{"date":"2026-06-08","schema_version":1,"captured_at":"2026-08-25T23:11:36.703Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":17},"attention":[{"title":"Alexander Zverev","views":476702,"rank":3,"extract":"Alexander \"Sascha\" Zverev is a German professional tennis player. He has a career-high singles ranking of world No. 2 by the ATP achieved in June 2022. Zverev has won 25 ATP Tour singles titles, including the 2026 French Open, a gold medal at the 2020 Tokyo Ol","url":"https://en.wikipedia.org/wiki/Alexander_Zverev","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/2/2f/Alexander_Zverev.jpg/330px-Alexander_Zverev.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Obsession (2025 film)","views":303010,"rank":5,"extract":"Obsession is a 2025 American supernatural horror film written, directed, and edited by Curry Barker. 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On the NYU-v2 dataset, our method achieves much better depth estimation accuracy compared to single-image based methods.","authors":["Huang, Zhengyu","Du, Weizhi","Norris, Theodore B."],"url":"https://arxiv.org/abs/2203.07904","arxiv_id":"2203.07904","published":"2026-06-08"},{"title":"Characterizing Learning Dynamics under Relative Reparameterization of Singular Models","abstract":"A common way to analyze learning of statistical models is to consider operations in the models parameter space, however this becomes challenging when there is no one-to-one mapping between the parameter space and the underlying statistical model space. Such ``singular models'' occur frequently and exhibit a characteristic decrease in convergence speed of learning trajectories due to attractor behaviors. In th","authors":["Esser, Pascal Mattia","Nielsen, Frank"],"url":"https://arxiv.org/abs/2206.08598","arxiv_id":"2206.08598","published":"2026-06-08"},{"title":"Hermes: Accelerating Long-Latency Load Requests via Perceptron-Based Off-Chip Load Prediction","abstract":"Long-latency load requests continue to limit the performance of high-performance processors. To increase the latency tolerance of a processor, architects have primarily relied on two key techniques: sophisticated data prefetchers and large on-chip caches. In this work, we show that: 1) even a sophisticated state-of-the-art prefetcher can only predict half of the off-chip load requests on average across a wide range o","authors":["Bera, Rahul","Kanellopoulos, Konstantinos","Balachandran, Shankar","Novo, David","Olgun, Ataberk","Sadrosadati, Mohammad"],"url":"https://arxiv.org/abs/2209.00188","arxiv_id":"2209.00188","published":"2026-06-08"},{"title":"A fuzzy adaptive metaheuristic algorithm for identifying sustainable, economical, lightweight, and earthquake-resistant reinforced concrete cantilever retaining walls","abstract":"In earthquake-prone zones, the seismic performance of reinforced concrete cantilever (RCC) retaining walls is significant. In this study, the seismic performance was investigated using horizontal and vertical pseudo-static coefficients. To tackle RCC weights and forces resulting from these earth pressures, 26 constraints for structural strengths and geotechnical stability along with 12 geometric variables are associa","authors":["Keivanian, Farshid","Chiong, Raymond","Kashani, Ali R.","Gandomi, Amir H."],"url":"https://arxiv.org/abs/2302.00198","arxiv_id":"2302.00198","published":"2026-06-08"},{"title":"A fuzzy adaptive evolutionary-based feature selection and machine learning framework for single and multi-objective body fat prediction","abstract":"Predicting body fat can provide medical practitioners and users with essential information for preventing and diagnosing heart diseases. Hybrid machine learning models offer better performance than simple regression analysis methods by selecting relevant body measurements and capturing complex nonlinear relationships among selected features in modelling body fat prediction problems. There are, however, some disadvant","authors":["Keivanian, Farshid","Chiong, Raymond","Fan, Zongwen"],"url":"https://arxiv.org/abs/2303.11949","arxiv_id":"2303.11949","published":"2026-06-08"},{"title":"A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning","abstract":"Symmetry is present throughout nature and continues to play an increasingly central role in physics and machine learning. Fundamental symmetries, such as Poincaré invariance, allow physical laws discovered in laboratories on Earth to be extrapolated to the farthest reaches of the universe. Symmetry is essential to achieving this extrapolatory power in machine learning applications. For example, translation invariance","authors":["Otto, Samuel E.","Zolman, Nicholas","Kutz, J. Nathan","Brunton, Steven L."],"url":"https://arxiv.org/abs/2311.00212","arxiv_id":"2311.00212","published":"2026-06-08"},{"title":"Twin: Tuning Learning Rate and Weight Decay of Deep Homogeneous Classifiers without Validation","abstract":"We introduce Tune without Validation (Twin), a simple and effective pipeline for tuning learning rate and weight decay of homogeneous classifiers without validation sets, eliminating the need to hold out data and avoiding the two-step process. Twin leverages the margin-maximization dynamics of homogeneous networks and an empirical scaling law that links training and test losses across hyper-parameter configurations. ","authors":["Brigato, Lorenzo","Mougiakakou, Stavroula"],"url":"https://arxiv.org/abs/2403.05532","arxiv_id":"2403.05532","published":"2026-06-08"},{"title":"SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning","abstract":"Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in complex environments, such as stabilizing a tokamak fusion reactor or minimizing the drag force on an object in a fluid flow. However, DRL requires an abundance of training examples and may become prohibitively expensive for many applications. In addition, the reliance on deep neural networks","authors":["Zolman, Nicholas","Lagemann, Christian","Fasel, Urban","Kutz, J. Nathan","Brunton, Steven L."],"url":"https://arxiv.org/abs/2403.09110","arxiv_id":"2403.09110","published":"2026-06-08"},{"title":"pTNAS: Progressive Neural Architecture Search for Tabular Data","abstract":"Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost that scales poorly with context size. Deep neural networks remain a highly competitive and more efficient modeling paradigm when equipped with well-designed architectures; however, identifying such architectures in a data-adaptive and budget-aware manner remains challengi","authors":["Xing, Naili","Cai, Shaofeng","Zeng, Lingze","Zhu, Jiaqi","Lu, Peng","Pei, Jian"],"url":"https://arxiv.org/abs/2403.10318","arxiv_id":"2403.10318","published":"2026-06-08"},{"title":"Robustly estimating heterogeneity in factorial data using Rashomon Partitions","abstract":"In both observational data and randomized control trials, researchers select statistical models to articulate how the outcome of interest varies with combinations of observable covariates. Choosing a model that is too simple can obfuscate important heterogeneity in outcomes between covariate groups, while too much complexity risks identifying spurious patterns. In this paper, we propose a novel Bayesian framework for","authors":["Venkateswaran, Aparajithan","Sankar, Anirudh","Chandrasekhar, Arun G.","McCormick, Tyler H."],"url":"https://arxiv.org/abs/2404.02141","arxiv_id":"2404.02141","published":"2026-06-08"},{"title":"T2LM: Long-Term 3D Human Motion Generation from Multiple Sentences","abstract":"In this paper, we address the challenging problem of long-term 3D human motion generation. Specifically, we aim to generate a long sequence of smoothly connected actions from a stream of multiple sentences (i.e., paragraph). Previous long-term motion generating approaches were mostly based on recurrent methods, using previously generated motion chunks as input for the next step. However, this approach has two drawbac","authors":["Lee, Taeryung","Baradel, Fabien","Lucas, Thomas","Lee, Kyoung Mu","Rogez, Gregory"],"url":"https://arxiv.org/abs/2406.00636","arxiv_id":"2406.00636","published":"2026-06-08"},{"title":"Certified Robustness to Data Poisoning in Gradient-Based Training","abstract":"Modern machine learning pipelines leverage large amounts of public data, making it infeasible to guarantee data quality and leaving models open to poisoning and backdoor attacks. Provably bounding model behavior under such attacks remains an open problem. In this work, we address this challenge by developing the first framework providing provable guarantees on the behavior of models trained with potentially manipulat","authors":["Sosnin, Philip","Müller, Mark N.","Baader, Maximilian","Tsay, Calvin","Wicker, Matthew"],"url":"https://arxiv.org/abs/2406.05670","arxiv_id":"2406.05670","published":"2026-06-08"},{"title":"The Rlign Algorithm for Enhanced Electrocardiogram Analysis through R-Peak Alignment for Explainable Classification and Clustering","abstract":"Electrocardiogram (ECG) recordings have long been vital in diagnosing different cardiac conditions. Recently, research in the field of automatic ECG processing using machine learning methods has gained importance, mainly by utilizing deep learning methods on raw ECG signals. A major advantage of models like convolutional neural networks (CNNs) is their ability to effectively process biomedical imaging or signal data.","authors":["Plagwitz, Lucas","Bickmann, Lucas","Fujarski, Michael","Brenner, Alexander","Gobalakrishnan, Warnes","Eckardt, Lars"],"url":"https://arxiv.org/abs/2407.15555","arxiv_id":"2407.15555","published":"2026-06-08"},{"title":"Conformal Disentanglement and Latent-Space Curation: A Neural Framework for Perspective Synthesis, Differentiation and Targeted Generation","abstract":"Many scientific and engineering problems involve observing a common phenomenon through multiple heterogeneous sensors or measurement modalities. Such observations typically contain both information shared across sensors, reflecting the underlying system, and sensor-specific or extraneous components arising from measurement processes or environmental effects. Disentangling these contributions is essential when sensor-","authors":["Kevrekidis, George A.","Koronaki, Eleni D.","Giovanis, Dimitris G.","Kevrekidis, Yannis G."],"url":"https://arxiv.org/abs/2408.15344","arxiv_id":"2408.15344","published":"2026-06-08"},{"title":"A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling","abstract":"Since the various MR contrasts of a given anatomy contain redundant information, one contrast can be used to guide the reconstruction of another undersampled contrast acquired subsequently in the same session. To solve this reconstruction problem leveraging multi-contrast side information, several end-to-end learning-based methods have been proposed. However, a key challenge is the requirement for large paired traini","authors":["Rao, Chinmay","van Osch, Matthias","Pezzotti, Nicola","de Bresser, Jeroen","van Buchem, Mark","Beljaards, Laurens"],"url":"https://arxiv.org/abs/2409.13477","arxiv_id":"2409.13477","published":"2026-06-08"},{"title":"Graph-Dictionary Signal Model for Sparse Representations of Multivariate Data","abstract":"Representing and exploiting multivariate signals requires capturing relations between variables, which we can represent by graphs. Graph dictionaries allow to describe complex relational information as a sparse sum of simpler structures, but no prior model exists to infer such underlying structure elements from data. We define a novel Graph-Dictionary signal model, where a finite set of graphs characterizes relations","authors":["Cappelletti, William","Frossard, Pascal"],"url":"https://arxiv.org/abs/2411.05729","arxiv_id":"2411.05729","published":"2026-06-08"},{"title":"Modeling AdaGrad, RMSProp, and Adam with Integro-Differential Equations","abstract":"In this paper, we propose a continuous-time formulation for the AdaGrad, RMSProp, and Adam optimization algorithms by modeling them as first-order integro-differential equations. We perform numerical simulations of these equations, along with stability and convergence analyses, to demonstrate their validity as accurate approximations of the original algorithms. Our results indicate a strong agreement between the beha","authors":["Heredia, Carlos"],"url":"https://arxiv.org/abs/2411.09734","arxiv_id":"2411.09734","published":"2026-06-08"},{"title":"The Utility and Complexity of in- and out-of-Distribution Machine Unlearning","abstract":"Machine unlearning, the process of selectively removing data from trained models, is increasingly crucial for addressing privacy concerns and knowledge gaps post-deployment. Despite this importance, existing approaches are often heuristic and lack formal guarantees. In this paper, we analyze the fundamental utility, time, and space complexity trade-offs of approximate unlearning, providing rigorous certification anal","authors":["Allouah, Youssef","Kazdan, Joshua","Guerraoui, Rachid","Koyejo, Sanmi"],"url":"https://arxiv.org/abs/2412.09119","arxiv_id":"2412.09119","published":"2026-06-08"},{"title":"Training-free Ultra Small Model for Universal Sparse Reconstruction in Compressed Sensing","abstract":"Pre-trained large models attract widespread attention in recent years, but they face challenges in applications that require high interpretability or have limited resources, such as physical sensing, medical imaging, and bioinformatics. Compressed Sensing (CS) is a well-proved theory that drives many recent breakthroughs in these applications. However, as a typical under-determined linear system, CS suffers from exce","authors":["Tang, Chaoqing","Zhuang, Huanze","Tian, Guiyun","Zeng, Zhenli","Ding, Yi","Liu, Wenzhong"],"url":"https://arxiv.org/abs/2501.11592","arxiv_id":"2501.11592","published":"2026-06-08"},{"title":"Should You Use Your Large Language Model to Explore or Exploit?","abstract":"We evaluate the ability of the current generation of large language models (LLMs) to help a decision-making agent facing an exploration-exploitation tradeoff. While previous work has largely study the ability of LLMs to solve combined exploration-exploitation tasks, we take a more systematic approach and use LLMs to explore and exploit in silos in various (contextual) bandit tasks. We find that reasoning models show ","authors":["Harris, Keegan","Slivkins, Aleksandrs"],"url":"https://arxiv.org/abs/2502.00225","arxiv_id":"2502.00225","published":"2026-06-08"},{"title":"PolarQuant: Leveraging Polar Transformation for Efficient Key Cache Quantization and Decoding Acceleration","abstract":"The KV cache in large language models is a dominant factor in memory usage, limiting their broader applicability. Quantizing the cache to lower bit widths is an effective way to reduce computational costs; however, previous methods struggle with quantizing key vectors due to outliers, resulting in excessive overhead. We propose a novel quantization approach called PolarQuant, which efficiently addresses the outlier c","authors":["Wu, Songhao","Lv, Ang","Feng, Xiao","Zhang, Yufei","Zhang, Xun","Yin, Guojun"],"url":"https://arxiv.org/abs/2502.00527","arxiv_id":"2502.00527","published":"2026-06-08"},{"title":"3D-Grounded Vision-Language Framework for Robotic Task Planning: Automated Prompt Synthesis and Supervised Reasoning","abstract":"Vision-language models (VLMs) have achieved remarkable success in scene understanding and perception tasks, enabling robots to plan and execute actions adaptively in dynamic environments. However, most multimodal large language models lack robust 3D scene localization capabilities, limiting their effectiveness in fine-grained robotic operations. Additionally, challenges such as low recognition accuracy, inefficiency,","authors":["Tang, Guoqin","Jia, Qingxuan","Huang, Zeyuan","Chen, Gang","Ji, Ning","Yao, Zhipeng"],"url":"https://arxiv.org/abs/2502.08903","arxiv_id":"2502.08903","published":"2026-06-08"},{"title":"Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models","abstract":"Ensuring trustworthiness in machine learning (ML) systems is crucial as they become increasingly embedded in high-stakes domains. This paper advocates for integrating causal methods into machine learning to navigate the trade-offs among key principles of trustworthy ML, including fairness, privacy, robustness, accuracy, and explainability. While these objectives should ideally be satisfied simultaneously, they are of","authors":["Binkyte, Ruta","Sheth, Ivaxi","Jin, Zhijing","Havaei, Mohammad","Schölkopf, Bernhard","Fritz, Mario"],"url":"https://arxiv.org/abs/2502.21123","arxiv_id":"2502.21123","published":"2026-06-08"},{"title":"ADAGE: Active Defenses Against GNN Extraction","abstract":"Graph Neural Networks (GNNs) achieve high performance in various real-world applications, such as drug discovery, traffic states prediction, and recommendation systems. The fact that building powerful GNNs requires a large amount of training data, powerful computing resources, and human expertise turns the models into lucrative targets for model stealing attacks. Prior work has revealed that the threat vector of stea","authors":["Xu, Jing","Boenisch, Franziska","Dziedzic, Adam"],"url":"https://arxiv.org/abs/2503.00065","arxiv_id":"2503.00065","published":"2026-06-08"},{"title":"Chunking the Critic: A Transformer-based Soft Actor-Critic with N-Step Returns","abstract":"We introduce a sequence-conditioned critic for Soft Actor-Critic (SAC) that models trajectory context with a lightweight Transformer and trains on aggregated $N$-step targets. Unlike prior approaches that (i) score state-action pairs in isolation or (ii) rely on actor-side action chunking to handle long horizons, our method strengthens the critic itself by conditioning on short trajectory segments and integrating mul","authors":["Tian, Dong","Celik, Onur","Neumann, Gerhard"],"url":"https://arxiv.org/abs/2503.03660","arxiv_id":"2503.03660","published":"2026-06-08"}],"history":{"events":[{"year":2009,"text":"Two American journalists, having been arrested for illegal entry into North Korea, were sentenced to twelve years hard labor before being pardoned two months later.","url":"https://en.wikipedia.org/wiki/2009_imprisonment_of_American_journalists_by_North_Korea"},{"year":2008,"text":"Seven people were killed and 10 others were injured in a vehicle and knife attack in Tokyo, Japan.","url":"https://en.wikipedia.org/wiki/Akihabara_massacre"},{"year":2007,"text":"A major storm caused the bulk carrier Pasha Bulker to run aground in New South Wales, Australia.","url":"https://en.wikipedia.org/wiki/2007_New_South_Wales_storms"},{"year":1982,"text":"Falklands War: The Argentine Air Force attacked the neutral oil tanker Hercules while she was traversing the South Atlantic.","url":"https://en.wikipedia.org/wiki/Falklands_War"},{"year":1982,"text":"Falklands War: The Argentine Air Force attacked British transport ships (damaged ship pictured) while unloading supplies off Bluff Cove in the Falkland Islands, killing 56 British servicemen and wounding 150 others.","url":"https://en.wikipedia.org/wiki/Falklands_War"},{"year":1972,"text":"Vietnam War: Associated Press photographer Nick Ut took a Pulitzer Prize–winning photograph of a naked nine-year-old Phan Thi Kim Phuc fleeing after being burned by napalm.","url":"https://en.wikipedia.org/wiki/Vietnam_War"},{"year":1967,"text":"The Israeli Air Force attacked the U.S. Navy intelligence ship USS Liberty in international waters, killing 34 people and wounding 171 others.","url":"https://en.wikipedia.org/wiki/Israeli_Air_Force"},{"year":1953,"text":"An F5 tornado struck Flint and Beecher, Michigan, causing 116 fatalities, 844 injuries and $19 million in damage during a larger tornado outbreak sequence.","url":"https://en.wikipedia.org/wiki/1953_Flint%E2%80%93Beecher_tornado"},{"year":1950,"text":"Thomas Blamey became the only Australian to attain the rank of field marshal.","url":"https://en.wikipedia.org/wiki/Thomas_Blamey"},{"year":1949,"text":"Nineteen Eighty-Four, a dystopian political novel by the English writer George Orwell about life under a fictional totalitarian government, was published.","url":"https://en.wikipedia.org/wiki/Nineteen_Eighty-Four"},{"year":1941,"text":"World War II: The Allies commenced the Syria–Lebanon campaign against Vichy French possessions in the Levant.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1929,"text":"Margaret Bondfield became the first female member of the Cabinet of the United Kingdom when she was named Minister of Labour by Ramsay MacDonald.","url":"https://en.wikipedia.org/wiki/Margaret_Bondfield"},{"year":1862,"text":"American Civil War: The Confederate Army won a resounding victory at the Battle of Cross Keys, one of the two decisive battles in Jackson's Valley campaign.","url":"https://en.wikipedia.org/wiki/American_Civil_War"},{"year":1826,"text":"In York, Upper Canada, members of the Family Compact destroyed William Lyon Mackenzie's printing press in the Types Riot after Mackenzie accused them of corruption.","url":"https://en.wikipedia.org/wiki/York%2C_Upper_Canada"},{"year":1783,"text":"Laki, a volcanic fissure in Iceland, began an eight-month eruption, triggering a major famine and causing widespread fluoride poisoning.","url":"https://en.wikipedia.org/wiki/Laki"},{"year":1776,"text":"American Revolutionary War: British forces defeated the Continental Army at the Battle of Trois-Rivières, the last battle of the American invasion of Quebec.","url":"https://en.wikipedia.org/wiki/American_Revolutionary_War"},{"year":218,"text":"Led by the inexperienced Gannys, Elagabalus's legions defeated the forces of Roman emperor Macrinus at the Battle of Antioch.","url":"https://en.wikipedia.org/wiki/Gannys"}],"births":[{"year":2004,"text":"Francesca Capaldi, American actress","url":"https://en.wikipedia.org/wiki/Francesca_Capaldi"},{"year":2000,"text":"Jan Paul van Hecke, Dutch footballer","url":"https://en.wikipedia.org/wiki/Jan_Paul_van_Hecke"},{"year":1997,"text":"Jeļena Ostapenko, Latvian tennis player","url":"https://en.wikipedia.org/wiki/Je%C4%BCena_Ostapenko"},{"year":1995,"text":"Jean Butez, French footballer","url":"https://en.wikipedia.org/wiki/Jean_Butez"},{"year":1995,"text":"Ferland Mendy, French footballer","url":"https://en.wikipedia.org/wiki/Ferland_Mendy"},{"year":1994,"text":"Liv Morgan, American professional wrestler","url":"https://en.wikipedia.org/wiki/Liv_Morgan"},{"year":1989,"text":"Timea Bacsinszky, Swiss tennis player","url":"https://en.wikipedia.org/wiki/Timea_Bacsinszky"},{"year":1986,"text":"Keith Gill, American financial analyst and investor","url":"https://en.wikipedia.org/wiki/Keith_Gill"}],"deaths":[{"year":2024,"text":"Ramoji Rao, Indian businessman, media proprietor and film producer (born 1936)","url":"https://en.wikipedia.org/wiki/Ramoji_Rao"},{"year":2024,"text":"Chet Walker, American basketball player (born 1940)","url":"https://en.wikipedia.org/wiki/Chet_Walker"},{"year":2023,"text":"Pat Robertson, American televangelist (born 1930)","url":"https://en.wikipedia.org/wiki/Pat_Robertson"},{"year":2022,"text":"Paula Rego, Portuguese-British visual artist (born 1935)","url":"https://en.wikipedia.org/wiki/Paula_Rego"},{"year":2019,"text":"Andre Matos, Brazilian heavy metal musician (born 1971)","url":"https://en.wikipedia.org/wiki/Andre_Matos"},{"year":2018,"text":"Anthony Bourdain, American chef and travel documentarian (born 1956)","url":"https://en.wikipedia.org/wiki/Anthony_Bourdain"},{"year":2015,"text":"Chea Sim, Cambodian commander and politician (born 1932)","url":"https://en.wikipedia.org/wiki/Chea_Sim"},{"year":2014,"text":"Alexander Imich, Polish-American chemist, parapsychologist, and academic (born 1903)","url":"https://en.wikipedia.org/wiki/Alexander_Imich"}]},"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."}