{"date":"2026-08-13","schema_version":1,"captured_at":"2026-08-25T23:19:43.180Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":13},"attention":[{"title":"Joshua Kushner","views":525043,"rank":4,"extract":"Joshua Kushner is an American businessman and venture capitalist. He is a founder and managing partner of the venture capital firm Thrive Capital, co-founder and vice-chairman of Oscar Health, and the youngest son of Charles Kushner. 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That is putting off users","points":164,"comments":213,"url":"https://www.economist.com/business/2026/08/12/ai-agents-lie-cheat-and-steal-that-is-putting-off-users","discussion":"https://news.ycombinator.com/item?id=49285604"}],"research":[{"title":"Soft-Attention Improves Skin Cancer Classification Performance","abstract":"In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network toachieve this goal. This paper investigates the effectiveness of Soft-Attention in deep neural architectures. The central aim of Soft-Attention is to boost the value of important features and suppress the noise-inducing features. We compare the performanc","authors":["Datta, Soumyya Kanti","Hashemi, Seyed Mohammad Abuzar","Srihari, Sargur N.","Gao, Mingchen"],"url":"https://arxiv.org/abs/2105.03358","arxiv_id":"2105.03358","published":"2026-08-13"},{"title":"Joint AP Probing and Scheduling: A Contextual Bandit Approach","abstract":"We consider a set of APs with unknown data rates that cooperatively serve a mobile client. The data rate of each link is i.i.d. sampled from a distribution that is unknown a priori. In contrast to traditional link scheduling problems under uncertainty, we assume that in each time step, the device can probe a subset of links before deciding which one to use. We model this problem as a contextual bandit problem with pr","authors":["Xu, Tianyi","Zhang, Ding","Pathak, Parth H.","Zheng, Zizhan"],"url":"https://arxiv.org/abs/2108.03297","arxiv_id":"2108.03297","published":"2026-08-13"},{"title":"Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor","abstract":"Rule extraction is a central problem in interpretable machine learning because it seeks to convert opaque predictive behavior into human-readable symbolic structure. This paper presents Chat Incremental Pattern Constructor (ChatIPC), a lightweight incremental symbolic learning system that extracts ordered token-transition rules from text, enriches them with definition-based expansion, and constructs responses by simi","authors":["Nwokocha, Caleb Princewill"],"url":"https://arxiv.org/abs/2208.00335","arxiv_id":"2208.00335","published":"2026-08-13"},{"title":"Online Learning for Adaptive Probing and Scheduling in Dense WLANs","abstract":"Existing solutions to network scheduling typically assume that the instantaneous link rates are completely known before a scheduling decision is made or consider a bandit setting where the accurate link quality is discovered only after it has been used for data transmission. In practice, the decision maker can obtain (relatively accurate) channel information, e.g., through beamforming in mmWave networks, right before","authors":["Xu, Tianyi","Zhang, Ding","Zheng, Zizhan"],"url":"https://arxiv.org/abs/2212.13585","arxiv_id":"2212.13585","published":"2026-08-13"},{"title":"Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering","abstract":"Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. Howe","authors":["Wen, Yan","Gao, Chen","Yi, Lingling","Qiu, Liwei","Wang, Yaqing","Li, Yong"],"url":"https://arxiv.org/abs/2307.11004","arxiv_id":"2307.11004","published":"2026-08-13"},{"title":"Un-EVIMO: Unsupervised Event-Based Independent Motion Segmentation","abstract":"Event cameras are a novel type of biologically inspired vision sensor known for their high temporal resolution, high dynamic range, and low power consumption. Because of these properties, they are well-suited for processing fast motions that require rapid reactions. Although event cameras have recently shown competitive performance in unsupervised optical flow estimation, performance in detecting independently moving","authors":["Wang, Ziyun","Guo, Jinyuan","Daniilidis, Kostas"],"url":"https://arxiv.org/abs/2312.00114","arxiv_id":"2312.00114","published":"2026-08-13"},{"title":"Weak Correlations as the Underlying Principle for Linearization of Gradient-Based Learning Systems","abstract":"Deep learning models, such as wide neural networks, can be conceptualized as nonlinear dynamical physical systems characterized by a multitude of interacting degrees of freedom. Such systems in the infinite limit, tend to exhibit simplified dynamics. This paper delves into gradient descent-based learning algorithms, that display a linear structure in their parameter dynamics, reminiscent of the neural tangent kernel.","authors":["Shem-Ur, Ori","Cohen, Khen","Orly, Aviv","Oz, Yaron"],"url":"https://arxiv.org/abs/2401.04013","arxiv_id":"2401.04013","published":"2026-08-13"},{"title":"A Rate-Distortion-Classification Approach for Lossy Image Compression","abstract":"In lossy image compression, the objective is to achieve minimal signal distortion while compressing images to a specified bit rate. The increasing demand for visual analysis applications, particularly in classification tasks, has emphasized the significance of considering semantic distortion in compressed images. To bridge the gap between image compression and visual analysis, we propose a Rate-Distortion-Classificat","authors":["Zhang, Yuefeng"],"url":"https://arxiv.org/abs/2405.03500","arxiv_id":"2405.03500","published":"2026-08-13"},{"title":"Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis","abstract":"Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations. Existing deep learning models tend to overlook the semantic interdependencies among activities and households and rely on restricted GPS data, while activity-based models depend on rigid assumptions and extensive data, making them costly and difficult to adapt to new regions, es","authors":["Liao, Xishun","Jiang, Qinhua","He, Brian Yueshuai","Liu, Yifan","Kuai, Chenchen","Ma, Jiaqi"],"url":"https://arxiv.org/abs/2405.17468","arxiv_id":"2405.17468","published":"2026-08-13"},{"title":"qMRI Diffuser: Quantitative T1 Mapping of the Brain using a Denoising Diffusion Probabilistic Model","abstract":"Quantitative MRI (qMRI) offers significant advantages over weighted images by providing objective parameters related to tissue properties. Deep learning-based methods have demonstrated effectiveness in estimating quantitative maps from series of weighted images. In this study, we present qMRI Diffuser, a novel approach to qMRI utilising deep generative models. Specifically, we implemented denoising diffusion probabil","authors":["Wang, Shishuai","Ma, Hua","Hernandez-Tamames, Juan A.","Klein, Stefan","Poot, Dirk H. J."],"url":"https://arxiv.org/abs/2407.16477","arxiv_id":"2407.16477","published":"2026-08-13"},{"title":"Causal Agent based on Large Language Model","abstract":"The large language model (LLM) has achieved significant success across various domains. However, the inherent complexity of causal problems and causal theory poses challenges in accurately describing them in natural language, making it difficult for LLM to comprehend and use them effectively. Causal methods are not easily conveyed through natural language, which hinders LLM's ability to apply them accurately. Add","authors":["Han, Kairong","Kuang, Kun","Zhao, Ziyu","Ye, Junjian","Wu, Fei"],"url":"https://arxiv.org/abs/2408.06849","arxiv_id":"2408.06849","published":"2026-08-13"},{"title":"RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency","abstract":"Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization. However, aligning ST data with histology images poses challenges due to inherent spatial distortions and modality-specific variations. Existing methods largely rely on direct alignment, which often fails to capture complex cross-modal rela","authors":["Huang, Wentao","Xu, Meilong","Hu, Xiaoling","Abousamra, Shahira","Ganguly, Aniruddha","Kapse, Saarthak"],"url":"https://arxiv.org/abs/2411.15076","arxiv_id":"2411.15076","published":"2026-08-13"},{"title":"ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation","abstract":"AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively evaluating their performance. To address this, we present ReXrank, https://rexrank.ai, a public leaderboard and challenge for assessing AI-powered radiology report generation. Our framework incorporates ReXGradient, the largest test dataset co","authors":["Zhang, Xiaoman","Zhou, Hong-Yu","Yang, Xiaoli","Banerjee, Oishi","Acosta, Julián N.","Baharoon, Mohammed"],"url":"https://arxiv.org/abs/2411.15122","arxiv_id":"2411.15122","published":"2026-08-13"},{"title":"Heterogeneous transfer learning for high-dimensional regression with feature mismatch","abstract":"We study Heterogeneous Transfer Learning (HTL) for high-dimensional regression with differing feature sets. Such feature mismatch arises when some variables available in a data-rich source domain are unavailable in a data-poor target domain. Yet most homogeneous TL methods require the same feature space in both the source and target domains, limiting their practical applicability. Conversely, existing HTL methods lac","authors":["Chang, Jae Ho","Russo, Massimiliano","Paul, Subhadeep"],"url":"https://arxiv.org/abs/2412.18081","arxiv_id":"2412.18081","published":"2026-08-13"},{"title":"Explainability in Practice: A Survey of Explainable NLP Across Various Domains","abstract":"Natural Language Processing (NLP) is now embedded in critical sectors including healthcare, finance, and customer relationship management, where models such as GPT-4o, Gemini, and BERT increasingly inform decisions. The black-box nature of these models has created an urgent need for transparency. This review examines explainable NLP (XNLP) as it is actually deployed, working through seven application domains: medicin","authors":["Mohammadi, Hadi","Bagheri, Robert A.","Giachanou, Anastasia","Oberski, Daniel L."],"url":"https://arxiv.org/abs/2502.00837","arxiv_id":"2502.00837","published":"2026-08-13"},{"title":"A Variational Analysis of Kernel Learning with Learnable Linear Transformations","abstract":"The classical kernel ridge regression problem aims to find the best fit for the output $Y$ as a function of the input data $X\\in \\mathbb{R}^d$, with a fixed choice of regularization term imposed by a given choice of a reproducing kernel Hilbert space, such as a Sobolev space. Here we consider a generalization of the kernel ridge regression problem, by introducing an extra matrix parameter $U$, which aims to detect th","authors":["Li, Yang","Ruan, Feng"],"url":"https://arxiv.org/abs/2502.11665","arxiv_id":"2502.11665","published":"2026-08-13"},{"title":"A dataset of high-resolution plantar pressures for gait analysis across varying footwear and walking speeds","abstract":"Gait refers to the patterns of limb movement generated during walking, which are unique to each individual due to both physical and behavioral traits. Walking patterns have been widely studied in biometrics, biomechanics, sports, and rehabilitation. While traditional methods rely on video and motion capture, advances in plantar pressure sensing technology now offer deeper insights into gait. However, underfoot pressu","authors":["Larracy, Robyn","Phinyomark, Angkoon","Salehi, Ala","MacDonald, Eve","Kazemi, Saeed","Bashar, Shikder Shafiul"],"url":"https://arxiv.org/abs/2502.17244","arxiv_id":"2502.17244","published":"2026-08-13"},{"title":"On Benchmarking Human-Like Intelligence in Machines","abstract":"Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition. However, we argue that many current evaluation paradigms for AI are insufficient for assessing human-like cognitive capabilities in these models. We identify a set of key shortcomings: a lack of human-","authors":["Ying, Lance","Collins, Katherine M.","Wong, Lionel","Sucholutsky, Ilia","Liu, Ryan","Weller, Adrian"],"url":"https://arxiv.org/abs/2502.20502","arxiv_id":"2502.20502","published":"2026-08-13"},{"title":"Proportional Committee Elections with Positive and Negative Votes","abstract":"In the classic committee election setting each voter approves a subset of candidates and the goal is to select $k$ winners based on these preferences. A central focus of recent research in the area has been to achieve proportional representation. In this work, we explore notions of proportionality in a more expressive setting that allows voters to vote against candidates---a common feature on online polling platforms","authors":["Kraiczy, Sonja","Papasotiropoulos, Georgios","Pierczyński, Grzegorz","Skowron, Piotr"],"url":"https://arxiv.org/abs/2503.01985","arxiv_id":"2503.01985","published":"2026-08-13"},{"title":"Program Semantic Inequivalence Game with Large Language Models","abstract":"Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics. Finding training examples to teach LLMs to solve these tasks can be challenging. In this work, we explore a method to synthetically generate code reasoning training data based on a semantic inequivalence game (SInQ): a generator agent crea","authors":["Miceli-Barone, Antonio Valerio","Belle, Vaishak","Payani, Ali"],"url":"https://arxiv.org/abs/2505.03818","arxiv_id":"2505.03818","published":"2026-08-13"},{"title":"3D Scene Generation: A Survey","abstract":"3D scene generation seeks to synthesize spatially structured, semantically meaningful, and photorealistic environments for applications such as immersive media, robotics, autonomous driving, and embodied AI. Early methods based on procedural rules offered scalability but limited diversity. Recent advances in deep generative models (e.g., GANs, diffusion models) and 3D representations (e.g., NeRF, 3D Gaussians) have e","authors":["Xie, Haozhe","Wen, Beichen","Chen, Zhaoxi","Hong, Fangzhou","Liu, Ziwei"],"url":"https://arxiv.org/abs/2505.05474","arxiv_id":"2505.05474","published":"2026-08-13"},{"title":"MedScore: Generalizable Factuality Evaluation of Free-Form Medical Answers by Domain-adapted Claim Decomposition and Verification","abstract":"While Large Language Models (LLMs) can generate fluent and convincing responses, they are not necessarily correct. This is especially apparent in the popular decompose-then-verify factuality evaluation pipeline, where LLMs evaluate generations by decomposing the generations into individual, valid claims. Factuality evaluation is especially important for medical answers, since incorrect medical information could serio","authors":["Huang, Heyuan","DeLucia, Alexandra","Tiyyala, Vijay Murari","Dredze, Mark"],"url":"https://arxiv.org/abs/2505.18452","arxiv_id":"2505.18452","published":"2026-08-13"},{"title":"BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases","abstract":"Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks. However, current text-to-SQL systems often struggle to map qualitative scientific questions into executable SQL, particularly when implicit domain reasoning is required. We introduce BiomedSQL, the first benchmark explicitly designed to evaluate scientific reasoning in text-to-SQL generation over a real-world bi","authors":["Koretsky, Mathew J.","Willey, Maya","Bianchi, Owen","Alvarado, Chelsea X.","Nayak, Tanay","Kuznetsov, Nicole"],"url":"https://arxiv.org/abs/2505.20321","arxiv_id":"2505.20321","published":"2026-08-13"},{"title":"Computational Algebra with Attention: Transformer Oracles for Border Basis Algorithms","abstract":"Solving systems of polynomial equations, particularly those with finitely many solutions, is a crucial challenge across many scientific fields. Traditional methods like Gröbner and Border bases are fundamental but suffer from high computational costs, which have motivated recent Deep Learning approaches to improve efficiency, albeit at the expense of output correctness. In this work, we introduce the Oracle Border Ba","authors":["Kera, Hiroshi","Pelleriti, Nico","Ishihara, Yuki","Zimmer, Max","Pokutta, Sebastian"],"url":"https://arxiv.org/abs/2505.23696","arxiv_id":"2505.23696","published":"2026-08-13"},{"title":"Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons","abstract":"We propose a new ternary spiking neuron model to improve the representation capacity of binary spiking neurons in deep Q-learning. Although a ternary neuron model has recently been introduced to overcome the limited representation capacity offered by binary spiking neurons, we show that its performance is worse than that of binary models in deep Q-learning tasks, contradicting previous findings from recent studies. T","authors":["Ghoreishee, Aref","Mishra, Abhishek","Walsh, John","Das, Anup","Kandasamy, Nagarajan"],"url":"https://arxiv.org/abs/2506.03392","arxiv_id":"2506.03392","published":"2026-08-13"}],"history":{"events":[{"year":2010,"text":"After being boarded by Canadian authorities, MV Sun Sea docked in British Columbia and the 492 Sri Lankan Tamil refugee claimants on board were placed in detention.","url":"https://en.wikipedia.org/wiki/MV_Sun_Sea_incident"},{"year":2004,"text":"Merely 22 hours after Tropical Storm Bonnie struck the U.S. state of Florida, Hurricane Charley inflicted further damage to the region (example pictured).","url":"https://en.wikipedia.org/wiki/Tropical_Storm_Bonnie_(2004)"},{"year":1999,"text":"The Act on National Flag and Anthem was adopted, formally establishing the Hinomaru and \"Kimigayo\" as the Japanese national flag and anthem, respectively.","url":"https://en.wikipedia.org/wiki/Act_on_National_Flag_and_Anthem"},{"year":1940,"text":"World War II: The German Luftwaffe launched an air operation, codenamed Adlertag, to destroy the British Royal Air Force; its failure indefinitely postponed the German invasion of the United Kingdom.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1918,"text":"Opha May Johnson became the first woman to enlist in the United States Marine Corps.","url":"https://en.wikipedia.org/wiki/Opha_May_Johnson"},{"year":1906,"text":"Members of the U.S. Army's all-black 25th Infantry Regiment were accused of killing a white bartender and wounding a white police officer in Brownsville, Texas, despite exculpatory evidence.","url":"https://en.wikipedia.org/wiki/25th_Infantry_Regiment_(United_States)"},{"year":1876,"text":"A performance of Das Rheingold (scene pictured) opened the first performance of Richard Wagner's Ring cycle at the inaugural Bayreuth Festival, attended by emperors and other composers.","url":"https://en.wikipedia.org/wiki/Das_Rheingold"},{"year":1868,"text":"A major earthquake near Arica, Peru (now in Chile), caused an estimated 25,000 casualties; the subsequent tsunami caused considerable damage as far away as Hawaii and New Zealand.","url":"https://en.wikipedia.org/wiki/1868_Arica_earthquake"},{"year":1762,"text":"Anglo-Spanish War: The United Kingdom captured Havana after a five-week siege, holding it until the Treaty of Paris the following year.","url":"https://en.wikipedia.org/wiki/Anglo-Spanish_War_(1762%E2%80%931763)"},{"year":1724,"text":"Bach led the Thomanerchor in Leipzig in the first performance of the chorale cantata, Nimm von uns, Herr, du treuer Gott, BWV 101.","url":"https://en.wikipedia.org/wiki/Johann_Sebastian_Bach"},{"year":1704,"text":"War of the Spanish Succession: The Duke of Marlborough led Allied forces to a crucial victory at the Battle of Blenheim.","url":"https://en.wikipedia.org/wiki/War_of_the_Spanish_Succession"},{"year":1650,"text":"General George Monck founded Monck's Regiment of Foot, the predecessor to the Coldstream Guards, the oldest regular regiment of the British Army in continuous active service.","url":"https://en.wikipedia.org/wiki/George_Monck%2C_1st_Duke_of_Albemarle"},{"year":582,"text":"Maurice was crowned as Byzantine emperor, succeeding Tiberius II Constantine.","url":"https://en.wikipedia.org/wiki/Maurice_(emperor)"}],"births":[{"year":2000,"text":"Na Jaemin, South Korean rapper, singer, dancer and actor","url":"https://en.wikipedia.org/wiki/Jaemin"},{"year":1999,"text":"Lennon Stella, Canadian singer and actress","url":"https://en.wikipedia.org/wiki/Lennon_Stella"},{"year":1998,"text":"Dalma Gálfi, Hungarian tennis player","url":"https://en.wikipedia.org/wiki/Dalma_G%C3%A1lfi"},{"year":1996,"text":"Antonia Lottner, German tennis player","url":"https://en.wikipedia.org/wiki/Antonia_Lottner"},{"year":1994,"text":"Filip Forsberg, Swedish ice hockey player","url":"https://en.wikipedia.org/wiki/Filip_Forsberg"},{"year":1993,"text":"Johnny Gaudreau, American ice hockey player (died 2024)","url":"https://en.wikipedia.org/wiki/Johnny_Gaudreau"},{"year":1993,"text":"Moses Mbye, Australian rugby league player","url":"https://en.wikipedia.org/wiki/Moses_Mbye"},{"year":1992,"text":"Katrina Gorry, Australian football player","url":"https://en.wikipedia.org/wiki/Katrina_Gorry"}],"deaths":[{"year":2026,"text":"Prichard Colón, Puerto Rican boxer (born 1992)","url":"https://en.wikipedia.org/wiki/Prichard_Col%C3%B3n"},{"year":2024,"text":"Richard Alatorre, American politician (born 1943)","url":"https://en.wikipedia.org/wiki/Richard_Alatorre"},{"year":2024,"text":"Wally Amos, American entrepreneur, founder of Famous Amos (born 1936)","url":"https://en.wikipedia.org/wiki/Wally_Amos"},{"year":2024,"text":"Sergio Donati, Italian screenwriter (born 1933)","url":"https://en.wikipedia.org/wiki/Sergio_Donati"},{"year":2024,"text":"Greg Kihn, American singer-songwriter and guitarist (born 1949)","url":"https://en.wikipedia.org/wiki/Greg_Kihn"},{"year":2024,"text":"Frank Selvy, American basketball player and coach (born 1932)","url":"https://en.wikipedia.org/wiki/Frank_Selvy"},{"year":2021,"text":"Nanci Griffith, American singer-songwriter (born 1953)","url":"https://en.wikipedia.org/wiki/Nanci_Griffith"},{"year":2018,"text":"Jim Neidhart, American wrestler (born 1955)","url":"https://en.wikipedia.org/wiki/Jim_Neidhart"}]},"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."}