Knowledge Of World Research Articles

Chapter 10. Intensification does not require modification: Tropical Swidden and the Maya

AbstractWhat is involved in finding fields? Agricultural intensification and its archaeological correlates are not always obvious. Archaeologists frequently equate capital‐based investment and arable farming as the sole path to intensified production. The presence of terraces to slow water flows across land, canals to bring water to drier lands, and raised and drained fields to reduce water, are methods to bring marginal lands into productive use. Labor‐based economies, especially those of the Americas before European conquest, present an entirely distinct pathway toward intensification based on tending the landscape. Tropical societies in general, and the Maya in particular, demonstrate a comprehensive knowledge of the natural world, cultivating biological capital as a product of their culture with skill, hand tools, scheduling, and fire. Asynchronous and embedded fields transform into forests in a poly‐cultivation practice, emphasizing the diversity that prevails in tropical woodlands. As with most traditional land‐use systems around the world, the Maya milpa cycle reduces temperature and evapotranspiration, conserves water, maintains biodiversity, builds soil fertility, inhibits erosion, and nurtures people. Labor investments per se do not leave direct evidence on the landscape, apart from the implicit density of settlement, yet the imprint of their management lies in the forest landscape itself.

Keeping an Eye Out for Change: Anxiety Disrupts Adaptive Resolution of Policy Uncertainty

BackgroundHuman learning unfolds under uncertainty. Uncertainty is heterogeneous with different forms exerting distinct influences on learning. While one can be uncertain about what to do to maximize rewarding outcomes, known as policy uncertainty, one can also be uncertain about general world knowledge, known as epistemic uncertainty (EU). In complex and naturalistic environments such as the social world, adaptive learning may hinge on striking a balance between attending to and resolving each type of uncertainty. Prior work illustrates that people with anxiety—those with increased threat and uncertainty sensitivity—learn less from aversive outcomes, particularly as outcomes become more uncertain. How does a learner adaptively trade-off between attending to these distinct sources of uncertainty to successfully learn about their social environment? MethodsWe developed a novel eye-tracking method to capture highly granular estimates of policy uncertainty and EU based on gaze patterns and pupil diameter (a physiological estimate of arousal). ResultsThese empirically derived uncertainty measures revealed that humans (N = 94) flexibly switched between resolving policy uncertainty and EU to adaptively learn about which individuals can be trusted and which should be avoided. However, those with increased anxiety (n = 49) did not flexibly switch between resolving policy uncertainty and EU and instead expressed less uncertainty overall. ConclusionsCombining modeling and eye-tracking techniques, we show that altered learning in people with anxiety emerged from an insensitivity to policy uncertainty and rigid choice policies, leading to maladaptive behaviors with untrustworthy people.

The Role of Gender and Social Power in the Use of Advice-giving Strategies in Algerian Arabic

This study explores the influence of gender and social status on advice-giving strategies in Algerian Arabic. The study involved 50 native Algerian Arabic speakers (25 males and 25 females) who completed a Discourse Completion Task. This task, designed to measure the gender and social status variables, presented three types of advice-giving situations: high-to-low, low-to-high, and equal social power. Hinkel’s (1997) taxonomy was used to categorize advice-giving strategies as direct, hedged, or indirect. The study's key finding was that male and female Algerians tend to give direct advice, particularly imperatives, regardless of the advisee’s social status. This suggests that Algerians perceive advice-giving as a cooperative and supportive act that does not require mitigation or implication. Importantly, these findings have practical implications. They can help individuals and organizations avoid cross-cultural misunderstandings with Algerians. In cultures where advice-giving may be perceived as threatening one’s freedom of action, logical thinking ability, and world knowledge, Algerians may be misunderstood as impolite and overly assertive despite their well-intentioned advice.

South America holds the greatest diversity of native daisies (Asteraceae) in the world: an updated catalogue supporting continental-scale conservation.

Asteraceae is the world's richest plant family and is found on all continents, in environments ranging from the coast to the highest mountains. The family shows all growth forms and, as in other angiosperm families, species richness is concentrated in tropical regions. South America has the highest diversity of Asteraceae in the world, yet taxonomic and distributional knowledge gaps remain. This study compiles an updated catalog of Asteraceae native to South America, based on national and regional checklists and ongoing large-scale flora projects. The resulting checklist includes a total of 6,940 species and 564 genera native to South America to date, which represent about a quarter of the family's global diversity. Countries already considered to be megadiverse show the greatest diversity, such as Brazil with 2,095 species, followed by Peru (1,588), Argentina (1,377), and Colombia (1,244), with this diversity mainly focused on the Brazilian Highlands and the Andes. Species endemism also peaks in Brazil, but Sørensen distances reveal the Chilean flora to be eminently different from the rest of the continent. Tribes better represented in the continent are Eupatorieae, Senecioneae and Astereae, also with a remarkably presence of entirely South American subfamilies representing earliest diverging lineages of the Asteraceae, such as Barnadesioideae, Wunderlichioideae, Famatinanthoideae, and Stifftioideae. It is estimated that the discovery and description curves have not yet stabilized, and the number of species is likely to increase by 5 to 10% in the coming years, posing major challenges to continental-scale conservation.

THE ENGLISH LITERACY PRACTICE, WORLD KNOWLEDGE PROFICIENCY, AND TEXT SKIMMING OF TENTH GRADES

Literacy is vital in understanding pupils' knowledge of learning content. However, learners have encountered several learning challenges, including vocabulary mastering and reading comprehension. The purpose of this study was to see if there was a link between students' literacy practice, world knowledge proficiency, and text skimming in senior high school. This study's data was analyzed using quantitative methodologies and a correlation design. There were 141 tenth grade students from SMAN 4 Kota Kediri who took part. They completed the vocabulary and reading comprehension tests by multiple-choice questions and items for the reading habits questionnaire. The researcher utilized Pearson Product Moment Coefficient and using SPSS ver 26.0 for Windows to assess the correlation. Following data analysis, it was discovered that all variables have a significant (sig.) value of 0.00. while coefficient The correlation between reading literacy and vocabulary mastery was 0.504, whereas the correlation between reading literacy and text skimming was 0.257 and the correlation between vocabulary mastery and text skimming was 0.441. Based on the above results, there was a substantial connection between all variables, therefore Ho is rejected but Ha should be approved. When pupils have a high degree of reading literacy, it can be argued that they have a greater skill in vocabulary mastery and text skimming. The higher the degree of students' reading literacy and vocabulary knowledge, the better the students' reading comprehension ability.

CoRTEx: contrastive learning for representing terms via explanations with applications on constructing biomedical knowledge graphs.

Biomedical Knowledge Graphs play a pivotal role in various biomedical research domains. Concurrently, term clustering emerges as a crucial step in constructing these knowledge graphs, aiming to identify synonymous terms. Due to a lack of knowledge, previous contrastive learning models trained with Unified Medical Language System (UMLS) synonyms struggle at clustering difficult terms and do not generalize well beyond UMLS terms. In this work, we leverage the world knowledge from large language models (LLMs) and propose Contrastive Learning for Representing Terms via Explanations (CoRTEx) to enhance term representation and significantly improves term clustering. The model training involves generating explanations for a cleaned subset of UMLS terms using ChatGPT. We employ contrastive learning, considering term and explanation embeddings simultaneously, and progressively introduce hard negative samples. Additionally, a ChatGPT-assisted BIRCH algorithm is designed for efficient clustering of a new ontology. We established a clustering test set and a hard negative test set, where our model consistently achieves the highest F1 score. With CoRTEx embeddings and the modified BIRCH algorithm, we grouped 35 580 932 terms from the Biomedical Informatics Ontology System (BIOS) into 22 104 559 clusters with O(N) queries to ChatGPT. Case studies highlight the model's efficacy in handling challenging samples, aided by information from explanations. By aligning terms to their explanations, CoRTEx demonstrates superior accuracy over benchmark models and robustness beyond its training set, and it is suitable for clustering terms for large-scale biomedical ontologies.

Exploring Failure Cases in Multimodal Reasoning About Physical Dynamics

In this paper, we present an exploration of LLMs' abilities to problem solve with physical reasoning in situated environments. We construct a simple simulated environment and demonstrate examples of where, in a zero-shot setting, both text and multimodal LLMs display atomic world knowledge about various objects but fail to compose this knowledge in correct solutions for an object manipulation and placement task. We also use BLIP, a vision-language model trained with more sophisticated cross-modal attention, to identify cases relevant to object physical properties that that model fails to ground. Finally, we present a procedure for discovering the relevant properties of objects in the environment and propose a method to distill this knowledge back into the LLM.

A survey of context in neural machine translation and its evaluation

Abstract The question of context in neural machine translation often focuses on topics related to document-level translation or intersentential context. However, there is a wide range of other aspects that can be considered under the umbrella of context. In this work, we survey ways that researchers have incorporated context into neural machine translation systems and the evaluation thereof. This includes building translation systems that operate at the paragraph level or the document level or ones that translate at the sentence level but incorporate information from other sentences. We also consider how issues like terminology consistency, anaphora, and world knowledge or external information can be considered as types of context relevant to the task of machine translation and its evaluation. Closely tied to these topics is the question of how to best evaluate machine translation output in a way that is sensitive to the contexts in which it appears. To this end, we discuss work on incorporating context into both human and automatic evaluations of machine translation quality. Furthermore, we also discuss recent experiments in the field as they relate to the use of large language models in translation and evaluation. We conclude with a view of the future of machine translation, where we expect to see issues of context continue to come to the forefront.

The Negative Impact of Broken Home Children in the Increase in Criminal Cases that Occur in Society in terms of Analysis of Islamic Family Law

This study will examine the impact of broken home families on children's behaviour. The effect of a broken home family condition can be analysed by observing a person's patterns and attitudes in daily activities. The observation procedure used is library research, using sources such as journals, books, and e-books relevant to this article's topic. In the analysis process, data is reviewed through the stages of review, sorting, grouping, and categorising to understand the impact of broken home families on children in increasing criminal cases in the community. The results showed that there are impacts of broken home families, including psychological effects that affect children's social life, children's knowledge of the criminal world, and spiritual effects that affect their religious behaviour.

A stochastic world model on gravity for stability inference.

The fact that objects without proper support will fall to the ground is not only a natural phenomenon, but also common sense in mind. Previous studies suggest that humans may infer objects' stability through a world model that performs mental simulations with a priori knowledge of gravity acting upon the objects. Here we measured participants' sensitivity to gravity to investigate how the world model works. We found that the world model on gravity was not a faithful replica of the physical laws, but instead encoded gravity's vertical direction as a Gaussian distribution. The world model with this stochastic feature fit nicely with participants' subjective sense of objects' stability and explained the illusion that taller objects are perceived as more likely to fall. Furthermore, a computational model with reinforcement learning revealed that the stochastic characteristic likely originated from experience-dependent comparisons between predictions formed by internal simulations and the realities observed in the external world, which illustrated the ecological advantage of stochastic representation in balancing accuracy and speed for efficient stability inference. The stochastic world model on gravity provides an example of how a priori knowledge of the physical world is implemented in mind that helps humans operate flexibly in open-ended environments.

A stochastic world model on gravity for stability inference

The fact that objects without proper support will fall to the ground is not only a natural phenomenon, but also common sense in mind. Previous studies suggest that humans may infer objects’ stability through a world model that performs mental simulations with a priori knowledge of gravity acting upon the objects. Here we measured participants’ sensitivity to gravity to investigate how the world model works. We found that the world model on gravity was not a faithful replica of the physical laws, but instead encoded gravity’s vertical direction as a Gaussian distribution. The world model with this stochastic feature fit nicely with participants’ subjective sense of objects’ stability and explained the illusion that taller objects are perceived as more likely to fall. Furthermore, a computational model with reinforcement learning revealed that the stochastic characteristic likely originated from experience-dependent comparisons between predictions formed by internal simulations and the realities observed in the external world, which illustrated the ecological advantage of stochastic representation in balancing accuracy and speed for efficient stability inference. The stochastic world model on gravity provides an example of how a priori knowledge of the physical world is implemented in mind that helps humans operate flexibly in open-ended environments.

An Analysis of George Orwell 's Animal Farm with Discourse Coherence Theory

Animal Farm is a political allegory that reflects the social and political issues of the Soviet Union under Stalin's regime. The narrative provides an insightful critique of the corruption and abuse of power that occurred during the communist regime. It highlights the process of social control and manipulation, the importance of language and discourse, and the effects of propaganda. The discourse coherence theory aims to explain the factors contributing to the coherence and coherence problems in texts. The theory suggests that coherence is achieved through the connections between linguistic elements, world knowledge, and situational and contextual factors. An analysis of Animal Farm from the perspective of discourse coherence theory could reveal how the language and discourse used in the narrative contribute to the coherence and the ways in which coherence is disrupted. Therefore, Animal Farm could be a suitable text for studying discourse coherence theory due to its complex narrative structure and the use of language and discourse to convey political messages.

PENGARUH MODEL PEMBELAJARAN CONTEXTUAL TEACHING AND LEARNING (CTL) TERHADAP KEMAMPUAN PEMAHAMAN KONSEP MATEMATIS SISWA PADA MATERI DIMENSI TIGA

This research aims to determine whether there is an influence of the CTL learning model on students' ability to understand mathematical concepts in three-dimensional material. This research approach is a quantitative approach with a quasi-experimental type of research. The population of this study was class XII of SMA Negeri 1 Teluk Dalam in the 2023/2024 academic year, consisting of 262 students. Samples were taken using the Cluster Random Sampling technique, namely class XII Science-Melati (Experiment) 34 students and XII Science-Teratai (Control) 26 students. The instrument used is a test of students' ability to understand mathematical concepts in three-dimensional material. The results of research data processing showed that the average Pretest score for the experimental class was 65.59 and the control class was 65.38 and the Posttest for the experimental class was 72.03 and the control class was 66.12. Hypothesis testing using the Independent-Samples T Test shows that the sig (2-tiled) value is 0.048 < 0.05, meaning there is a significant difference in the posttest scores for the two classes. It was concluded that the CTL learning model influenced students' ability to understand mathematical concepts in three-dimensional material. This is influenced by students who play an active role in discovering concepts of learning material through real experience and collaboration in discussions with friends and teachers. Researchers suggest (1) The CTL learning model is an alternative for teachers to build students' ability to understand mathematical concepts (2) Students are advised to seek and build understanding of learning material through their knowledge of the real world (3) The results of this research should be taken into consideration by researchers Next in choosing a learning model.

Perceptions of University Students Participating in Student Club Activities: Kültürel Miras Student Club Example

The study aims determine and examine the perceptions of students who participate in student club activities as organizers and participants regarding skills, values, and abilities/skills within the scope of these activities. The study group consisted of 14 university students who participated in the activities of a student club operating within the faculty of education of a state university in the 2022-2023 academic year. The study was carried out within the framework of phenomenology research design. “Activity Diary Interview Form’’ was used to collect the study data, and the data were analyzed with the descriptive analysis method. As a result of the study, it was determined that university students who participated in student club activities as organizers and participants made statements about skills 64 times and values 33 times according to the 2023 Social Studies Curriculum. In addition, it was determined that in these student club activities, university students put forward expressions regarding skills and abilities such as “world knowledge, love of learning, collaborative work, self-management, examples of altruism, language ability, initiative and volunteering activities’’ according to developed Paul's Transcript.

Marcación media en español: afección, control y sujetos no canónicos

Abstract Middle markers (such as reflexives in Spanish) are used to encode both semantically connected situations and some semantic properties of the subject participants. Previous literature on the topic usually mentioned two of these properties: affectedness (Klaiman 1988 and 1991; Kemmer 1993; Maldonado 1999; Zúñiga/Kittilä 2019) and control (potential control, Klaiman 1991; non-canonical control, Kaufmann 2007). Middle marking in Spanish has been also related to both affected/patient subjects (García-Miguel 1985; Mendikoetxea 1999; Jiménez Juliá 2016) and agentive/intentional subjects (Martín Zorraquino 1979; Morimoto 2011; Di Tullio 2012). Nevertheless, there is not a comprehensive study on affectedness, control and middle marking in Spanish. This paper seeks to shade some light on the topic offering a detailed analysis of affectedness, control, and their correlations within a verbal sample from 13 semantic classes of the middle domain. In particular, we claim that middle marking changes the status of the subject entity, which can be [+affected], [+control], or [-control] depending on the semantics of the verb, the semantics of the sentence, the status of the entity in the active voice, and on our world knowledge. Lastly, we argue that the general function of middle marking is to encode non-canonical subjects on the control construct.

Knowledge Ply Chat

Despite their ability to store information and excel at many NLP tasks with fine-tuning, large language models tend to have issues about accurately accessing and altering knowledge, which leads to performance gaps in knowledge-intensive tasks compared to domain-specific architectures. Additionally, these models face problems when it comes to having transparent decision-making processes or updating their world knowledge. To mitigate these limitations, we propose a Retrieval Augmented Generation (RAG) system by improving the Mistral7B model specifically for RAG tasks. The novel training technique includes Parameter-Efficient Fine-Tuning (PEFT) which enables efficient adaptation of large pre- trained models on-the-fly according to task-specific requirements while reducing computational costs. In addition, this system combines pre-trained embedding models that use pre-trained cross-encoders for effective retrieval and reranking of information. This RAG system will thus leverage these state-of-the-art methodologies towards achieving top performances in a range of NLP tasks such as question answering and summarization.

The Role of Academic Ethics in the Professional Relationships of University Academic Staff

The subject of the research in the paper is academic ethics in the professional relationships of university teachers. The final results are presented on the basis of in-depth interviews with representatives of the academic staff of Russian universities (N=35). The purpose of the paper is to show how academic ethics as a value-normative phenomenon supports the professional relationships of academic staff at the university and forms certain values that ensure the effectiveness of the scientific and educational process. In this interpretation, the emphasis is placed on identifying the creative potential of academic ethics, its impact on the scientific and educational process at the university in terms of professional interaction of its main subjects. An assessment of the structure and content of academic ethics is given. It is established that academic ethics, being an element of academism in the higher education system, influences the formation of value-normative attitudes of respect and recognition. In addition, the importance of this phenomenon for the strategic development of the university and at the same time the preservation of academic traditions is revealed. The article also summarizes the assessments of the state of academic ethics at universities formulated by informants. In such assessments, value-semantic reflection prevails, which makes it possible to identify academic ethics as a factor of effective professional interaction between subjects of the educational process in order to achieve joint goals for the formation of their value world and professional knowledge and skills.

Russell's Theories of Events and Instants from the Perspective of Point-Free Ontologies in the Tradition of the Lvov-Warsaw School

We classify two of Bertrand Russell's theories of events within the point-free ontology. The first of such approaches was presented informally by Russell in ‘The World of Physics and the World of Sense’ (Lecture IV in Our Knowledge of the External World of 1914). Based on this theory, Russell sketched ways to construct instants as collections of events. This paper formalizes Russell's approach from 1914. We will also show that in such a reconstructed theory, we obtain all axioms of Russell's second theory from 1936 and all axioms of Thomason's theory of events from 1989. Russell's work certainly influenced the works of Stanisław Leśniewski, his student Alfred Tarski, and Czesław Lejewski – prominent members of the Lvov-Warsaw School (LWS). We see our work in the tradition of the research of Leśniewski and Tarski. Building on the technical tools developed in this environment and in the spirit of the traditional research of the LWS, we engage here, in particular, with two classic works by Russell on fundamental ontology.

Teaching vocabulary through invoking conceptually accessible epistemic domains

ABSTRACT While existing research into L2 classroom discourse has highlighted how knowledge and knowledge positions are negotiated, no attention seems to have been paid to what might be called conceptual epistemic domains. Drawing on 22 hours of video-recorded data from Iranian EFL classrooms, our conversation analytic study illustrates how invoking conceptually accessible epistemic domains creates interactional space for teachers to introduce conditionally relevant vocabulary. We define conceptual epistemic domains as areas of world knowledge to which learners have ostensible access. The bottom-up analysis of our data showcases that such practices serve two pivotal pedagogical functions: (1) providing contingent assistance; (2) initiating and maintaining warm-up activities. In the former, the teachers invoke the conceptually accessible epistemic domain facing learners’ claims of insufficient knowledge to elicit relevant further talk. In the latter, the teachers sequentially build up a shared conceptual access to conduct a warm-up activity. Both cases unfold interactional space where relevant vocabulary items emerge as learnables. We argue that by bringing to the fore what is conceptually accessible to the learners, teachers orient to relevant vocabulary items as teachable objects. Our findings thus shine light on an interactional resource through which the need for vocabulary teaching is talked into being.

SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge

Large Language Models (LLMs) have demonstrated impressive planning abilities due to their vast "world knowledge". Yet, obtaining plans that are both feasible (grounded in affordances) and cost-effective (in plan length), remains a challenge, despite recent progress. This contrasts with heuristic planning methods that employ domain knowledge (formalized in action models such as PDDL) and heuristic search to generate feasible, optimal plans. Inspired by this, we propose to combine the power of LLMs and heuristic planning by leveraging the world knowledge of LLMs and the principles of heuristic search. Our approach, SayCanPay, employs LLMs to generate actions (Say) guided by learnable domain knowledge, that evaluates actions' feasibility (Can) and long-term reward/payoff (Pay), and heuristic search to select the best sequence of actions. Our contributions are (1) a novel framing of the LLM planning problem in the context of heuristic planning, (2) integrating grounding and cost-effective elements into the generated plans, and (3) using heuristic search over actions. Our extensive evaluations show that our model surpasses other LLM planning approaches.