.

Non-random exploration of infant speech-like vocalizations (e.g., squeals, growls, and vowel-like sounds or "vocants") is pivotal in speech development. This type of vocal exploration, often noticed when infants produce particular vocal types in clusters, serves two crucial purposes: it establishes a foundation for speech because speech requires formation of new vocal categories, and it serves as a basis for vocal signaling of wellness and interaction with caregivers. Despite the significance of clustering, existing research has largely relied on subjective descriptions and anecdotal observations regarding early vocal category formation. In this study, we aim to address this gap by presenting the first large-scale empirical evidence of vocal category exploration and clustering throughout the first year of life. We observed infant vocalizations longitudinally using all-day home recordings from 130 typically developing infants across the entire first year of life. To identify clustering patterns, we conducted Fisher's exact tests to compare the occurrence of squeals versus vocants, as well as growls versus vocants. We found that across the first year, infants demonstrated clear clustering patterns of squeals and growls, indicating that these categories were not randomly produced, but rather, it seemed, infants actively engaged in practice of these specific categories. The findings lend support to the concept of infants as manifesting active vocal exploration and category formation, a key foundation for vocal language.

Looking for It: Language, Literacy, and History in Place

Within this article, we explore how teachers, researchers, and community members—including youth—worked in collaborative conversations and place-based projects to explore the languages, stories, and histories of their local Georgia communities. By examining the process of “looking for it,” as one youth researcher puts it, this article explores three inquiry practices Georgia youth use to identify and sustain community language and literacy practices: personal storytelling, walking histories, and breaking bread. These community literacies resulted in youth having a stronger sense of self and community and understanding the relationship between them. Additionally, the practices spurred critical thinking, historical inquiry, and socioemotional learning. Community exploration through community literacies created the foundation for place-based language, literacy, and history research to take root and flourish.

Research on Training Model of English Talents under the Background of New Liberal Arts

Under the background of the construction of the new liberal arts, local application-oriented universities should explore the inter-disciplinarity of foreign language courses and application-oriented professional courses in combination with school-based characteristics and talent training objectives, and establish interdisciplinary curriculum groups. The model proposed in this paper combines the instrumental goal of improving students' language foundation and cross-cultural ability with the humanistic goal of cultivating students' independent learning ability, critical thinking ability and cultural accomplishment, so as to enable regional economic and social development.

Learning the Meanings of Function Words From Grounded Language Using a Visual Question Answering Model.

Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network-based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learned by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on the frequency of models' input. Our findings offer proof-of-concept evidence that it is possible to learn the nuanced interpretations of function words in a visually grounded context by using non-symbolic general statistical learning algorithms, without any prior knowledge of linguisticmeaning.

SDN: Semantic Decoupling Network for Temporal Language Grounding.

Temporal language grounding (TLG) is one of the most challenging cross-modal video understanding tasks, which aims at retrieving the most relevant video segment from an untrimmed video according to a natural language sentence. The existing methods can be separated into two dominant types: 1) proposal-based and 2) proposal-free methods, where the former conduct contextual interactions and the latter localizes timestamps flexibly. However, the constant-scale candidates in proposal-based methods limit the localization precision and bring extra computational costs. In contrast, the proposal-free methods perform well on high-precision metrics-based on the fine-grained features but suffer from a lack of coarse-grained interactions, which cause degeneration when the video becomes complex. In this article, we propose a novel framework termed semantic decoupling network (SDN) that combines the advantages of proposal-based and proposal-free methods and overcomes their defects. It contains three key components: 1) semantic decoupling module (SDM); 2) context modeling block (CMB); and 3) semantic cross-level aggregation module (SCAM). By capturing the video-text contexts in multilevel semantics, the SDM and CMB effectively utilize the benefits of proposal-based methods. Meanwhile, the SCAM maintains the merit of proposal-free methods in that it localizes timestamps precisely. The experiments on three challenge datasets, i.e., Charades-STA, TACoS, and ActivityNet-Caption, show that our proposed SDN method significantly outperforms recent state-of-the-art methods, especially the proposal-free methods. Extensive analyses, as well as the implementation code of the proposed SDN method, are provided at https://github.com/CFM-MSG/Code_SDN.

Vision–language foundation model for echocardiogram interpretation

The development of robust artificial intelligence models for echocardiography has been limited by the availability of annotated clinical data. Here, to address this challenge and improve the performance of cardiac imaging models, we developed EchoCLIP, a vision–language foundation model for echocardiography, that learns the relationship between cardiac ultrasound images and the interpretations of expert cardiologists across a wide range of patients and indications for imaging. After training on 1,032,975 cardiac ultrasound videos and corresponding expert text, EchoCLIP performs well on a diverse range of benchmarks for cardiac image interpretation, despite not having been explicitly trained for individual interpretation tasks. EchoCLIP can assess cardiac function (mean absolute error of 7.1% when predicting left ventricular ejection fraction in an external validation dataset) and identify implanted intracardiac devices (area under the curve (AUC) of 0.84, 0.92 and 0.97 for pacemakers, percutaneous mitral valve repair and artificial aortic valves, respectively). We also developed a long-context variant (EchoCLIP-R) using a custom tokenizer based on common echocardiography concepts. EchoCLIP-R accurately identified unique patients across multiple videos (AUC of 0.86), identified clinical transitions such as heart transplants (AUC of 0.79) and cardiac surgery (AUC 0.77) and enabled robust image-to-text search (mean cross-modal retrieval rank in the top 1% of candidate text reports). These capabilities represent a substantial step toward understanding and applying foundation models in cardiovascular imaging for preliminary interpretation of echocardiographic findings.

Research on the Teaching Pathways of English Major Curriculum Ideological and Political Education from the Perspective of “Cultural Confidence”

With the rapid development of global information technology and the continuous deepening of the new educational philosophy of College English majors, College English teaching is no longer limited to helping students lay a solid foundation in English language, but also to cultivate students’ cross-cultural awareness, cultural identity, and national pride. College teachers should fully integrate China’s excellent traditional culture, integrate “curriculum ideological and political education” into practical teaching to enhance students’ cultural confidence, and promote China’s excellent traditional cultural ideas[1]. “Integrated English” as a core professional course shoulders the dual mission of language learning and cultural exchange. The long-standing Chinese culture provides a rich source for the ideological and political courses of College English majors. This article, from the perspective of cultural confidence, combines the current situation of English major teaching in university education, analyzes the significance of developing ideological and political courses for College English majors from the perspective of cultural confidence, and explores effective ways to implement ideological and political education for College English majors from the perspective of cultural confidence. The author takes the course Contemporary College English Intensive Reading 1 as an example, explores the ideological and political elements in the textbook from the perspective of cultural confidence, designs a teaching outline that combines teaching objectives and ideological and political objectives, and explores the practical path of implementing the function of ideological and political education in College English teaching.

DRAGON: A Dialogue-Based Robot for Assistive Navigation With Visual Language Grounding

DRAGON: A Dialogue-Based Robot for Assistive Navigation With Visual Language Grounding