{"date":"2026-06-25","schema_version":1,"captured_at":"2026-08-25T23:13:38.812Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":13},"attention":[{"title":"2026 FIFA World Cup","views":838922,"rank":2,"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. The tournament began on June 11, 2026, and concluded on July 19 with Spain winning ","url":"https://en.wikipedia.org/wiki/2026_FIFA_World_Cup","thumb":"https://upload.wikimedia.org/wikipedia/en/thumb/1/17/2026_FIFA_World_Cup_emblem.svg/330px-2026_FIFA_World_Cup_emblem.svg.png?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"2026 NBA draft","views":260054,"rank":5,"extract":"The 2026 NBA draft was the 80th edition of the National Basketball Association's annual draft. 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The existing literature largely focuses on denoising methods for channel estimation that depend on either (i) channel analysis in the time-domain with prior channel knowledge or (ii) supervised learning techniques which require large pre-l","authors":["Oh, Myeung Suk","Hosseinalipour, Seyyedali","Kim, Taejoon","Brinton, Christopher G.","Love, David J."],"url":"https://arxiv.org/abs/2101.10300","arxiv_id":"2101.10300","published":"2026-06-25"},{"title":"Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification","abstract":"The problem of linear predictions has been extensively studied for the past century under pretty generalized frameworks. Recent advances in the robust statistics literature allow us to analyze robust versions of classical linear models through the prism of Median of Means (MoM). Combining these approaches in a piecemeal way might lead to ad-hoc procedures, and the restricted theoretical conclusions that underpin each","authors":["Chakraborty, Saptarshi","Paul, Debolina","Das, Swagatam"],"url":"https://arxiv.org/abs/2201.01973","arxiv_id":"2201.01973","published":"2026-06-25"},{"title":"Learning Non-Vacuous Generalization Bounds from Optimization","abstract":"One of the fundamental challenges in the deep learning community is to theoretically understand how well a deep neural network generalizes to unseen data. However, current approaches often yield generalization bounds that are either too loose to be informative of the true generalization error or only valid to the compressed nets. In this study, we present a simple yet non-vacuous generalization bound from the optimiz","authors":["Tan, Chengli","Zhang, Jiangshe","Liu, Junmin","Gong, Yihong"],"url":"https://arxiv.org/abs/2206.04359","arxiv_id":"2206.04359","published":"2026-06-25"},{"title":"A stability theorem for bigraded persistence barcodes","abstract":"We define bigraded persistent homology modules and bigraded barcodes of a finite pseudo-metric space X using the ordinary and double homology of the moment-angle complex associated with the Vietoris-Rips filtration of X. We prove a stability theorem for the bigraded persistent double homology modules and barcodes.","authors":["Bahri, Anthony","Limonchenko, Ivan","Panov, Taras","Song, Jongbaek","Stanley, Donald"],"url":"https://arxiv.org/abs/2303.14694","arxiv_id":"2303.14694","published":"2026-06-25"},{"title":"Why Shallow Networks Struggle to Approximate and Learn High Frequencies","abstract":"In this work, we present a comprehensive study combining mathematical and computational analysis to explain why a two-layer neural network struggles to handle high frequencies in both approximation and learning, especially when machine precision, numerical noise, and computational cost are significant factors in practice. Specifically, we investigate the following fundamental computational issues: (1) the minimal num","authors":["Zhang, Shijun","Zhao, Hongkai","Zhong, Yimin","Zhou, Haomin"],"url":"https://arxiv.org/abs/2306.17301","arxiv_id":"2306.17301","published":"2026-06-25"},{"title":"Deep Network Approximation: Beyond ReLU to Diverse Activation Functions","abstract":"This paper explores the expressive power of deep neural networks for a diverse range of activation functions. An activation function set $\\mathscr{A}$ is defined to encompass the majority of commonly used activation functions, such as $\\mathtt{ReLU}$, $\\mathtt{LeakyReLU}$, $\\mathtt{ReLU}^2$, $\\mathtt{ELU}$, $\\mathtt{CELU}$, $\\mathtt{SELU}$, $\\mathtt{Softplus}$, $\\mathtt{GELU}$, $\\mathtt{SiLU}$, $\\mathtt{Swish}$, $\\ma","authors":["Zhang, Shijun","Lu, Jianfeng","Zhao, Hongkai"],"url":"https://arxiv.org/abs/2307.06555","arxiv_id":"2307.06555","published":"2026-06-25"},{"title":"Structured Approximations of Measures","abstract":"We study the approximation of probability measures in the Wasserstein-$p$ distance by structured classes of approximators, motivated by applications in imaging, machine learning, and physical measurement under sensor constraints. We obtain three sets of results. First, for measures with densities bounded away from zero on a bounded Lipschitz domain $Ω$, we prove that any approximation scheme for functions in $\\mathrm","authors":["Hamm, Keaton","Khurana, Varun"],"url":"https://arxiv.org/abs/2310.09149","arxiv_id":"2310.09149","published":"2026-06-25"},{"title":"A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records","abstract":"Foundation models hold promise for transforming AI in healthcare by providing modular components that are easily adaptable to downstream healthcare tasks, making AI development more scalable and cost-effective. Structured EHR foundation models, trained on coded medical records from millions of patients, demonstrated benefits including increased performance with fewer training labels, and improved robustness to distri","authors":["Guo, Lin Lawrence","Fries, Jason","Steinberg, Ethan","Fleming, Scott Lanyon","Morse, Keith","Aftandilian, Catherine"],"url":"https://arxiv.org/abs/2311.11483","arxiv_id":"2311.11483","published":"2026-06-25"},{"title":"SparseGS: Sparse View Synthesis using 3D Gaussian Splatting","abstract":"3D Gaussian Splatting (3DGS) has recently enabled real-time rendering of unbounded 3D scenes for novel view synthesis. However, this technique requires dense training views to accurately reconstruct 3D geometry. A limited number of input views will significantly degrade reconstruction quality, resulting in artifacts such as "floaters" and "background collapse" at unseen viewpoints. In this work, we in","authors":["Xiong, Haolin","Muttukuru, Sairisheek","Xiao, Hanyuan","Upadhyay, Rishi","Chari, Pradyumna","Zhao, Yajie"],"url":"https://arxiv.org/abs/2312.00206","arxiv_id":"2312.00206","published":"2026-06-25"},{"title":"Transfer Learning With Densenet201 Architecture Model For Potato Leaf Disease Classification","abstract":"Potato plants are plants that are beneficial to humans. Like other plants in general, potato plants also have diseases; if this disease is not treated immediately, there will be a significant decrease in food production. Therefore, it is necessary to detect diseases quickly and precisely so that disease control can be carried out effectively and efficiently. Classification of potato leaf disease can be done directly.","authors":["Charisma, Rifqi Alfinnur","Adhinata, Faisal Dharma"],"url":"https://arxiv.org/abs/2402.03347","arxiv_id":"2402.03347","published":"2026-06-25"},{"title":"Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure","abstract":"Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving cancer treatment outcomes and safety. This study examined machine learning (ML) models to identify cancer patients at risk of HF using electronic health records (EHRs), including traditional ML, Time-Aware long short-term memory (T-","authors":["Chen, Ziyi","Zhang, Mengyuan","Ahmed, Mustafa Mohammed","Guo, Yi","George, Thomas J.","Bian, Jiang"],"url":"https://arxiv.org/abs/2403.11425","arxiv_id":"2403.11425","published":"2026-06-25"},{"title":"PVF:Understanding AI Vulnerability Against SDCs","abstract":"Reliability of AI systems is a fundamental concern for the successful deployment and widespread adoption of AI technologies. Unfortunately, the escalating complexity and heterogeneity of AI hardware systems make them increasingly susceptible to hardware faults, e.g., silent data corruptions (SDC), that can potentially corrupt model parameters. When this occurs during AI inference/servicing, it can potentially lead to","authors":["Jiao, Xun","Lin, Fred","Dixit, Harish D.","Coburn, Joel","Nair, Sajin","Pandey, Abhinav"],"url":"https://arxiv.org/abs/2405.01741","arxiv_id":"2405.01741","published":"2026-06-25"},{"title":"Privacy-Aware Visual Language Models","abstract":"As Visual Language Models (VLMs) become increasingly embedded in everyday applications, ensuring they can recognise and appropriately handle privacy-sensitive content is thus essential to protect users. To this end, we conduct a comprehensive evaluation of twelve state-of-the-art VLMs and identify limitations in their understanding of visual privacy. However, existing privacy-related datasets often suffer from label ","authors":["Samson, Laurens","Barazani, Nimrod","Ghebreab, Sennay","Asano, Yuki M."],"url":"https://arxiv.org/abs/2405.17423","arxiv_id":"2405.17423","published":"2026-06-25"},{"title":"Structured and Balanced Multi-Component and Multi-Layer Neural Networks","abstract":"In this work, we propose a balanced multi-component and multi-layer neural network (MMNN) structure to accurately and efficiently approximate functions with complex features, in terms of both degrees of freedom and computational cost. The main idea is inspired by a multi-component approach, in which each component can be effectively approximated by a single-layer network, combined with a multi-layer decomposition str","authors":["Zhang, Shijun","Zhao, Hongkai","Zhong, Yimin","Zhou, Haomin"],"url":"https://arxiv.org/abs/2407.00765","arxiv_id":"2407.00765","published":"2026-06-25"},{"title":"RotRNN: Modelling Long Sequences with Rotations","abstract":"Linear recurrent neural networks, such as State Space Models (SSMs) and Linear Recurrent Units (LRUs), have recently shown state-of-the-art performance on long sequence modelling benchmarks. Despite their success, their empirical performance is not well understood and they come with a number of drawbacks, most notably their complex initialisation and normalisation schemes. In this work, we address some of these issue","authors":["Biegun, Kai","Dolga, Rares","Cunningham, Jake","Barber, David"],"url":"https://arxiv.org/abs/2407.07239","arxiv_id":"2407.07239","published":"2026-06-25"},{"title":"Don't Fear Peculiar Activation Functions: EUAF and Beyond","abstract":"In this paper, we propose a new super-expressive activation function called the Parametric Elementary Universal Activation Function (PEUAF). We demonstrate the effectiveness of PEUAF through systematic and comprehensive experiments on various industrial and image datasets, including CIFAR10, Tiny-ImageNet, and ImageNet. Moreover, we significantly generalize the family of super-expressive activation functions, whose e","authors":["Wang, Qianchao","Zhang, Shijun","Zeng, Dong","Xie, Zhaoheng","Guo, Hengtao","Fan, Feng-Lei"],"url":"https://arxiv.org/abs/2407.09580","arxiv_id":"2407.09580","published":"2026-06-25"},{"title":"Certified Robust Invariant Polytope Training in Neural Controlled ODEs","abstract":"We propose a framework for training neural network controllers with certified robust forward invariant polytopes. First, we parameterize a family of lifted control systems in a higher dimensional space, where the original neural controlled system evolves on an invariant subspace of each lifted system. We use interval analysis and neural network verifiers to further construct a family of lifted embedding systems, care","authors":["Harapanahalli, Akash","Coogan, Samuel"],"url":"https://arxiv.org/abs/2408.01273","arxiv_id":"2408.01273","published":"2026-06-25"},{"title":"Explaining a probabilistic prediction on the simplex with Shapley compositions","abstract":"Originating in game theory, Shapley values are widely used for explaining a machine learning model's prediction by quantifying the contribution of each feature's value to the prediction. This requires a scalar prediction as in binary classification, whereas a multiclass probabilistic prediction is a discrete probability distribution, living on a multidimensional simplex. In such a multiclass setting the Shapl","authors":["Noé, Paul-Gauthier","Perelló-Nieto, Miquel","Bonastre, Jean-François","Flach, Peter"],"url":"https://arxiv.org/abs/2408.01382","arxiv_id":"2408.01382","published":"2026-06-25"},{"title":"Bayesian uncertainty-aware deep learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection","abstract":"Deep learning is advancing EEG processing for automated epileptic seizure detection and onset zone localization, yet its performance relies heavily on high-quality annotated training data. However, scalp EEG is susceptible to high noise levels, which in turn leads to imprecise annotations of the seizure timing and characteristics. This "label noise" presents a significant challenge in model training and gener","authors":["Shama, Deeksha M.","Venkataraman, Archana"],"url":"https://arxiv.org/abs/2410.19815","arxiv_id":"2410.19815","published":"2026-06-25"},{"title":"Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics","abstract":"High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on Flow Annealed importance sampling Bootstrap (FAB) that evaluates the differentiable target density durin","authors":["Kofler, Annalena","Stimper, Vincent","Mikhasenko, Mikhail","Kagan, Michael","Heinrich, Lukas"],"url":"https://arxiv.org/abs/2411.16234","arxiv_id":"2411.16234","published":"2026-06-25"},{"title":"Hengqin-RA-v1: Advanced Large Language Model for Diagnosis and Treatment of Rheumatoid Arthritis with Dataset based Traditional Chinese Medicine","abstract":"Large language models (LLMs) primarily trained on English texts, often face biases and inaccuracies in Chinese contexts. Their limitations are pronounced in fields like Traditional Chinese Medicine (TCM), where cultural and clinical subtleties are vital, further hindered by a lack of domain-specific data, such as rheumatoid arthritis (RA). To address these issues, this paper introduces Hengqin-RA-v1, the first large ","authors":["Liu, Yishen"],"url":"https://arxiv.org/abs/2501.02471","arxiv_id":"2501.02471","published":"2026-06-25"},{"title":"ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning","abstract":"Learning efficient representations for decision-making policies is a challenge in imitation learning (IL). Current IL methods require expert demonstrations, which are expensive to collect. Additionally, they are not explicitly trained to understand the environment. Consequently, they have underdeveloped world models. Self-supervised learning (SSL) offers an alternative, as it can learn a world model from diverse, unl","authors":["Vujinovic, Aleksandar","Kovacevic, Aleksandar"],"url":"https://arxiv.org/abs/2501.14622","arxiv_id":"2501.14622","published":"2026-06-25"},{"title":"Safe Learning Control with Optimality and Stability Guarantees","abstract":"Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance. How to guarantee both safety and performance in learning-based control problems is an interesting yet challenging issue. This paper aims to enhance system performance with a safety guarantee by solving reinforcement learning (RL)-based optimal control problems for nonlinear systems s","authors":["Wang, Xinyang","Zhang, Hongwei","Wang, Shimin","Xiao, Wei","Guay, Martin"],"url":"https://arxiv.org/abs/2501.15373","arxiv_id":"2501.15373","published":"2026-06-25"},{"title":"LLM Program Optimization via Retrieval Augmented Search","abstract":"Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We propose a blackbox adaptation method called Retrieval Augmented Search (RAS) that performs beam search over candidate optimizations; at each step, it retrieves in-context examples from a given training dataset of slow-fast program pairs to guide the LLM. Critically, we find","authors":["Anupam, Sagnik","Shypula, Alexander","Bastani, Osbert"],"url":"https://arxiv.org/abs/2501.18916","arxiv_id":"2501.18916","published":"2026-06-25"},{"title":"From Uncertain to Safe: Conformal Adaptation of Diffusion Models for Safe PDE Control","abstract":"The application of deep learning for partial differential equation (PDE)-constrained control is gaining increasing attention. However, existing methods rarely consider safety requirements crucial in real-world applications. To address this limitation, we propose Safe Diffusion Models for PDE Control (SafeDiffCon), which introduce the uncertainty quantile as model uncertainty quantification to achieve optimal control ","authors":["Hu, Peiyan","Qian, Xiaowei","Deng, Wenhao","Wang, Rui","Feng, Haodong","Feng, Ruiqi"],"url":"https://arxiv.org/abs/2502.02205","arxiv_id":"2502.02205","published":"2026-06-25"}],"history":{"events":[{"year":2022,"text":"Russian invasion of Ukraine: Russian forces captured the city of Sievierodonetsk, Ukraine, after six weeks of fighting.","url":"https://en.wikipedia.org/wiki/Russo-Ukrainian_war_(2022%E2%80%93present)"},{"year":2013,"text":"In response to a Freedom of Information Act request, the CIA acknowledged the existence of Area 51 (diagram pictured), a secretive U.S. Air Force facility in Nevada and a subject of various UFO, and other, conspiracy theories.","url":"https://en.wikipedia.org/wiki/Freedom_of_Information_Act_(United_States)"},{"year":2009,"text":"Singer Michael Jackson died as a result of the combination of drugs in his body.","url":"https://en.wikipedia.org/wiki/Michael_Jackson"},{"year":1978,"text":"The rainbow flag (original version pictured) representing gay pride was first flown at the San Francisco Gay Freedom Day parade.","url":"https://en.wikipedia.org/wiki/Rainbow_flag_(LGBTQ)"},{"year":1967,"text":"More than an estimated 400 million people viewed Our World, the first live international satellite television production.","url":"https://en.wikipedia.org/wiki/Our_World_(1967_TV_program)"},{"year":1960,"text":"Two cryptographers working for the U.S. National Security Agency left on vacation to Mexico, and proceeded to defect to the Soviet Union.","url":"https://en.wikipedia.org/wiki/Cryptography"},{"year":1950,"text":"The Korean War began with North Korean forces launching a pre-dawn raid over the 38th parallel into South Korea.","url":"https://en.wikipedia.org/wiki/Korean_War"},{"year":1950,"text":"The United Nations Security Council passed Resolution 82 condemning the North Korean invasion of South Korea.","url":"https://en.wikipedia.org/wiki/United_Nations_Security_Council"},{"year":1944,"text":"World War II: U.S. Navy and Royal Navy ships bombarded Cherbourg, France, to support U.S. Army units engaged in the Battle of Cherbourg.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1940,"text":"Second World War: Operation Aerial, an evacuation of nearly 200,000 Allied soldiers from French ports, was completed.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1926,"text":"French police arrested Los Errantes, while they were preparing to assassinate Spanish king Alfonso XIII, as part of a mass raid against Spanish immigrants in Paris.","url":"https://en.wikipedia.org/wiki/Los_Errantes"},{"year":1910,"text":"The United States Congress passed the Mann Act, which prohibited the interstate transport of females for \"immoral purposes\".","url":"https://en.wikipedia.org/wiki/United_States_Congress"},{"year":1658,"text":"Anglo-Spanish War: The largest battle ever fought on Jamaica, the three-day Battle of Rio Nuevo, began.","url":"https://en.wikipedia.org/wiki/Anglo-Spanish_War_(1654%E2%80%931660)"}],"births":[{"year":2006,"text":"Mckenna Grace, American actress, singer and songwriter","url":"https://en.wikipedia.org/wiki/Mckenna_Grace"},{"year":2005,"text":"Kylie Cantrall, American actress, singer, songwriter, dancer, and social media personality","url":"https://en.wikipedia.org/wiki/Kylie_Cantrall"},{"year":2002,"text":"Benson Boone, American singer-songwriter","url":"https://en.wikipedia.org/wiki/Benson_Boone"},{"year":2001,"text":"Philip Broberg, Swedish professional ice hockey player","url":"https://en.wikipedia.org/wiki/Philip_Broberg"},{"year":1998,"text":"Kyle Chalmers, Australian swimmer","url":"https://en.wikipedia.org/wiki/Kyle_Chalmers"},{"year":1996,"text":"Pietro Fittipaldi, Brazilian-American race car driver","url":"https://en.wikipedia.org/wiki/Pietro_Fittipaldi"},{"year":1990,"text":"Andi Eigenmann, Filipino actress","url":"https://en.wikipedia.org/wiki/Andi_Eigenmann"},{"year":1988,"text":"Jhonas Enroth, Swedish ice hockey player","url":"https://en.wikipedia.org/wiki/Jhonas_Enroth"}],"deaths":[{"year":2024,"text":"Sika Anoa‘i, American Samoan professional wrestler (born 1945)","url":"https://en.wikipedia.org/wiki/Sika_Anoa%CA%BBi"},{"year":2024,"text":"Bill Cobbs, American actor (born 1934)","url":"https://en.wikipedia.org/wiki/Bill_Cobbs"},{"year":2023,"text":"Simon Crean, Australian trade union leader and politician (born 1949)","url":"https://en.wikipedia.org/wiki/Simon_Crean"},{"year":2016,"text":"Adam Small, South African writer of apartheid-period (born 1936)","url":"https://en.wikipedia.org/wiki/Adam_Small"},{"year":2015,"text":"Patrick Macnee, English actor (born 1922)","url":"https://en.wikipedia.org/wiki/Patrick_Macnee"},{"year":2015,"text":"Nerses Bedros XIX Tarmouni, Egyptian-Armenian patriarch (born 1940)","url":"https://en.wikipedia.org/wiki/Nerses_Bedros_XIX_Tarmouni"},{"year":2014,"text":"Nigel Calder, English journalist, author, and screenwriter (born 1931)","url":"https://en.wikipedia.org/wiki/Nigel_Calder"},{"year":2014,"text":"Ana María Matute, Spanish author and academic (born 1925)","url":"https://en.wikipedia.org/wiki/Ana_Mar%C3%ADa_Matute"}]},"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."}