Record 30062026 · captured 2026-08-25
The world looked up 2026 FIFA World Cup. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
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What the most people looked up, ranked by Wikipedia pageviews for that day.
The 2026 FIFA World Cup was the 23rd FIFA World Cup, the quadrennial international men's soccer championship contested by the national teams of the member associations of FIFA. The tournament began on June 11, 2026, and concluded on July 19 with Spain winning
Zion Suzuki is a professional footballer who plays as a goalkeeper for Premier League club Aston Villa. Born in the United States, he represents the Japan national team.
Dame Penelope Anne Constance Keith was an English actress. Active in film, radio, stage and television, where she was also a presenter, Keith was primarily known for her roles in the British sitcoms The Good Life and To the Manor Born. She succeeded Laurence O
.xyz is a top-level domain name that was proposed in ICANN's new generic top-level domain (gTLD) Program for consisting of the last three letters of the Latin-script alphabet. XYZ.com and CentralNic are the registries for the domain, which was created by entre
Supergirl is a 2026 American superhero film based on the DC Comics superheroine Kara Zor-El / Supergirl. Directed by Craig Gillespie and written by Ana Nogueira, it is the second film in the DC Universe (DCU). Milly Alcock stars in the title role, alongside Ma
Stephen Antunes Eustáquio is a Canadian professional soccer player who plays as a midfielder for EFL Championship side Swansea City and vice-captains the Canada national team.
Citizen Vigilante is a 2026 action-thriller film produced, written, and directed by Uwe Boll. It stars Armie Hammer as Michael Sanders, a vigilante enraged by the breakdown of law and order who targets criminals and rapists, most of whom are migrants, and the
Orlando Daniel Gill Noldin is a Paraguayan professional footballer who plays as a goalkeeper for the Argentine Primera División club San Lorenzo and the Paraguay national team.
The 2026 Forbidden Door was a professional wrestling pay-per-view (PPV) event co-produced by All Elite Wrestling (AEW), New Japan Pro-Wrestling (NJPW), Consejo Mundial de Lucha Libre (CMLL), and World Wonder Ring Stardom. It was the fifth annual Forbidden Door
The FIFA World Cup is an international association football competition among the senior men's national teams of the members of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The tournament has been held every
Christopher Duan Johnson is an American former professional football running back. Born in Orlando, Florida, he emerged as a senior for East Carolina University, breaking out for 2,960 all-purpose yards and 24 touchdowns. Johnson was selected by the Tennessee
The following notable deaths occurred in 2026. Names are reported under the date of death, in alphabetical order. A typical entry reports information in the following sequence:Name, age, country of citizenship at birth, subsequent nationality, what subject was
Daveigh Elizabeth Chase was an American actress.
Carlo Ancelotti is an Italian professional football manager and former player who is the head coach of the Brazil national team. Nicknamed Carletto in Italy and Don Carlo in Spain, he is regarded as one of the greatest football managers of all time.
Obsession is a 2025 American supernatural horror film written, directed, and edited by Curry Barker. The film follows Bear, a music store employee who buys a supernatural toy that grants his wish for his friend Nikki to fall in love with him, which makes her b
The third season of the American fantasy drama television series House of the Dragon premiered on HBO on June 21, 2026, in the United States and concluded on August 9, 2026. It consists of eight episodes, each of approximately one hour. The season covers the e
2026 FIFA World Cup knockout stage
The knockout stage of the 2026 FIFA World Cup was the second and final stage of the competition, following the group stage. Played from June 28 to July 19, 2026, the knockout stage ended with the final, held at MetLife Stadium in East Rutherford, New Jersey. T
The FIFA World Cup is an international association football competition contested by the senior men's national teams of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The championship has been awarded every fou
House of the Dragon is an American fantasy drama television series created by George R. R. Martin and Ryan Condal for HBO. A prequel to Game of Thrones (2011–2019), it is the second television series in Martin's A Song of Ice and Fire franchise. Based on parts
Paraguay, officially the Republic of Paraguay, is a landlocked country located in the central region of South America. It borders Bolivia to the northwest and north, Brazil to the northeast and east, and Argentina to the southeast, south, and west. Paraguay ha
The FIFA Men's World Ranking is a ranking system for men's national teams in association football, first introduced in December 1992. The men's teams of the member nations of FIFA, football's world governing body, are ranked based on their game results with th
Lucas Federico Trejo is an Argentine football player who plays as a defender for Venezuelan Segunda División club Marítimo.
The Japan national football team , also known by the nickname Samurai Blue , represents Japan in men's international football. It is controlled by the Japan Football Association (JFA), the governing body for football in Japan.
I Will Find You is an American crime drama miniseries made for Netflix, adapted from the 2023 novel of the same name by Harlan Coben, who served as executive producer. The miniseries stars Sam Worthington, Britt Lower, Milo Ventimiglia, and Erin Richards. It p
Lauryn Noelle Hill is an American rapper, singer, and songwriter. She is considered one of the most influential musicians of her time. A definitive figure in neo soul and a pioneer of rap-singing and melodic rap, The Telegraph credited Hill with the populariza
Lionel Andrés "Leo" Messi is an Argentine professional footballer who plays as a forward for and captains both Major League Soccer (MLS) club Inter Miami and the Argentina national team. Widely regarded as one of the greatest players in history, Messi has set
Neymar da Silva Santos Júnior, known mononymously as Neymar, is a Brazilian professional footballer who plays as an attacking midfielder or a forward for Campeonato Brasileiro Série A club Santos. A goalscorer and playmaker, he is known for his dribbling, tech
The Sheep Detectives is a 2026 mystery comedy-drama film directed by Kyle Balda and written by Craig Mazin, based on the 2005 novel Three Bags Full by Leonie Swann. The film features an ensemble cast including Hugh Jackman, Nicholas Braun, Nicholas Galitzine,
Cristiano Ronaldo dos Santos Aveiro is a Portuguese professional footballer who plays as a forward for and captains the Saudi Pro League club Al-Nassr and the Portugal national team. Nicknamed CR7, he is widely regarded as one of the greatest players in histor
Paraguay national football team
The Paraguay national football team represents Paraguay in men's international football competitions, and are controlled by the Asociación Paraguaya de Fútbol. Paraguay is a member of CONMEBOL. The Albirroja has qualified for nine FIFA World Cup competitions,
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
TCSPs (Temporal Constraint Satisfaction Problems) [Dechter et al. 1991] get rid of unary constraints by binarizing them after having added an "origin of the world" variable. In this work, we look at the constraints between the "origin of the world" variable and the other variables, as the (binarized) domains of these other variables. With this in mind, we define a notion of arc-consistency for TCSPs,
Ensemble Learning Based Classification Algorithm Recommendation
Selecting an appropriate classification algorithm for a given data set remains a challenging problem in data mining and machine learning. Existing algorithm recommendation models are typically trained with individual learners and rely on only one type of meta-feature, which may limit their ability to capture the diverse characteristics of classification problems. This paper proposes a multi-view ensemble meta-learnin
Multiply Robust Causal Mediation Analysis with Continuous Treatments
In many applications, researchers are interested in the direct and indirect causal effects of a treatment or exposure on an outcome of interest. Mediation analysis offers a rigorous framework for identifying and estimating these causal effects. For binary treatments, efficient estimators for the direct and indirect effects are presented by Tchetgen Tchetgen and Shpitser (2012) based on the influence function of the p
Adversarial learning is used to test the robustness of machine learning algorithms under attack and create attacks that deceive the anomaly detection methods in Industrial Control System (ICS). Given that security assessment of an ICS demands that an exhaustive set of possible attack patterns is studied, in this work, we propose an association rule mining-based attack generation technique. The technique has been impl
Lagrangian Inference for Ranking Problems
We propose a novel combinatorial inference framework to conduct general uncertainty quantification in ranking problems. We consider the widely adopted Bradley-Terry-Luce (BTL) model, where each item is assigned a positive preference score that determines the Bernoulli distributions of pairwise comparisons' outcomes. Our proposed method aims to infer general ranking properties of the BTL model. The general ranking
Deep learning-based NLP Data Pipeline for EHR Scanned Document Information Extraction
Scanned documents in electronic health records (EHR) have been a challenge for decades, and are expected to stay in the foreseeable future. Current approaches for processing often include image preprocessing, optical character recognition (OCR), and text mining. However, there is limited work that evaluates the choice of image preprocessing methods, the selection of NLP models, and the role of document layout. The im
A Data-Centric Approach to Generate Invariants for a Smart Grid Using Machine Learning
Cyber-Physical Systems (CPS) have gained popularity due to the increased requirements on their uninterrupted connectivity and process automation. Due to their connectivity over the network including intranet and internet, dependence on sensitive data, heterogeneous nature, and large-scale deployment, they are highly vulnerable to cyber-attacks. Cyber-attacks are performed by creating anomalies in the normal operation
Reproducing sensory induced hallucinations via neural fields
Understanding sensory-induced cortical patterns in the primary visual cortex V1 is an important challenge both for physiological motivations and for improving our understanding of human perception and visual organisation. In this work, we focus on pattern formation in the visual cortex when the cortical activity is driven by a geometric visual hallucination-like stimulus. In particular, we present a theoretical frame
Resolution for Constrained Pseudo-Propositional Logic
This work, shows how propositional resolution can be generalized to obtain a resolution proof system for constrained pseudo-propositional logic (CPPL), which is an extension resulted from inserting the natural numbers with few constraints symbols into the alphabet of propositional logic and adjusting the underling language accordingly. Unlike the construction of CNF formulas which are restricted to a finite set of cl
Assortment Planning with Sponsored Products
In the rapidly evolving landscape of retail, assortment planning plays a crucial role in determining the success of a business. With the rise of sponsored products and their increasing prominence in online marketplaces, retailers face new challenges in effectively managing their product assortment in the presence of sponsored products. Remarkably, previous research in assortment planning largely overlooks the existen
Modelling Human Values for Value-Aware Multi-Agent Systems
One of today's most pressing societal challenges is building AI systems whose behaviour, or the behaviour it enables within communities of interacting human and artificial agents, aligns with relevant human values. To address this challenge, we propose a formal computational framework for representing human values that provides the foundational structures required for value-aware reasoning in multi-agent systems.
We show that a deep neural network (DNN) trained to construct a stochastic discount factor (SDF) admits an additive decomposition separating nonlinear characteristic discovery from the pricing rule that aggregates them. This decomposition yields a linear factor representation governed by the Portfolio Tangent Kernel (PTK), which summarizes the network's learned features. In population, the implied SDF converges t
Radiology report generation (RRG) has attracted significant attention due to its potential to reduce the workload of radiologists. The performance of current RRG approaches remains unsatisfactory against clinical standards. This paper introduces a novel RRG method, MLLM-RRG, that integrates multimodal large language models (MLLMs) with various types of clinical knowledge to generate accurate and comprehensive chest X
SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces
Given the remarkable achievements in image generation through diffusion models, the research community has shown increasing interest in extending these models to video generation. Recent diffusion models for video generation have predominantly utilized attention layers to extract temporal features. However, attention layers are limited by their computational costs, which increase quadratically with the sequence lengt
New methods to compute the generalized chi-square distribution
We present four new mathematical methods, two exact and two approximate, along with open-source software, to compute the cdf, pdf and inverse cdf of the generalized chi-square distribution. Some methods are geared for speed, while others are designed to be accurate far into the tails, using which we can also measure large values of the discriminability index $d'$ between multivariate normal distributions. We comp
Mitigating cybersecurity risk in electric vehicle (EV) charging demand forecasting plays a crucial role in the safe operation of collective EV chargings, the stability of the power grid, and the cost-effective infrastructure expansion. However, existing methods either suffer from the data privacy issue and the susceptibility to cyberattacks or fail to consider the spatial correlation among different stations. To addr
Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver
In modern intelligent transportation systems (ITS), particularly in freight transportation and logistics, real-time route planning is crucial. It presents unique challenges driven by high uncertainty in service requests, where the number of service customers can vary drastically, ranging from hundreds to thousands. Existing neural methods struggle to maintain performance under such significant variations, which sever
Personalized Additive Modeling for Multi-level Federated Learning
Contemporary AI faces the challenge of balancing generality with user-specific personalization. In federated learning (FL), this challenge is amplified by highly heterogeneous client data with complex non-IID patterns beyond standard IID assumptions. Many existing FL methods are designed for relatively restricted heterogeneity settings (e.g., a fixed number of clusters or a fixed form of personalization), limiting th
BEACON: A Bayesian Optimization Inspired Strategy for Efficient Novelty Search
Novelty search (NS) aims to uncover diverse system behaviors through simulation or experiment without requiring a pre-specified scalar objective. This capability is especially relevant to modern discovery problems in chemistry, materials science, and molecular design, where researchers often seek broad coverage of attainable property space rather than a single optimum and where each evaluation may require a costly co
Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework
Existing evaluations of tabular synthesis models rely primarily on low-order statistics and downstream task performance, leaving multivariate causal relationships that go beyond pairwise correlations largely unmeasured. We argue that a systematic evaluation on high-order structural information is a crucial first step in addressing this issue in tabular data synthesis. In this paper, we present high-order structural c
Generalization error of min-norm interpolators in transfer learning
This paper establishes the generalization error of pooled min-$\ell_2$-norm interpolation in transfer learning, where data from diverse distributions are available. Min-norm interpolators arise naturally as implicit regularized limits of modern machine learning algorithms. Prior work has characterized their out-of-distribution risk when samples from the test distribution are unavailable during training. In many appli
Towards Complete Causal Explanation with Expert Knowledge
We study the problem of restricting a Markov equivalence class of maximal ancestral graphs (MAGs) to only those MAGs that contain certain edge marks, which we refer to as expert or orientation knowledge. Such a restriction of the Markov equivalence class can be uniquely represented by a restricted essential ancestral graph. Our contributions are several-fold. First, we prove certain properties for the entire Markov e
OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale
Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than optimally by state-of-the-art solvers because the expertise required to formulate and solve these problems limits the widespread adoption of optimization tools and techniques. We introduce a Large Language Model (LLM)-based system designed to
ProSpec RL: Plan Ahead, then Execute
Imagining potential outcomes of actions before execution helps agents make more informed decisions, a prospective thinking ability fundamental to human cognition. However, mainstream model-free Reinforcement Learning (RL) methods lack the ability to proactively envision future scenarios, plan, and guide strategies. These methods typically rely on trial and error to adjust policy functions, aiming to maximize cumulati
Factor analysis, often regarded as a Bayesian variant of matrix factorization, offers superior capabilities in capturing uncertainty, modeling complex dependencies, and ensuring robustness. As the deep learning era arrives, factor analysis is receiving less and less attention due to their limited expressive ability. On the contrary, contrastive learning has emerged as a potent technique with demonstrated efficacy in
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