Record 25062026 · 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
The 2026 NBA draft was the 80th edition of the National Basketball Association's annual draft. This was the first draft since 2021 with 60 picks, as no teams forfeited second-round draft picks for free agency violations. The first round of the draft was held o
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
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
Anicet Francois Dybantsa Jr. is an American professional basketball player for the Washington Wizards of the National Basketball Association (NBA). Dybantsa played one year of college basketball for the BYU Cougars, earning the Julius Erving Award after leadin
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
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
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
Yaxel Okari Lendeborg is an American-Dominican basketball player for the Golden State Warriors of the National Basketball Association (NBA). He was drafted 11th overall in the 2026 NBA draft by the Warriors. Lendeborg played college basketball for the Arizona
Erling Braut Haaland is a Norwegian professional footballer who plays as a striker for Premier League club Manchester City and the Norway national team. Regarded as one of the best players in the world and the greatest Norwegian player of all time, he is known
Andrew Murray Burnham is a British politician who has served as Prime Minister of the United Kingdom and Leader of the Labour Party since July 2026. He has been Member of Parliament (MP) for Makerfield in Greater Manchester since June 2026, and was Mayor of Gr
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
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
List of FIFA World Cup top goalscorers
Players have scored more than 3,000 goals in the 23 men's FIFA World Cup tournaments, the goal record includes own goals scored, but not counting penalty shoot-outs. Since the first goal, by French player Lucien Laurent in 1930, nearly 1,300 footballers have s
Zendaya Maree Stoermer Coleman, known mononymously as Zendaya, is an American actress and singer-songwriter. Known for her work in television and blockbusters, her films as a leading actress have grossed over $9.8 billion worldwide. Her accolades include two P
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
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
Bradford Scott Lander is an American politician and urban planner who is the Democratic Party nominee for New York's 10th congressional district, having defeated incumbent representative Dan Goldman in the primary. A progressive, he previously served as New Yo
Thea Helseth is a Norwegian competitive rower. Her achievements include winning several national titles, and a gold medal in double scull at the 2024 European Rowing Championships.
Toy Story 5 is a 2026 American animated comedy-drama film produced by Pixar Animation Studios for Walt Disney Pictures. Directed by Andrew Stanton and written by Stanton and Kenna Harris, it is the fifth main installment in the Toy Story film series and the se
Carlos Austin Boozer Jr. is an American former professional basketball player. A two-time NBA All-Star, he played for the Cleveland Cavaliers, Utah Jazz, Chicago Bulls, and Los Angeles Lakers, and then spent his last season playing overseas with the Guangdong
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
Darializa Avila Chevalier, also known as DAC, is an American political candidate and community organizer. A member of the Democratic Party and the Democratic Socialists of America, she is the Democratic nominee for New York's 13th congressional district in 202
Cameron Nicholas Boozer is an American professional basketball player for the Memphis Grizzlies of the National Basketball Association (NBA). Boozer played college basketball for the Duke Blue Devils. He played high school basketball at Christopher Columbus Hi
Neatsville is an unincorporated community in Adair County, in the U.S. state of Kentucky. It is located at the junction of Kentucky Route 206 and Kentucky Route 76. Its elevation is 705 feet (215 m). For unknown reasons, the town's name was spelled as Neetsvil
Karim Hiram López Mondaca is a Mexican professional basketball player for the Memphis Grizzlies of the National Basketball Association (NBA). After playing for Joventut Badalona and the New Zealand Breakers between 2024 and 2026, López was selected with the 21
Patrice Émery Lumumba was a Congolese politician, independence leader and revolutionary who served as the first prime minister of the First Congolese Republic from June until September 1960, following his party's success in the May 1960 election. Lumumba was t
Kylian Mbappé Lottin is a French professional footballer who plays as a forward for La Liga club Real Madrid and captains the France national team. Widely regarded as one of the best players in the world and one of the greatest French players of all time, he i
Aday Mara Gómez is a Spanish basketball player for the Oklahoma City Thunder of the National Basketball Association (NBA). He played college basketball for the UCLA Bruins and Michigan Wolverines. Mara was an NCAA national champion and the Big Ten Defensive Pl
Voicemails for Isabelle is a 2026 American romantic comedy-drama film written and directed by Leah McKendrick and starring Zoey Deutch and Nick Robinson. The plot follows Jill who, to cope with the loss of her sister Isabelle, begins leaving voicemails on Isab
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach
In general, reliable communication via multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) requires accurate channel estimation at the receiver. The existing literature largely focuses on denoising methods for channel estimation that depend on either (i) channel analysis in the time-domain with prior channel knowledge or (ii) supervised learning techniques which require large pre-l
Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification
The problem of linear predictions has been extensively studied for the past century under pretty generalized frameworks. Recent advances in the robust statistics literature allow us to analyze robust versions of classical linear models through the prism of Median of Means (MoM). Combining these approaches in a piecemeal way might lead to ad-hoc procedures, and the restricted theoretical conclusions that underpin each
Learning Non-Vacuous Generalization Bounds from Optimization
One of the fundamental challenges in the deep learning community is to theoretically understand how well a deep neural network generalizes to unseen data. However, current approaches often yield generalization bounds that are either too loose to be informative of the true generalization error or only valid to the compressed nets. In this study, we present a simple yet non-vacuous generalization bound from the optimiz
A stability theorem for bigraded persistence barcodes
We define bigraded persistent homology modules and bigraded barcodes of a finite pseudo-metric space X using the ordinary and double homology of the moment-angle complex associated with the Vietoris-Rips filtration of X. We prove a stability theorem for the bigraded persistent double homology modules and barcodes.
Why Shallow Networks Struggle to Approximate and Learn High Frequencies
In this work, we present a comprehensive study combining mathematical and computational analysis to explain why a two-layer neural network struggles to handle high frequencies in both approximation and learning, especially when machine precision, numerical noise, and computational cost are significant factors in practice. Specifically, we investigate the following fundamental computational issues: (1) the minimal num
Deep Network Approximation: Beyond ReLU to Diverse Activation Functions
This paper explores the expressive power of deep neural networks for a diverse range of activation functions. An activation function set $\mathscr{A}$ is defined to encompass the majority of commonly used activation functions, such as $\mathtt{ReLU}$, $\mathtt{LeakyReLU}$, $\mathtt{ReLU}^2$, $\mathtt{ELU}$, $\mathtt{CELU}$, $\mathtt{SELU}$, $\mathtt{Softplus}$, $\mathtt{GELU}$, $\mathtt{SiLU}$, $\mathtt{Swish}$, $\ma
Structured Approximations of Measures
We study the approximation of probability measures in the Wasserstein-$p$ distance by structured classes of approximators, motivated by applications in imaging, machine learning, and physical measurement under sensor constraints. We obtain three sets of results. First, for measures with densities bounded away from zero on a bounded Lipschitz domain $Ω$, we prove that any approximation scheme for functions in $\mathrm
A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records
Foundation models hold promise for transforming AI in healthcare by providing modular components that are easily adaptable to downstream healthcare tasks, making AI development more scalable and cost-effective. Structured EHR foundation models, trained on coded medical records from millions of patients, demonstrated benefits including increased performance with fewer training labels, and improved robustness to distri
SparseGS: Sparse View Synthesis using 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) has recently enabled real-time rendering of unbounded 3D scenes for novel view synthesis. However, this technique requires dense training views to accurately reconstruct 3D geometry. A limited number of input views will significantly degrade reconstruction quality, resulting in artifacts such as "floaters" and "background collapse" at unseen viewpoints. In this work, we in
Transfer Learning With Densenet201 Architecture Model For Potato Leaf Disease Classification
Potato plants are plants that are beneficial to humans. Like other plants in general, potato plants also have diseases; if this disease is not treated immediately, there will be a significant decrease in food production. Therefore, it is necessary to detect diseases quickly and precisely so that disease control can be carried out effectively and efficiently. Classification of potato leaf disease can be done directly.
Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving cancer treatment outcomes and safety. This study examined machine learning (ML) models to identify cancer patients at risk of HF using electronic health records (EHRs), including traditional ML, Time-Aware long short-term memory (T-
PVF:Understanding AI Vulnerability Against SDCs
Reliability of AI systems is a fundamental concern for the successful deployment and widespread adoption of AI technologies. Unfortunately, the escalating complexity and heterogeneity of AI hardware systems make them increasingly susceptible to hardware faults, e.g., silent data corruptions (SDC), that can potentially corrupt model parameters. When this occurs during AI inference/servicing, it can potentially lead to
Privacy-Aware Visual Language Models
As Visual Language Models (VLMs) become increasingly embedded in everyday applications, ensuring they can recognise and appropriately handle privacy-sensitive content is thus essential to protect users. To this end, we conduct a comprehensive evaluation of twelve state-of-the-art VLMs and identify limitations in their understanding of visual privacy. However, existing privacy-related datasets often suffer from label
Structured and Balanced Multi-Component and Multi-Layer Neural Networks
In this work, we propose a balanced multi-component and multi-layer neural network (MMNN) structure to accurately and efficiently approximate functions with complex features, in terms of both degrees of freedom and computational cost. The main idea is inspired by a multi-component approach, in which each component can be effectively approximated by a single-layer network, combined with a multi-layer decomposition str
RotRNN: Modelling Long Sequences with Rotations
Linear recurrent neural networks, such as State Space Models (SSMs) and Linear Recurrent Units (LRUs), have recently shown state-of-the-art performance on long sequence modelling benchmarks. Despite their success, their empirical performance is not well understood and they come with a number of drawbacks, most notably their complex initialisation and normalisation schemes. In this work, we address some of these issue
Don't Fear Peculiar Activation Functions: EUAF and Beyond
In this paper, we propose a new super-expressive activation function called the Parametric Elementary Universal Activation Function (PEUAF). We demonstrate the effectiveness of PEUAF through systematic and comprehensive experiments on various industrial and image datasets, including CIFAR10, Tiny-ImageNet, and ImageNet. Moreover, we significantly generalize the family of super-expressive activation functions, whose e
Certified Robust Invariant Polytope Training in Neural Controlled ODEs
We propose a framework for training neural network controllers with certified robust forward invariant polytopes. First, we parameterize a family of lifted control systems in a higher dimensional space, where the original neural controlled system evolves on an invariant subspace of each lifted system. We use interval analysis and neural network verifiers to further construct a family of lifted embedding systems, care
Explaining a probabilistic prediction on the simplex with Shapley compositions
Originating in game theory, Shapley values are widely used for explaining a machine learning model's prediction by quantifying the contribution of each feature's value to the prediction. This requires a scalar prediction as in binary classification, whereas a multiclass probabilistic prediction is a discrete probability distribution, living on a multidimensional simplex. In such a multiclass setting the Shapl
Deep learning is advancing EEG processing for automated epileptic seizure detection and onset zone localization, yet its performance relies heavily on high-quality annotated training data. However, scalp EEG is susceptible to high noise levels, which in turn leads to imprecise annotations of the seizure timing and characteristics. This "label noise" presents a significant challenge in model training and gener
Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on Flow Annealed importance sampling Bootstrap (FAB) that evaluates the differentiable target density durin
Large language models (LLMs) primarily trained on English texts, often face biases and inaccuracies in Chinese contexts. Their limitations are pronounced in fields like Traditional Chinese Medicine (TCM), where cultural and clinical subtleties are vital, further hindered by a lack of domain-specific data, such as rheumatoid arthritis (RA). To address these issues, this paper introduces Hengqin-RA-v1, the first large
ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning
Learning efficient representations for decision-making policies is a challenge in imitation learning (IL). Current IL methods require expert demonstrations, which are expensive to collect. Additionally, they are not explicitly trained to understand the environment. Consequently, they have underdeveloped world models. Self-supervised learning (SSL) offers an alternative, as it can learn a world model from diverse, unl
Safe Learning Control with Optimality and Stability Guarantees
Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance. How to guarantee both safety and performance in learning-based control problems is an interesting yet challenging issue. This paper aims to enhance system performance with a safety guarantee by solving reinforcement learning (RL)-based optimal control problems for nonlinear systems s
LLM Program Optimization via Retrieval Augmented Search
Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We propose a blackbox adaptation method called Retrieval Augmented Search (RAS) that performs beam search over candidate optimizations; at each step, it retrieves in-context examples from a given training dataset of slow-fast program pairs to guide the LLM. Critically, we find
From Uncertain to Safe: Conformal Adaptation of Diffusion Models for Safe PDE Control
The application of deep learning for partial differential equation (PDE)-constrained control is gaining increasing attention. However, existing methods rarely consider safety requirements crucial in real-world applications. To address this limitation, we propose Safe Diffusion Models for PDE Control (SafeDiffCon), which introduce the uncertainty quantile as model uncertainty quantification to achieve optimal control
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