{"date":"2026-07-02","schema_version":1,"captured_at":"2026-08-25T23:14:26.499Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":15},"attention":[{"title":"2026 FIFA World Cup","views":807303,"rank":3,"extract":"The 2026 FIFA World Cup was the 23rd FIFA World Cup, the quadrennial international men's soccer championship contested by the national teams of the member associations of FIFA. 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promise","points":347,"comments":487,"url":"https://news.ubc.ca/2026/06/mrna-vaccines-are-safe-effective-and-full-of-promise/","discussion":"https://news.ycombinator.com/item?id=48754963"},{"title":"Zuckerberg says AI agent development going slower than expected","points":342,"comments":616,"url":"https://www.reuters.com/business/zuckerberg-says-ai-agent-development-going-slower-than-expected-2026-07-02/","discussion":"https://news.ycombinator.com/item?id=48767058"},{"title":"Why I'm Forced to Say Farewell: Google Management Has Lost Its Moral Compass","points":335,"comments":253,"url":"https://docs.google.com/document/d/1SH9QRTAlL02THgAN2AGmWe9El0_2ZJF6hhgDBx8k97c/edit?tab=t.0","discussion":"https://news.ycombinator.com/item?id=48758970"},{"title":"Exapunks (2018)","points":333,"comments":123,"url":"https://www.zachtronics.com/exapunks/","discussion":"https://news.ycombinator.com/item?id=48765663"},{"title":"Google loses fight over record $4.7B EU antitrust fine","points":301,"comments":234,"url":"https://www.cnbc.com/2026/07/02/alphabet-google-android-eu-antitrust-fine-4-1-billion-euro-appeal.html","discussion":"https://news.ycombinator.com/item?id=48758309"},{"title":"The fall of the theorem economy","points":285,"comments":122,"url":"https://davidbessis.substack.com/p/the-fall-of-the-theorem-economy","discussion":"https://news.ycombinator.com/item?id=48758048"},{"title":"Introduction to Genomics for Engineers","points":273,"comments":43,"url":"https://learngenomics.dev/docs/biological-foundations/cells-genomes-dna-chromosomes/","discussion":"https://news.ycombinator.com/item?id=48760424"}],"research":[{"title":"Leveraging High-Level Synthesis and Large Language Models to Generate, Simulate, and Deploy a Uniform Random Number Generator Hardware Design","abstract":"We present a new high-level synthesis methodology for using large language model tools to generate hardware designs. The methodology uses exclusively open-source tools excluding the large language model. As a case study, we use our methodology to generate a permuted congruential random number generator design with a wishbone interface. We verify the functionality and quality of the random number generator design usin","authors":["Meech, James T."],"url":"https://arxiv.org/abs/2311.03489","arxiv_id":"2311.03489","published":"2026-07-02"},{"title":"Semi-Bandit Learning for Monotone Stochastic Optimization","abstract":"Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area. However, a significant limitation of this approach is that it requires full knowledge of the underlying probability distributions. Can we still get good (approximation)","authors":["Agarwal, Arpit","Ghuge, Rohan","Nagarajan, Viswanath","Zhuo, Zhengjia"],"url":"https://arxiv.org/abs/2312.15427","arxiv_id":"2312.15427","published":"2026-07-02"},{"title":"DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis","abstract":"Real-world time series analysis faces significant challenges when dealing with irregular and incomplete data. While Neural Differential Equation (NDE) based methods have shown promise, they struggle with limited expressiveness, scalability issues, and stability concerns. Conversely, Neural Flows offer stability but falter with irregular data. We introduce 'DualDynamics', a novel framework that synergistically","authors":["Oh, YongKyung","Lim, Dong-Young","Kim, Sungil"],"url":"https://arxiv.org/abs/2401.04979","arxiv_id":"2401.04979","published":"2026-07-02"},{"title":"Prediction of Cellular Identities from Trajectory and Cell Fate Information","abstract":"Determining cell identities in imaging sequences is an important yet challenging task. The conventional method for cell identification is via cell tracking, which is complex and can be time-consuming. In this study, we propose an innovative approach to cell identification during early $\\textit{C. elegans}$ embryogenesis using machine learning. Cell identification during $\\textit{C. elegans}$ embryogenesis would provi","authors":["Dai, Baiyang","Yang, Jiamin","Shroff, Hari","La Riviere, Patrick"],"url":"https://arxiv.org/abs/2401.06182","arxiv_id":"2401.06182","published":"2026-07-02"},{"title":"Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data","abstract":"Irregular sampling intervals and missing values in real-world time series data present challenges for conventional methods that assume consistent intervals and complete data. Neural Ordinary Differential Equations (Neural ODEs) offer an alternative approach, utilizing neural networks combined with ODE solvers to learn continuous latent representations through parameterized vector fields. Neural Stochastic Differentia","authors":["Oh, YongKyung","Lim, Dong-Young","Kim, Sungil"],"url":"https://arxiv.org/abs/2402.14989","arxiv_id":"2402.14989","published":"2026-07-02"},{"title":"Learn Once, Edit Anywhere: Visual Direction Transfer for Diffusion Models","abstract":"The rapid advancement of diffusion models has enabled the generation of high-fidelity images from textual prompts, yet achieving precise, disentangled control over specific attributes remains a significant challenge. A fundamental limitation arises because visual differences between images are often far more descriptive and nuanced than what can be captured through human-crafted text descriptions, which frequently fa","authors":["Dalva, Yusuf","Yesiltepe, Hidir","Yanardag, Pinar"],"url":"https://arxiv.org/abs/2403.19645","arxiv_id":"2403.19645","published":"2026-07-02"},{"title":"Neural Dynamic Data Valuation via Stochastic State-Adjoint Trajectories","abstract":"Classical data valuation defines a data point's value through the finite marginal contribution $U(C\\cup\\{i\\})-U(C)$, but estimating this quantity over coalitions requires repeated training and does not describe the contribution made along a stochastic training path. We ask whether marginal contributions of data points can be estimated from one coupled trajectory while retaining a verifiable relation to coalition-","authors":["Liang, Zhangyong","Zhang, Ji","Gao, Huanhuan"],"url":"https://arxiv.org/abs/2404.19557","arxiv_id":"2404.19557","published":"2026-07-02"},{"title":"Generated Contents Enrichment","abstract":"We study Generated Contents Enrichment (GCE), a conditional image-generation task in which a sparse scene description is first enriched through an explicit scene representation and then rendered into semantically richer visual content. Conventional image-generation systems can produce visually realistic outputs from limited scene descriptions, but the added content is usually implicit in the generator rather than rep","authors":["Naseri, Mahdi","Qiu, Jiayan","Wang, Zhou"],"url":"https://arxiv.org/abs/2405.03650","arxiv_id":"2405.03650","published":"2026-07-02"},{"title":"Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique","abstract":"Growing concerns over the theft and misuse of Large Language Models (LLMs) underscore the need for effective fingerprinting to link a model to its original version and detect misuse. We define five essential properties for a successful fingerprint: Transparency, Efficiency, Persistence, Robustness, and Unforgeability. We present a novel fingerprinting framework that provides verifiable proof of ownership while preser","authors":["Russinovich, Mark","Cai, Yanan","Salem, Ahmed"],"url":"https://arxiv.org/abs/2407.10887","arxiv_id":"2407.10887","published":"2026-07-02"},{"title":"Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms","abstract":"Industry is moving toward autonomous, network-connected machines that detect and adapt to changing conditions, including hardware faults. Conventional fault-tolerant design duplicates hardware and reroutes control logic; reinforcement learning (RL) offers a learning-based alternative. This paper presents the first systematic comparison of two RL algorithms -- Proximal Policy Optimization (PPO) and Soft Actor-Critic (","authors":["Schoepp, Sheila","Taghian, Mehran","Miwa, Shotaro","Mitsuka, Yoshihiro","Golestan, Shadan","Zaïane, Osmar"],"url":"https://arxiv.org/abs/2407.15283","arxiv_id":"2407.15283","published":"2026-07-02"},{"title":"Path Following and Stabilisation of a Bicycle Model using a Reinforcement Learning Approach","abstract":"Over the years, complex control approaches have been developed to control the motion of a bicycle. Reinforcement Learning (RL), a branch of machine learning, promises easy deployment of so-called agents. Deployed agents are increasingly considered as an alternative to controllers for mechanical systems. The present work introduces an RL approach to do path following with a virtual bicycle model while simultaneously s","authors":["Weyrer, Sebastian","Manzl, Peter","Schwab, A. L.","Gerstmayr, Johannes"],"url":"https://arxiv.org/abs/2407.17156","arxiv_id":"2407.17156","published":"2026-07-02"},{"title":"Unexplainability of Artificial Intelligence Judgments and Functional Implementation in Kant's Perspective","abstract":"Kant's Critique of Pure Reason, a major contribution to the history of epistemology, proposes a table of categories to elucidate the structure of the a priori principles underlying human judgment. Artificial intelligence (AI) technology claims to simulate or replicate human judgment. To evaluate this claim, it is necessary to examine whether AI judgments exhibit the essential characteristics of human judgment. Th","authors":["Seo, Jongwoo"],"url":"https://arxiv.org/abs/2407.18950","arxiv_id":"2407.18950","published":"2026-07-02"},{"title":"Novel adaptation of video segmentation to 3D MRI: efficient zero-shot knee segmentation with SAM2","abstract":"Intelligent medical image segmentation methods are rapidly evolving and being increasingly applied, yet they face the challenge of domain transfer, where algorithm performance degrades due to different data distributions between source and target domains. To address this, we introduce a method for zero-shot, single-prompt segmentation of 3D knee MRI by adapting Segment Anything Model 2 (SAM2), a general-purpose segme","authors":["Yu, Andrew Seohwan","Hariri, Mohsen","Zhang, Xuecen","Yang, Mingrui","Chaudhary, Vipin","Li, Xiaojuan"],"url":"https://arxiv.org/abs/2408.04762","arxiv_id":"2408.04762","published":"2026-07-02"},{"title":"2DGH: 2D Gaussian-Hermite Splatting for High-quality Rendering and Better Geometry Features","abstract":"2D Gaussian Splatting has recently emerged as a significant method in 3D reconstruction, enabling novel view synthesis and geometry reconstruction simultaneously. While the well-known Gaussian kernel is broadly used, its lack of anisotropy and deformation ability leads to dim and vague edges at object silhouettes, limiting the reconstruction quality of current Gaussian splatting methods. To enhance the representation","authors":["Yu, Ruihan","Huang, Tianyu","Ling, Jingwang","Xu, Feng"],"url":"https://arxiv.org/abs/2408.16982","arxiv_id":"2408.16982","published":"2026-07-02"},{"title":"From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems","abstract":"To effectively address potential harms from Artificial Intelligence (AI) systems, it is essential to identify and mitigate system-level hazards. Current analysis approaches focus on individual components of an AI system, like training data or models, in isolation, overlooking hazards from component interactions or how they are situated within a company's development process. To this end, we draw from the establis","authors":["Rismani, Shalaleh","Dobbe, Roel","Moon, AJung"],"url":"https://arxiv.org/abs/2410.22526","arxiv_id":"2410.22526","published":"2026-07-02"},{"title":"Continuous Speculative Decoding for Autoregressive Image Generation","abstract":"Continuous visual autoregressive (AR) models have demonstrated promising performance in image generation, but their inherently sequential nature results in slow inference speed. Speculative decoding, a successful acceleration technique for large language models (LLMs), has effectively accelerated discrete visual AR models. However, the absence of an analogous theory for continuous distributions precludes its use in a","authors":["Wang, Zili","Zhang, Zheng","Ding, Kun","Yang, Qi","Li, Fei","Xiang, Shiming"],"url":"https://arxiv.org/abs/2411.11925","arxiv_id":"2411.11925","published":"2026-07-02"},{"title":"Histopathology Multi-modal Embedding for Pathology Composed Retrieval","abstract":"To overcome the black-box nature of predictive AI and the hallucination risks of generative models, retrieval-based models offer an interpretable, evidence-based paradigm for pathology clinical workflow. However, real-world clinical queries are inherently interleaved (e.g., pathology images and text). Current dual-encoders suffer from an \\textbf{Architectural Mismatch}, lacking the mechanism to fuse such composed que","authors":["Zhou, Qifeng","Zhong, Wenliang","Dang, Thao M.","Ma, Hehuan","Na, Saiyang","Guo, Yuzhi"],"url":"https://arxiv.org/abs/2502.07221","arxiv_id":"2502.07221","published":"2026-07-02"},{"title":"Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage Planning","abstract":"Reinforcement learning (RL) has successfully solved various deterministic and stochastic planning problems. However, conventional RL struggles with complex real-world constraints, particularly when feasibility is explicit and depends on the current state or trajectory. In this work, we address stochastic sequential decision-making with state-dependent constraints through a real-world case study of the master stowage ","authors":["van Twiller, Jaike","Adulyasak, Yossiri","Delage, Erick","Grbic, Djordje","Jensen, Rune Møller"],"url":"https://arxiv.org/abs/2502.12756","arxiv_id":"2502.12756","published":"2026-07-02"},{"title":"Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes","abstract":"Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels and avoid dysglycemia, including hyperglycemia and hypoglycemia. Despite advances in automated insulin delivery (AID) systems, achieving optimal glycemic control remains challenging. These systems integrate data from wearable devices such as insulin pumps and continuous glucose monitors (CGMs), helpi","authors":["Khamesian, Saman","Arefeen, Asiful","Grando, Maria Adela","Thompson, Bithika M.","Ghasemzadeh, Hassan"],"url":"https://arxiv.org/abs/2502.14183","arxiv_id":"2502.14183","published":"2026-07-02"},{"title":"ForAug: Mitigating Biases in Image Classification via Controlled Image Compositions","abstract":"Large-scale image classification datasets exhibit strong compositional biases: objects tend to be centered, appear at characteristic scales, and co-occur with class-specific context. By exploiting such biases, models attain high in-distribution accuracy but remain fragile under distribution shifts. To address this issue, we introduce ForAug, a controlled composition augmentation scheme that factorizes each training i","authors":["Nauen, Tobias Christian","Moser, Brian","Raue, Federico","Frolov, Stanislav","Dengel, Andreas"],"url":"https://arxiv.org/abs/2503.09399","arxiv_id":"2503.09399","published":"2026-07-02"},{"title":"Verbosity Tradeoffs and the Impact of Scale on the Faithfulness of LLM Self-Explanations","abstract":"When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans. But are these explanations faithful, i.e. do they convey the factors actually responsible for the decision? In this work, we analyse counterfactual faithfulness across 75 models from 13 families. We analyze the tradeoff between conciseness and comprehensiveness, how correlational faithfulness metrics assess this t","authors":["Siegel, Noah Y.","Heess, Nicolas","Perez-Ortiz, Maria","Camburu, Oana-Maria"],"url":"https://arxiv.org/abs/2503.13445","arxiv_id":"2503.13445","published":"2026-07-02"},{"title":"Optimization on the Oblique Manifold for Sparse Simplex Constraints via Multiplicative Updates","abstract":"Low-rank optimization problems with sparse simplex constraints involve variables that must satisfy nonnegativity, sparsity, and sum-to-1 conditions, making their optimization particularly challenging due to the interplay between low-rank structures and constraints. These problems arise in various applications, including machine learning, signal processing, environmental fields, and computational biology. In this work","authors":["Esposito, Flavia","Ang, Andersen"],"url":"https://arxiv.org/abs/2503.24075","arxiv_id":"2503.24075","published":"2026-07-02"},{"title":"PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks","abstract":"Proteins move and deform to ensure their biological functions. Despite significant progress in protein structure prediction, approximating conformational ensembles at physiological conditions remains a fundamental open problem. This paper presents a novel perspective on the problem by directly targeting continuous compact representations of protein motions inferred from sparse experimental observations. We develop a ","authors":["Lombard, Valentin","Van, Julien Nguyen","Grudinin, Sergei","Laine, Elodie"],"url":"https://arxiv.org/abs/2504.02839","arxiv_id":"2504.02839","published":"2026-07-02"},{"title":"Explainability in mulimodal deep transformation models for stroke outcome prediction","abstract":"Multimodal prediction models based on imaging and clinical data are increasingly used for clinical decision support, yet their interpretability remains limited. We present multimodal Deep Transformation Models (DTMs) combining statistical approaches and neural networks to achieve strong predictive performance while preserving interpretability for tabular data. A key contribution of this work is the adaption of the xA","authors":["Herzog, Lisa","Brändli, Jonas","Schneeberger, Maurice","Avci, Loran","Dari, Nordin","Hänsel, Martin"],"url":"https://arxiv.org/abs/2504.06299","arxiv_id":"2504.06299","published":"2026-07-02"},{"title":"Towards Weaker Variance Assumptions for Stochastic Optimization","abstract":"We revisit a classical assumption for analyzing stochastic gradient algorithms where the squared norm of the stochastic subgradient (or the variance for smooth problems) is allowed to grow as fast as the squared norm of the optimization variable. We contextualize this assumption in view of its inception in the 1960s, its seemingly independent appearance in the recent literature, its relationship to weakest-known vari","authors":["Alacaoglu, Ahmet","Malitsky, Yura","Wright, Stephen J."],"url":"https://arxiv.org/abs/2504.09951","arxiv_id":"2504.09951","published":"2026-07-02"}],"history":{"events":[{"year":2020,"text":"A landslide at a jade mine in Hpakant killed 175–200 miners, the deadliest mining accident in Burmese history.","url":"https://en.wikipedia.org/wiki/2020_Hpakant_jade_mine_disaster"},{"year":2013,"text":"In the Indonesian province of Aceh on the northern end of Sumatra, a Mw 6.1 strike-slip earthquake killed at least 35 people and injured 276 others.","url":"https://en.wikipedia.org/wiki/Aceh"},{"year":2013,"text":"The International Astronomical Union announced that the fourth and fifth moons of Pluto to be discovered would be named Kerberos and Styx, respectively.","url":"https://en.wikipedia.org/wiki/International_Astronomical_Union"},{"year":1998,"text":"Harry Potter and the Chamber of Secrets by J. K. Rowling, the second novel of the Harry Potter series, was published.","url":"https://en.wikipedia.org/wiki/Harry_Potter_and_the_Chamber_of_Secrets"},{"year":1990,"text":"Singing Revolution: The Soviet economic blockade of Lithuania (pictured) was lifted when the Lithuanian parliament agreed to suspend the effects of their act to re-establish Lithuania as a state.","url":"https://en.wikipedia.org/wiki/Singing_Revolution"},{"year":1976,"text":"More than a year after the end of the Vietnam War, North and South Vietnam officially merged under communist rule to form the Socialist Republic of Vietnam.","url":"https://en.wikipedia.org/wiki/Vietnam_War"},{"year":1964,"text":"The Civil Rights Act was signed into law, outlawing segregation in schools, at the workplace, and other facilities that served the general public in the United States.","url":"https://en.wikipedia.org/wiki/Civil_Rights_Act_of_1964"},{"year":1941,"text":"A German SS unit arrived in Vilnius, Lithuania, and began the systematic execution of up to 100,000 people over the next three years.","url":"https://en.wikipedia.org/wiki/Schutzstaffel"},{"year":1881,"text":"U.S. president James A. Garfield (pictured) was fatally shot by Charles J. Guiteau at the Baltimore and Potomac Railroad station in Washington, D.C.","url":"https://en.wikipedia.org/wiki/James_A._Garfield"},{"year":1816,"text":"The French frigate Méduse ran aground off the coast of present-day Mauritania, with the survivors escaping on a makeshift raft, depicted in Théodore Géricault's painting The Raft of the Medusa.","url":"https://en.wikipedia.org/wiki/French_frigate_M%C3%A9duse_(1810)"},{"year":1779,"text":"American Revolutionary War: French troops landed near St. George's, Grenada, and began their capture of the island.","url":"https://en.wikipedia.org/wiki/American_Revolutionary_War"},{"year":1724,"text":"On the Feast of the Visitation, Bach led the first performance of his Meine Seel erhebt den Herren, BWV 10, based on the German Magnificat.","url":"https://en.wikipedia.org/wiki/Visitation_(Christianity)"},{"year":1644,"text":"First English Civil War: The combined forces of Scottish Covenanters and English Parliamentarians defeated Royalist troops at the Battle of Marston Moor.","url":"https://en.wikipedia.org/wiki/First_English_Civil_War"},{"year":706,"text":"The bodies of Emperor Gaozong of Tang and Empress Wu Zetian were interred in the Qianling Mausoleum.","url":"https://en.wikipedia.org/wiki/Emperor_Gaozong_of_Tang"},{"year":626,"text":"Li Shimin (pictured) led his forces to assassinate his rival brothers in a coup for the imperial throne of Tang China.","url":"https://en.wikipedia.org/wiki/Emperor_Taizong_of_Tang"}],"births":[{"year":1996,"text":"Julia Grabher, Austrian tennis player","url":"https://en.wikipedia.org/wiki/Julia_Grabher"},{"year":1995,"text":"Ryan Murphy, American swimmer","url":"https://en.wikipedia.org/wiki/Ryan_Murphy_(swimmer)"},{"year":1994,"text":"Henrik Kristoffersen, Norwegian skier","url":"https://en.wikipedia.org/wiki/Henrik_Kristoffersen"},{"year":1994,"text":"Derrick White, American basketball player","url":"https://en.wikipedia.org/wiki/Derrick_White"},{"year":1993,"text":"Vince Staples, American rapper and actor","url":"https://en.wikipedia.org/wiki/Vince_Staples"},{"year":1993,"text":"Saweetie, American rapper","url":"https://en.wikipedia.org/wiki/Saweetie"},{"year":1992,"text":"Madison Chock, American ice dancer","url":"https://en.wikipedia.org/wiki/Madison_Chock"},{"year":1992,"text":"Jānis Timma, Latvian basketball player","url":"https://en.wikipedia.org/wiki/J%C4%81nis_Timma"}],"deaths":[{"year":2025,"text":"Sophia Hutchins, American socialite (born 1996)","url":"https://en.wikipedia.org/wiki/Sophia_Hutchins"},{"year":2025,"text":"Julian McMahon, Australian-American actor (born 1968)","url":"https://en.wikipedia.org/wiki/Julian_McMahon"},{"year":2020,"text":"Ángela Jeria, Chilean archaeologist and human rights activist (born 1926)","url":"https://en.wikipedia.org/wiki/%C3%81ngela_Jeria"},{"year":2020,"text":"Byron Bernstein, American Twitch streamer (born 1989)","url":"https://en.wikipedia.org/wiki/Reckful"},{"year":2019,"text":"Lee Iacocca, American automotive executive (born 1924)","url":"https://en.wikipedia.org/wiki/Lee_Iacocca"},{"year":2018,"text":"Alan Longmuir, Scottish musician (born 1948)","url":"https://en.wikipedia.org/wiki/Alan_Longmuir"},{"year":2017,"text":"Vladislav Rastorotsky, a Russian (and former Soviet) artistic gymnastics coach, (born 1933)","url":"https://en.wikipedia.org/wiki/Vladislav_Rastorotsky"},{"year":2017,"text":"Smith Hart, American-born Canadian professional wrestler (born 1948)","url":"https://en.wikipedia.org/wiki/Smith_Hart"}]},"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."}