Hasil untuk "Encyclopedias"

R. G. Lerner, G. L. Trigg, R. Beyer

J. Habermas, S. Lennox, F. Lennox

Robert A. Wilson, F. Keil

P. Schreiner, N. L. Allinger, T. Clark et al.

M. Flickinger, S. Drew

J. Piekarewicz

The aim of this chapter, focused on relativistic mean-field models and part of the Encyclopedia of Nuclear Physics, is to provide an introductory, self-contained discussion accessible to a broad audience, including advanced undergraduate students. The chapter surveys the fundamental ideas, assumptions, and theoretical framework underlying relativistic mean-field models, and illustrates their wide range of applications across nuclear science. Particular emphasis is placed on the central role that these models play in the construction of equations of state for strongly interacting matter, as well as on the intimate connections between nuclear experiments, astrophysical observations, and theoretical modeling. In this context, relativistic mean-field theory is shown to provide a unified description of bulk nuclear properties and dense neutron-rich matter, enabling the interpretation of the remarkable structural and observational properties of neutron stars in the emerging era of multi-messenger astronomy.

Zachary F. Mainen

Externalized reasoning is already exploited by transformer-based agents through chain-of-thought, but structured retrieval -- indexing over one's own reasoning state -- remains underexplored. We formalize the transformer context window as an I/O page and prove that tool-augmented agents with indexed external memory achieve exponentially lower retrieval cost than agents restricted to sequential scanning: $O(\log_b N)$ versus $Ω(N)$ page reads per query, and $O(T \log_b T)$ versus $Θ(T^2)$ cumulative cost over $T$ reasoning steps -- a gap that widens as deliberation deepens. We test these predictions on a controlled lookup benchmark across three content types -- random hashes, ordered integers, and encyclopedia entries -- varying store size from 50 to 5,000 items, and replicate key conditions across two model generations (GPT-4o-mini and GPT-5.4). On abstract content, the indexed agent achieves median 1 page read regardless of store size, confirming the $O(1)$ prediction. Sorted pages without an index fail to close the gap: the weaker model cannot sustain binary search at scale, and the stronger model achieves near-optimal $\log_2 N$ search but still loses to the index by $5\times$. On familiar content (encyclopedia entries), a competing failure mode emerges: the model recognizes the domain, bypasses the retrieval protocol, and generates answers from parametric memory, producing catastrophic token expenditure even when the index is sound. This parametric memory competition dissociates the two cognitive operations that indexing combines: understanding content (where language models excel) and following navigational protocols (where they fail when understanding tempts them to shortcut). The result argues for a separation of concerns: use language models for index construction, where semantic understanding helps, and deterministic algorithms for index traversal, where it hurts.

J. Cochran, L. Cox

H. Roginski, J. W. Fuquay, P. Fox

Silvia Cappa, Lingxiao Kong, Pille-Riin Peet et al.

Wikipedia serves as a globally accessible knowledge source with content in over 300 languages. Despite covering the same topics, the different versions of Wikipedia are written and updated independently. This leads to factual inconsistencies that can impact the neutrality and reliability of the encyclopedia and AI systems, which often rely on Wikipedia as a main training source. This study investigates cross-lingual inconsistencies in Wikipedia's structured content, with a focus on tabular data. We developed a methodology to collect, align, and analyze tables from Wikipedia multilingual articles, defining categories of inconsistency. We apply various quantitative and qualitative metrics to assess multilingual alignment using a sample dataset. These insights have implications for factual verification, multilingual knowledge interaction, and design for reliable AI systems leveraging Wikipedia content.

Aprilia Cyahyani Rupilu, Ika Maryani

The low level of science literacy and student motivation demands innovative, relevant, and engaging learning media. This study aims to measure the effectiveness of a contextual-based plant encyclopedia in improving science literacy and student motivation in elementary schools. This study uses a quantitative method with a quasi-experimental one-group pretest-posttest design. The research population consists of 4th-grade students from SD Kotagede 3, Yogyakarta, with a sample of 20 students selected through simple random sampling. The research instruments include pretest-posttest questions to measure science literacy, Guttman scale questionnaires for learning motivation, as well as observation sheets and interviews to support qualitative data. We analyzed the data using descriptive statistics and the non-parametric Wilcoxon test, as the assumption test results indicated that the data did not meet the criteria for normality and homogeneity. The research results show a significant increase in students' science literacy (p < 0.001), although there was no significant increase in learning motivation (p = 0.492). These findings indicate that context-based encyclopedias are highly effective in supporting students' understanding of science concepts through the presentation of engaging and structured information. However, the minimal impact on learning motivation indicates the need for additional strategies, such as teacher guidance and increased media interactivity. The implications of this research include the integration of context-based encyclopedias as effective learning media and the need for supporting strategies, such as longer intervention durations and teacher guidance for more optimal results.

Bulat L. Khamidullin

The article briefly discusses the primary objectives, intended goals, and main content of the planned academic encyclopedias «Volga Bulgaria» and «Golden Horde». These initiatives aim to address the spiritual demands of the progressive part of our society, who are interested in the history and culture of our Homeland and the peoples living here. Moreover, the encyclopedias are designed to provide comprehensive reference material regarding the history, culture, and geography of Volga Bulgaria and the Golden Horde.

S. Bullard, Mélina E. Griss, S. Greene et al.

Contributor Jennifer A. W. Wright, M. Khosrow-Pour

Mykola Zhelezniak, Oleksandr Ishchenko

The article explores potential directions for employing generative artificial intelligence to enhance encyclopedia articles. Specifically, it proposes the idea of building additional knowledge-based content on encyclopedia platforms designed to summarize the information contained within articles (e.g., generating highlights, annotations, summaries, etc., through AI tools). According to the authors, such supplementary text blocks would be both interesting and beneficial for readers, aiming to improve article comprehension and retention of information while enabling them to quickly grasp the essence of extensive articles. A case study using selected articles from the <em>Encyclopedia of Modern Ukraine</em> demonstrates that virtual assistants like ChatGPT and Gemini exhibit a satisfactory level of proficiency in generating this type of supplementary content. However, the authors also emphasize the risks of editorial misuse of artificial intelligence, as it could be employed to generate primary content of articles replacing human authors. This raises concerns regarding the accuracy, reliability, and overall value of such content. Finally, the article underscores the need for scholarly discourse on ethical standards for the use of artificial intelligence by encyclopedia editorial officers.

Hugh M. Culbertson

Ziauddin Ahmed, Z. Begum, M. Hassan et al.

Jack Belzer

"Algorithmic Learning Theory Akira Marouka and Eiji Takimoto Augmented Reality Reinhold Behringer Chinese Text Spelling Check and Configuration Kin Hong Lee, Qin Lu, and Mau Kit Michael Ng Cluster Computing and Application Mark Baker, Amy Apon, Rajkumar Buyya, and Hai Jin Evaluation of Software Systems Ivo Duntsch, Gunther Gedifa, and Kai-Christoph Hamborg Logic Programming and Deductive Databases with Uncertainty: A Survey Laks V. S. Lakshmanan and Nematollaah Shiri Multilayter Perceptrons and Fractals Jennifter L. Pittman Multimedia Abstract Machine Timothy K. Shih and Anthony Y. Chang Parallel Computing Helmar Burkhart and Jens Volkert Real-Time Constraints Pao-Ann Hsiung Shape Modeling Vladimir Savchenko and Alexander Pasko Statistical Language Modeling Elvira I. Sicilia-Garcia and F. Jack Smith Systems Documentation Sanjay Singh Visual Information Querying Tiziana Catarci and Stefano Spaccapietra Wards and Upgma Clustering of Data with Very High Dimensionality Athman Bouguettaya, Hongming Qi, Je-Ho Park, and Alex Delis "

Temilola Adeleye, Skye Berghel, Damien Desfontaines et al.

For almost 20 years, the Wikimedia Foundation has been publishing statistics about how many people visited each Wikipedia page on each day. This data helps Wikipedia editors determine where to focus their efforts to improve the online encyclopedia, and enables academic research. In June 2023, the Wikimedia Foundation, helped by Tumult Labs, addressed a long-standing request from Wikipedia editors and academic researchers: it started publishing these statistics with finer granularity, including the country of origin in the daily counts of page views. This new data publication uses differential privacy to provide robust guarantees to people browsing or editing Wikipedia. This paper describes this data publication: its goals, the process followed from its inception to its deployment, the algorithms used to produce the data, and the outcomes of the data release.

Vladislav I. Goldin

The article characterizes the study of the Civil War in Russia at the present stage in connection with the centenary of this epoch. The author points out the most important research projects implemented in Russia and abroad. The article summarizes the research results and reviews the current state of histori-ography of the Civil War and intervention in the Russian North. The author presents an overview of the consideration of problems and events of this epoch in Northern Russia in the Russian encyclopedias about the Civil War. The article gives a detailed critical analysis of the key problems of the Civil War in the North of Russia and the life of this region, its population on the pages of 3-volumes encyclopedia “Russia in the Civil War”, issued in Moscow in 2021. In contrast to simplistic representations and distortions of facts, the author reveals the real processes that took place on the northern territories of Russia during the dramatic era of Civil War, reflects on the prospects of further research.