Hasil untuk "Encyclopedias"

F. Plum, J. Posner

D. Suvin

S. Levin

R. Meyers

Jan Derezinski, Vojkan Jaksic, Claude-Alain Pillet

We presents a unified and concise exposition of key topics in the mathematical theory of open quantum systems, developed within the framework of operator algebras. The manuscript consolidates and extends a series of invited articles originally prepared for the Modern Encyclopedia of Mathematical Physics, combining foundational material with modern perspectives on non-equilibrium quantum statistical mechanics. After introducing the C*- and W*-algebraic formulation of quantum mechanics, the paper reviews quantum dynamical systems, KMS states, and Tomita-Takesaki modular theory, as well as CCR and CAR algebras for bosonic and fermionic systems. Particular emphasis is placed on infinite systems, non-equilibrium steady states, entropy production, and linear response theory. The later sections develop a systematic treatment of small systems coupled to reservoirs, open lattice quantum spin systems, culminating in a detailed discussion of competing notions of quantum entropy production. The presentation highlights structural insights, conceptual clarity, and connections between equilibrium and non-equilibrium phenomena, providing a self-contained reference for researchers and graduate students in mathematical physics.

Walter Moreira, Joe Stubbs

The On-Line Encyclopedia of Integer Sequences (OEIS) is a web-accessible database cataloging interesting integer sequences and associated theorems. With more than 12,000 citations, the OEIS is one of the most highly cited resources in all of theoretical mathematics. In this paper, we present Sequencelib, a project to formalize the mathematics contained within the OEIS using the Lean programming language. Sequencelib includes a library of Lean formalizations of OEIS sequences as well as metaprogramming tools for programmatically attaching OEIS metadata to Lean definitions and deriving theorems about their values. Further, we describe OEIS-LT, a highly scalable Lean server that exposes these tools via a low-latency API. Finally, using OEIS-LT and prior work of Gauthier, et al., we describe a computational pipeline that formalized more than 25,000 sequences from the OEIS and proved more than 1.6 million theorems about their values. Our method makes use of a transpiler, available in OEIS-LT, that is capable of translating a subset of Standard ML to Lean, together with a set of performance improvement transformations and proofs of correctness.

Y.-S. Kang, B. Yoon

Artur Niewiarowski, Anna Plichta

This publication presents the results of a study on text similarity between Belarusian and Ukrainian, utilizing a matrix-based analysis method grounded in edit distance. A distinctive feature of this approach is the absence of language-specific vocabulary rules, highlighting the algorithm’s linguistic universality in similarity analysis. The analyzed texts were sourced from excerpts of online encyclopedias, translated using AI-powered online translation  services provided by well-known companies. The primary objective of this study is to determine whether it is possible to compare texts written in these languages without prior translation into a common language. Additionally, it aims to assess whether a method that does not belong to the large language model (LLM) family or the broader category of AI-based approaches can effectively compare languages within the same linguistic group. Furthermore, the study provides insights into the degree of similarity between Belarusian and Ukrainian, investigating the extent to which speakers of one language might partially understand the other.

Muhammad Shoaib, Ghassan Husnain, Nasir Sayed et al.

This work presents a novel perspective towards generating automated multiple-choice questions (MCQs)-a task fundamentally different due to the highly dynamic nature of computer science education, which spans several sub-domains. Taking advantage of Conditional Generative Adversarial Networks (cGANs), our model provides a versatile approach to addressing the need for diversity and context in relevant MCQ generation across proficiency levels, topic areas. Resulting MCQs inspire implementations within a variety of educational environments - from classrooms, to online courses, and finally exams - equipping teachers with an instrument that could be easily adapted based on the specific needs o students. The model is trained on a carefully constructed dataset that includes material from more than 20 subareas in computer science, consisting of materials such as textbooks, online encyclopedias and Q&A websites. Through rigorous evaluation using comprehensive performance metrics, including Question Relevance Score (QRS), Diversity Index (DI), and Difficulty Alignment Accuracy (DAA), we demonstrate the efficacy and robustness of our framework in generating high-quality MCQs. Moreover, we address ethical considerations inherent in AI-driven educational assessment, ensuring fairness, transparency, and accountability in the MCQ generation process. The cGAN architecture facilitates the generation of contextually relevant MCQs across various proficiency levels and subject domains, enhancing the educational assessment process. The comprehensive dataset developed for this study encompasses diverse computer science topics curated from authoritative textbooks, online resources, question banks, and instructor-generated content. Additionally, a user-friendly QT application has been developed, enabling seamless integration of the cGAN model into educational environments. Through rigorous evaluation and ethical considerations, this framework demonstrates its efficacy, ensuring fairness, transparency, and accountability in MCQ generation. This interdisciplinary work represents a significant advancement in computer science education, providing educators with a powerful tool to enhance student engagement and learning outcomes.

Jiwei Fu, Chunyu Yang

Metastasis is the leading cause of cancer-related mortality, yet most predictive models rely on shallow architectures and neglect patient-specific regulatory mechanisms. Here, we integrate classical machine learning and deep learning to predict metastatic potential across multiple cancer types. Gene expression profiles from the Cancer Cell Line Encyclopedia were combined with a transcription factor-target prior from DoRothEA, focusing on nine metastasis-associated regulators. After selecting differential genes using the Kruskal-Wallis test, ElasticNet, Random Forest, and XGBoost models were trained for benchmarking. Personalized gene regulatory networks were then constructed using PANDA and LIONESS and analyzed through a graph attention neural network (GATv2) to learn topological and expression-based representations. While XGBoost achieved the highest AUROC (0.7051), the GNN captured non-linear regulatory dependencies at the patient level. These results demonstrate that combining traditional machine learning with graph-based deep learning enables a scalable and interpretable framework for metastasis risk prediction in precision oncology.

Rebekah Fitzsimmons

In lieu of an abstract: When I was asked to review Beyond Nancy Drew: U.S. Girls’ Series Fiction in the Twentieth Century, I had to pause and really consider whether I could be objective or whether my nostalgic love for the Nancy Drew series might cloud my judgement. Growing up, my grandparents’ home featured shelves and shelves of books, including a full shelf of Nancy Drew and Bobbsey Twins books. The books were bound in matching blue tweed covers with decorative details on the spine, in what my scholarly-trained mind’s eye now recognizes as a publisher’s play for middlebrow respectability. The uniform size, binding, and height of both book series offered a decorative element and fit in well with the shelves of encyclopedias, back issues of National Geographic (arranged in date order), and mass-produced copies of Western classics with faux leather bindings. When I was old enough, I devoured every copy of Nancy Drew on that shelf; The Mystery at Lilac Inn was and remains my favorite. As I got older, I read further in the series and moved into the slightly older and more contemporary (i.e. sexy and soap-opera-esque) Nancy Drew Files. I read other series fiction, namely The Baby-Sitters Club, and then eventually found YA dystopian trilogies. I certainly feel comfortable ascribing much of my professional interest in children’s and young adult literature to those copies of Nancy Drew. (I also harbor a deeply hidden desire to one day be described as ‘plucky’, but that is, perhaps, a different essay.)

Soyoung Park, Youngchang Oh, Bongwan Ku et al.

This study, using Korean-English Encyclopedia of Korean Culture as a case, investigates the principled design and methodologies of headwords essential for crafting culturally-informed dictionaries with educational functionalities in the field of Korean studies. In a broader context, this study aims to contribute to the academic discourse surrounding the creation of cultural encyclopedias within the realm of Korean studies. Specifically, it initiates with an analysis of the essence and characteristics of the Korean-English Encyclopedia of Korean Culture. Subsequently, it proceeds to outline the theoretical underpinnings of headword design, offer an overview of the collected data, and delineates the headword extraction methods. Additionally, this study is anticipated to provide inspiration for the scholarly advancement of cultural encyclopedias with educational objectives within the context of Korean studies.

Anjaneya Teja Kalvakolanu, NagaSai Chandra, Michael Fekadu

FAQ documents are commonly used with text documents and websites to provide important information in the form of question answer pairs to either aid in reading comprehension or provide a shortcut to the key ideas. We suppose that salient sentences from a given document serve as a good proxy fro the answers to an aggregated set of FAQs from readers. We propose a system for generating FAQ documents that extract the salient questions and their corresponding answers from sizeable text documents scraped from the Stanford Encyclopedia of Philosophy. We use existing text summarization, sentence ranking via the Text rank algorithm, and question-generation tools to create an initial set of questions and answers. Finally, we apply some heuristics to filter out invalid questions. We use human evaluation to rate the generated questions on grammar, whether the question is meaningful, and whether the question's answerability is present within a summarized context. On average, participants thought 71 percent of the questions were meaningful.

Paramita Das, Amartya Roy, Ritabrata Chakraborty et al.

Although Wikipedia is the largest multilingual encyclopedia, it remains inherently incomplete. There is a significant disparity in the quality of content between high-resource languages (HRLs, e.g., English) and low-resource languages (LRLs, e.g., Hindi), with many LRL articles lacking adequate information. To bridge these content gaps, we propose a lightweight framework to enhance knowledge equity between English and Hindi. In case the English Wikipedia page is not up-to-date, our framework extracts relevant information from external resources readily available (such as English books) and adapts it to align with Wikipedia's distinctive style, including its \textit{neutral point of view} (NPOV) policy, using in-context learning capabilities of large language models. The adapted content is then machine-translated into Hindi for integration into the corresponding Wikipedia articles. On the other hand, if the English version is comprehensive and up-to-date, the framework directly transfers knowledge from English to Hindi. Our framework effectively generates new content for Hindi Wikipedia sections, enhancing Hindi Wikipedia articles respectively by 65% and 62% according to automatic and human judgment-based evaluations.

R. Meyers

Jürgen Fuchs, Christoph Schweigert, Simon Wood et al.

This is an invited contribution to the 2nd edition of the Encyclopedia of Mathematical Physics. We review the following algebraic structures which appear in two-dimensional conformal field theory (CFT): The symmetries of two-dimensional conformal field theories (CFTs) can be formalised as chiral algebras, vertex operator algebras or nets of observable algebras. Their representation categories are abelian categories having additional structures, which are induced by properties of conformal blocks, i.e. of vector bundles over the moduli space of curves with marked points, which can be constructed from the symmetry structure. These mathematical notions pertain to the description of chiral CFTs. In a full local CFT one deals in addition with correlators, which are specific elements in the spaces of conformal blocks. In fact, a full CFT is the same as a consistent system of correlators for arbitrary conformal surfaces with any number and type of field insertions in the bulk as well as on boundaries and on topological defect lines. We present algebraic structures that allow one to construct such systems of correlators.

Michael D. Ekstrand, Graham McDonald, Amifa Raj et al.

The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide a fair exposure to a mixture of demographics or attributes, such as ethnicity, that are represented by relevant documents in response to a search query. For example, particular demographics or attributes can be represented by the documents' topical content or authors. The 2021 Fair Ranking Track adopted a resource allocation task. The task focused on supporting Wikipedia editors who are looking to improve the encyclopedia's coverage of topics under the purview of a WikiProject. WikiProject coordinators and/or Wikipedia editors search for Wikipedia documents that are in need of editing to improve the quality of the article. The 2021 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia. The under-representation of particular protected characteristics in Wikipedia can result in systematic biases that can have a negative human, social, and economic impact, particularly for disadvantaged or protected societal groups.

Michael D. Ekstrand, Graham McDonald, Amifa Raj et al.

The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide a fair exposure to a mixture of demographics or attributes, such as ethnicity, that are represented by relevant documents in response to a search query. For example, particular demographics or attributes can be represented by the documents topical content or authors. The 2022 Fair Ranking Track adopted a resource allocation task. The task focused on supporting Wikipedia editors who are looking to improve the encyclopedia's coverage of topics under the purview of a WikiProject. WikiProject coordinators and/or Wikipedia editors search for Wikipedia documents that are in need of editing to improve the quality of the article. The 2022 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia. The under-representation of particular protected characteristics in Wikipedia can result in systematic biases that can have a negative human, social, and economic impact, particularly for disadvantaged or protected societal groups.

K. Rosenbloom, T. Dreszer, Michael Pheasant et al.

The Encyclopedia of DNA Elements (ENCODE) project is an international consortium of investigators funded to analyze the human genome with the goal of producing a comprehensive catalog of functional elements. The ENCODE Data Coordination Center at The University of California, Santa Cruz (UCSC) is the primary repository for experimental results generated by ENCODE investigators. These results are captured in the UCSC Genome Bioinformatics database and download server for visualization and data mining via the UCSC Genome Browser and companion tools (Rhead et al. The UCSC Genome Browser Database: update 2010, in this issue). The ENCODE web portal at UCSC (http://encodeproject.org or http://genome.ucsc.edu/ENCODE) provides information about the ENCODE data and convenient links for access.

O. Melnik, R. Sparks, Antonio Costa et al.