{"date":"2026-03-17","schema_version":1,"captured_at":"2026-08-25T23:32:19.288Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"98th Academy Awards","views":1463343,"rank":2,"extract":"The 98th Academy Awards ceremony, presented by the Academy of Motion Picture Arts and Sciences (AMPAS), took place on March 15, 2026, at the Dolby Theatre in Hollywood, Los Angeles. During the gala, the AMPAS presented Academy Awards in 24 categories honoring ","url":"https://en.wikipedia.org/wiki/98th_Academy_Awards","thumb":"https://upload.wikimedia.org/wikipedia/en/6/6a/98th_Academy_Awards_poster.png?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail_unscaled"},{"title":"One Battle After Another","views":1147838,"rank":3,"extract":"One Battle After Another is a 2025 American action thriller film written, directed, and produced by Paul Thomas Anderson. 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It follows sisters Nora and Agnes in their reunion with their estranged father Gustav, who is preparing to make his next film with young American actress Rachel.","url":"https://en.wikipedia.org/wiki/Sentimental_Value","thumb":"https://upload.wikimedia.org/wikipedia/en/5/5c/Sentimental_Value_2025.jpeg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail_unscaled"}],"frontier":[{"title":"Kagi Translate now supports LinkedIn Speak as an output language","points":1469,"comments":345,"url":"https://translate.kagi.com/?from=en&to=LinkedIn+speak","discussion":"https://news.ycombinator.com/item?id=47408703"},{"title":"Reddit User Uncovers Who Is Behind Meta's $2B Lobbying for Age Verification Tech","points":907,"comments":22,"url":"https://www.gadgetreview.com/reddit-user-uncovers-who-is-behind-metas-2b-lobbying-for-invasive-age-verification-tech","discussion":"https://news.ycombinator.com/item?id=47410870"},{"title":"Microsoft's 'unhackable' Xbox One has been hacked by 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Bill","points":290,"comments":453,"url":"https://www.ilga.gov/Legislation/BillStatus?DocTypeID=HB&DocNum=5511","discussion":"https://news.ycombinator.com/item?id=47416131"},{"title":"Java 26 is here","points":277,"comments":339,"url":"https://hanno.codes/2026/03/17/java-26-is-here/","discussion":"https://news.ycombinator.com/item?id=47416548"},{"title":"Node.js needs a virtual file system","points":275,"comments":251,"url":"https://blog.platformatic.dev/why-nodejs-needs-a-virtual-file-system","discussion":"https://news.ycombinator.com/item?id=47413195"},{"title":"GPT‑5.4 Mini and Nano","points":248,"comments":145,"url":"https://openai.com/index/introducing-gpt-5-4-mini-and-nano","discussion":"https://news.ycombinator.com/item?id=47415441"},{"title":"Meta Horizon Worlds on Meta Quest is being discontinued","points":240,"comments":264,"url":"https://communityforums.atmeta.com/blog/AnnouncementsBlog/updates-to-your-meta-quest-experience-in-2026/1369435","discussion":"https://news.ycombinator.com/item?id=47416940"},{"title":"VisiCalc Reconstructed","points":231,"comments":80,"url":"https://zserge.com/posts/visicalc/","discussion":"https://news.ycombinator.com/item?id=47410871"}],"research":[{"title":"Forest structure in epigenetic landscapes","abstract":"Morphogenesis is the biological process that causes the emergence and changes of patterns (tissues and organs) in living organisms. It is a robust, self-organising mechanism, governed by Genetic Regulatory Networks (GRN), that hasn't been thoroughly understood. In this work we propose Epigenetic Forests as a tool to study morphogenesis and to extract valuable information from GRN. Our method unfolds the richness ","authors":["Cortes-Poza, Yuriria","Perez-Buendia, J. Rogelio"],"url":"https://arxiv.org/abs/1903.09386","arxiv_id":"1903.09386","published":"2026-03-17"},{"title":"Unsupervised Point Cloud Pre-Training via Contrasting and Clustering","abstract":"Annotating large-scale point clouds is highly time-consuming and often infeasible for many complex real-world tasks. Point cloud pre-training has therefore become a promising strategy for learning discriminative representations without labeled data. In this paper, we propose a general unsupervised pre-training framework, termed ConClu, which jointly integrates contrasting and clustering. The contrasting objective max","authors":["Mei, Guofeng","Huang, Xiaoshui","Liu, Juan","Zhang, Jian","Wu, Qiang"],"url":"https://arxiv.org/abs/2202.02543","arxiv_id":"2202.02543","published":"2026-03-17"},{"title":"Conceptual Views of Neural Networks: A Framework for Neuro-Symbolic Analysis","abstract":"We introduce \\emph{conceptual views} as a formal framework grounded in Formal Concept Analysis for globally explaining neural networks. Experiments on twenty-four ImageNet models and Fruits-360 show that these views faithfully represent the original models, enable architecture comparison via Gromov--Wasserstein distance, and support abductive learning of human-comprehensible rules from neurons.","authors":["Hirth, Johannes","Hanika, Tom"],"url":"https://arxiv.org/abs/2209.13517","arxiv_id":"2209.13517","published":"2026-03-17"},{"title":"Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity","abstract":"In this work, we study a natural nonparametric estimator of the transition probability matrices of a finite controlled Markov chain. We consider an offline setting with a fixed dataset, collected using a so-called logging policy. We develop sample complexity bounds for the estimator and establish conditions for minimaxity. Our statistical bounds depend on the logging policy through its mixing properties. We show that","authors":["Banerjee, Imon","Honnappa, Harsha","Rao, Vinayak"],"url":"https://arxiv.org/abs/2211.07092","arxiv_id":"2211.07092","published":"2026-03-17"},{"title":"Skeleton Regression: A Graph-Based Approach to Estimation with Manifold Structure","abstract":"We introduce a new regression framework designed to deal with large-scale, complex data that lies around a low-dimensional manifold with noises. Our approach first constructs a graph representation, referred to as the skeleton, to capture the underlying geometric structure. We then define metrics on the skeleton graph and apply nonparametric regression techniques, along with feature transformations based on the graph","authors":["Wei, Zeyu","Chen, Yen-Chi"],"url":"https://arxiv.org/abs/2303.11786","arxiv_id":"2303.11786","published":"2026-03-17"},{"title":"Measure transfer via stochastic slicing and matching","abstract":"This paper studies iterative schemes for measure transfer and approximation problems, which are defined through a slicing-and-matching procedure. Similar to the sliced Wasserstein distance, these schemes benefit from the availability of closed-form solutions for the one-dimensional optimal transport problem and the associated computational advantages. While such schemes have already been successfully utilized in data","authors":["Li, Shiying","Moosmueller, Caroline","Wang, Yongzhe"],"url":"https://arxiv.org/abs/2307.05705","arxiv_id":"2307.05705","published":"2026-03-17"},{"title":"Faster Stochastic Algorithms for Minimax Optimization under Polyak--Łojasiewicz Conditions","abstract":"This paper considers stochastic first-order algorithms for minimax optimization under Polyak--Łojasiewicz (PL) conditions. We propose SPIDER-GDA for solving the finite-sum problem of the form $\\min_x \\max_y f(x,y)\\triangleq \\frac{1}{n} \\sum_{i=1}^n f_i(x,y)$, where the objective function $f(x,y)$ is $μ_x$-PL in $x$ and $μ_y$-PL in $y$; and each $f_i(x,y)$ is $L$-smooth. We prove SPIDER-GDA could find an $ε$-optimal s","authors":["Chen, Lesi","Yao, Boyuan","Luo, Luo"],"url":"https://arxiv.org/abs/2307.15868","arxiv_id":"2307.15868","published":"2026-03-17"},{"title":"Optimal Projections for Discriminative Dictionary Learning using the JL-lemma","abstract":"Dimensionality reduction-based dictionary learning methods in the literature have often used iterative random projections. The dimensionality of such a random projection matrix is a random number that might not lead to a separable subspace structure in the transformed space. The convergence of such methods highly depends on the initial seed values used. Also, gradient descent-based updates might result in local minim","authors":["Madhuri, G.","Negi, Atul","Rangarao, Kaluri V."],"url":"https://arxiv.org/abs/2308.13991","arxiv_id":"2308.13991","published":"2026-03-17"},{"title":"Tensor Completion Leveraging Graph Information: A Dynamic Regularization Approach with Statistical Guarantees","abstract":"We consider the problem of tensor completion with graphs serving as side information to represent interrelationships among variables. Existing approaches suffer from several limitations: (1) they are often task-specific and lack generality or systematic formulation; (2) they typically treat graphs as static structures, ignoring their inherent dynamism in tensor-based settings; (3) they lack theoretical guarantees on ","authors":["Wang, Kaidong","Yi, Qianxin","Wang, Yao","Liao, Xiuwu","Tang, Shaojie","Yang, Can"],"url":"https://arxiv.org/abs/2310.02543","arxiv_id":"2310.02543","published":"2026-03-17"},{"title":"Harvest Video Foundation Models via Efficient Post-Pretraining","abstract":"Building video-language foundation models is costly and difficult due to the redundant nature of video data and the lack of high-quality video-language datasets. In this paper, we propose an efficient framework to harvest video foundation models from image ones. Our method is intuitively simple by randomly dropping input video patches and masking out input text during the post-pretraining procedure. The patch droppin","authors":["Li, Yizhuo","Li, Kunchang","He, Yinan","Wang, Yi","Wang, Yali","Wang, Limin"],"url":"https://arxiv.org/abs/2310.19554","arxiv_id":"2310.19554","published":"2026-03-17"},{"title":"Fast Estimations of Hitting Time of Elitist Evolutionary Algorithms from Fitness Levels","abstract":"The fitness level method is a widely used technique for estimating the mean hitting time of elitist evolutionary algorithms on level-based fitness functions. However, this paper identifies its main limitation: the linear lower bound derived from traditional fitness level partitioning is not tight when applied to many non-level-based fitness functions. A new subset level method is introduced to address this limitation","authors":["He, Jun","Chong, Siang Yew","Yao, Xin"],"url":"https://arxiv.org/abs/2311.10502","arxiv_id":"2311.10502","published":"2026-03-17"},{"title":"On Meta-Prompting","abstract":"Modern large language models (LLMs) are capable of interpreting input strings as instructions, or prompts, and carry out tasks based on them. Unlike traditional learners, LLMs cannot use back-propagation to obtain feedback, and condition their output in situ in a phenomenon known as in-context learning (ICL). Many approaches to prompting and pre-training these models involve the automated generation of these prompts,","authors":["de Wynter, Adrian","Wang, Xun","Gu, Qilong","Chen, Si-Qing"],"url":"https://arxiv.org/abs/2312.06562","arxiv_id":"2312.06562","published":"2026-03-17"},{"title":"3D-LFM: Lifting Foundation Model","abstract":"The lifting of 3D structure and camera from 2D landmarks is at the cornerstone of the entire discipline of computer vision. Traditional methods have been confined to specific rigid objects, such as those in Perspective-n-Point (PnP) problems, but deep learning has expanded our capability to reconstruct a wide range of object classes (e.g. C3DPO and PAUL) with resilience to noise, occlusions, and perspective distortio","authors":["Dabhi, Mosam","Jeni, Laszlo A.","Lucey, Simon"],"url":"https://arxiv.org/abs/2312.11894","arxiv_id":"2312.11894","published":"2026-03-17"},{"title":"Sparse Training for Federated Learning with Regularized Error Correction","abstract":"Federated Learning (FL) has attracted much interest due to the significant advantages it brings to training deep neural network (DNN) models. However, since communications and computation resources are limited, training DNN models in FL systems face challenges such as elevated computational and communication costs in complex tasks. Sparse training schemes gain increasing attention in order to scale down the dimension","authors":["Greidi, Ran","Cohen, Kobi"],"url":"https://arxiv.org/abs/2312.13795","arxiv_id":"2312.13795","published":"2026-03-17"},{"title":"Input Convex Lipschitz Recurrent Neural Networks for Robust and Efficient Process Modeling and Optimization","abstract":"Computational efficiency and robustness are essential in process modeling, optimization, and control for real-world engineering applications. While neural network-based approaches have gained significant attention in recent years, conventional neural networks often fail to address these two critical aspects simultaneously or even independently. Inspired by natural physical systems and established literature, input co","authors":["Wang, Zihao","Li, Yuhan","Shi, Yao","Wu, Zhe"],"url":"https://arxiv.org/abs/2401.07494","arxiv_id":"2401.07494","published":"2026-03-17"},{"title":"All-weather Multi-Modality Image Fusion: Unified Framework and 100k Benchmark","abstract":"Multi-modality image fusion (MMIF) combines complementary information from different image modalities to provide a comprehensive and objective interpretation of scenes. However, existing fusion methods cannot resist different weather interferences in real-world scenes, limiting their practical applicability. To bridge this gap, we propose an end-to-end, unified all-weather MMIF model. Rather than focusing solely on p","authors":["Li, Xilai","Liu, Wuyang","Li, Xiaosong","Zhou, Fuqiang","Li, Huafeng","Nie, Feiping"],"url":"https://arxiv.org/abs/2402.02090","arxiv_id":"2402.02090","published":"2026-03-17"},{"title":"Combining Evidence Across Filtrations","abstract":"In sequential anytime-valid inference, any admissible procedure must be based on e-processes: generalizations of test martingales that quantify the accumulated evidence against a composite null hypothesis at any stopping time. This paper proposes a method for combining e-processes constructed in different filtrations but for the same null. Although e-processes in the same filtration can be combined effortlessly (by a","authors":["Choe, Yo Joong","Ramdas, Aaditya"],"url":"https://arxiv.org/abs/2402.09698","arxiv_id":"2402.09698","published":"2026-03-17"},{"title":"Survey of Computerized Adaptive Testing: A Machine Learning Perspective","abstract":"Computerized Adaptive Testing (CAT) offers an efficient and personalized method for assessing examinee proficiency by dynamically adjusting test questions based on individual performance. Compared to traditional, non-personalized testing methods, CAT requires fewer questions and provides more accurate assessments. As a result, CAT has been widely adopted across various fields, including education, healthcare, sports,","authors":["Zhuang, Yan","Liu, Qi","Bi, Haoyang","Huang, Zhenya","Huang, Weizhe","Li, Jiatong"],"url":"https://arxiv.org/abs/2404.00712","arxiv_id":"2404.00712","published":"2026-03-17"},{"title":"Federated Multi-Agent Mapping for Planetary Exploration","abstract":"Multi-agent robotic exploration stands to play an important role in space exploration as the next generation of robotic systems ventures to far-flung environments. A key challenge in this new paradigm will be to effectively share and utilize the vast amount of data generated onboard while operating in bandwidth-constrained regimes typical of space missions. Federated learning (FL) is a promising tool for bridging thi","authors":["Szatmari, Tiberiu-Ioan","Cauligi, Abhishek"],"url":"https://arxiv.org/abs/2404.02289","arxiv_id":"2404.02289","published":"2026-03-17"},{"title":"Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty","abstract":"This paper studies continuous-time risk-sensitive reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation with the exponential-form objective. The risk-sensitive objective arises either as the agent's risk attitude or as a distributionally robust approach against the model uncertainty. Owing to the martingale perspective in Jia and Zhou (J Mach Learn Res 24(161): 1--61","authors":["Jia, Yanwei"],"url":"https://arxiv.org/abs/2404.12598","arxiv_id":"2404.12598","published":"2026-03-17"},{"title":"Simple-RF: Regularizing Sparse Input Radiance Fields with Simpler Solutions","abstract":"Neural Radiance Fields (NeRF) show impressive performance in photo-realistic free-view rendering of scenes. Recent improvements on the NeRF such as TensoRF and ZipNeRF employ explicit models for faster optimization and rendering, as compared to the NeRF that employs an implicit representation. However, both implicit and explicit radiance fields require dense sampling of images in the given scene. Their performance de","authors":["Somraj, Nagabhushan","Mupparaju, Sai Harsha","Karanayil, Adithyan","Soundararajan, Rajiv"],"url":"https://arxiv.org/abs/2404.19015","arxiv_id":"2404.19015","published":"2026-03-17"},{"title":"Policy Iteration for Two-Player General-Sum Stochastic Stackelberg Games","abstract":"We address two-player general-sum stochastic Stackelberg games (SSGs), where the leader's policy is optimized considering the best-response follower whose policy is optimal for its reward under the leader. Existing policy gradient and value iteration approaches for SSGs do not guarantee monotone improvement in the leader's policy under the best-response follower. Consequently, their performance is not guarant","authors":["Kudo, Mikoto","Akimoto, Youhei"],"url":"https://arxiv.org/abs/2405.06689","arxiv_id":"2405.06689","published":"2026-03-17"},{"title":"Random Scaling and Momentum for Non-smooth Non-convex Optimization","abstract":"Training neural networks requires optimizing a loss function that may be highly irregular, and in particular neither convex nor smooth. Popular training algorithms are based on stochastic gradient descent with momentum (SGDM), for which classical analysis applies only if the loss is either convex or smooth. We show that a very small modification to SGDM closes this gap: simply scale the update at each time point by a","authors":["Zhang, Qinzi","Cutkosky, Ashok"],"url":"https://arxiv.org/abs/2405.09742","arxiv_id":"2405.09742","published":"2026-03-17"},{"title":"MetaGS: A Meta-Learned Gaussian-Phong Model for Out-of-Distribution 3D Scene Relighting","abstract":"Out-of-distribution (OOD) 3D relighting requires novel view synthesis under unseen lighting conditions that differ significantly from the observed images. Existing relighting methods, which assume consistent light source distributions between training and testing, often degrade in OOD scenarios. We introduce MetaGS to tackle this challenge from two perspectives. First, we propose a meta-learning approach to train 3D ","authors":["He, Yumeng","Wang, Yunbo","Yang, Xiaokang"],"url":"https://arxiv.org/abs/2405.20791","arxiv_id":"2405.20791","published":"2026-03-17"},{"title":"TraffiDent: A Dataset for Understanding the Interplay Between Traffic Dynamics and Incidents","abstract":"Long-separated research has been conducted on two highly correlated tracks: traffic and incidents. Traffic track witnesses complicating deep learning models, e.g., to push the prediction a few percent more accurate, and the incident track only studies the incidents alone, e.g., to infer the incident risk. We, for the first time, spatiotemporally aligned the two tracks in a large-scale region (16,972 traffic nodes) fr","authors":["Gou, Xiaochuan","Li, Ziyue","Lan, Tian","Lin, Junpeng","Li, Zhishuai","Zhao, Bingyu"],"url":"https://arxiv.org/abs/2407.11477","arxiv_id":"2407.11477","published":"2026-03-17"}],"history":{"events":[{"year":2011,"text":"First Libyan Civil War: The United Nations Security Council adopted Resolution 1973, authorizing military intervention in Libya to protect civilians.","url":"https://en.wikipedia.org/wiki/Libyan_civil_war_(2011)"},{"year":2004,"text":"Unrest in Kosovo broke out, resulting in the deaths of 28, the wounding of more than 600 others, and the destruction of several Serb Orthodox churches and shrines.","url":"https://en.wikipedia.org/wiki/2004_unrest_in_Kosovo"},{"year":2000,"text":"Hundreds of followers of the Movement for the Restoration of the Ten Commandments of God, a Ugandan sect, died in a mass murder committed by its leaders.","url":"https://en.wikipedia.org/wiki/Movement_for_the_Restoration_of_the_Ten_Commandments_of_God"},{"year":1988,"text":"Eritrean War of Independence: Eritrean People's Liberation Front troops encircled Ethiopian forces in the Battle of Afabet, gaining a decisive victory three days later.","url":"https://en.wikipedia.org/wiki/Eritrean_War_of_Independence"},{"year":1979,"text":"The Penmanshiel Tunnel in the Scottish Borders collapsed during refurbishing construction, killing two workers, and leading to the abandonment of the tunnel.","url":"https://en.wikipedia.org/wiki/Penmanshiel_Tunnel"},{"year":1973,"text":"Slava Veder took his Pulitzer Prize-winning photograph Burst of Joy, which came to symbolize the end of United States involvement in the Vietnam War.","url":"https://en.wikipedia.org/wiki/Pulitzer_Prize_for_Feature_Photography"},{"year":1968,"text":"Six thousand sheep were killed on ranches near Dugway Proving Ground in Utah as a result of the U.S. Army spraying a nerve agent.","url":"https://en.wikipedia.org/wiki/Dugway_Proving_Ground"},{"year":1963,"text":"An eruption of Mount Agung on Bali, Indonesia, killed at least 1,100 people.","url":"https://en.wikipedia.org/wiki/Mount_Agung"},{"year":1957,"text":"A plane crash on the slopes of Mount Manunggal killed Philippine president Ramon Magsaysay and 24 others.","url":"https://en.wikipedia.org/wiki/1957_Cebu_Douglas_C-47_crash"},{"year":1955,"text":"Ice hockey fans in Montreal rioted to protest the suspension of Montreal Canadiens star Maurice Richard for hitting an official.","url":"https://en.wikipedia.org/wiki/Ice_hockey"},{"year":1950,"text":"Scientists at Lawrence Berkeley National Laboratory announced the synthesis of californium, a radioactive transuranium element.","url":"https://en.wikipedia.org/wiki/Lawrence_Berkeley_National_Laboratory"},{"year":1942,"text":"The Holocaust: The first mass killings of Jews began at Belzec extermination camp in occupied Poland, the first of the Operation Reinhard camps to begin operation.","url":"https://en.wikipedia.org/wiki/The_Holocaust"},{"year":1917,"text":"World War I: Hugh Grosvenor, 2nd Duke of Westminster led a force to rescue 92 prisoners of war from Bir Hakeim, killing the Senussi guards and their families.","url":"https://en.wikipedia.org/wiki/World_War_I"},{"year":1902,"text":"The Dorchester Heights Monument , memorializing the siege of Boston during the American Revolutionary War, was dedicated.","url":"https://en.wikipedia.org/wiki/Dorchester_Heights_Monument"},{"year":1891,"text":"The transatlantic steamship Utopia accidentally collided with the battleship HMS Anson in the Bay of Gibraltar, sinking in less than twenty minutes and killing 562.","url":"https://en.wikipedia.org/wiki/SS_Utopia"},{"year":1864,"text":"Second Schleswig War: In an attempt to end a Danish blockade, Eduard von Jachmann led a Prussian squadron in an attack against a Danish fleet led by Edvard van Dockum.","url":"https://en.wikipedia.org/wiki/Second_Schleswig_War"},{"year":1776,"text":"American Revolutionary War: The 11‑month Siege of Boston ended with the evacuation of the city's British garrison (depicted).","url":"https://en.wikipedia.org/wiki/American_Revolutionary_War"},{"year":1677,"text":"Franco-Dutch War: France captured the town of Valenciennes in the Spanish Netherlands.","url":"https://en.wikipedia.org/wiki/Franco-Dutch_War"}],"births":[{"year":2001,"text":"Pietro Pellegri, Italian footballer","url":"https://en.wikipedia.org/wiki/Pietro_Pellegri"},{"year":1998,"text":"Brandon Aiyuk, American football player","url":"https://en.wikipedia.org/wiki/Brandon_Aiyuk"},{"year":1997,"text":"Katie Ledecky, American swimmer","url":"https://en.wikipedia.org/wiki/Katie_Ledecky"},{"year":1997,"text":"Daniel Sprong, Dutch ice hockey player","url":"https://en.wikipedia.org/wiki/Daniel_Sprong"},{"year":1995,"text":"Claressa Shields, American boxer and mixed martial artist","url":"https://en.wikipedia.org/wiki/Claressa_Shields"},{"year":1994,"text":"DeForest Buckner, American football player","url":"https://en.wikipedia.org/wiki/DeForest_Buckner"},{"year":1994,"text":"Terry Rozier, American basketball player","url":"https://en.wikipedia.org/wiki/Terry_Rozier"},{"year":1994,"text":"Ivan Provedel, Italian footballer","url":"https://en.wikipedia.org/wiki/Ivan_Provedel"}],"deaths":[{"year":2025,"text":"John Hemingway, Irish fighter pilot, last surviving Battle of Britain pilot (born 1919)","url":"https://en.wikipedia.org/wiki/John_Hemingway_(RAF_officer)"},{"year":2025,"text":"Lee Shau-kee, Hong Kong real estate billionaire (born 1928)","url":"https://en.wikipedia.org/wiki/Lee_Shau-kee"},{"year":2023,"text":"Lance Reddick, American actor (born 1962)","url":"https://en.wikipedia.org/wiki/Lance_Reddick"},{"year":2021,"text":"John Magufuli, the fifth President of Tanzania (born 1959)","url":"https://en.wikipedia.org/wiki/John_Magufuli"},{"year":2018,"text":"Mike MacDonald, Canadian comedian (born 1954)","url":"https://en.wikipedia.org/wiki/Mike_MacDonald_(comedian)"},{"year":2018,"text":"Phan Văn Khải, the fifth Prime Minister of Vietnam (born 1933)","url":"https://en.wikipedia.org/wiki/Phan_V%C4%83n_Kh%E1%BA%A3i"},{"year":2016,"text":"Meir Dagan, Israeli general (born 1945)","url":"https://en.wikipedia.org/wiki/Meir_Dagan"},{"year":2016,"text":"Zoltán Kamondi, Hungarian director, producer, and screenwriter (born 1960)","url":"https://en.wikipedia.org/wiki/Zolt%C3%A1n_Kamondi"}]},"census":null,"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","licence":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","licence":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"licence":"Records are CC BY-SA 4.0, inheriting from Wikipedia. Every item links to its source."}