{"date":"2025-12-19","schema_version":1,"captured_at":"2026-08-25T23:05:26.732Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":13},"attention":[{"title":"Greg Biffle","views":815802,"rank":3,"extract":"Gregory Jack Biffle, nicknamed \"the Biff\", was an American professional stock car racing driver. He most notably raced from 2002 to 2022 in the NASCAR Cup Series, most notably driving the No. 16 Ford for Roush Fenway Racing from 2002 to 2016 and last competed ","url":"https://en.wikipedia.org/wiki/Greg_Biffle","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Greg_Biffle_2003_%282764335252%29.jpg/330px-Greg_Biffle_2003_%282764335252%29.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Rob Reiner","views":466368,"rank":5,"extract":"Robert Reiner was an American filmmaker, actor, and political activist. 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datacenters","points":722,"comments":182,"url":"https://garagehq.deuxfleurs.fr/","discussion":"https://news.ycombinator.com/item?id=46326984"},{"title":"Amazon will allow ePub and PDF downloads for DRM-free eBooks","points":634,"comments":339,"url":"https://www.kdpcommunity.com/s/article/New-eBook-Download-Options-for-Readers-Coming-in-2026?language=en_US","discussion":"https://news.ycombinator.com/item?id=46324078"},{"title":"GotaTun – Mullvad's WireGuard Implementation in Rust","points":632,"comments":131,"url":"https://mullvad.net/en/blog/announcing-gotatun-the-future-of-wireguard-at-mullvad-vpn","discussion":"https://news.ycombinator.com/item?id=46324543"},{"title":"Noclip.website – A digital museum of video game levels","points":485,"comments":57,"url":"https://noclip.website/","discussion":"https://news.ycombinator.com/item?id=46321619"},{"title":"What makes you 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say","points":192,"comments":267,"url":"https://www.washingtonpost.com/nation/2025/12/18/brown-university-shooting-person-of-interest/","discussion":"https://news.ycombinator.com/item?id=46321947"},{"title":"A better zip bomb (2019)","points":191,"comments":56,"url":"https://www.bamsoftware.com/hacks/zipbomb/","discussion":"https://news.ycombinator.com/item?id=46331216"},{"title":"Show HN: Hacker News, but every headline is hysterical clickbait","points":188,"comments":74,"url":"https://dosaygo-studio.github.io/hn-front-page-2035/news-max.html","discussion":"https://news.ycombinator.com/item?id=46324579"}],"research":[{"title":"Learning-Driven Exploration for Reinforcement Learning","abstract":"Effective and intelligent exploration has been an unresolved problem for reinforcement learning. Most contemporary reinforcement learning relies on simple heuristic strategies such as $ε$-greedy exploration or adding Gaussian noise to actions. These heuristics, however, are unable to intelligently distinguish the well explored and the unexplored regions of state space, which can lead to inefficient use of training ti","authors":["Usama, Muhammad","Chang, Dong Eui"],"url":"https://arxiv.org/abs/1906.06890","arxiv_id":"1906.06890","published":"2025-12-19"},{"title":"Neural networks for dengue forecasting: a systematic review","abstract":"Background: Early forecasts of dengue are an important tool for disease mitigation. Neural networks are powerful predictive models that have made contributions to many areas of public health. In this study, we reviewed the application of neural networks in the dengue forecasting literature, with the objective of informing model design for future work. Methods: Following PRISMA guidelines, we conducted a systematic se","authors":["Lober, Luiza","Rodrigues, Francisco A.","Roster, Kirstin"],"url":"https://arxiv.org/abs/2106.12905","arxiv_id":"2106.12905","published":"2025-12-19"},{"title":"Optimization with Access to Auxiliary Information","abstract":"We investigate the fundamental optimization question of minimizing a target function $f$, whose gradients are expensive to compute or have limited availability, given access to some auxiliary side function $h$ whose gradients are cheap or more available. This formulation captures many settings of practical relevance, such as i) re-using batches in SGD, ii) transfer learning, iii) federated learning, iv) training with","authors":["Chayti, El Mahdi","Karimireddy, Sai Praneeth"],"url":"https://arxiv.org/abs/2206.00395","arxiv_id":"2206.00395","published":"2025-12-19"},{"title":"Low-Resolution Action Recognition for Tiny Actions Challenge","abstract":"Tiny Actions Challenge focuses on understanding human activities in real-world surveillance. Basically, there are two main difficulties for activity recognition in this scenario. First, human activities are often recorded at a distance, and appear in a small resolution without much discriminative clue. Second, these activities are naturally distributed in a long-tailed way. It is hard to alleviate data bias for such ","authors":["Chen, Boyu","Qiao, Yu","Wang, Yali"],"url":"https://arxiv.org/abs/2209.14711","arxiv_id":"2209.14711","published":"2025-12-19"},{"title":"The Emergence of Chunking Structures with Hierarchical RNN","abstract":"In Natural Language Processing (NLP), predicting linguistic structures, such as parsing and chunking, has mostly relied on manual annotations of syntactic structures. This paper introduces an unsupervised approach to chunking, a syntactic task that involves grouping words in a non-hierarchical manner. We present a Hierarchical Recurrent Neural Network (HRNN) designed to model word-to-chunk and chunk-to-sentence compo","authors":["Wu, Zijun","Deshmukh, Anup Anand","Wu, Yongkang","Lin, Jimmy","Mou, Lili"],"url":"https://arxiv.org/abs/2309.04919","arxiv_id":"2309.04919","published":"2025-12-19"},{"title":"Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning","abstract":"Exploring exoplanets has transformed our understanding of the universe by revealing many planetary systems that defy our current understanding. To study their atmospheres, spectroscopic observations are used to infer essential atmospheric properties that are not directly measurable. Estimating atmospheric parameters that best fit the observed spectrum within a specified atmospheric model is a complex problem that is ","authors":["Giobergia, Flavio","Koudounas, Alkis","Baralis, Elena"],"url":"https://arxiv.org/abs/2310.01227","arxiv_id":"2310.01227","published":"2025-12-19"},{"title":"Percept, Chat, and then Adapt: Multimodal Knowledge Transfer of Foundation Models for Open-World Video Recognition","abstract":"Open-world video recognition is challenging since traditional networks are not generalized well on complex environment variations. Alternatively, foundation models with rich knowledge have recently shown their generalization power. However, how to apply such knowledge has not been fully explored for open-world video recognition. To this end, we propose a generic knowledge transfer pipeline, which progressively exploi","authors":["Chen, Boyu","Chen, Siran","Li, Kunchang","Xu, Qinglin","Qiao, Yu","Wang, Yali"],"url":"https://arxiv.org/abs/2402.18951","arxiv_id":"2402.18951","published":"2025-12-19"},{"title":"MALTO at SemEval-2024 Task 6: Leveraging Synthetic Data for LLM Hallucination Detection","abstract":"In Natural Language Generation (NLG), contemporary Large Language Models (LLMs) face several challenges, such as generating fluent yet inaccurate outputs and reliance on fluency-centric metrics. This often leads to neural networks exhibiting "hallucinations". The SHROOM challenge focuses on automatically identifying these hallucinations in the generated text. To tackle these issues, we introduce two key compo","authors":["Borra, Federico","Savelli, Claudio","Rosso, Giacomo","Koudounas, Alkis","Giobergia, Flavio"],"url":"https://arxiv.org/abs/2403.00964","arxiv_id":"2403.00964","published":"2025-12-19"},{"title":"Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review","abstract":"The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable. In particular, the challenge of identifying thyroid cancer (TC) has seen progress with the use of machine learning (ML) and big data analysis, incorporating Transformers to evaluate TC prognosis and determine the risk of malignancy in individuals. This review art","authors":["Habchi, Yassine","Kheddar, Hamza","Himeur, Yassine","Ghanem, Mohamed Chahine"],"url":"https://arxiv.org/abs/2403.13843","arxiv_id":"2403.13843","published":"2025-12-19"},{"title":"Houston we have a Divergence: A Subgroup Performance Analysis of ASR Models","abstract":"The Fearless Steps APOLLO Community Resource provides unparalleled opportunities to explore the potential of multi-speaker team communications from NASA Apollo missions. This study focuses on discovering the characteristics that make Apollo recordings more or less intelligible to Automatic Speech Recognition (ASR) methods. We extract, for each audio recording, interpretable metadata on recordings (signal-to-noise rat","authors":["Koudounas, Alkis","Giobergia, Flavio"],"url":"https://arxiv.org/abs/2404.07226","arxiv_id":"2404.07226","published":"2025-12-19"},{"title":"Four-hour thunderstorm nowcasting using a deep diffusion model of satellite data","abstract":"Convection (thunderstorm) develops rapidly within hours and is highly destructive, posing a significant challenge for nowcasting and resulting in substantial losses to infrastructure and society. After the emergence of artificial intelligence (AI)-based methods, convection nowcasting has experienced rapid advancements, with its performance surpassing that of physics-based numerical weather prediction and other conven","authors":["Dai, Kuai","Li, Xutao","Fang, Junying","Ye, Yunming","Yu, Demin","Su, Hui"],"url":"https://arxiv.org/abs/2404.10512","arxiv_id":"2404.10512","published":"2025-12-19"},{"title":"Online Bandits with (Biased) Offline Data: Adaptive Learning under Distribution Mismatch","abstract":"Traditional online learning models are typically initialized from scratch. By contrast, contemporary real-world applications often have access to historical datasets that can potentially enhanced the online learning processes. We study how offline data can be leveraged to facilitate online learning in stochastic multi-armed bandits and combinatorial bandits. In our study, the probability distributions that govern the","authors":["Cheung, Wang Chi","Lyu, Lixing"],"url":"https://arxiv.org/abs/2405.02594","arxiv_id":"2405.02594","published":"2025-12-19"},{"title":"A Sparse Tensor Generator with Efficient Feature Extraction","abstract":"Sparse tensor operations are increasingly important in diverse applications such as social networks, deep learning, diagnosis, crime, and review analysis. However, a major obstacle in sparse tensor research is the lack of large-scale sparse tensor datasets. Another challenge lies in analyzing sparse tensor features, which are essential not only for understanding the nonzero pattern but also for selecting the most sui","authors":["Torun, Tugba","Taweel, Ameer","Unat, Didem"],"url":"https://arxiv.org/abs/2405.04944","arxiv_id":"2405.04944","published":"2025-12-19"},{"title":"Sparse-Tuning: Adapting Vision Transformers with Efficient Fine-tuning and Inference","abstract":"Parameter-efficient fine-tuning (PEFT) has emerged as a popular solution for adapting pre-trained Vision Transformer (ViT) models to downstream applications by updating only a small subset of parameters. While current PEFT methods have achieved fine-tuning efficiency, they overlook the efficiency of computation and GPU memory during inference, falling short of practical requirements. To address this limitation, we pr","authors":["Liu, Ting","Liu, Xuyang","Shi, Liangtao","Xu, Zunnan","Hu, Yue","Huang, Siteng"],"url":"https://arxiv.org/abs/2405.14700","arxiv_id":"2405.14700","published":"2025-12-19"},{"title":"Models That Prove Their Own Correctness","abstract":"How can we trust the correctness of a learned model on a particular input of interest? Model accuracy is typically measured on average over a distribution of inputs, giving no guarantee for any fixed input. This paper proposes a theoretically-founded solution to this problem: to train Self-Proving models that prove the correctness of their output to a verification algorithm $V$ via an Interactive Proof. Self-Proving ","authors":["Amit, Noga","Goldwasser, Shafi","Paradise, Orr","Rothblum, Guy"],"url":"https://arxiv.org/abs/2405.15722","arxiv_id":"2405.15722","published":"2025-12-19"},{"title":"PILA: Physics-Informed Low Rank Augmentation for Interpretable Earth Observation","abstract":"Physically meaningful representations are essential for Earth Observation (EO), yet existing physical models are often simplified and incomplete. This leads to discrepancies between simulation and observations that hinder reliable forward model inversion. Common approaches to EO inversion either ignored this incompleteness or relied on case-specific preprocessing. More recent methods use physics-informed autoencoders","authors":["She, Yihang","Blake, Andrew","Atzberger, Clement","Gualandi, Adriano","Keshav, Srinivasan"],"url":"https://arxiv.org/abs/2405.18953","arxiv_id":"2405.18953","published":"2025-12-19"},{"title":"MMRel: Benchmarking Relation Understanding in Multi-Modal Large Language Models","abstract":"Though Multi-modal Large Language Models (MLLMs) have recently achieved significant progress, they often struggle to understand diverse and complicated inter-object relations. Specifically, the lack of large-scale and high-quality relation data has greatly hindered the progress of MLLMs in various vision-language perception tasks. We attempt to address this challenge by contributing the Multi-Modal Relation Understan","authors":["Nie, Jiahao","Zhang, Gongjie","An, Wenbin","Xing, Yun","Tan, Yap-Peng","Kot, Alex C."],"url":"https://arxiv.org/abs/2406.09121","arxiv_id":"2406.09121","published":"2025-12-19"},{"title":"Bandits with Preference Feedback: A Stackelberg Game Perspective","abstract":"Bandits with preference feedback present a powerful tool for optimizing unknown target functions when only pairwise comparisons are allowed instead of direct value queries. This model allows for incorporating human feedback into online inference and optimization and has been employed in systems for fine-tuning large language models. The problem is well understood in simplified settings with linear target functions or","authors":["Pásztor, Barna","Kassraie, Parnian","Krause, Andreas"],"url":"https://arxiv.org/abs/2406.16745","arxiv_id":"2406.16745","published":"2025-12-19"},{"title":"InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios","abstract":"Perception systems of autonomous vehicles are susceptible to occlusion, especially when examined from a vehicle-centric perspective. Such occlusion can lead to overlooked object detections, e.g., larger vehicles such as trucks or buses may create blind spots where cyclists or pedestrians could be obscured, accentuating the safety concerns associated with such perception system limitations. To mitigate these challenge","authors":["Zhang, Xiaofei","Li, Yining","Wang, Jinping","Qin, Xiangyi","Shen, Ying","Fan, Zhengping"],"url":"https://arxiv.org/abs/2407.21581","arxiv_id":"2407.21581","published":"2025-12-19"},{"title":"DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs","abstract":"Dynamic graph modeling aims to uncover evolutionary patterns in real-world systems, enabling accurate social recommendation and early detection of cancer cells. Inspired by the success of recent state space models in efficiently capturing long-term dependencies, we propose DyG-Mamba by translating dynamic graph modeling into a long-term sequence modeling problem. Specifically, inspired by Ebbinghaus' forgetting c","authors":["Li, Dongyuan","Tan, Shiyin","Zhang, Ying","Jin, Ming","Pan, Shirui","Okumura, Manabu"],"url":"https://arxiv.org/abs/2408.06966","arxiv_id":"2408.06966","published":"2025-12-19"},{"title":"Unsupervised discovery of the shared and private geometry in multi-view data","abstract":"Studying complex real-world phenomena often involves data from multiple views (e.g. sensor modalities or brain regions), each capturing different aspects of the underlying system. Within neuroscience, there is growing interest in large-scale simultaneous recordings across multiple brain regions. Understanding the relationship between views (e.g., the neural activity in each region recorded) can reveal fundamental ins","authors":["Koukuntla, Sai","Julian, Joshua B.","Kaminsky, Jesse C.","Schottdorf, Manuel","Tank, David W.","Brody, Carlos D."],"url":"https://arxiv.org/abs/2408.12091","arxiv_id":"2408.12091","published":"2025-12-19"},{"title":"Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review","abstract":"Recent technological advancements in multimodal machine learning--including the rise of large language models (LLMs)--have improved our ability to collect, process, and analyze diverse multimodal data such as speech, video, and eye gaze in learning and training contexts. While prior reviews have addressed individual components of the multimodal pipeline (e.g., conceptual models, data fusion), a comprehensive review o","authors":["Cohn, Clayton","Davalos, Eduardo","Vatral, Caleb","Fonteles, Joyce Horn","Wang, Hanchen David","Coursey, Austin"],"url":"https://arxiv.org/abs/2408.14491","arxiv_id":"2408.14491","published":"2025-12-19"},{"title":"WildFit: Autonomous In-situ Model Adaptation for Resource-Constrained IoT Systems","abstract":"Resource-constrained IoT devices increasingly rely on deep learning models, however, these models experience significant accuracy drops due to domain shifts when encountering variations in lighting, weather, and seasonal conditions. While cloud-based retraining can address this issue, many IoT deployments operate with limited connectivity and energy constraints, making traditional fine-tuning approaches impractical. ","authors":["Rastikerdar, Mohammad Mehdi","Huang, Jin","Guan, Hui","Ganesan, Deepak"],"url":"https://arxiv.org/abs/2409.07796","arxiv_id":"2409.07796","published":"2025-12-19"},{"title":"Enhancing Long-term RAG Chatbots with Psychological Models of Memory Importance and Forgetting","abstract":"While Retrieval-Augmented Generation (RAG) has shown promise in enhancing long-term conversations, the increasing memory load as conversations progress degrades retrieval accuracy. Drawing on psychological insights, we propose LUFY, a simple yet effective method that focuses on emotionally arousing memories and retains less than 10% of the conversation. In the user experiment, participants interacted with three types","authors":["Sumida, Ryuichi","Inoue, Koji","Kawahara, Tatsuya"],"url":"https://arxiv.org/abs/2409.12524","arxiv_id":"2409.12524","published":"2025-12-19"},{"title":"Matérn Kernels for Tunable Implicit Surface Reconstruction","abstract":"We propose to use the family of Matérn kernels for implicit surface reconstruction, building upon the recent success of kernel methods for 3D reconstruction of oriented point clouds. As we show from a theoretical and practical perspective, Matérn kernels have some appealing properties which make them particularly well suited for surface reconstruction -- outperforming state-of-the-art methods based on the arc-cosine ","authors":["Weiherer, Maximilian","Egger, Bernhard"],"url":"https://arxiv.org/abs/2409.15466","arxiv_id":"2409.15466","published":"2025-12-19"}],"history":{"events":[{"year":2016,"text":"Andrei Karlov (pictured), Russia's ambassador to Turkey, was assassinated at an art gallery in Ankara.","url":"https://en.wikipedia.org/wiki/Andrei_Karlov"},{"year":2013,"text":"The European Space Agency's spacecraft Gaia was launched with the goal of constructing the largest and most precise star catalogue ever made.","url":"https://en.wikipedia.org/wiki/European_Space_Agency"},{"year":1997,"text":"SilkAir Flight 185 crashed into the Musi River in Indonesia, killing 104 people.","url":"https://en.wikipedia.org/wiki/SilkAir_Flight_185"},{"year":1997,"text":"Titanic, the fourth-highest-grossing film of all time with a worldwide total of more than US$2.2 billion, was released in the United States.","url":"https://en.wikipedia.org/wiki/Titanic_(1997_film)"},{"year":1985,"text":"Aeroflot Flight 101/435 was hijacked by its co-pilot; it landed in a cow pasture in China, where he was apprehended.","url":"https://en.wikipedia.org/wiki/Aeroflot_Flight_101%2F435"},{"year":1984,"text":"China and the United Kingdom signed the Sino-British Joint Declaration, agreeing to the transfer of sovereignty of Hong Kong to China on 1 July 1997.","url":"https://en.wikipedia.org/wiki/Sino-British_Joint_Declaration"},{"year":1983,"text":"The Jules Rimet Trophy, awarded to the winner of the FIFA World Cup, was stolen from the offices of the Brazilian Football Confederation.","url":"https://en.wikipedia.org/wiki/FIFA_World_Cup_Trophy"},{"year":1964,"text":"The ruling junta of South Vietnam, led by Nguyễn Khánh, initiated a coup, dissolving the High National Council, a civilian advisory body.","url":"https://en.wikipedia.org/wiki/South_Vietnam"},{"year":1956,"text":"British physician and suspected serial killer John Bodkin Adams was arrested in connection with the death of Edith Alice Morrell.","url":"https://en.wikipedia.org/wiki/John_Bodkin_Adams"},{"year":1843,"text":"A Christmas Carol (illustration pictured), a novella by Charles Dickens about the miser Ebenezer Scrooge and his transformation after being visited by ghosts, was published.","url":"https://en.wikipedia.org/wiki/A_Christmas_Carol"},{"year":1828,"text":"Nullification crisis: American vice president John C. Calhoun's South Carolina Exposition and Protest, written to protest the Tariff of Abominations, was presented to the South Carolina House of Representatives.","url":"https://en.wikipedia.org/wiki/Nullification_crisis"},{"year":1776,"text":"Thomas Paine published the first in a series of pamphlets entitled The American Crisis, opening with the line: \"These are the times that try men's souls.\"","url":"https://en.wikipedia.org/wiki/Thomas_Paine"},{"year":1154,"text":"Henry II (depicted) was crowned king of England in Westminster Abbey, London.","url":"https://en.wikipedia.org/wiki/Henry_II_of_England"}],"births":[{"year":1998,"text":"King Princess, American singer-songwriter and musician","url":"https://en.wikipedia.org/wiki/King_Princess"},{"year":1997,"text":"Gabriel Magalhães, Brazilian footballer","url":"https://en.wikipedia.org/wiki/Gabriel_Magalh%C3%A3es"},{"year":1997,"text":"Fikayo Tomori, English footballer","url":"https://en.wikipedia.org/wiki/Fikayo_Tomori"},{"year":1996,"text":"Franck Kessié, Ivorian footballer","url":"https://en.wikipedia.org/wiki/Franck_Kessi%C3%A9"},{"year":1994,"text":"Maudy Ayunda, Indonesian actress and singer-songwriter","url":"https://en.wikipedia.org/wiki/Maudy_Ayunda"},{"year":1994,"text":"M'Baye Niang, French footballer","url":"https://en.wikipedia.org/wiki/M'Baye_Niang"},{"year":1993,"text":"Young K, South Korean singer-songwriter","url":"https://en.wikipedia.org/wiki/Young_K"},{"year":1993,"text":"Isiah Koech, Kenyan runner","url":"https://en.wikipedia.org/wiki/Isiah_Koech"}],"deaths":[{"year":2024,"text":"Michael Leunig, Australian cartoonist (born 1945)","url":"https://en.wikipedia.org/wiki/Michael_Leunig"},{"year":2024,"text":"Wincey Willis, British broadcaster (born 1948)","url":"https://en.wikipedia.org/wiki/Wincey_Willis"},{"year":2021,"text":"Sally Ann Howes, English-American singer and actress (born 1930)","url":"https://en.wikipedia.org/wiki/Sally_Ann_Howes"},{"year":2021,"text":"Johnny Isakson, American politician (born 1944)","url":"https://en.wikipedia.org/wiki/Johnny_Isakson"},{"year":2020,"text":"Rosalind Knight, English actress (born 1933)","url":"https://en.wikipedia.org/wiki/Rosalind_Knight"},{"year":2016,"text":"Andrei Karlov, Russian diplomat, Ambassador to Turkey (born 1954)","url":"https://en.wikipedia.org/wiki/Andrei_Karlov"},{"year":2015,"text":"Jimmy Hill, English footballer, manager, and sportscaster (born 1928)","url":"https://en.wikipedia.org/wiki/Jimmy_Hill"},{"year":2015,"text":"Greville Janner, Baron Janner of Braunstone, Welsh-English lawyer and politician (born 1928)","url":"https://en.wikipedia.org/wiki/Greville_Janner"}]},"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."}