{"date":"2026-07-09","schema_version":1,"captured_at":"2026-08-25T23:15:19.977Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Arthur Fery","views":597106,"rank":4,"extract":"Arthur Fery is a British professional tennis player. He has a career-high ATP singles ranking of world No. 36 achieved on 13 July 2026 and a doubles ranking of No. 201 achieved on 29 July 2024. 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Additionally, regulators demand these models to be transparent and auditable. Thus, in credit scoring, very simple predictive models such as logistic regression or decision trees are still widely used and the superior predictive power of modern machine learning algorithms cannot be fully leveraged. Significant potential ","authors":["Bücker, Michael","Szepannek, Gero","Gosiewska, Alicja","Biecek, Przemyslaw"],"url":"https://arxiv.org/abs/2009.13384","arxiv_id":"2009.13384","published":"2026-07-09"},{"title":"Research on Domain Information Mining and Theme Evolution of Scientific Papers","abstract":"In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Cross-disciplinary research results have gradually become an emerging frontier research direction. There is a certain dependence between a large number of research results. It is difficult to effectively analyze today's scientific research results when looki","authors":["Zheng, Changwei","Xue, Zhe","Liang, Meiyu","Kou, Feifei","Guan, Zeli"],"url":"https://arxiv.org/abs/2204.08476","arxiv_id":"2204.08476","published":"2026-07-09"},{"title":"Symbolic Recovery of Differential Equations: The Identifiability Problem","abstract":"Symbolic recovery of differential equations is the ambitious attempt at automating the derivation of governing equations with the use of machine learning techniques. In contrast to classical methods which assume the structure of the equation to be known and focus on the estimation of specific parameters, these algorithms aim to learn the structure and the parameters simultaneously. While the uniqueness and, therefore","authors":["Scholl, Philipp","Bacho, Aras","Boche, Holger","Kutyniok, Gitta"],"url":"https://arxiv.org/abs/2210.08342","arxiv_id":"2210.08342","published":"2026-07-09"},{"title":"Adversarial Rademacher Complexity of Deep Neural Networks","abstract":"Deep neural networks (DNNs) are highly vulnerable to adversarial attacks. Ideally, a robust model should perform well on both perturbed training data and unseen perturbed test data. While DNNs can fit perturbed training data, generalizing to perturbed test data remains a significant challenge. This motivates the study of generalization guarantees from a learning theory perspective. This paper focuses on adversarial R","authors":["Xiao, Jiancong","Fan, Yanbo","Sun, Ruoyu","Luo, Zhi-Quan"],"url":"https://arxiv.org/abs/2211.14966","arxiv_id":"2211.14966","published":"2026-07-09"},{"title":"COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images","abstract":"As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent further spread of the virus and lessen the burden on healthcare providers is a necessity. As a cheap and widely accessible medical image modality, point-of-care ultrasound (POCUS) imaging allows radiologists to identify symptoms and ass","authors":["Song, Jessy","Ebadi, Ashkan","Florea, Adrian","Xi, Pengcheng","Tremblay, Stéphane","Wong, Alexander"],"url":"https://arxiv.org/abs/2301.01679","arxiv_id":"2301.01679","published":"2026-07-09"},{"title":"InferNet: Exploiting Aggregate GPU Profiles as Side-Channel for DNN Architecture Inference","abstract":"Deep Neural Networks (DNNs) have become ubiquitous for their ability to solve problems across various domains, including computer vision, natural language processing, and speech recognition. However, as their adoption grows, they face a range of security threats, such as model stealing, architecture extraction, and manipulation, which can compromise their integrity, privacy, and functionality. Past works have relied ","authors":["Anwar, Raja Hasnain","Weiss, Jonah O'Brien","Alves, Tiago","Kundu, Sandip"],"url":"https://arxiv.org/abs/2304.03388","arxiv_id":"2304.03388","published":"2026-07-09"},{"title":"Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering","abstract":"Federated learning is a distributed machine learning technology, which realizes the balance between data privacy protection and data sharing computing. To protect data privacy, feder-ated learning learns shared models by locally executing distributed training on participating devices and aggregating local models into global models. There is a problem in federated learning, that is, the negative impact caused by the n","authors":["Zhao, Tianyu","Du, Junping","Shao, Yingxia","Guan, Zeli"],"url":"https://arxiv.org/abs/2306.12859","arxiv_id":"2306.12859","published":"2026-07-09"},{"title":"From system models to class models: An in-context learning paradigm","abstract":"Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same class? This central question drives the study presented in this paper. In response to this query, we introduce a novel paradigm for system identification, addressing two primary tasks: one-step-ahead prediction and multi-step simulation. Unlik","authors":["Forgione, Marco","Pura, Filippo","Piga, Dario"],"url":"https://arxiv.org/abs/2308.13380","arxiv_id":"2308.13380","published":"2026-07-09"},{"title":"Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification","abstract":"Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks. Existing defense mechanisms face critical limitations in deployments, such as relying on impractical assumptions (e.g., adversaries acknowledging the presence of attacks before attacking) or undermining accuracy in model training, even in benign scenarios. To address these challenges, we propo","authors":["Han, Shanshan","Wu, Wenxuan","Buyukates, Baturalp","Jin, Weizhao","Zhang, Qifan","Yao, Yuhang"],"url":"https://arxiv.org/abs/2310.04055","arxiv_id":"2310.04055","published":"2026-07-09"},{"title":"A Distributionally Robust Optimisation Approach to Fair Credit Scoring","abstract":"Credit scoring has been catalogued by the European Commission and the Executive Office of the US President as a high-risk classification task, in light of the potential harms of making loan approval decisions based on models that would be biased against certain groups. To address this concern, recent credit scoring research has considered a range of fairness-enhancing techniques put forward by the machine learning co","authors":["Casas, Pablo","Yu, Huan","Mues, Christophe"],"url":"https://arxiv.org/abs/2402.01811","arxiv_id":"2402.01811","published":"2026-07-09"},{"title":"Model-Based Reinforcement Learning Control of Reaction-Diffusion Problems","abstract":"Mathematical and computational tools have proven to be reliable in decision-making processes. In recent times, in particular, machine learning-based methods are becoming increasingly popular as advanced support tools. When dealing with control problems, reinforcement learning has been applied to decision-making in several applications, most notably in games. The success of these methods in finding solutions to comple","authors":["Schenk, Christina","Vasudevan, Aditya","Haranczyk, Maciej","Romero, Ignacio"],"url":"https://arxiv.org/abs/2402.14446","arxiv_id":"2402.14446","published":"2026-07-09"},{"title":"Lipschitz-Regularized Critics Lead to Policy Robustness Against Transition Dynamics Uncertainty","abstract":"Uncertainties in transition dynamics pose a critical challenge in reinforcement learning (RL), often resulting in performance degradation of trained policies when deployed on hardware. Many robust RL approaches follow two strategies: enforcing smoothness in actor or actor-critic modules with Lipschitz regularization, or learning robust Bellman operators. However, the first strategy does not investigate the impact of ","authors":["Chen, Xulin","Liu, Ruipeng","Gan, Zhenyu","Katz, Garrett E."],"url":"https://arxiv.org/abs/2404.13879","arxiv_id":"2404.13879","published":"2026-07-09"},{"title":"Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees","abstract":"Adversarial Imitation Learning (AIL) faces challenges with sample inefficiency because of its reliance on sufficient on-policy data to evaluate the performance of the current policy during reward function updates. In this work, we study the convergence properties and sample complexity of off-policy AIL algorithms. We show that, even in the absence of importance sampling correction, reusing samples generated by the $o","authors":["Chen, Yilei","Giammarino, Vittorio","Queeney, James","Paschalidis, Ioannis Ch."],"url":"https://arxiv.org/abs/2405.16668","arxiv_id":"2405.16668","published":"2026-07-09"},{"title":"MEGO: Learning Mixture-of-Experts for General-Purpose Binary Optimization","abstract":"Discrete optimization is ubiquitous in science and engineering. The vast array of existing discrete optimization problems, coupled with the continuous emergence of new ones, necessitates off-the-shelf optimizers capable of generating high-quality solutions for a large variety of optimization problems. This article introduces MEGO, a novel general-purpose neural optimizer for binary optimization under the black-box se","authors":["Liu, Shengcai","Wang, Zhiyuan","Ong, Yew-Soon","Yao, Xin","Tang, Ke"],"url":"https://arxiv.org/abs/2405.18884","arxiv_id":"2405.18884","published":"2026-07-09"},{"title":"Faster and Simpler Greedy Algorithm for $k$-Median and $k$-Means","abstract":"Clustering problems such as $k$-means and $k$-median are staples of unsupervised learning, and many algorithmic techniques have been developed to tackle their numerous aspects. In this paper, we focus on the class of greedy approximation algorithm, that attracted less attention than local-search or primal-dual counterparts. In particular, we study the recursive greedy algorithm developed by Mettu and Plaxton [SIAM J.","authors":["la Tour, Max Dupré","Saulpic, David"],"url":"https://arxiv.org/abs/2407.11217","arxiv_id":"2407.11217","published":"2026-07-09"},{"title":"The Costs of Pretending That There Are Data-Generating Probability Distributions in the Social World","abstract":"Machine Learning research, including work promoting fair or equitable algorithms, often relies on the concept of a data-generating probability distribution. The standard presumption is that since data points are 'sampled from' such a distribution, one can learn from observed data about this distribution and, thus, predict future data points which are also drawn from it. We argue, however, that such true proba","authors":["Höltgen, Benedikt","Williamson, Robert C."],"url":"https://arxiv.org/abs/2407.17395","arxiv_id":"2407.17395","published":"2026-07-09"},{"title":"Learning to Explore for Stochastic Gradient MCMC","abstract":"Bayesian Neural Networks(BNNs) with high-dimensional parameters pose a challenge for posterior inference due to the multi-modality of the posterior distributions. Stochastic Gradient MCMC(SGMCMC) with cyclical learning rate scheduling is a promising solution, but it requires a large number of sampling steps to explore high-dimensional multi-modal posteriors, making it computationally expensive. In this paper, we prop","authors":["Kim, SeungHyun","Jung, Seohyeon","Kim, Seonghyeon","Lee, Juho"],"url":"https://arxiv.org/abs/2408.09140","arxiv_id":"2408.09140","published":"2026-07-09"},{"title":"A Tutorial on Explainable Image Classification for Dementia Stages Using Convolutional Neural Network and Gradient-weighted Class Activation Mapping","abstract":"This paper presents a tutorial of an explainable approach using Convolutional Neural Network (CNN) and Gradient-weighted Class Activation Mapping (Grad-CAM) to classify four progressive dementia stages based on open MRI brain images. The detailed implementation steps are demonstrated with an explanation. Whilst the proposed CNN architecture is demonstrated to achieve more than 99% accuracy for the test dataset, the c","authors":["Yuen, Kevin Kam Fung"],"url":"https://arxiv.org/abs/2408.10572","arxiv_id":"2408.10572","published":"2026-07-09"},{"title":"Efficient Scene Appearance Aggregation for Level-of-Detail Rendering","abstract":"Creating an appearance-preserving level-of-detail (LoD) representation for arbitrary 3D scenes is a challenging problem. The appearance of a scene is an intricate combination of both geometry and material models, and is further complicated by correlation due to the spatial configuration of scene elements. We present a novel volumetric representation for the aggregated appearance of complex scenes and an efficient pip","authors":["Zhou, Yang","Huang, Tao","Ramamoorthi, Ravi","Sen, Pradeep","Yan, Ling-Qi"],"url":"https://arxiv.org/abs/2409.03761","arxiv_id":"2409.03761","published":"2026-07-09"},{"title":"How Learning Dynamics Drive Adversarially Robust Generalization?","abstract":"Despite being widely adopted as a canonical framework for learning robust models, adversarial training suffers from robust overfitting. Existing empirical and theoretical explorations fail to provide a satisfactory mechanistic interpretation of the phenomenon. By modeling adversarial training with momentum SGD as a discrete-time dynamical system, we propose a PAC-Bayesian analytical framework that proves time-resolve","authors":["Xu, Yuelin","Zhang, Xiao"],"url":"https://arxiv.org/abs/2410.07719","arxiv_id":"2410.07719","published":"2026-07-09"},{"title":"ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Driving","abstract":"Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic understanding of what is happening within the scene for safe interaction with other road users. Few datasets exist for the purpose of developing and training algorithms to comprehend the actions of other road users. This paper presents ROAD-Waymo, an extensive dataset for ","authors":["Khan, Salman","Teeti, Izzeddin","Alitappeh, Reza Javanmard","Stoian, Mihaela C.","Giunchiglia, Eleonora","Singh, Gurkirt"],"url":"https://arxiv.org/abs/2411.01683","arxiv_id":"2411.01683","published":"2026-07-09"},{"title":"ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts","abstract":"As Classifier-Free Guidance (CFG) has proven effective in conditional diffusion model sampling for improved condition alignment, many applications use a negated CFG term as a Negative Prompting (NP) to filter out unwanted features from samples. However, simply negating CFG guidance creates an inverted probability distribution, often distorting samples away from the marginal distribution. Inspired by recent advances i","authors":["Chang, Jinho","Lee, Changsun","Chung, Hyungjin","Ye, Jong Chul"],"url":"https://arxiv.org/abs/2411.17077","arxiv_id":"2411.17077","published":"2026-07-09"},{"title":"Deep Operator BSDE: a Numerical Scheme to Approximate Solution Operators","abstract":"Motivated by dynamic risk measures and conditional $g$-expectations, in this work we propose a numerical method to approximate the solution operator given by a Backward Stochastic Differential Equation (BSDE). The main ingredients for this are the Wiener chaos decomposition and the classical Euler scheme for BSDEs. We show convergence of this scheme under very mild assumptions, and provide a rate of convergence in mo","authors":["Diaz-Lozano, Pere","Di Nunno, Giulia"],"url":"https://arxiv.org/abs/2412.03405","arxiv_id":"2412.03405","published":"2026-07-09"},{"title":"GraphTool-Instruction: Revolutionizing Graph Reasoning in LLMs through Decomposed Subtask Instruction","abstract":"Large language models (LLMs) have been demonstrated to possess the capabilities to understand fundamental graph properties and address various graph reasoning tasks. Existing methods fine-tune LLMs to understand and execute graph reasoning tasks by specially designed task instructions. However, these Text-Instruction methods generally exhibit poor performance. Inspired by tool learning, researchers propose Tool-Instr","authors":["Wang, Rongzheng","Liang, Shuang","Chen, Qizhi","Zhang, Jiasheng","Qin, Ke"],"url":"https://arxiv.org/abs/2412.12152","arxiv_id":"2412.12152","published":"2026-07-09"},{"title":"Network Dynamics-Based Framework for Understanding Deep Neural Networks","abstract":"Advancements in artificial intelligence call for a deeper understanding of the fundamental mechanisms underlying deep learning. In this work, we propose a theoretical framework to analyze learning dynamics through the lens of dynamical systems theory. We redefine the notions of linearity and nonlinearity in neural networks by introducing two fundamental transformation units at the neuron level: order-preserving trans","authors":["Lin, Yuchen","Zhang, Yong","Feng, Sihan","Zhao, Hong"],"url":"https://arxiv.org/abs/2501.02436","arxiv_id":"2501.02436","published":"2026-07-09"}],"history":{"events":[{"year":2008,"text":"Under the belief that Israel and the United States were planning to attack its nuclear program, Iran conducted the Great Prophet III missile test and war games.","url":"https://en.wikipedia.org/wiki/Nuclear_program_of_Iran"},{"year":2006,"text":"Italy won their fourth FIFA World Cup title, defeating France 5–3 on penalties following a 1–1 draw after extra time (players pictured).","url":"https://en.wikipedia.org/wiki/Italy_national_football_team"},{"year":1995,"text":"Sri Lankan civil war: After advising civilians to take shelter in places of worship, the Sri Lanka Air Force bombed a church in Navaly, killing at least 147 people.","url":"https://en.wikipedia.org/wiki/Sri_Lankan_civil_war"},{"year":1982,"text":"A British man named Michael Fagan broke into Buckingham Palace and entered Queen Elizabeth II's bedroom when she was asleep.","url":"https://en.wikipedia.org/wiki/Michael_Fagan"},{"year":1981,"text":"Nintendo released the arcade game Donkey Kong, which featured the debut of Mario, one of the most famous characters in video-game history.","url":"https://en.wikipedia.org/wiki/Nintendo"},{"year":1962,"text":"The United States conducted the Starfish Prime high-altitude nuclear test, the largest nuclear explosion in outer space.","url":"https://en.wikipedia.org/wiki/Starfish_Prime"},{"year":1962,"text":"In a seminal moment for pop art, Andy Warhol's Campbell's Soup Cans exhibition opened at the Ferus Gallery in Los Angeles.","url":"https://en.wikipedia.org/wiki/Pop_art"},{"year":1958,"text":"An earthquake struck Lituya Bay, Alaska; the subsequent megatsunami, the largest in modern times, reached an elevation of 1,720 ft (524 m).","url":"https://en.wikipedia.org/wiki/1958_Lituya_Bay_earthquake_and_megatsunami"},{"year":1955,"text":"Albert Einstein, Bertrand Russell (pictured) and nine other preeminent intellectuals and scientists issued the Russell–Einstein Manifesto, calling for a conference to assess the dangers posed by weapons of mass destruction.","url":"https://en.wikipedia.org/wiki/Albert_Einstein"},{"year":1943,"text":"World War II: The Battle of Saipan concluded after a mass Japanese suicide attack was repelled by American forces.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1937,"text":"Nitrate film being stored in a 20th Century Fox facility spontaneously combusted, destroying more than 40,000 reels of negatives and film prints.","url":"https://en.wikipedia.org/wiki/Nitrocellulose"},{"year":1896,"text":"Politician William Jennings Bryan made his Cross of Gold speech advocating bimetallism, considered one of the greatest political speeches in American history.","url":"https://en.wikipedia.org/wiki/William_Jennings_Bryan"},{"year":1877,"text":"The inaugural Wimbledon Championship, the world's oldest tennis tournament, began in London.","url":"https://en.wikipedia.org/wiki/1877_Wimbledon_Championship"},{"year":1868,"text":"The Fourteenth Amendment to the United States Constitution, including the Citizenship Clause and the Equal Protection Clause, was ratified by the minimum required twenty-eight states.","url":"https://en.wikipedia.org/wiki/Fourteenth_Amendment_to_the_United_States_Constitution"},{"year":1850,"text":"Following Zachary Taylor's death, Millard Fillmore (pictured) became president of the United States, the last member of the Whig Party to hold that office.","url":"https://en.wikipedia.org/wiki/Zachary_Taylor"},{"year":1811,"text":"British explorer David Thompson posted a notice at the confluence of the Columbia and Snake Rivers (in modern Washington state, U.S.), claiming the area for Great Britain.","url":"https://en.wikipedia.org/wiki/David_Thompson_(explorer)"},{"year":1790,"text":"Russo-Swedish War: During the Battle of Svensksund in the Baltic Sea, the Swedish Navy captured a third of the Russian fleet.","url":"https://en.wikipedia.org/wiki/Russo-Swedish_War_(1788%E2%80%931790)"},{"year":1763,"text":"The Mozart family grand tour began, presenting the child prodigies Maria Anna and Wolfgang Amadeus across Western Europe.","url":"https://en.wikipedia.org/wiki/Mozart_family_grand_tour"}],"births":[{"year":2003,"text":"Conor Bradley, Northern Irish footballer","url":"https://en.wikipedia.org/wiki/Conor_Bradley"},{"year":1999,"text":"Claire Corlett, American voice actress","url":"https://en.wikipedia.org/wiki/Claire_Corlett"},{"year":1993,"text":"Mitch Larkin, Australian swimmer","url":"https://en.wikipedia.org/wiki/Mitch_Larkin"},{"year":1993,"text":"DeAndre Yedlin, American footballer","url":"https://en.wikipedia.org/wiki/DeAndre_Yedlin"},{"year":1991,"text":"Mitchel Musso, American actor and singer","url":"https://en.wikipedia.org/wiki/Mitchel_Musso"},{"year":1990,"text":"Earl Bamber, New Zealand race car driver","url":"https://en.wikipedia.org/wiki/Earl_Bamber"},{"year":1990,"text":"Fábio, Brazilian footballer","url":"https://en.wikipedia.org/wiki/Fabio_(footballer%2C_born_1990)"},{"year":1990,"text":"Rafael, Brazilian footballer","url":"https://en.wikipedia.org/wiki/Rafael_(footballer%2C_born_1990)"}],"deaths":[{"year":2025,"text":"Ian Blair, British police officer (born 1953)","url":"https://en.wikipedia.org/wiki/Ian_Blair"},{"year":2025,"text":"Glen Michael, British television presenter and entertainer (born 1926)","url":"https://en.wikipedia.org/wiki/Glen_Michael"},{"year":2024,"text":"Joe Bonsall, American country/gospel singer (born 1948)","url":"https://en.wikipedia.org/wiki/Joe_Bonsall"},{"year":2024,"text":"Diana Hill, New Zealand biochemist (born 1943)","url":"https://en.wikipedia.org/wiki/Diana_Hill_(scientist)"},{"year":2024,"text":"Jim Inhofe, American politician (born 1934)","url":"https://en.wikipedia.org/wiki/Jim_Inhofe"},{"year":2024,"text":"Maxine Singer, American biologist (born 1931)","url":"https://en.wikipedia.org/wiki/Maxine_Singer"},{"year":2024,"text":"Jerzy Stuhr, Polish actor (born 1947)","url":"https://en.wikipedia.org/wiki/Jerzy_Stuhr"},{"year":2022,"text":"John Gwynne, English reporter and commentator (born 1945)","url":"https://en.wikipedia.org/wiki/John_Gwynne_(commentator)"}]},"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 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Every item links to its source."}