{"date":"2026-08-28","schema_version":1,"captured_at":"2026-08-29T11:23:21.086Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":16},"attention":[{"title":"Tim Curry","views":1571639,"rank":2,"extract":"Timothy James Curry was an English actor and singer. In a career that spanned nearly 60 years, he was noted for his performances across theatre, cinema, television, and animation.","url":"https://en.wikipedia.org/wiki/Tim_Curry","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Tim_Curry_%281978_A%26M_Records_publicity_photo%29_%28Cropped%29.jpg/330px-Tim_Curry_%281978_A%26M_Records_publicity_photo%29_%28Cropped%29.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Dolly Parton","views":1269538,"rank":3,"extract":"Dolly Rebecca Parton Dean was an American singer-songwriter, actress and businesswoman. 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It is located at the junction of Kentucky Route 206 and Kentucky Route 76. Its elevation is 705 feet (215 m). For unknown reasons, the town's name was spelled as Neetsvil","url":"https://en.wikipedia.org/wiki/Neatsville%2C_Kentucky","thumb":null},{"title":"Rachel Parton George","views":75173,"rank":33,"extract":"Rachel Ann Parton George is an American singer and actress and the youngest sibling of entertainer Dolly Parton.","url":"https://en.wikipedia.org/wiki/Rachel_Parton_George","thumb":null},{"title":"Kenny Rogers","views":73391,"rank":34,"extract":"Kenneth Donald Ray Rogers was an American country music singer-songwriter. 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I wouldn't do it again","points":178,"comments":128,"url":"https://joshkaramuth.com/blog/aws-cognito-authentication-startup-nightmare/","discussion":"https://news.ycombinator.com/item?id=49478091"},{"title":"State of the Map 2026","points":167,"comments":54,"url":"https://2026.stateofthemap.org/","discussion":"https://news.ycombinator.com/item?id=49478401"},{"title":"I accidentally turned LLM memory into program analysis","points":164,"comments":35,"url":"https://pwning.systems/posts/llm-memory-program-analysis/","discussion":"https://news.ycombinator.com/item?id=49485416"},{"title":"EasyEffects can improve laptop speaker sound quality","points":158,"comments":65,"url":"https://www.osnews.com/story/145883/easyeffects-should-be-part-of-every-linux-distribution-and-desktop-environment-to-massively-improve-laptop-speaker-sound-quality/","discussion":"https://news.ycombinator.com/item?id=49479924"},{"title":"Smaller reactors bring nuclear power closer to fulfilling its promise","points":141,"comments":202,"url":"https://www.nature.com/articles/d41586-026-02506-4","discussion":"https://news.ycombinator.com/item?id=49477559"}],"research":[{"title":"Reveal of Vision Transformers Robustness against Adversarial Attacks","abstract":"The major part of the vanilla vision transformer (ViT) is the attention block that brings the power of mimicking the global context of the input image. For better performance, ViT needs large-scale training data. To overcome this data hunger limitation, many ViT-based networks, or hybrid-ViT, have been proposed to include local context during the training. The robustness of ViTs and its variants against adversarial a","authors":["Aldahdooh, Ahmed","Hamidouche, Wassim","Deforges, Olivier"],"url":"https://arxiv.org/abs/2106.03734","arxiv_id":"2106.03734","published":"2026-08-28"},{"title":"Federated Adversarial Training with Transformers","abstract":"Federated learning (FL) has emerged to enable global model training over distributed clients' data while preserving its privacy. However, the global trained model is vulnerable to the evasion attacks especially, the adversarial examples (AEs), carefully crafted samples to yield false classification. Adversarial training (AT) is found to be the most promising approach against evasion attacks and it is widely studi","authors":["Aldahdooh, Ahmed","Hamidouche, Wassim","Déforges, Olivier"],"url":"https://arxiv.org/abs/2206.02131","arxiv_id":"2206.02131","published":"2026-08-28"},{"title":"Using representation balancing to learn conditional-average dose responses from clustered data","abstract":"Estimating a unit's responses to interventions with an associated dose, the &#34;conditional average dose response&#34; (CADR), is relevant in a variety of domains, from healthcare to business, economics, and beyond. Such a response typically needs to be estimated from observational data, which introduces several challenges. That is why the machine learning (ML) community has proposed several tailored CADR estima","authors":["Bockel-Rickermann, Christopher","Vanderschueren, Toon","Berrevoets, Jeroen","Verdonck, Tim","Verbeke, Wouter"],"url":"https://arxiv.org/abs/2309.03731","arxiv_id":"2309.03731","published":"2026-08-28"},{"title":"Recurrent Reinforcement Learning with Memoroids","abstract":"Memory models such as Recurrent Neural Networks (RNNs) and Transformers address Partially Observable Markov Decision Processes (POMDPs) by mapping trajectories to latent Markov states. Neither model scales particularly well to long sequences, especially compared to an emerging class of memory models called Linear Recurrent Models. We discover that the recurrent update of these models resembles a monoid, leading us to","authors":["Morad, Steven","Lu, Chris","Kortvelesy, Ryan","Liwicki, Stephan","Foerster, Jakob","Prorok, Amanda"],"url":"https://arxiv.org/abs/2402.09900","arxiv_id":"2402.09900","published":"2026-08-28"},{"title":"A Dynamic Likelihood Approach to Filtering for Advection-Diffusion Dynamics","abstract":"A Bayesian data assimilation scheme is formulated for advection-dominated advective and diffusive evolutionary problems, based upon the Dynamic Likelihood (DLF) approach to filtering. The DLF was developed specifically for hyperbolic problems -waves-, and in this paper, it is extended via a split step formulation, to handle advection-diffusion problems. In the dynamic likelihood approach, observations and their stati","authors":["Krotz, Johannes","Restrepo, Juan M.","Ramirez, Jorge"],"url":"https://arxiv.org/abs/2406.06837","arxiv_id":"2406.06837","published":"2026-08-28"},{"title":"Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation","abstract":"Estimating conditional average dose responses (CADR) is an important but challenging problem. Estimators must correctly model the potentially complex relationships between covariates, interventions, doses, and outcomes. In recent years, the machine learning community has shown great interest in developing tailored CADR estimators that target specific challenges. Their performance is typically evaluated against other ","authors":["Bockel-Rickermann, Christopher","Vanderschueren, Toon","Verdonck, Tim","Verbeke, Wouter"],"url":"https://arxiv.org/abs/2406.08206","arxiv_id":"2406.08206","published":"2026-08-28"},{"title":"CollaFuse: Collaborative Diffusion Models","abstract":"In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffusion models poses numerous challenges, particularly concerning data availability, computational requirements, and privacy. Traditional approaches to address these shortcomings, like federated learning, often impose significant computational","authors":["Allmendinger, Simeon","Zipperling, Domenique","Struppek, Lukas","Kühl, Niklas"],"url":"https://arxiv.org/abs/2406.14429","arxiv_id":"2406.14429","published":"2026-08-28"},{"title":"The BS-meter: Detecting Politics and Labour through ChatGPT's Language","abstract":"What can we learn about language from studying how it is used by ChatGPT and other large language model (LLM)-based chatbots? In this paper, we analyse the distinctive character of language generated by ChatGPT, in relation to questions raised by natural language processing pioneer, and student of Wittgenstein, Margaret Masterman. Following frequent complaints that LLM-based chatbots produce &#34;bullshit,&#34; in th","authors":["Trevisan, Alessandro","Giddens, Harry","Dillon, Sarah","Blackwell, Alan F."],"url":"https://arxiv.org/abs/2411.15129","arxiv_id":"2411.15129","published":"2026-08-28"},{"title":"Designing Cellular Manufacturing Systems in the Presence of Alternative Process Plans","abstract":"In the design of cellular manufacturing systems (CMS), numerous technological and managerial decisions must be made at both the design and operational stages. The first step in designing a CMS involves grouping parts and machines. In this paper, four integer programming formulations are presented for grouping parts and machines in a CMS at both the design and operational levels for a generalised grouping problem, whe","authors":["Uddin, Md. Kutub","Islam, Md. Saiful","Jahin, Md Abrar","Irfan, Md. Tanjid Hossen","Seam, Md. Saiful Islam","Mridha, M. F."],"url":"https://arxiv.org/abs/2411.15361","arxiv_id":"2411.15361","published":"2026-08-28"},{"title":"Optimal Control with Natural Images: Efficient Reinforcement Learning using Overcomplete Sparse Codes","abstract":"Optimal control and sequential decision making are widely used in many complex tasks. Optimal control over a sequence of natural images is a first step towards understanding the role of vision in control. Here, we formalize this problem as a reinforcement learning task, and derive general conditions under which an image includes enough information to implement an optimal policy. Reinforcement learning is shown to pro","authors":["Loxley, Peter N."],"url":"https://arxiv.org/abs/2412.08893","arxiv_id":"2412.08893","published":"2026-08-28"},{"title":"Guided Data Generation for Understanding Model Behavior","abstract":"We propose a method for generating distributions over the input space as an inspection tool for understanding trained models. Our framework poses questions of the form ``which inputs would make a trained model exhibit a specified behavior?'' and encodes each question through a guidance function. The generated data provide insights into how the models behave. To showcase our framework, we pose queries such as ","authors":["Kıral, Eren Mehmet","Aydın, Nurşen","Birbil, Ş. İlker"],"url":"https://arxiv.org/abs/2502.06658","arxiv_id":"2502.06658","published":"2026-08-28"},{"title":"Investigating Memory in Model-Free RL with POPGym Arcade","abstract":"How should we analyze memory in deep RL? We introduce tools for analyzing policies under partial observability and revealing how agents use memory to make decisions. To utilize these tools, we present POPGym Arcade, a collection of Atari-inspired, hardware-accelerated environments sharing a single observation and action space. Each environment provides fully and partially observable variants, enabling counterfactual ","authors":["Wang, Zekang","He, Zhe","Zhang, Borong","Toledo, Edan","Morad, Steven"],"url":"https://arxiv.org/abs/2503.01450","arxiv_id":"2503.01450","published":"2026-08-28"},{"title":"MODIS: Multi-Omics Data Integration for Small and unpaired datasets","abstract":"An important objective in computational biology is the efficient integration of multi-omics data. The task of integration comes with challenges: multi-omics data are most often unpaired (requiring diagonal integration), partially labeled with information about biological conditions, and in some situations such as rare diseases, only very small datasets are available. We present MODIS, a semi supervised framework desi","authors":["Lepe-Soltero, Daniel","Artières, Thierry","Baudot, Anaïs","Villoutreix, Paul"],"url":"https://arxiv.org/abs/2503.18856","arxiv_id":"2503.18856","published":"2026-08-28"},{"title":"Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling","abstract":"Dense retrieval is a crucial task in Information Retrieval (IR), serving as the basis for downstream tasks such as re-ranking and augmenting generation. Recently, large language models (LLMs) have demonstrated impressive semantic understanding capabilities, making them attractive to researchers focusing on dense retrieval. While LLMs, as decoder-style generative models, excel in language generation, they often fall s","authors":["Zhang, Hengran","Bi, Keping","Guo, Jiafeng","Sun, Xiaojie","Liu, Shihao","Shi, Daiting"],"url":"https://arxiv.org/abs/2504.05216","arxiv_id":"2504.05216","published":"2026-08-28"},{"title":"pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks","abstract":"Motivated by the growing demand for reduced-precision arithmetic in computational science, we exploit lower-precision emulation in Python{--}widely regarded as the dominant programming language for numerical analysis and machine learning. Low-precision paradigms have revolutionized deep learning by enabling more efficient computation and reduced memory footprint while maintaining model fidelity. To better enable nume","authors":["Carson, Erin","Chen, Xinye"],"url":"https://arxiv.org/abs/2504.07835","arxiv_id":"2504.07835","published":"2026-08-28"},{"title":"Bregman Linearized Augmented Lagrangian Method for Nonconvex Constrained Stochastic Zeroth-order Optimization","abstract":"In this paper, we study nonconvex constrained stochastic zeroth-order optimization problems, for which we have access to exact information of constraints and noisy function values of the objective. We propose a Bregman linearized augmented Lagrangian method that utilizes stochastic zeroth-order gradient estimators combined with a variance reduction technique. We analyze its oracle complexity, in terms of the total nu","authors":["Shi, Qiankun","Yuan, Han","Wang, Xiao","Wang, Hao"],"url":"https://arxiv.org/abs/2504.09409","arxiv_id":"2504.09409","published":"2026-08-28"},{"title":"Plain Transformers Can be Powerful Graph Learners","abstract":"Transformers have attained outstanding performance across various modalities, owing to their simple but powerful scaled-dot-product (SDP) attention mechanisms. Researchers have attempted to migrate Transformers to graph learning, but most advanced Graph Transformers (GTs) have strayed far from plain Transformers, exhibiting major architectural differences either by integrating message-passing or incorporating sophist","authors":["Ma, Liheng","Pal, Soumyasundar","Zhang, Yingxue","Torr, Philip H. S.","Coates, Mark"],"url":"https://arxiv.org/abs/2504.12588","arxiv_id":"2504.12588","published":"2026-08-28"},{"title":"Communication styles and reader preferences of LLM- and human-authored COVID-19 information explanations: a case study","abstract":"With the wide adoption of large language models (LLMs) in information assistance, it is essential to examine their alignment with human communication styles and values. We situate this study within health fact-checking, where effective communication is critical for correcting misconceptions and building trust. Although recent studies have explored LLMs for fact-checking and health communication, differences between L","authors":["Zhou, Jiawei","Venkatachalam, Kritika","Choi, Minje","Saha, Koustuv","De Choudhury, Munmun"],"url":"https://arxiv.org/abs/2505.08143","arxiv_id":"2505.08143","published":"2026-08-28"},{"title":"Temporally-Grounded Language Generation: Towards Real-Time Vision-Language Models","abstract":"Vision-language models (VLMs) have shown remarkable progress in offline tasks such as image captioning and video question answering. However, real-time interactive environments impose new demands on VLMs, requiring them to generate utterances that are not only semantically accurate but also temporally precise. We identify two core capabilities necessary for such settings---\\textit{perceptual updating} and \\textit{con","authors":["Yu, Keunwoo Peter","Chai, Joyce"],"url":"https://arxiv.org/abs/2505.11326","arxiv_id":"2505.11326","published":"2026-08-28"},{"title":"PiT: Progressive Diffusion Transformer","abstract":"Diffusion Transformers (DiTs) achieve remarkable performance within image generation via the transformer architecture. Conventionally, DiTs are constructed by stacking serial isotropic global modeling transformers, which face significant quadratic computational cost. However, through empirical analysis, we find that DiTs do not rely as heavily on global information as previously believed. In fact, most layers exhibit","authors":["Wu, Jiafu","Wang, Yabiao","Li, Jian","Peng, Jinlong","Cao, Yun","Wang, Chengjie"],"url":"https://arxiv.org/abs/2505.13219","arxiv_id":"2505.13219","published":"2026-08-28"},{"title":"Legal Rule Induction: Towards Generalizable Principle Discovery from Analogous Judicial Precedents","abstract":"Legal rules encompass not only codified statutes but also implicit adjudicatory principles derived from precedents that contain discretionary norms, social morality, and policy. While computational legal research has advanced in applying established rules to cases, inducing legal rules from judicial decisions remains understudied across jurisdictions. The advent of Large Language Models (LLMs) offers unprecedented po","authors":["Fan, Wei","Zheng, Tianshi","Hu, Yiran","Deng, Zheye","Wang, Weiqi","Xu, Baixuan"],"url":"https://arxiv.org/abs/2505.14104","arxiv_id":"2505.14104","published":"2026-08-28"},{"title":"HybridProver: Augmenting Theorem Proving with LLM-Driven Proof Synthesis and Refinement","abstract":"Formal methods play a crucial role in ensuring the reliability of critical systems through rigorous mathematical verification. However, their adoption remains limited due to the labor-intensive nature of manual proof construction. Recent advances in large language models (LLMs) have opened new opportunities for automated theorem proving. Two main paradigms have emerged: stepwise tactic-based generation and whole-proo","authors":["Hu, Jilin","Zhang, Jianyu","Zhao, Yongwang","Ringer, Talia"],"url":"https://arxiv.org/abs/2505.15740","arxiv_id":"2505.15740","published":"2026-08-28"},{"title":"STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation","abstract":"Off-policy evaluation (OPE) estimates the performance of a target policy using offline data collected from a behavior policy, and is crucial in domains such as robotics or healthcare where direct interaction with the environment is costly or unsafe. Existing OPE methods are ineffective for high-dimensional, long-horizon problems, due to exponential blow-ups in variance from importance weighting or compounding errors ","authors":["Goli, Hossein","Gimelfarb, Michael","de Lara, Nathan Samuel","Nishimura, Haruki","Itkina, Masha","Shkurti, Florian"],"url":"https://arxiv.org/abs/2505.20781","arxiv_id":"2505.20781","published":"2026-08-28"},{"title":"MObyGaze: a film dataset of multimodal objectification densely annotated by experts","abstract":"Characterizing and quantifying gender representation disparities in audiovisual storytelling contents is necessary to grasp how stereotypes may perpetuate on screen. In this article, we consider the high-level construct of objectification and introduce a new AI task to the ML community: characterize and quantify complex multimodal (visual, speech, audio) temporal patterns producing objectification in films. Building ","authors":["Ancarani, Elisa","Tores, Julie","Sassatelli, Lucile","Wu, Hui-Yin","Sun, Remy","Mouret, Paul"],"url":"https://arxiv.org/abs/2505.22084","arxiv_id":"2505.22084","published":"2026-08-28"},{"title":"From Accuracy to Robustness: A Study of Rule- and Model-based Verifiers in Mathematical Reasoning","abstract":"Trustworthy verifiers are essential for the success of reinforcement learning with verifiable reward (RLVR), which is the core methodology behind various large reasoning models such as DeepSeek-R1. In complex domains like mathematical reasoning, rule-based verifiers have been widely adopted in previous works to train strong reasoning models. However, the reliability of these verifiers and their impact on the RL train","authors":["Huang, Yuzhen","Zeng, Weihao","Zeng, Xingshan","Zhu, Qi","He, Junxian"],"url":"https://arxiv.org/abs/2505.22203","arxiv_id":"2505.22203","published":"2026-08-28"}],"history":{"events":[{"year":2021,"text":"The second phase of the Thomson-East Coast MRT line was opened for service.","url":"https://en.wikipedia.org/wiki/Thomson%E2%80%93East_Coast_Line"},{"year":1993,"text":"The NASA spacecraft Galileo flew by the asteroid 243 Ida and took photographs that later revealed the first known asteroid moon (both pictured).","url":"https://en.wikipedia.org/wiki/Galileo_(spacecraft)"},{"year":1993,"text":"The NASA spacecraft Galileo flew by the asteroid 243 Ida and took photographs that later revealed the first known asteroid moon.","url":"https://en.wikipedia.org/wiki/Galileo_(spacecraft)"},{"year":1987,"text":"Construction began on the Ryugyong Hotel (pictured) in Pyongyang, the tallest building in North Korea.","url":"https://en.wikipedia.org/wiki/Ryugyong_Hotel"},{"year":1973,"text":"Swedish police used gas bombs to end a seven-day hostage situation in Stockholm; the hostages had bonded with their captors during the incident, leading to the term Stockholm syndrome.","url":"https://en.wikipedia.org/wiki/Norrmalmstorg_robbery"},{"year":1963,"text":"American civil-rights leader Martin Luther King Jr. delivered the speech \"I Have a Dream\" during the March on Washington, calling for civil and economic rights and an end to racism in the United States.","url":"https://en.wikipedia.org/wiki/Martin_Luther_King_Jr."},{"year":1955,"text":"African-American teenager Emmett Till was lynched near Money, Mississippi, for allegedly flirting with a white woman, energizing the nascent American civil rights movement.","url":"https://en.wikipedia.org/wiki/Emmett_Till"},{"year":1950,"text":"American tennis player Althea Gibson became the first African-American woman to compete at the U.S. National Championships.","url":"https://en.wikipedia.org/wiki/Althea_Gibson"},{"year":1916,"text":"A confrontation at Blargies prison camp in France led to a charge of mutiny for New Zealand private John Braithwaite, for which he was later executed.","url":"https://en.wikipedia.org/wiki/Blargies_prison_camp"},{"year":1909,"text":"The 1909 Monterrey hurricane dissipated; one of the deadliest Atlantic tropical cyclones on record, it killed an estimated 4,000 people throughout Mexico.","url":"https://en.wikipedia.org/wiki/1909_Monterrey_hurricane"},{"year":1830,"text":"Tom Thumb (replica pictured), the first American-built steam locomotive, took part in an impromptu race against a horse-drawn car in Maryland.","url":"https://en.wikipedia.org/wiki/Tom_Thumb_(locomotive)"},{"year":1789,"text":"With the first use of his 1.2-metre (3.9 ft) telescope, then the largest in the world, William Herschel discovered a new moon of Saturn, later named Enceladus (pictured).","url":"https://en.wikipedia.org/wiki/William_Herschel"},{"year":1640,"text":"Bishops' Wars: Scottish Covenanter forces led by Alexander Leslie defeated the English army near Newburn, England.","url":"https://en.wikipedia.org/wiki/Bishops'_Wars"},{"year":1619,"text":"Ferdinand II, King of Bohemia and Hungary, was unanimously elected Holy Roman Emperor.","url":"https://en.wikipedia.org/wiki/Ferdinand_II%2C_Holy_Roman_Emperor"},{"year":1542,"text":"Ottoman–Portuguese conflicts: During the Battle of Wofla, the Portuguese commander Cristóvão da Gama was captured by the Adal Sultanate and executed the next day.","url":"https://en.wikipedia.org/wiki/Ottoman%E2%80%93Portuguese_conflicts_(1538%E2%80%931560)"},{"year":475,"text":"Orestes took control of Ravenna, the capital of the Western Roman Empire, forcing Emperor Julius Nepos to flee.","url":"https://en.wikipedia.org/wiki/Orestes_(father_of_Romulus_Augustulus)"}],"births":[{"year":2004,"text":"Kano Fujihira, Japanese entertainer","url":"https://en.wikipedia.org/wiki/Kano_Fujihira"},{"year":2003,"text":"Quvenzhané Wallis, American actress","url":"https://en.wikipedia.org/wiki/Quvenzhan%C3%A9_Wallis"},{"year":2001,"text":"Kamilla Rakhimova, Russian tennis player","url":"https://en.wikipedia.org/wiki/Kamilla_Rakhimova"},{"year":2000,"text":"Marissa Bode, American actress","url":"https://en.wikipedia.org/wiki/Marissa_Bode"},{"year":1998,"text":"Weston McKennie, American soccer player","url":"https://en.wikipedia.org/wiki/Weston_McKennie"},{"year":1996,"text":"Kim Se-jeong, South Korean actress and singer","url":"https://en.wikipedia.org/wiki/Kim_Se-jeong"},{"year":1994,"text":"Manon Arcangioli, French tennis player","url":"https://en.wikipedia.org/wiki/Manon_Arcangioli"},{"year":1994,"text":"Ons Jabeur, Tunisian tennis player","url":"https://en.wikipedia.org/wiki/Ons_Jabeur"}],"deaths":[{"year":2024,"text":"Obi Ndefo, American actor (born 1972)","url":"https://en.wikipedia.org/wiki/Obi_Ndefo"},{"year":2024,"text":"Steve Silberman, American writer and journalist (born 1957)","url":"https://en.wikipedia.org/wiki/Steve_Silberman"},{"year":2020,"text":"Chadwick Boseman, American actor and playwright (born 1976)","url":"https://en.wikipedia.org/wiki/Chadwick_Boseman"},{"year":2017,"text":"Mireille Darc, French actress and model (born 1938)","url":"https://en.wikipedia.org/wiki/Mireille_Darc"},{"year":2016,"text":"Juan Gabriel, Mexican singer and songwriter (born 1950)","url":"https://en.wikipedia.org/wiki/Juan_Gabriel"},{"year":2016,"text":"Mr. Fuji, American professional wrestler and manager (born 1934)","url":"https://en.wikipedia.org/wiki/Mr._Fuji"},{"year":2015,"text":"Al Arbour, Canadian-American ice hockey player and coach (born 1932)","url":"https://en.wikipedia.org/wiki/Al_Arbour"},{"year":2015,"text":"Mark Krasniqi, Kosovan ethnographer, poet, and translator (born 1920)","url":"https://en.wikipedia.org/wiki/Mark_Krasniqi"}]},"census":{"panel_version":2,"probed":14770,"error_rate":24.54,"degraded":false,"rates":{"head":10,"tail":5.5,"ai_native":75.45}},"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","license":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","license":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","license":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","license":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"license":"Records are CC BY-SA 4.0, inheriting from Wikipedia. Every item links to its source."}