{"date":"2026-10-09","schema_version":1,"captured_at":"2026-10-10T10:52:41.434Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Nana Patekar","views":1665865,"rank":2,"extract":"Vishwanath Patekar, nicknamed Nana, was an Indian actor and filmmaker, mainly working in Hindi and Marathi cinema. Widely regarded as one of the greatest and most influential actors in Indian Cinema, Patekar was recipient of the three National Film Awards, fou","url":"https://en.wikipedia.org/wiki/Nana_Patekar","thumb":"https://thumb.wikimedia.org/wikipedia/commons/thumb/6/6f/Nana_Patekar_2025.jpg/330px-Nana_Patekar_2025.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Christa Pike","views":422519,"rank":5,"extract":"Christa Gail Pike is an American convicted murderer on death row in Tennessee. 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It w","url":"https://en.wikipedia.org/wiki/2009_Fort_Hood_shooting","thumb":"https://thumb.wikimedia.org/wikipedia/commons/thumb/a/ac/Flickr_-_The_U.S._Army_-_Preparing_the_wounded_for_transport_at_Fort_Hood.jpg/330px-Flickr_-_The_U.S._Army_-_Preparing_the_wounded_for_transport_at_Fort_Hood.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Mia Goth","views":66330,"rank":32,"extract":"Mia Gypsy Mello da Silva Goth is a British actress and model. After modelling as a teenager, Goth made her acting debut in the erotic drama Nymphomaniac (2013). She earned recognition with the films A Cure for Wellness (2016), High Life (2018), Suspiria (2018)","url":"https://en.wikipedia.org/wiki/Mia_Goth","thumb":"https://thumb.wikimedia.org/wikipedia/commons/thumb/b/b1/Mia_Goth_at_82nd_Venice_International_Film_Festival-1_%28cropped%29.jpg/330px-Mia_Goth_at_82nd_Venice_International_Film_Festival-1_%28cropped%29.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Ansel Adams","views":65003,"rank":33,"extract":"Ansel Easton Adams was an American landscape photographer and environmentalist known for his black-and-white images of the American West. 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Recent studies show that convolutional neural networks (CNNs) perform well to identify skin lesions from images. Lightweight CNN based pre-scanner applications for resource-constrained mobile devices can help users with early diagnosis of Lyme disease and prevent the tr","authors":["Hossain, Sk Imran","de Herve, Jocelyn de Goër","Hassan, Md Shahriar","Martineau, Delphine","Petrosyan, Evelina","Corbain, Violaine"],"url":"https://arxiv.org/abs/2106.14465","arxiv_id":"2106.14465","published":"2026-10-09"},{"title":"Expert Opinion Elicitation for Assisting Deep Learning based Lyme Disease Classifier with Patient Data","abstract":"Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Physicians rely on patient information about the background of the skin le","authors":["Hossain, Sk Imran","de Herve, Jocelyn de Goër","Abrial, David","Emillion, Richard","Lebertb, Isabelle","Frendo, Yann"],"url":"https://arxiv.org/abs/2208.14384","arxiv_id":"2208.14384","published":"2026-10-09"},{"title":"Perturbed Iterate SGD for Lipschitz Continuous Loss Functions with Numerical Error and Adaptive Step Sizes","abstract":"Motivated by neural network training in finite-precision arithmetic environments, this work studies the convergence of perturbed iterate SGD using adaptive step sizes in an environment with numerical error. Considering a general stochastic Lipschitz continuous loss function, an asymptotic convergence result to a Clarke stationary point is proven as well as the non-asymptotic convergence to an approximate stationary p","authors":["Metel, Michael R."],"url":"https://arxiv.org/abs/2211.04655","arxiv_id":"2211.04655","published":"2026-10-09"},{"title":"Enabling Quantum Natural Language Processing for Hindi Language","abstract":"Quantum Natural Language Processing (QNLP) is taking huge leaps in solving the shortcomings of classical Natural Language Processing (NLP) techniques and moving towards a more "Explainable" NLP system. The current literature around QNLP focuses primarily on implementing QNLP techniques in sentences in the English language. In this paper, we propose to enable the QNLP approach to HINDI, which is the third most","authors":["Srivastava, Naman","Belekar, Gaurang","Naik, Gautami Sanjay","Saumya, Sunil","H, Aswath Babu"],"url":"https://arxiv.org/abs/2312.01221","arxiv_id":"2312.01221","published":"2026-10-09"},{"title":"Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection","abstract":"This work aims to improve the sample efficiency of parallel large-scale ranking and selection (R&S) problems by leveraging correlation information. We modify the commonly used "divide and conquer" framework in parallel computing by adding a correlation-based clustering step, transforming it into "clustering and conquer". Theoretically, we develop a novel gradient-based analysis framework and show ","authors":["Zhang, Zishi","Peng, Yijie"],"url":"https://arxiv.org/abs/2402.02196","arxiv_id":"2402.02196","published":"2026-10-09"},{"title":"Machine learning and information theory concepts towards an AI Mathematician","abstract":"The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning. What could be missing? Can we learn something useful about that gap from how the brains of mathematicians go about their craft? This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities -- which correspond to our int","authors":["Bengio, Yoshua","Whitammer, Esmeralda S."],"url":"https://arxiv.org/abs/2403.04571","arxiv_id":"2403.04571","published":"2026-10-09"},{"title":"Accurate and Efficient Object Pose Estimation via the Aggregation of Diffusion Features","abstract":"Estimating the pose of objects from images is a crucial task of 3D scene understanding, and recent approaches have shown promising results on very large benchmarks. However, these methods experience a significant performance drop when dealing with unseen objects. To address this problem, we have an in-depth analysis on the features of diffusion models, e.g. Stable Diffusion, which hold substantial potential for model","authors":["Wang, Tianfu","Hu, Guosheng","Wang, Hongguang"],"url":"https://arxiv.org/abs/2403.18791","arxiv_id":"2403.18791","published":"2026-10-09"},{"title":"Policy Learning with a Language Bottleneck","abstract":"Modern AI systems such as self-driving cars and game-playing agents can achieve superhuman performance, but often lack human-like generalization, interpretability, and inter-operability with human users. Inspired by the rich interactions between language and decision-making in humans, we introduce Policy Learning with a Language Bottleneck (PLLB), a framework enabling AI agents to generate linguistic rules that captu","authors":["Srivastava, Megha","Colas, Cedric","Sadigh, Dorsa","Andreas, Jacob"],"url":"https://arxiv.org/abs/2405.04118","arxiv_id":"2405.04118","published":"2026-10-09"},{"title":"Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning","abstract":"Machine unlearning aims to remove the influence of specified training data or knowledge from a trained model without requiring full retraining. This capability is particularly important for modern image classifiers and large language models (LLMs), where retraining can be computationally expensive. However, machine unlearning on complex data remains difficult because the target information is often distributed across","authors":["Chang, Wenhan","Zhu, Tianqing","Xu, Heng","Liu, Wenjian","Zhou, Wanlei"],"url":"https://arxiv.org/abs/2405.15662","arxiv_id":"2405.15662","published":"2026-10-09"},{"title":"Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving","abstract":"Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI. It features a KVCache-centric disaggregated architecture that separates the prefill and decoding clusters. It also leverages the underutilized CPU, DRAM, and SSD resources of the GPU cluster to implement a disaggregated cache of KVCache. The core of Mooncake is its KVCache-centric scheduler, which balances maximizing overall eff","authors":["Qin, Ruoyu","Li, Zheming","He, Weiran","Zhang, Mingxing","Wu, Yongwei","Zheng, Weimin"],"url":"https://arxiv.org/abs/2407.00079","arxiv_id":"2407.00079","published":"2026-10-09"},{"title":"Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Bootstrapped Prolog-based Chain-of-Thought","abstract":"Large language models have demonstrated remarkable capabilities as general-purpose assistants, excelling in a wide range of reasoning tasks and supporting various aspects of daily web usage. This achievement represents a significant step toward achieving artificial general intelligence. Despite these advancements, the effectiveness of large language models often hinges on the specific prompting strategies employed, a","authors":["Qiu, Xihe","Deng, Yongxin","Tan, Xiaoyu","Fang, Zhen","Chen, Ling"],"url":"https://arxiv.org/abs/2407.14562","arxiv_id":"2407.14562","published":"2026-10-09"},{"title":"Graphons of Line Graphs","abstract":"We consider the problem of estimating graph limits, known as graphons, from observations of sequences of sparse finite graphs. In this paper we show a simple method that can shed light on a subset of sparse graphs. The method involves mapping the original graphs to their line graphs. We show that graphs satisfying a particular property, which we call the square-degree property are sparse, but give rise to dense line ","authors":["Kandanaarachchi, Sevvandi","Ong, Cheng Soon"],"url":"https://arxiv.org/abs/2409.01656","arxiv_id":"2409.01656","published":"2026-10-09"},{"title":"Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Review","abstract":"The integration of Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) in healthcare, through Machine Learning (ML) and Deep Learning (DL) techniques, has advanced the prediction and diagnosis of chronic diseases. AI-driven models such as XGBoost, Random Forest, CNNs, and LSTM RNNs have achieved over 98\\% accuracy in predicting heart disease, chronic kidney disease (CKD), Alzheimer's disease, a","authors":["Otapo, Akeem Temitope","Othmani, Alice","Khodabandelou, Ghazaleh","Ming, Zuheng"],"url":"https://arxiv.org/abs/2410.00034","arxiv_id":"2410.00034","published":"2026-10-09"},{"title":"Attention when you need","abstract":"Paying attention improves performance, but attention is metabolically costly, so how should a resource-efficient agent allocate it? We study optimal allocation strategies using a normative model of a signal detection task in which attention comes at a cost. The model reveals that optimal attention is temporally structured in one of two patterns depending on task conditions: when attention costs penalize intense focus","authors":["Matthew, Grayson","Boominathan, Lokesh","Chen, Yizhou","McGinley, Matthew","Pitkow, Xaq"],"url":"https://arxiv.org/abs/2501.07440","arxiv_id":"2501.07440","published":"2026-10-09"},{"title":"Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion Planning","abstract":"Multi-agent path finding (MAPF) in large networks is computationally challenging. An approach for MAPF is prioritized planning (PP), in which agents plan sequentially according to their priority. Albeit a computationally efficient approach for MAPF, the solution quality strongly depends on the prioritization. Most prioritizations rely either on heuristics, which do not generalize well, or iterate to find adequate pri","authors":["Scheffe, Patrick","Kahle, Julius","Alrifaee, Bassam"],"url":"https://arxiv.org/abs/2501.10781","arxiv_id":"2501.10781","published":"2026-10-09"},{"title":"BEAT: Balanced Frequency Adaptive Tuning for Long-Term Time-Series Forecasting","abstract":"Long-term time-series forecasting supports a wide range of applications, including weather prediction and electricity demand planning. Frequency-domain methods address this task by decomposing observations into components that describe temporal variations at different scales. However, separate representations do not by themselves provide an explicit mechanism for adjusting the training emphasis across components. Und","authors":["Li, Zhixuan","Chen, Naipeng","Choi, Seonghwa","Lee, Sanghoon","Lin, Weisi"],"url":"https://arxiv.org/abs/2501.19065","arxiv_id":"2501.19065","published":"2026-10-09"},{"title":"Networks with Finite VC Dimension: Pro and Contra","abstract":"Approximation and learning of classifiers of large data sets by neural networks in terms of high-dimensional geometry and statistical learning theory are investigated. The influence of the VC dimension of sets of input-output functions of networks on approximation capabilities is compared with its influence on consistency in learning from samples of data. It is shown that, whereas finite VC dimension is desirable for","authors":["Kurkova, Vera","Sanguineti, Marcello"],"url":"https://arxiv.org/abs/2502.02679","arxiv_id":"2502.02679","published":"2026-10-09"},{"title":"A Survey on Industrial Anomaly Synthesis","abstract":"This paper presents a comprehensive review of industrial anomaly synthesis (IAS). Existing surveys on industrial anomalies mainly focus on anomaly detection, while IAS is typically treated as an auxiliary component rather than as an independent topic. However, owing to its increasing importance in data augmentation, downstream model training, and controllable industrial inspection, IAS has become a research direction","authors":["Wang, Yanshu","Xu, Xichen","Wang, Jinbao","Ma, Chengbin","Lei, Xiaoning","Xie, Guoyang"],"url":"https://arxiv.org/abs/2502.16412","arxiv_id":"2502.16412","published":"2026-10-09"},{"title":"C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Models","abstract":"Pre-trained visual models have become fundamental in computer vision, but they face challenges in continual learning scenarios where data and tasks evolve over time. Low-Rank Adaptation (LoRA) offers efficient fine-tuning capabilities but remains limited for such dynamic environments. Standard LoRA cannot distinguish important subspaces, causing critical knowledge to be overwritten in sequential training. Existing ap","authors":["Zhang, Xin","Bai, Liang","Yang, Xian"],"url":"https://arxiv.org/abs/2502.17920","arxiv_id":"2502.17920","published":"2026-10-09"},{"title":"Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning","abstract":"In self-supervised contrastive learning, negative pairs are typically constructed using an anchor image and a sample drawn from the entire dataset, excluding the anchor. However, this approach can result in the creation of negative pairs with similar semantics, referred to as "false negatives", leading to their embeddings being falsely pushed apart. To address this issue, we introduce GloFND, an optimization-","authors":["Balmaseda, Vicente","Wang, Bokun","Lin, Ching-Long","Yang, Tianbao"],"url":"https://arxiv.org/abs/2502.20612","arxiv_id":"2502.20612","published":"2026-10-09"},{"title":"Boosting the Local Invariance for Better Adversarial Transferability","abstract":"Transfer-based attacks pose a significant threat to real-world applications by directly targeting victim models with adversarial examples generated on surrogate models. While numerous approaches have been proposed to enhance adversarial transferability, existing works often overlook the intrinsic relationship between adversarial perturbations and input images. In this work, we find that the adversarial perturbations ","authors":["Liu, Bohan","Wang, Xiaosen"],"url":"https://arxiv.org/abs/2503.06140","arxiv_id":"2503.06140","published":"2026-10-09"},{"title":"Sample-Efficient Optimization over Generative Priors via Coarse Learnability","abstract":"We study zeroth-order optimization where solutions must minimize a cost $d(s)$ while maintaining high probability under a complex generative prior $L(s)$ (e.g., a parameterized model). This reduces to sampling from a target distribution proportional to $L(s) e^{-T \\cdot d(s)}$. Since classical model-based optimization (MBO) lacks finite-sample guarantees for expressive approximate learners, we introduce "coarse l","authors":["Awasthi, Pranjal","Gollapudi, Sreenivas","Kumar, Ravi","Munagala, Kamesh"],"url":"https://arxiv.org/abs/2503.06917","arxiv_id":"2503.06917","published":"2026-10-09"},{"title":"A Survey on Archetypal Analysis","abstract":"Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure for extracting distinct aspects, so-called archetypes, from observations, with each observational record approximated as a mixture (i.e., convex combination) of these archetypes. AA thereby provides straightforward, interpretable, and explainable representations for feature extraction and dimensionali","authors":["Alcacer, Aleix","Epifanio, Irene","Mair, Sebastian","Mørup, Morten"],"url":"https://arxiv.org/abs/2504.12392","arxiv_id":"2504.12392","published":"2026-10-09"},{"title":"InfiFPO: Implicit Model Fusion via Preference Optimization in Large Language Models","abstract":"Model fusion combines multiple Large Language Models (LLMs) with different strengths into a more powerful, integrated model through lightweight training methods. Existing works on model fusion focus primarily on supervised fine-tuning (SFT), leaving preference alignment (PA) --a critical phase for enhancing LLM performance--largely unexplored. The current few fusion methods on PA phase, like WRPO, simplify the proces","authors":["Gu, Yanggan","Wang, Yuanyi","Yan, Zhaoyi","Zhang, Yiming","Zhou, Qi","Wu, Fei"],"url":"https://arxiv.org/abs/2505.13878","arxiv_id":"2505.13878","published":"2026-10-09"},{"title":"LLM-powered Query Expansion for Enhancing Boundary Prediction in Language-driven Action Localization","abstract":"Language-driven action localization in videos requires not only semantic alignment between language query and video segment, but also prediction of action boundaries. However, the language query primarily describes the main content of an action and usually lacks specific details of action start and end boundaries, which increases the subjectivity of manual boundary annotation and leads to boundary uncertainty in trai","authors":["Shang, Zirui","Wu, Xinxiao","Yang, Shuo"],"url":"https://arxiv.org/abs/2505.24282","arxiv_id":"2505.24282","published":"2026-10-09"}],"history":{"events":[{"year":2019,"text":"Syrian civil war: Turkish forces began an offensive into north-eastern Syria following the withdrawal of U.S. troops from the region.","url":"https://en.wikipedia.org/wiki/Syrian_civil_war"},{"year":2012,"text":"Pakistani activist Malala Yousafzai (pictured) was severely injured by a Taliban gunman in a failed assassination attempt.","url":"https://en.wikipedia.org/wiki/Malala_Yousafzai"},{"year":1986,"text":"The Phantom of the Opera, a musical by Andrew Lloyd Webber and currently the longest-running Broadway show in history, opened in London's West End.","url":"https://en.wikipedia.org/wiki/The_Phantom_of_the_Opera_(1986_musical)"},{"year":1962,"text":"Nick Holonyak, an engineer for General Electric, gave the first public demonstration of a light-emitting diode.","url":"https://en.wikipedia.org/wiki/Nick_Holonyak"},{"year":1952,"text":"A footman shot and killed two colleagues and wounded the lady of the house at Knowsley Hall, England.","url":"https://en.wikipedia.org/wiki/Knowsley_Hall_shootings"},{"year":1942,"text":"World War II: American forces defeated the Japanese at the Third Battle of the Matanikau in Guadalcanal, Solomon Islands, reversing the Japanese victory a couple of weeks earlier.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1921,"text":"The Mortimer War Memorial (pictured), designed by Herbert Maryon in memory of local servicemen who died in the First World War, was dedicated.","url":"https://en.wikipedia.org/wiki/Mortimer_War_Memorial"},{"year":1914,"text":"World War I: The civilian authorities of Antwerp surrendered and allowed the German army to capture the city.","url":"https://en.wikipedia.org/wiki/World_War_I"},{"year":1913,"text":"Carrying a cargo hold full of highly flammable chemicals, the ocean liner SS Volturno caught fire in the north Atlantic and sank, resulting in 136 deaths.","url":"https://en.wikipedia.org/wiki/SS_Volturno_(1906)"},{"year":1912,"text":"Following a reduction in pay, textile workers in Little Falls, New York, walked out of their mill, starting a three-month strike.","url":"https://en.wikipedia.org/wiki/Little_Falls%2C_New_York"},{"year":1888,"text":"The Washington Monument in Washington, D.C., at the time the world's tallest building, officially opened to the general public.","url":"https://en.wikipedia.org/wiki/Washington_Monument"},{"year":1874,"text":"The Universal Postal Union, then known as the General Postal Union, was established with the signing of the Treaty of Bern to unify disparate postal services and regulations so that international mail could be exchanged easily.","url":"https://en.wikipedia.org/wiki/Universal_Postal_Union"},{"year":1813,"text":"Late in the Napoleonic Wars, Empress Marie Louise (pictured) issued decrees conscripting tens of thousands of French teenagers, who became known as Marie-Louises.","url":"https://en.wikipedia.org/wiki/Napoleonic_Wars"},{"year":1793,"text":"French Revolution: After a month-long siege, the leaders of Lyon surrendered, ending their revolt against the National Convention.","url":"https://en.wikipedia.org/wiki/French_Revolution"},{"year":1780,"text":"The deadliest Atlantic hurricane on record began to impact the Caribbean, killing at least 20,000 people across the Antilles over the subsequent days.","url":"https://en.wikipedia.org/wiki/Great_Hurricane_of_1780"},{"year":1740,"text":"European soldiers and Javanese collaborators started massacring Chinese Indonesians in the port city of Batavia, modern-day Jakarta: at least 10,000 people were killed.","url":"https://en.wikipedia.org/wiki/1740_Batavia_massacre"},{"year":1708,"text":"Great Northern War: Russia defeated Sweden at the Battle of Lesnaya on the Russian–Polish border, in present-day Belarus.","url":"https://en.wikipedia.org/wiki/Great_Northern_War"},{"year":1676,"text":"Antonie van Leeuwenhoek wrote a letter to the Royal Society describing \"animalcules\" – the first known description of protozoa (pictured).","url":"https://en.wikipedia.org/wiki/Antonie_van_Leeuwenhoek"}],"births":[{"year":2002,"text":"Ben Shelton, American tennis player","url":"https://en.wikipedia.org/wiki/Ben_Shelton"},{"year":2001,"text":"Kyla Leibel, Canadian swimmer","url":"https://en.wikipedia.org/wiki/Kyla_Leibel"},{"year":2000,"text":"Penei Sewell, American football player","url":"https://en.wikipedia.org/wiki/Penei_Sewell"},{"year":1997,"text":"Jharrel Jerome, American actor","url":"https://en.wikipedia.org/wiki/Jharrel_Jerome"},{"year":1997,"text":"Megan Moroney, American singer-songwriter.","url":"https://en.wikipedia.org/wiki/Megan_Moroney"},{"year":1996,"text":"Jacob Batalon, Filipino-American actor","url":"https://en.wikipedia.org/wiki/Jacob_Batalon"},{"year":1996,"text":"Bella Hadid, American model","url":"https://en.wikipedia.org/wiki/Bella_Hadid"},{"year":1994,"text":"Jodelle Ferland, Canadian actress","url":"https://en.wikipedia.org/wiki/Jodelle_Ferland"}],"deaths":[{"year":2026,"text":"Mike Ditka, American football coach and tight end for the Chicago Bears (born 1939)","url":"https://en.wikipedia.org/wiki/Mike_Ditka"},{"year":2024,"text":"George Baldock, British-born Greek international footballer (born 1993)","url":"https://en.wikipedia.org/wiki/George_Baldock"},{"year":2024,"text":"Dieter Burdenski, German footballer (born 1950)","url":"https://en.wikipedia.org/wiki/Dieter_Burdenski"},{"year":2024,"text":"Lily Ebert, Hungarian-born Holocaust survivor (born 1923)","url":"https://en.wikipedia.org/wiki/Lily_Ebert"},{"year":2024,"text":"Lee Wei Ling, Singaporean neurologist (born 1955)","url":"https://en.wikipedia.org/wiki/Lee_Wei_Ling"},{"year":2024,"text":"Clark R. Rasmussen, American politician (born 1934)","url":"https://en.wikipedia.org/wiki/Clark_R._Rasmussen"},{"year":2024,"text":"Leif Segerstam, Finnish conductor and composer (born 1944)","url":"https://en.wikipedia.org/wiki/Leif_Segerstam"},{"year":2024,"text":"Ratan Tata, Indian businessman and philanthropist (born 1937)","url":"https://en.wikipedia.org/wiki/Ratan_Tata"}]},"census":{"panel_version":2,"probed":14770,"error_rate":23.07,"degraded":false,"rates":{"head":13,"tail":5.1,"ai_native":74.55}},"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. 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