{"date":"2026-08-05","schema_version":1,"captured_at":"2026-08-25T23:18:53.270Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Spider-Man: Brand New Day","views":638274,"rank":4,"extract":"Spider-Man: Brand New Day is a 2026 American superhero film based on the Marvel Comics character Spider-Man. 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life","points":225,"comments":122,"url":"https://resobscura.substack.com/p/the-mechanical-miracle-that-ruined","discussion":"https://news.ycombinator.com/item?id=49184220"},{"title":"The \"Disability Dongle\": Why Silicon Valley Hates Me and You","points":210,"comments":204,"url":"https://sightlessscribbles.com/disability-dongle/","discussion":"https://news.ycombinator.com/item?id=49183665"},{"title":"Nvidia’s Vera Whitepaper Has a Thread Loose","points":208,"comments":46,"url":"https://chipsandcheese.com/p/nvidias-vera-whitepaper-has-a-thread","discussion":"https://news.ycombinator.com/item?id=49189234"}],"research":[{"title":"Whittle index based Q-learning for restless bandits with average reward","abstract":"A novel reinforcement learning algorithm is introduced for multiarmed restless bandits with average reward, using the paradigms of Q-learning and Whittle index. Specifically, we leverage the structure of the Whittle index policy to reduce the search space of Q-learning, resulting in major computational gains. Rigorous convergence analysis is provided, supported by numerical experiments. The numerical experiments show","authors":["Avrachenkov, Konstantin E.","Borkar, Vivek S."],"url":"https://arxiv.org/abs/2004.14427","arxiv_id":"2004.14427","published":"2026-08-05"},{"title":"Dynamic social learning under graph constraints","abstract":"We introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively $α$-homogeneous rewards. We show that its empirical process, which can be written as a stochastic approximation recursion with Markov noise, has the same probability law as a certain vertex reinforced random walk. We use this equivalence to show that for $α> 0$, the asymptotic outcome concentrates around the optimum i","authors":["Avrachenkov, Konstantin","Borkar, Vivek S.","Moharir, Sharayu","Shah, Suhail M."],"url":"https://arxiv.org/abs/2007.03983","arxiv_id":"2007.03983","published":"2026-08-05"},{"title":"The Representation Theory of Neural Networks","abstract":"In this work, we show that neural networks can be represented via the mathematical theory of quiver representations. More specifically, we prove that a neural network is a quiver representation with activation functions, a mathematical object that we represent using a network quiver. Also, we show that network quivers gently adapt to common neural network concepts such as fully-connected layers, convolution operation","authors":["Armenta, Marco Antonio","Jodoin, Pierre-Marc"],"url":"https://arxiv.org/abs/2007.12213","arxiv_id":"2007.12213","published":"2026-08-05"},{"title":"Full Gradient DQN Reinforcement Learning: A Provably Convergent Scheme","abstract":"We analyze the DQN reinforcement learning algorithm as a stochastic approximation scheme using the o.d.e. (for 'ordinary differential equation') approach and point out certain theoretical issues. We then propose a modified scheme called Full Gradient DQN (FG-DQN, for short) that has a sound theoretical basis and compare it with the original scheme on sample problems. We observe a better performance for FG-DQN","authors":["Avrachenkov, K. E.","Borkar, V. S.","Dolhare, H. P.","Patil, K."],"url":"https://arxiv.org/abs/2103.05981","arxiv_id":"2103.05981","published":"2026-08-05"},{"title":"Multi-Camera Trajectory Forecasting with Trajectory Tensors","abstract":"We introduce the problem of multi-camera trajectory forecasting (MCTF), which involves predicting the trajectory of a moving object across a network of cameras. While multi-camera setups are widespread for applications such as surveillance and traffic monitoring, existing trajectory forecasting methods typically focus on single-camera trajectory forecasting (SCTF), limiting their use for such applications. Furthermor","authors":["Styles, Olly","Guha, Tanaya","Sanchez, Victor"],"url":"https://arxiv.org/abs/2108.04694","arxiv_id":"2108.04694","published":"2026-08-05"},{"title":"Modeling Long-Term Memory and Temporal Attention Shifts for Video Salient Object Ranking with a New Benchmark","abstract":"Salient Object Ranking (SOR) aims to estimate the relative saliency order among multiple salient objects. While SOR has been extensively studied in static images, Video Salient Object Ranking (VSOR) remains largely underexplored due to the lack of effective temporal saliency modeling. In particular, existing VSOR methods rely on short input frame clips, which limits their ability to capture long-term saliency evoluti","authors":["Lin, Jiaying","Song, Rui","Guan, Huankang","Li, Shuanglin","Ye, Shuquan","Lau, Rynson W. H."],"url":"https://arxiv.org/abs/2203.17257","arxiv_id":"2203.17257","published":"2026-08-05"},{"title":"Representing Random Utility Choice Models with Neural Networks","abstract":"Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, inspired by the random utility maximization (RUM) framework. This model formulates the agents' random utility function using a sample average approximation. We show that RUMnets sharply approximate the class of RUM discrete choice models: any model derived from random utility maximizatio","authors":["Aouad, Ali","Désir, Antoine"],"url":"https://arxiv.org/abs/2207.12877","arxiv_id":"2207.12877","published":"2026-08-05"},{"title":"Meta Prompting for AI Systems","abstract":"We introduce Meta Prompting (MP), a framework that emphasizes the formal structure of a task rather than content-specific worked examples. We give a categorical formalization in which a functor maps typed task transformations to typed prompt transformations. Functoriality encodes preservation of identities and composition, but it does not by itself guarantee semantic correctness. We extend MP to Recursive Meta Prompt","authors":["Zhang, Yifan","Yuan, Yang","Yao, Andrew Chi-Chih"],"url":"https://arxiv.org/abs/2311.11482","arxiv_id":"2311.11482","published":"2026-08-05"},{"title":"A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures","abstract":"Given their increasing size and complexity, the need for efficient execution of deep neural networks has become increasingly pressing in the design of heterogeneous High-Performance Computing (HPC) and edge platforms, leading to a wide variety of proposals for specialized deep learning architectures and hardware accelerators. The design of such architectures and accelerators requires a multidisciplinary approach comb","authors":["Curzel, Serena","Ferrandi, Fabrizio","Fiorin, Leandro","Ielmini, Daniele","Silvano, Cristina","Conti, Francesco"],"url":"https://arxiv.org/abs/2311.17815","arxiv_id":"2311.17815","published":"2026-08-05"},{"title":"Deep Frequency-Aware Functional Maps for Robust Shape Matching","abstract":"Deep functional map frameworks are widely employed for 3D shape matching. However, most existing deep functional map methods cannot adaptively capture important frequency information for functional map estimation in specific matching scenarios, i.e., lacking \\textit{frequency awareness}, resulting in poor performance when dealing with large deformable shape matching. To this end, we propose a novel unsupervised learn","authors":["Luo, Feifan","Li, Qinsong","Hu, Ling","Wang, Haibo","Liu, Xinru","Liu, Shengjun"],"url":"https://arxiv.org/abs/2402.03904","arxiv_id":"2402.03904","published":"2026-08-05"},{"title":"MSTAR: Multi-Scale Backbone Architecture Search for Timeseries Classification","abstract":"Most of the previous approaches to Time Series Classification (TSC) highlight the significance of receptive fields and frequencies while overlooking the time resolution. Hence, unavoidably suffered from scalability issues as they integrated an extensive range of receptive fields into classification models. Other methods, while having a better adaptation for large datasets, require manual design and yet not being able","authors":["Cao, Tue M.","Tran, Nhat H.","Pham, Hieu H.","Nguyen, Hung T.","Nguyen, Le P."],"url":"https://arxiv.org/abs/2402.13822","arxiv_id":"2402.13822","published":"2026-08-05"},{"title":"CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification","abstract":"We propose CLIP-EBC, the first fully CLIP-based model for accurate crowd density estimation. While the CLIP model has demonstrated remarkable success in addressing recognition tasks such as zero-shot image classification, its potential for counting has been largely unexplored due to the inherent challenges in transforming a regression problem, such as counting, into a recognition task. In this work, we investigate an","authors":["Ma, Yiming","Sanchez, Victor","Guha, Tanaya"],"url":"https://arxiv.org/abs/2403.09281","arxiv_id":"2403.09281","published":"2026-08-05"},{"title":"Crowdsourced Multilingual Speech Intelligibility Testing","abstract":"With the advent of generative audio features, there is an increasing need for rapid evaluation of their impact on speech intelligibility. Beyond the existing laboratory measures, which are expensive and do not scale well, there has been comparatively little work on crowdsourced assessment of intelligibility. Standards and recommendations are yet to be defined, and publicly available multilingual test materials are la","authors":["Lechler, Laura","Wojcicki, Kamil"],"url":"https://arxiv.org/abs/2403.14817","arxiv_id":"2403.14817","published":"2026-08-05"},{"title":"Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian Adaptation","abstract":"For effective human-agent teaming, robots and other artificial intelligence (AI) agents must infer their human partner's abilities and behavioral response patterns and adapt accordingly. Most prior works make the unrealistic assumption that one or more teammates can act near-optimally. In real-world collaboration, humans and autonomous agents can be suboptimal, especially when each only has partial domain knowled","authors":["Natarajan, Manisha","Xue, Chunyue","van Waveren, Sanne","Feigh, Karen","Gombolay, Matthew"],"url":"https://arxiv.org/abs/2403.16178","arxiv_id":"2403.16178","published":"2026-08-05"},{"title":"Convergence analysis of controlled particle systems arising in deep learning: from finite to infinite sample size","abstract":"This paper deals with a class of neural SDEs and studies the limiting behavior of the associated sampled optimal control problems as the sample size grows to infinity. The neural SDEs with $N$ samples can be linked to the $N$-particle systems with centralized control. We analyze the Hamilton--Jacobi--Bellman equation corresponding to the $N$-particle system and establish regularity results which are uniform in $N$. T","authors":["Liao, Huafu","Mészáros, Alpár R.","Mou, Chenchen","Zhou, Chao"],"url":"https://arxiv.org/abs/2404.05185","arxiv_id":"2404.05185","published":"2026-08-05"},{"title":"MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting","abstract":"In recent years, Transformers have become the de-facto architecture for long-term time series forecasting (LTSF), yet they face challenges associated with the self-attention mechanism, including quadratic complexity and permutation-invariant bias. This raises an important question: \\emph{do we truly need self-attention to model long-range dependencies in LTSF?} To address this, we propose MambaTS, a linear-scan-based","authors":["Cai, Xiuding","Wang, Xueyao","Zhu, Yaoyao","Yao, Yu"],"url":"https://arxiv.org/abs/2405.16440","arxiv_id":"2405.16440","published":"2026-08-05"},{"title":"Robust Biharmonic Skinning Using Geometric Fields","abstract":"Bounded bihramonic weights are a popular tool used to rig and deform characters for animation, to compute reduced-order simulations, and to define feature descriptors for geometry processing. They necessitate tetrahedralizing the volume bounded by the surface, introducing the possibility of meshing artifacts or tetrahedralization failure. We introduce a mesh-free and robust automatic skinning technique that generates","authors":["Dodik, Ana","Sitzmann, Vincent","Solomon, Justin","Stein, Oded"],"url":"https://arxiv.org/abs/2406.00238","arxiv_id":"2406.00238","published":"2026-08-05"},{"title":"Tabular and Deep Learning for the Whittle Index","abstract":"The Whittle index policy is a heuristic that has shown remarkably good performance (with guaranteed asymptotic optimality) when applied to the class of problems known as Restless Multi-Armed Bandit Problems (RMABPs). In this paper we present QWI and QWINN, two reinforcement learning algorithms, respectively tabular and deep, to learn the Whittle index for the total discounted criterion. The key feature is the use of ","authors":["Relaño, Francisco Robledo","Borkar, Vivek","Ayesta, Urtzi","Avrachenkov, Konstantin"],"url":"https://arxiv.org/abs/2406.02057","arxiv_id":"2406.02057","published":"2026-08-05"},{"title":"Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry","abstract":"Reconstructing accurate surfaces from sparse multi-view images remains challenging due to severe geometric ambiguity and occlusions. Existing generalizable neural surface reconstruction methods primarily rely on cost volumes that summarize multi-view features using simple statistics (e.g., mean and variance), which discard critical view-dependent geometric structure and often lead to over-smoothed reconstructions. We","authors":["Chang, Xinhai","Zhou, Kaichen"],"url":"https://arxiv.org/abs/2406.04301","arxiv_id":"2406.04301","published":"2026-08-05"},{"title":"Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide images","abstract":"Accurate prediction of the likelihood of recurrence is important in the selection of postoperative treatment for patients with early-stage breast cancer. In this study, we investigated whether deep learning algorithms can predict patients' risk of recurrence by analyzing the pathology images of their cancer histology.We analyzed 125 hematoxylin and eosin-stained whole slide images (WSIs) from 125 patients across ","authors":["Lee, Geongyu","Lee, Joonho","Kwak, Tae-Yeong","Kim, Sun Woo","Kwon, Youngmee","Kim, Chungyeul"],"url":"https://arxiv.org/abs/2406.06650","arxiv_id":"2406.06650","published":"2026-08-05"},{"title":"CollaFuse: Collaborative Diffusion Models","abstract":"In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffusion models poses numerous challenges, particularly concerning data availability, computational requirements, and privacy. Traditional approaches to address these shortcomings, like federated learning, often impose significant computational","authors":["Allmendinger, Simeon","Zipperling, Domenique","Struppek, Lukas","Kühl, Niklas"],"url":"https://arxiv.org/abs/2406.14429","arxiv_id":"2406.14429","published":"2026-08-05"},{"title":"GMT: Guided Mask Transformer for Leaf Instance Segmentation","abstract":"Leaf instance segmentation is a challenging multi-instance segmentation task, aiming to separate and delineate each leaf in an image of a plant. Accurate segmentation of each leaf is crucial for plant-related applications such as the fine-grained monitoring of plant growth and crop yield estimation. This task is challenging because of the high similarity (in shape and colour), great size variation, and heavy occlusio","authors":["Chen, Feng","Tsaftaris, Sotirios A.","Giuffrida, Mario Valerio"],"url":"https://arxiv.org/abs/2406.17109","arxiv_id":"2406.17109","published":"2026-08-05"},{"title":"States Hidden in Hidden States: Implicit Discrete State Representations Emerge in LLMs' Hidden States","abstract":"Large Language Models (LLMs) exhibit emergent abilities that may reveal aspects of their internal mechanisms. We study one such capability: directly performing extended sequences of calculations without generating chain-of-thought solutions. The strongest models in our evaluation can directly output sums with up to 15 addends, where operands are sampled from 1 to 100. We hypothesize that models form Implicit Discrete","authors":["Chen, Junhao","Hu, Shengding","Liu, Zhiyuan","Sun, Maosong"],"url":"https://arxiv.org/abs/2407.11421","arxiv_id":"2407.11421","published":"2026-08-05"},{"title":"Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment","abstract":"Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data. Despite recent advances, existing methods often rely on fine-tuning large backbone models, which leads to high computational cost and limits scalability in resource-constrained environments. This limitation highlights the need for parameter-efficient approaches ","authors":["Abedi, Ali","Wu, Q. M. Jonathan","Zhang, Ning","Pourpanah, Farhad"],"url":"https://arxiv.org/abs/2407.21311","arxiv_id":"2407.21311","published":"2026-08-05"},{"title":"Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era","abstract":"This study proposes a novel, integrative framework for patient-centered data science in the digital health era. We developed a multidimensional model that combines traditional clinical data with patient-reported outcomes, social determinants of health, and multi-omic data to create comprehensive digital patient representations. Our framework employs a multi-agent artificial intelligence approach, utilizing various ma","authors":["Amoei, Mohsen","Poenaru, Dan"],"url":"https://arxiv.org/abs/2408.02677","arxiv_id":"2408.02677","published":"2026-08-05"}],"history":{"events":[{"year":2015,"text":"United States Environmental Protection Agency personnel accidentally caused a spillage (aftermath pictured) of 3 million gallons (11 million litres) of mine waste water and tailings trapped inside the Gold King Mine near Silverton, Colorado.","url":"https://en.wikipedia.org/wiki/United_States_Environmental_Protection_Agency"},{"year":2012,"text":"Radio Network House (pictured), damaged in the 2011 Christchurch earthquake, becomes the first building to be demolished by implosion in New Zealand.","url":"https://en.wikipedia.org/wiki/Implosion_of_Radio_Network_House"},{"year":2012,"text":"An American white supremacist carried out a mass shooting at a Sikh temple in Oak Creek, Wisconsin, killing six people and wounding four others.","url":"https://en.wikipedia.org/wiki/White_supremacy"},{"year":2012,"text":"Radio Network House, damaged in the 2011 Christchurch earthquake, becomes the first building to be demolished by implosion in New Zealand.","url":"https://en.wikipedia.org/wiki/Implosion_of_Radio_Network_House"},{"year":2011,"text":"NASA launched the Juno probe to Jupiter as part of the New Frontiers program.","url":"https://en.wikipedia.org/wiki/Juno_(spacecraft)"},{"year":1993,"text":"Wizards of the Coast released Magic: The Gathering, the first trading card game.","url":"https://en.wikipedia.org/wiki/Wizards_of_the_Coast"},{"year":1984,"text":"A Biman Bangladesh Airlines aircraft crashed while attempting to land in Dhaka, killing 49 people in the deadliest aviation accident in Bangladeshi history.","url":"https://en.wikipedia.org/wiki/Biman_Bangladesh_Airlines"},{"year":1981,"text":"U.S. president Ronald Reagan fired the 11,345 striking members of the Professional Air Traffic Controllers Organization en masse.","url":"https://en.wikipedia.org/wiki/Ronald_Reagan"},{"year":1973,"text":"Due to an athletics scandal, the National Collegiate Athletic Association applied the death penalty to the basketball program at the University of Southwestern Louisiana.","url":"https://en.wikipedia.org/wiki/University_of_Southwestern_Louisiana_basketball_scandal"},{"year":1969,"text":"Police raided a screening of the film Lonesome Cowboys in Atlanta, Georgia, leading to the creation of a Gay Liberation Front in that city.","url":"https://en.wikipedia.org/wiki/Lonesome_Cowboys_police_raid"},{"year":1962,"text":"American actress and model Marilyn Monroe was found dead of a barbiturate overdose in her home in Brentwood, Los Angeles.","url":"https://en.wikipedia.org/wiki/Marilyn_Monroe"},{"year":1949,"text":"An earthquake registering 6.4 Ms struck near Ambato, Ecuador, killing 5,050 people.","url":"https://en.wikipedia.org/wiki/1949_Ambato_earthquake"},{"year":1916,"text":"First World War: The British Empire's Sinai and Palestine campaign began with a victory at the Battle of Romani.","url":"https://en.wikipedia.org/wiki/World_War_I"},{"year":1888,"text":"Bertha Benz made the first long-distance automobile trip, driving 106 km (66 mi) from Mannheim to Pforzheim, Germany, in a Benz Patent-Motorwagen.","url":"https://en.wikipedia.org/wiki/Bertha_Benz"},{"year":1816,"text":"Sir John Barrow, secretary at the Admiralty, rejected a proposal to use Francis Ronalds's electrical telegraph, deeming it \"wholly unnecessary\".","url":"https://en.wikipedia.org/wiki/Sir_John_Barrow%2C_1st_Baronet"},{"year":1772,"text":"Russia, Prussia and Habsburg Austria began the First Partition of Poland, with the primary motive to restore the regional balance of power in Eastern Europe.","url":"https://en.wikipedia.org/wiki/Russian_Empire"},{"year":1716,"text":"Austro-Turkish War: The Ottoman army were defeated in their attempt to capture the Habsburg-controlled Petrovaradin Fortress despite having twice the number of soldiers.","url":"https://en.wikipedia.org/wiki/Austro-Turkish_War_(1716%E2%80%931718)"},{"year":1689,"text":"Beaver Wars: Aggravated by increased French incursions into their territory, a large force of Mohawk warriors substantially destroyed the settlement of Lachine in present-day Quebec.","url":"https://en.wikipedia.org/wiki/Beaver_Wars"}],"births":[{"year":2008,"text":"Hudson Meek, American actor (died 2024)","url":"https://en.wikipedia.org/wiki/Hudson_Meek"},{"year":2004,"text":"Gavi, Spanish footballer","url":"https://en.wikipedia.org/wiki/Gavi_(footballer)"},{"year":2003,"text":"Toni Shaw, British Paralympic swimmer","url":"https://en.wikipedia.org/wiki/Toni_Shaw"},{"year":2001,"text":"Anthony Edwards, American basketball player","url":"https://en.wikipedia.org/wiki/Anthony_Edwards_(basketball)"},{"year":2000,"text":"Tom Gilbert, Australian rugby league player","url":"https://en.wikipedia.org/wiki/Tom_Gilbert_(rugby_league)"},{"year":1999,"text":"Kim Si-hyeon, South Korean singer","url":"https://en.wikipedia.org/wiki/Kim_Si-hyeon"},{"year":1998,"text":"Adam Doueihi, Australian-Lebanese rugby league player","url":"https://en.wikipedia.org/wiki/Adam_Doueihi"},{"year":1997,"text":"Jack Cogger, Australian rugby league player","url":"https://en.wikipedia.org/wiki/Jack_Cogger"}],"deaths":[{"year":2025,"text":"Col Joye, Australian singer-songwriter and guitarist (born 1937)","url":"https://en.wikipedia.org/wiki/Col_Joye"},{"year":2022,"text":"Judith Durham, Australian singer-songwriter (born 1943)","url":"https://en.wikipedia.org/wiki/Judith_Durham"},{"year":2022,"text":"Cherie Gil, Filipino actress (born 1963)","url":"https://en.wikipedia.org/wiki/Cherie_Gil"},{"year":2022,"text":"Ali Haydar, Syrian army officer (born 1932)","url":"https://en.wikipedia.org/wiki/Ali_Haydar_(Syrian_army_officer)"},{"year":2022,"text":"Issey Miyake, Japanese fashion designer (born 1938)","url":"https://en.wikipedia.org/wiki/Issey_Miyake"},{"year":2022,"text":"Dillon Quirke, Irish hurler (born 1998)","url":"https://en.wikipedia.org/wiki/Dillon_Quirke"},{"year":2020,"text":"Hawa Abdi, Somali human rights activist and physician (born 1947)","url":"https://en.wikipedia.org/wiki/Hawa_Abdi"},{"year":2019,"text":"Toni Morrison, American author, Pulitzer Prize winner, and Nobel laureate (born 1931).","url":"https://en.wikipedia.org/wiki/Toni_Morrison"}]},"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 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Every item links to its source."}