{"date":"2026-05-18","schema_version":1,"captured_at":"2026-08-25T23:09:00.344Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Aaron Rai","views":613044,"rank":3,"extract":"Aaron Rai is an English professional golfer who plays on the PGA Tour and the European Tour. He has won one major championship, the 2026 PGA Championship.","url":"https://en.wikipedia.org/wiki/Aaron_Rai","thumb":"https://upload.wikimedia.org/wikipedia/en/thumb/1/1c/Aaron_Rai_signature.svg/330px-Aaron_Rai_signature.svg.png?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Eurovision Song Contest 2026","views":589937,"rank":4,"extract":"The Eurovision Song Contest 2026 was the 70th edition of the Eurovision Song Contest. 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Li, S. Tang and H. Yu, Commun. Comput. Phy. 27(2):379-411, 2020], it is shown that deep neural networks built with rectified power units (RePU) as activation functions can give better approximation for sufficient smooth functions than those built with rectified linear units, by converting polynomial approximations using power series into deep neural networks with optimal complexity and no appr","authors":["Tang, Shanshan","Li, Bo","Yu, Haijun"],"url":"https://arxiv.org/abs/1911.05467","arxiv_id":"1911.05467","published":"2026-05-18"},{"title":"Epistemic Monte Carlo Tree Search","abstract":"The AlphaZero/MuZero (A/MZ) family of algorithms has achieved remarkable success across various challenging domains by integrating Monte Carlo Tree Search (MCTS) with learned models. Learned models introduce epistemic uncertainty, which is caused by learning from limited data and is useful for exploration in sparse reward environments. MCTS does not account for the propagation of this uncertainty however. To address ","authors":["Oren, Yaniv","Vadocz, Viliam","Spaan, Matthijs T. J.","Böhmer, Wendelin"],"url":"https://arxiv.org/abs/2210.13455","arxiv_id":"2210.13455","published":"2026-05-18"},{"title":"Learning to Detect and Segment for Open Vocabulary Object Detection","abstract":"Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better genera","authors":["Wang, Tao","Li, Nan"],"url":"https://arxiv.org/abs/2212.12130","arxiv_id":"2212.12130","published":"2026-05-18"},{"title":"Generative Semantic Communication: Diffusion Models Beyond Bit Recovery","abstract":"Semantic communication is expected to be one of the cores of next-generation AI-based communications. One of the possibilities offered by semantic communication is the capability to regenerate, at the destination side, images or videos semantically equivalent to the transmitted ones, without necessarily recovering the transmitted sequence of bits. The current solutions still lack the ability to build complex scenes f","authors":["Grassucci, Eleonora","Barbarossa, Sergio","Comminiello, Danilo"],"url":"https://arxiv.org/abs/2306.04321","arxiv_id":"2306.04321","published":"2026-05-18"},{"title":"Approximate and Weighted Data Reconstruction Attack in Federated Learning","abstract":"Federated Learning (FL) is a distributed learning paradigm that enables multiple clients to collaborate on building a machine learning model without sharing their private data. Although FL is considered privacy-preserved by design, recent data reconstruction attacks demonstrate that an attacker can recover clients' training data based on the parameters shared in FL. However, most existing methods fail to attack t","authors":["Song, Yongcun","Wang, Ziqi","Zuazua, Enrique"],"url":"https://arxiv.org/abs/2308.06822","arxiv_id":"2308.06822","published":"2026-05-18"},{"title":"The Linear Representation Hypothesis and the Geometry of Large Language Models","abstract":"Informally, the 'linear representation hypothesis' is the idea that high-level concepts are represented linearly as directions in some representation space. In this paper, we address two closely related questions: What does "linear representation" actually mean? And, how do we make sense of geometric notions (e.g., cosine similarity or projection) in the representation space? To answer these, we use t","authors":["Park, Kiho","Choe, Yo Joong","Veitch, Victor"],"url":"https://arxiv.org/abs/2311.03658","arxiv_id":"2311.03658","published":"2026-05-18"},{"title":"FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision","abstract":"Explainability is a vital aspect of modern AI for real-world impact and usability. The main objective of this paper is to emphasise the need to understand the predictions of Computer Vision models, specifically Convolutional Neural Network (CNN) models. Existing methods for explaining CNN predictions are largely based on Gradient-weighted Class Activation Maps (Grad-CAM) and focus solely on a single target class; thi","authors":["Silva, Ravidu Suien Rammuni","Bird, Jordan J."],"url":"https://arxiv.org/abs/2312.05975","arxiv_id":"2312.05975","published":"2026-05-18"},{"title":"Subgraph-level Universal Prompt Tuning","abstract":"In the evolving landscape of machine learning, the adaptation of pre-trained models through prompt tuning has become increasingly prominent. This trend is particularly observable in the graph domain, where diverse pre-training strategies present unique challenges in developing effective prompt-based tuning methods for graph neural networks. Previous approaches have been limited, focusing on specialized prompting func","authors":["Lee, Junhyun","Yang, Wooseong","Kang, Jaewoo"],"url":"https://arxiv.org/abs/2402.10380","arxiv_id":"2402.10380","published":"2026-05-18"},{"title":"RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition","abstract":"CLIP (Contrastive Language-Image Pre-training) uses contrastive learning from noise image-text pairs to excel at recognizing a wide array of candidates, yet its focus on broad associations hinders the precision in distinguishing subtle differences among fine-grained items. Conversely, Multimodal Large Language Models (MLLMs) excel at classifying fine-grained categories, thanks to their substantial knowledge from pre-","authors":["Liu, Ziyu","Sun, Zeyi","Zang, Yuhang","Li, Wei","Zhang, Pan","Dong, Xiaoyi"],"url":"https://arxiv.org/abs/2403.13805","arxiv_id":"2403.13805","published":"2026-05-18"},{"title":"Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks","abstract":"Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper, we introduce NEON (Neural Epistemic Operator Networks), an architecture for generating predictions with uncertainty using a single operator network backbone, which presents orders of magnitude less trainable parameters than deep ensembles ","authors":["Guilhoto, Leonardo Ferreira","Perdikaris, Paris"],"url":"https://arxiv.org/abs/2404.03099","arxiv_id":"2404.03099","published":"2026-05-18"},{"title":"An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification","abstract":"Predictive models may generate biased predictions when classifying imbalanced datasets. This happens when the model favors the majority class, leading to low performance in accurately predicting the minority class. To address this issue, balancing or resampling methods are critical data-centric AI approaches in the modeling process to improve prediction performance. However, there have been debates and questions abou","authors":["Cavus, Mustafa","Biecek, Przemysław"],"url":"https://arxiv.org/abs/2405.01557","arxiv_id":"2405.01557","published":"2026-05-18"},{"title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","abstract":"Self-attention is central to the success of Transformer architectures; however, learning the query, key, and value projections from random initialization remains challenging and computationally expensive. In this paper, we propose two complementary methods that leverage the Discrete Cosine Transform (DCT) to enhance the efficiency and performance of Vision Transformers. First, we address the initialization problem by","authors":["Pan, Hongyi","Hamdan, Emadeldeen","Zhu, Xin","Cetin, Ahmet Enis","Bagci, Ulas"],"url":"https://arxiv.org/abs/2405.13901","arxiv_id":"2405.13901","published":"2026-05-18"},{"title":"Rethinking and Red-Teaming Protective Perturbation in Personalized Diffusion Models","abstract":"Personalized diffusion models (PDMs) have become prominent for adapting pre-trained text-to-image models to generate images of specific subjects using minimal training data. However, PDMs are susceptible to minor adversarial perturbations, leading to significant degradation when fine-tuned on corrupted datasets. These vulnerabilities are exploited to create protective perturbations that prevent unauthorized image gen","authors":["Liu, Yixin","Chen, Ruoxi","Chen, Xun","Sun, Lichao"],"url":"https://arxiv.org/abs/2406.18944","arxiv_id":"2406.18944","published":"2026-05-18"},{"title":"Prompt Stability Scoring for Text Annotation with Large Language Models","abstract":"Researchers are increasingly using language models (LMs) for text annotation. These approaches rely only on a prompt telling the model to return a given output according to a set of instructions. The reproducibility of LM outputs may nonetheless be vulnerable to small changes in the prompt design. This calls into question the replicability of classification routines. To tackle this problem, researchers have typically","authors":["Barrie, Christopher","Palaiologou, Elli","Törnberg, Petter"],"url":"https://arxiv.org/abs/2407.02039","arxiv_id":"2407.02039","published":"2026-05-18"},{"title":"Density Estimation via Binless Multidimensional Integration","abstract":"We introduce the Binless Multidimensional Thermodynamic Integration (BMTI) method for nonparametric, robust, and data-efficient density estimation. BMTI estimates the logarithm of the density by initially computing log-density differences between neighbouring data points. Subsequently, such differences are integrated, weighted by their associated uncertainties, using a maximum-likelihood formulation. This procedure c","authors":["Carli, Matteo","Rodriguez, Alex","Laio, Alessandro","Glielmo, Aldo"],"url":"https://arxiv.org/abs/2407.08094","arxiv_id":"2407.08094","published":"2026-05-18"},{"title":"Social and Ethical Risks Posed by General-Purpose LLMs for Settling Newcomers in Canada","abstract":"The non-profit settlement sector in Canada supports newcomers in achieving successful integration. This sector faces increasing operational pressures amidst rising immigration targets, which highlights a need for enhanced efficiency and innovation, potentially through reliable AI solutions. The ad-hoc use of general-purpose generative AI, such as ChatGPT, might become a common practice among newcomers and service pro","authors":["Nejadgholi, Isar","Molamohammadi, Maryam","Bakhtawar, Samir"],"url":"https://arxiv.org/abs/2407.20240","arxiv_id":"2407.20240","published":"2026-05-18"},{"title":"RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation","abstract":"Graph Neural Networks (GNNs) have exhibited remarkable efficacy in learning from multi-view graph data. In the framework of multi-view graph neural networks, a critical challenge lies in effectively combining diverse views, where each view has distinct graph structure features (GSFs). Existing approaches to this challenge primarily focus on two aspects: 1) prioritizing the most important GSFs, 2) utilizing GNNs for f","authors":["Chen, Junyu","Shi, Long","Chen, Badong"],"url":"https://arxiv.org/abs/2408.07331","arxiv_id":"2408.07331","published":"2026-05-18"},{"title":"On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments","abstract":"Large-scale multi-agent systems are often deployed across wide geographic areas, where agents interact with heterogeneous environments. There is an emerging interest in understanding the role of heterogeneity in the performance of the federated versions of classic reinforcement learning algorithms. In this paper, we study synchronous federated Q-learning, which aims to learn an optimal Q-function by having $K$ agents","authors":["Wang, Leo Muxing","Yang, Pengkun","Su, Lili"],"url":"https://arxiv.org/abs/2409.03897","arxiv_id":"2409.03897","published":"2026-05-18"},{"title":"DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition","abstract":"The advancements of Large Language Models (LLMs) have spurred a growing interest in their application to Named Entity Recognition (NER) methods. However, existing datasets are primarily designed for traditional machine learning methods and are inadequate for LLM-based methods, in terms of corpus selection and overall dataset design logic. Moreover, the prevalent fixed and relatively coarse-grained entity categorizati","authors":["Luo, Hanjun","Jin, Yingbin","Li, Xinfeng","Liu, Xuecheng","Chen, Ruizhe","Shang, Tong"],"url":"https://arxiv.org/abs/2409.11022","arxiv_id":"2409.11022","published":"2026-05-18"},{"title":"Deep Learning Alternatives of the Kolmogorov Superposition Theorem","abstract":"This paper explores alternative formulations of the Kolmogorov Superposition Theorem (KST) as a foundation for neural network design. The original KST formulation, while mathematically elegant, presents practical challenges due to its limited insight into the structure of inner and outer functions and the large number of unknown variables it introduces. Kolmogorov-Arnold Networks (KANs) leverage KST for function appr","authors":["Guilhoto, Leonardo Ferreira","Perdikaris, Paris"],"url":"https://arxiv.org/abs/2410.01990","arxiv_id":"2410.01990","published":"2026-05-18"},{"title":"FlipAttack: Jailbreak LLMs via Flipping","abstract":"This paper proposes a simple yet effective jailbreak attack named FlipAttack against black-box LLMs. First, from the autoregressive nature, we reveal that LLMs tend to understand the text from left to right and find that they struggle to comprehend the text when noise is added to the left side. Motivated by these insights, we propose to disguise the harmful prompt by constructing left-side noise merely based on the p","authors":["Liu, Yue","He, Xiaoxin","Xiong, Miao","Fu, Jinlan","Deng, Shumin","Ma, Yingwei"],"url":"https://arxiv.org/abs/2410.02832","arxiv_id":"2410.02832","published":"2026-05-18"},{"title":"The MediaSpin Dataset: Post-Publication News Headline Edits Annotated for Media Bias","abstract":"We present MediaSpin, a large-scale language resource capturing how major news outlets modify headlines after publication, and MediaSpin-in-the-Wild, a complementary dataset linking these revised headlines to their downstream engagement on social media. The increasing editability of online news headlines offers new opportunities to study linguistic framing and bias through the lens of editorial revisions. The dataset","authors":["Verma, Preetika","Jaidka, Kokil"],"url":"https://arxiv.org/abs/2412.02271","arxiv_id":"2412.02271","published":"2026-05-18"},{"title":"Tube Loss: A Novel Approach for Prediction Interval Estimation","abstract":"This paper proposes a novel loss function, called 'Tube Loss', for simultaneous estimation of bounds of a Prediction Interval (PI) in the regression setup. The PIs obtained by minimizing the empirical risk based on the Tube Loss are shown to be of better quality than the PIs obtained by the existing methods in the following sense. First, it yields intervals that attain the prespecified confidence level t $\\in","authors":["Anand, Pritam","Bandyopadhyay, Tathagata","Chandra, Suresh"],"url":"https://arxiv.org/abs/2412.06853","arxiv_id":"2412.06853","published":"2026-05-18"},{"title":"From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detection","abstract":"Predictive models often degrade in performance due to evolving data distributions, a phenomenon known as data drift. Among its forms, concept drift, where the relationship between explanatory variables and the response variable changes, is particularly challenging to detect and adapt to. Traditional drift detection methods often rely on metrics such as accuracy or marginal variable distributions, which may fail to ca","authors":["Dar, Ugur","Cavus, Mustafa"],"url":"https://arxiv.org/abs/2412.11308","arxiv_id":"2412.11308","published":"2026-05-18"},{"title":"TrainMover: An Interruption-Resilient Runtime for ML Training","abstract":"Large-scale ML training jobs are frequently interrupted by hardware and software anomalies, failures, and management events. Existing solutions like checkpoint-restart or runtime reconfiguration suffer from long downtimes and degraded performance. We present TrainMover, a resilient LLM training runtime that leverages elastic and standby machines to handle interruptions with minimal downtime and zero memory overhead. ","authors":["Lao, ChonLam","Gao, Jiaqi","Cao, Jiamin","Zhang, Zhipeng","Zhang, Pengcheng","Duan, Jiangfei"],"url":"https://arxiv.org/abs/2412.12636","arxiv_id":"2412.12636","published":"2026-05-18"}],"history":{"events":[{"year":2018,"text":"Cubana de Aviación Flight 0972 crashed near Santiago de las Vegas shortly after takeoff from José Martí International Airport, killing 112 of the 113 people on board.","url":"https://en.wikipedia.org/wiki/Cubana_de_Aviaci%C3%B3n_Flight_0972"},{"year":2013,"text":"Mark Carson, an openly gay man, was murdered in a hate crime incident in New York City, prompting a 1,500-person march against anti-LGBTQ violence.","url":"https://en.wikipedia.org/wiki/Murder_of_Mark_Carson"},{"year":2009,"text":"The Sri Lanka Army killed Velupillai Prabhakaran, the leader and founder of the Liberation Tigers of Tamil Eelam, to bring an end to the 26-year Sri Lankan civil war.","url":"https://en.wikipedia.org/wiki/Sri_Lanka_Army"},{"year":2006,"text":"The Parliament of Nepal unanimously voted to strip King Gyanendra of many of his powers.","url":"https://en.wikipedia.org/wiki/Federal_Parliament_of_Nepal"},{"year":2005,"text":"The Elder Scrolls IV: Oblivion publicly debuted at the Electronic Entertainment Expo.","url":"https://en.wikipedia.org/wiki/The_Elder_Scrolls_IV%3A_Oblivion"},{"year":1996,"text":"Ireland won the Eurovision song contest for the seventh time, the highest number of wins for any country before Sweden tied it in 2023.","url":"https://en.wikipedia.org/wiki/Republic_of_Ireland"},{"year":1980,"text":"Mount St. Helens explosively erupted (pictured), killing approximately 57 people in southern Washington state, reducing hundreds of square miles to wasteland, and causing more than US$1 billion in damage.","url":"https://en.wikipedia.org/wiki/Mount_St._Helens"},{"year":1974,"text":"India conducted its first nuclear test explosion at Pokhran, the first confirmed nuclear test by a nation outside the five permanent members of the United Nations Security Council.","url":"https://en.wikipedia.org/wiki/Smiling_Buddha"},{"year":1965,"text":"Eli Cohen, a spy who is credited with gathering significant intelligence used by Israel during the Six-Day War, was publicly hanged in Syria.","url":"https://en.wikipedia.org/wiki/Eli_Cohen"},{"year":1955,"text":"Operation Passage to Freedom, the evacuation of 310,000 Vietnamese civilians, soldiers and non-Vietnamese members of the French Army from communist North Vietnam to South Vietnam following the end of the First Indochina War, ended.","url":"https://en.wikipedia.org/wiki/Operation_Passage_to_Freedom"},{"year":1952,"text":"First Indochina War: Viet Minh forces overran a French and Laotian garrison at Muong Khoua, leaving only four survivors.","url":"https://en.wikipedia.org/wiki/First_Indochina_War"},{"year":1944,"text":"The Soviet Union forcibly deported hundreds of thousands of Crimean Tatars to the Uzbek SSR and elsewhere in the country.","url":"https://en.wikipedia.org/wiki/Deportation_of_the_Crimean_Tatars"},{"year":1936,"text":"In a crime that captivated Japan, Sada Abe strangled her lover, cut off his genitals, and carried them around with her for several days until her arrest.","url":"https://en.wikipedia.org/wiki/Sada_Abe"},{"year":1933,"text":"U.S. president Franklin D. Roosevelt signed an act establishing the Tennessee Valley Authority to stimulate the economic development of the Tennessee Valley, a region particularly impacted by the Great Depression.","url":"https://en.wikipedia.org/wiki/Franklin_D._Roosevelt"},{"year":1927,"text":"Disgruntled school board treasurer Andrew Kehoe set off explosives with timers and a rifle, causing the Bath School disaster in the Bath Consolidated School in Michigan, killing 44 people in the deadliest mass murder in a school in United States history.","url":"https://en.wikipedia.org/wiki/Board_of_education"},{"year":1926,"text":"Pentecostal evangelist Aimee Semple McPherson (pictured) was reportedly kidnapped near Venice Beach in Los Angeles before reappearing five weeks later in Mexico.","url":"https://en.wikipedia.org/wiki/Pentecostalism"},{"year":1896,"text":"Ruling in the landmark decision Plessy v. Ferguson, the U.S. Supreme Court upheld the legality of racial segregation in public transportation under the \"separate but equal\" doctrine.","url":"https://en.wikipedia.org/wiki/List_of_landmark_court_decisions_in_the_United_States"},{"year":1863,"text":"American Civil War: General Ulysses S. Grant led his Army of the Tennessee across the Big Black River in preparation for the Siege of Vicksburg.","url":"https://en.wikipedia.org/wiki/American_Civil_War"}],"births":[{"year":2009,"text":"Hala Finley, American actress","url":"https://en.wikipedia.org/wiki/Hala_Finley"},{"year":2003,"text":"Travis Hunter, American football player","url":"https://en.wikipedia.org/wiki/Travis_Hunter"},{"year":2002,"text":"Alina Zagitova, Russian figure skater","url":"https://en.wikipedia.org/wiki/Alina_Zagitova"},{"year":2001,"text":"Emma Navarro, American tennis player","url":"https://en.wikipedia.org/wiki/Emma_Navarro"},{"year":2000,"text":"Ryan Sessegnon, English footballer","url":"https://en.wikipedia.org/wiki/Ryan_Sessegnon"},{"year":2000,"text":"Steven Sessegnon, English footballer","url":"https://en.wikipedia.org/wiki/Steven_Sessegnon"},{"year":1999,"text":"Laura Omloop, Belgian singer-songwriter","url":"https://en.wikipedia.org/wiki/Laura_Omloop"},{"year":1998,"text":"Polina Edmunds, American figure skater","url":"https://en.wikipedia.org/wiki/Polina_Edmunds"}],"deaths":[{"year":2026,"text":"Tom Kane, American voice actor (born 1962)","url":"https://en.wikipedia.org/wiki/Tom_Kane"},{"year":2024,"text":"Bruce Nordstrom, American businessman (born 1933)","url":"https://en.wikipedia.org/wiki/Bruce_Nordstrom"},{"year":2024,"text":"Tony O'Reilly, Irish rugby player and businessman (born 1936)","url":"https://en.wikipedia.org/wiki/Tony_O'Reilly"},{"year":2024,"text":"Alice Stewart, American political commentator (born 1966)","url":"https://en.wikipedia.org/wiki/Alice_Stewart_(commentator)"},{"year":2023,"text":"Jim Brown, American football player, civil rights activist, and actor (born 1936)","url":"https://en.wikipedia.org/wiki/Jim_Brown"},{"year":2021,"text":"Charles Grodin, American actor and talk show host (born 1935)","url":"https://en.wikipedia.org/wiki/Charles_Grodin"},{"year":2021,"text":"Yolanda Tortolero, Venezuelan politician","url":"https://en.wikipedia.org/wiki/Yolanda_Tortolero"},{"year":2020,"text":"Ken Osmond, American actor and police officer (born 1943)","url":"https://en.wikipedia.org/wiki/Ken_Osmond"}]},"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."}