Hasil untuk "Biography"

Rusl Saeed Farhan Al-Dulaimi -, Majid Mohammed Khalifa -

Summary: Al-Alusi’s Biography and His Consensus in the Interpretation of Surah Al-Zukhruf      This study explores the biography of Mahmoud Shukri Al-Alusi (1217 AH - 1270 AH) and his tafsir "Ruh al-Ma'ani", which combines both traditional narrations and rational analysis, earning praise from scholars. The research also examines his reported consensus in the interpretation of Surah Al-Zukhruf, analyzing the accuracy of these claims by comparing them with other Quranic commentators.       Three key interpretative issues were studied: following ancestors without scrutiny, honoring magicians, and leaving them in misguidance until Judgment Day. The study found that Al-Alusi’s views align with the majority of scholars, including Al-Tabari, Al-Razi, and others. His tafsir remains an important reference due to its encyclopedic and analytical approach.

Alessandra Stramiglio, Andrea Schimmenti, Valentina Pasqual et al.

Text Implicitness has always been challenging in Natural Language Processing (NLP), with traditional methods relying on explicit statements to identify entities and their relationships. From the sentence "Zuhdi attends church every Sunday", the relationship between Zuhdi and Christianity is evident for a human reader, but it presents a challenge when it must be inferred automatically. Large language models (LLMs) have proven effective in NLP downstream tasks such as text comprehension and information extraction (IE). This study examines how textual implicitness affects IE tasks in pre-trained LLMs: LLaMA 2.3, DeepSeekV1, and Phi1.5. We generate two synthetic datasets of 10k implicit and explicit verbalization of biographic information to measure the impact on LLM performance and analyze whether fine-tuning implicit data improves their ability to generalize in implicit reasoning tasks. This research presents an experiment on the internal reasoning processes of LLMs in IE, particularly in dealing with implicit and explicit contexts. The results demonstrate that fine-tuning LLM models with LoRA (low-rank adaptation) improves their performance in extracting information from implicit texts, contributing to better model interpretability and reliability.

Anil Ramakrishna, Yixin Wan, Xiaomeng Jin et al.

We introduce SemEval-2025 Task 4: unlearning sensitive content from Large Language Models (LLMs). The task features 3 subtasks for LLM unlearning spanning different use cases: (1) unlearn long form synthetic creative documents spanning different genres; (2) unlearn short form synthetic biographies containing personally identifiable information (PII), including fake names, phone number, SSN, email and home addresses, and (3) unlearn real documents sampled from the target model's training dataset. We received over 100 submissions from over 30 institutions and we summarize the key techniques and lessons in this paper.

O. G. Turutanov

The Nobel Prize in Physics 2025 has been awarded to John Clarke, John Martinis, and Michel Devoret for "the discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit". Their achievements open up possibilities for developing the next generation of quantum technologies, including quantum cryptography, quantum computers, and quantum sensors. This article explains physical grounds of these discoveries and describes the role of earlier studies of weak superconductivity and macroscopic quantum systems by other scientists, highlighting the contribution of researchers from the B.I. Verkin Institute for Low Temperature Physics and Engineering of the National Academy of Sciences of Ukraine, who obtained pioneering results in this field. The paper includes short biographies of the Nobel laureates.

Joel Krueger, Lucy Osler

Generative AI chatbots like OpenAI's ChatGPT and Google's Gemini routinely make things up. They "hallucinate" historical events and figures, legal cases, academic papers, non-existent tech products and features, biographies, and news articles. Recently, some have argued that these hallucinations are better understood as bullshit. Chatbots produce rich streams of text that look truth-apt without any concern for the truthfulness of what this text says. But can they also gossip? We argue that they can. After some definitions and scene-setting, we focus on a recent example to clarify what AI gossip looks like before considering some distinct harms -- what we call "technosocial harms" -- that follow from it.

Johan Malmstedt, Giacomo Nanni, Dario Rodighiero

As biodiversity loss and climate change accelerate, botanical gardens serve as vital infrastructures for research, education, and conservation. This project focuses on the Arnold Arboretum of Harvard University, a 281-acre living museum founded in 1872 in Boston. Drawing on more than a century of curatorial data, the research combines historical analysis with computational methods to visualize the biographies of plants and people. The resulting platform reveals patterns of care and scientific observations, along with the collective dimensions embedded in botanical data. Using techniques from artificial intelligence, geospatial mapping, and information design, the project frames the arboretum as a system of shared agency--an active archive of more-than-human affinities that records the layered memory of curatorial labor, the situated nature of knowledge production, and the potential of design to bridge archival record and future care.

Samuel Bewiadzi Akakpo, Richard Awubomu

This study employed the qualitative method to study two indigenous women healers in Ghana – a traditional bone setter and a priestess healer. Placing the discussion within gender perspectives, the study chronicled the biography of these healers in the Volta and Oti regions of Ghana, their knowledge acquisition, healing practices, and the challenges that they face. The study found that the bone setter’s knowledge of healing was a family heritage, while the priestess acquired the shrine for reproductive purposes. The two practitioners have healed many patients in their communities in the areas of bone fractures and infertility issues. However, finance, poor record keeping, spiritual attacks, health challenges, and depletion of medicinal plants were identified as challenges confronting the female traditional healthcare practitioners in their respective communities. The study concludes that women, through their immense contribution to primary healthcare, support their family economy and provide a balance in a male-chauvinistic healthcare system. They therefore engage in their healing practices to navigate the male corridors of power and domain. This study contributes to scholarship on medical anthropology by discussing the role of women traditional healers in contemporary Ewe and Krachi societies. It also contributes to knowledge production in the field of gender studies, drawing insights from two distinct yet related geographical settings.

Elena I. Pogorelskaia, Alexandre F. Stroev

The archival documents from the collections of the National Library of France (BnF), the Russian State Archive of Literature and Art (RGALI), and the department of manuscript collections of the V.I. Dahl State Museum of the History of Russian Literature (ORF GLM) make significant additions and clarifications to Babel’s biography during his two long stays in France, expand our understanding of his Paris contacts are published for the first time. These are his letters of 1928 and 1932–1933 addressed to P.P. Suvchinsky, V.S. Pozner and the French writer, translator and journalist Nino Frank, as well as two letters to Suvchinsky of 1928 from the artist N.M. Davydova, in which Babel is mentioned, and a fragment of correspondence between Suvchinsky and A.M. Remizov of the early 1950s, associated with the unfulfilled project of Babel’s book. The publication concludes with a translation into Russian of Babel’s interview with the newspaper Les Nouvelles littéraires, artistiques et scientifiques in May 1928. The introductory article restores the biographical and political contexts of the published correspondence, talks about Babel’s relations with Russian emigrants, including Eurasians, and with Soviet diplomats, about his meeting with Remizov and, possibly, with N.A. Berdyaev.

Valeria de Paiva, Alexandre Rademaker

This short paper describes the first steps in a project to construct a knowledge graph for Brazilian history based on the Brazilian Dictionary of Historical Biographies (DHBB) and Wikipedia/Wikidata. We contend that large repositories of Brazilian-named entities (people, places, organizations, and political events and movements) would be beneficial for extracting information from Portuguese texts. We show that many of the terms/entities described in the DHBB do not have corresponding concepts (or Q items) in Wikidata, the largest structured database of entities associated with Wikipedia. We describe previous work on extracting information from the DHBB and outline the steps to construct a Wikidata-based historical knowledge graph.

David Eppstein, Joel Brewster Lewis, Russ Woodroofe et al.

Over the past 20 years, Wikipedia has gone from a rather outlandish idea to a major reference work, with more than 60 million articles across all languages, including nearly 7 million in English [Wiki01]. Around 27,000 of these articles concern mathematics [b], and Wikipedia is the first place that many of us go to learn about a new mathematical idea. In this overview, we will discuss how to go about creating or editing an article on a mathematical subject. (Most of this applies equally to topics from other technical fields.) We will also discuss biographies of mathematicians, articles on mathematical books, and the social dynamics of the Wikipedia editor community.

Ante Wang, Linfeng Song, Baolin Peng et al.

This work studies improving large language model (LLM) generations at inference time by mitigating fact-conflicting hallucinations. Particularly, we propose a self-endorsement framework that leverages the fine-grained fact-level comparisons across multiple sampled responses. Compared with prior ensemble methods (Wang et al., 2022;Chen et al., 2023)) that perform response-level selection, our approach can better alleviate hallucinations, especially for longform generation tasks. Our approach can broadly benefit smaller and open-source LLMs as it mainly conducts simple content-based comparisons. Experiments on Biographies show that our method can effectively improve the factuality of generations with simple and intuitive prompts across different scales of LLMs. Besides, comprehensive analyses on TriviaQA and GSM8K demonstrate the potential of self-endorsement for broader application.

Колонских Александр Геннадьевич

Рассматриваются данные, полученные в ходе изучения фортификационных сооружений Уразгильдинского городища (Татышлинский район Башкортостана). Основным археологическим материалом, обнаруженным в ходе раскопок, является керамика, которая позволяет датировать культурный слой и сооружения в широком диапазоне IV–VIII вв. н.э. и отнести памятник к бахмутинской культуре. Сами укрепления, вероятно, использовались недолго. В ходе проведенных исследований были изучены фортификационные сооружения, которые представляли собой дерево-земляную конструкцию в виде стены перекладного (крюкового) типа. Бревенчатая стена городища укреплялась вертикальными столбами. Укрепления были плотно забутованы грунтом. Многочисленные следы горения и воздействия высоких температур (уголь, прокалы, запекшаяся глина) свидетельствуют, что изученные сооружения были уничтожены пожаром, однако следов военного столкновения в ходе исследований не выявлено. Специфика выбора площадки строителями городища характеризуется использованием естественного рельефа местности –этот признак присущ для большинства городищ бахмутинской культуры. Примечательным оказывается то, что укрепления имели прямоугольную в плане конструкцию, что не является традиционным для памятников Южного Предуралья. Аналогии подобных конструкций на территории Уфимско-Бельского междуречья отсутствуют. Реконструкция исследованных сооружений даёт возможность предполагать их высокую фортификационную мощность, что также характеризует большинство известных укреплений бахмутинской культуры. Полученные результаты позволяют в значительной степени расширить современные представления об архитектурно-строительных и оборонительных традициях населения Южного Предуралья эпохи раннего средневековья.

István Fazakas

The paper explores the difference between semiology and hermeneutics of pain and suffering by focusing on narrativity and the body. First, it recapitulates some historical distinctions between explaining and understanding in the context of psychopathology. It shows how the hermeneutic method culminates in the idea of the cohesion of life, constituted through biography and narrative. The second section deals with the relationship between narrativity and selfhood in stories of suffering. The third part addresses the problem of the lived body and the ante-predicative embodiment of suffering and pain, which fuse with the ambiance, coloring the lifeworld.

Irsyad Leihitu, Ujang Hariadi

This article discusses the kampilan sword in Jambi People’s Struggle Museum. Kampilan is a traditional weapon originating from the Philippines but has spread to several regions in Indonesia, including Jambi. The kampilan sword collection is often overlooked, and there is not much information regarding these objects, despite the museum’s primary function is research and communication. Therefore, we conducted a study of a collection of kampilan swords to explore information related to these objects, allowing the museum to utilize and develop them in the future. The research model was based on the material culture study, using the social life of things and object biography approaches. Results indicate that, through these two approaches, new information and narratives about kampilan swords were successfully compiled. In the future, the museum can use these narratives to create new programs, create meanings and ideas related to struggle, and serve as the first step towards further research.

Lucia Giagnolini, Marilena Daquino, Francesca Mambelli et al.

In this article we propose a holistic approach to discover relations in art historical communities and enrich historians' biographies and archival descriptions with graph patterns relevant to art historiographic enquiry. We use exploratory data analysis to detect patterns, we select features, and we use them to evaluate classification models to predict new relations, to be recommended to archivists during the cataloguing phase. Results show that relations based on biographical information can be addressed with higher precision than relations based on research topics or institutional relations. Deterministic and a priori rules present better results than probabilistic methods.

Natalie Bolón Brun, Sofia Kypraiou, Natalia Gullón Altés et al.

The way Wikipedia's contributors think can influence how they describe individuals resulting in a bias based on gender. We use a machine learning model to prove that there is a difference in how women and men are portrayed on Wikipedia. Additionally, we use the results of the model to obtain which words create bias in the overview of the biographies of the English Wikipedia. Using only adjectives as input to the model, we show that the adjectives used to portray women have a higher subjectivity than the ones used to describe men. Extracting topics from the overview using nouns and adjectives as input to the model, we obtain that women are related to family while men are related to business and sports.

Angel Daza, Antske Fokkens, Tomaž Erjavec

Abbreviations present a significant challenge for NLP systems because they cause tokenization and out-of-vocabulary errors. They can also make the text less readable, especially in reference printed books, where they are extensively used. Abbreviations are especially problematic in low-resource settings, where systems are less robust to begin with. In this paper, we propose a new method for addressing the problems caused by a high density of domain-specific abbreviations in a text. We apply this method to the case of a Slovenian biographical lexicon and evaluate it on a newly developed gold-standard dataset of 51 Slovenian biographies. Our abbreviation identification method performs significantly better than commonly used ad-hoc solutions, especially at identifying unseen abbreviations. We also propose and present the results of a method for expanding the identified abbreviations in context.

Maryna Kolomiec

The purpose of the article is to highlight the process of formation of O. I. Stepanets as a scientist, basing on the analysis of documents of his personal archival fund, stored in the Institute for Archival Studies of Vernadsky National Library of Ukraine. The research methodology is based on general scientific methods of analysis, synthesis, comparison, description and generalization. Methods of archival heuristics, source analysis and chronological, historical-comparative, biographical, system methods are used. The application of these methods allowed to analyze the documentary sources deposited in the personal fund of the outstanding Ukrainian mathematician, for the reconstruction of the process of becoming O. I. Stepanets as a scientist. The scientific novelty of the research is that for the first time an array of documents of the personal archival fund of the corresponding member of the National Academy of Sciences of Ukraine O. I. Stepanets was introduced into scientific circulation, which reflect the process of his formation as a scientist. This made it possible to fill some gaps in the scientific biography of the mathematician in the early period of his scientific career. Conclusions. The documents of the personal archival fund of the corresponding member of the National Academy of Sciences of Ukraine O. I. Stepanets are a full-fledged informative source for the reconstruction of the process of his formation as a scientist. The formation of the young scientist took place under the influence of the Ukrainian mathematical scientific environment and his persistent and purposeful desire to acquire professional knowledge and solve mathematical problems.

Puyu Yang, Giovanni Colavizza

In recent decades, the rapid growth of Internet adoption is offering opportunities for convenient and inexpensive access to scientific information. Wikipedia, one of the largest encyclopedias worldwide, has become a reference in this respect, and has attracted widespread attention from scholars. However, a clear understanding of the scientific sources underpinning Wikipedia's contents remains elusive. In this work, we rely on an open dataset of citations from Wikipedia to map the relationship between Wikipedia articles and scientific journal articles. We find that most journal articles cited from Wikipedia belong to STEM fields, in particular biology and medicine ($47.6$\% of citations; $46.1$\% of cited articles). Furthermore, Wikipedia's biographies play an important role in connecting STEM fields with the humanities, especially history. These results contribute to our understanding of Wikipedia's reliance on scientific sources, and its role as knowledge broker to the public.

P. S. Dodds, J. R. Minot, M. V. Arnold et al.

Measuring the specific kind, temporal ordering, diversity, and turnover rate of stories surrounding any given subject is essential to developing a complete reckoning of that subject's historical impact. Here, we use Twitter as a distributed news and opinion aggregation source to identify and track the dynamics of the dominant day-scale stories around Donald Trump, the 45th President of the United States. Working with a data set comprising around 20 billion 1-grams, we first compare each day's 1-gram and 2-gram usage frequencies to those of a year before, to create day- and week-scale timelines for Trump stories for 2016 through 2020. We measure Trump's narrative control, the extent to which stories have been about Trump or put forward by Trump. We then quantify story turbulence and collective chronopathy -- the rate at which a population's stories for a subject seem to change over time. We show that 2017 was the most turbulent overall year for Trump. In 2020, story generation slowed dramatically during the first two major waves of the COVID-19 pandemic, with rapid turnover returning first with the Black Lives Matter protests following George Floyd's murder and then later by events leading up to and following the 2020 US presidential election, including the storming of the US Capitol six days into 2021. Trump story turnover for 2 months during the COVID-19 pandemic was on par with that of 3 days in September 2017. Our methods may be applied to any well-discussed phenomenon, and have potential to enable the computational aspects of journalism, history, and biography.