Record 26062026 · 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
Sebastián Andrés Beccacece is an Argentine professional football manager. He last served as the head coach of the Ecuador national team.
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 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
Francisco Guillermo Ochoa Magaña, commonly known as Memo Ochoa, is a Mexican former professional footballer who played as a goalkeeper. He was a full international with the Mexico national team, which he captained, and is widely considered to be one of the gre
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
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
On 24 June 2026, doublet large strike-slip earthquakes affected northwestern and central Venezuela. The epicenter of the first earthquake was in Veroes Municipality, west of San Felipe, the capital city of Yaracuy. This earthquake, which measured Mw 7.2, occur
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
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
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
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
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
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
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
Terrion Bernard Arnold is an American professional football cornerback. He played college football for the Alabama Crimson Tide, receiving All-American honors in 2023. Arnold was selected by the Detroit Lions in the first round of the 2024 NFL draft.
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
The Czech Republic, also known as Czechia and historically known as Bohemia, is a landlocked country in Central Europe. The country is bordered by Austria to the south, Germany to the west, Poland to the northeast, and Slovakia to the southeast. The Czech Repu
LaMelo LaFrance Ball is an American professional basketball player for the Minnesota Timberwolves of the National Basketball Association (NBA). He was selected by the Charlotte Hornets with the third overall pick of the 2020 NBA draft. Ball was voted the NBA R
Blast is a 2026 Indian Tamil-language action thriller film directed by Subash K. Raj in his debut and produced by AGS Entertainment. The film stars Arjun Sarja, Abhirami and Preity Mukhundhan, with John Kokken, Vivek Prasanna, Arjun Chidambaram, Dileepan and P
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
Ecuador national football team
The Ecuador national football team, nicknamed La Tricolor and La Tri represents Ecuador in men's international football and is controlled by the Federación Ecuatoriana de Fútbol. They joined FIFA in 1926 and CONMEBOL a year later.
Curaçao, officially the Country of Curaçao, is a constituent country within the Kingdom of the Netherlands. It is an island country located in the southern Caribbean Sea, specifically the Dutch Caribbean region, about 65 km (40 mi) north of Venezuela and 80 km
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
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
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
Michael Joseph Jackson was an American singer, songwriter, dancer, and philanthropist. Dubbed the "King of Pop", he is widely regarded as one of the most culturally significant figures of the 20th century. His musical achievements broke American racial barrier
Venezuela, officially the Bolivarian Republic of Venezuela, is a country on the northern coast of South America, consisting of a continental landmass and various islands and islets in the Caribbean Sea. It comprises an area of 912,050 km2 (352,140 sq mi), with
Amelia Dimoldenberg is an English comedian, writer, and presenter. She is the creator and host of the web series Chicken Shop Date, in which she interviews celebrities in fried chicken restaurants while subjecting them to her sarcastic, deadpan, and awkward se
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Theory of the Frequency Principle for General Deep Neural Networks
Along with fruitful applications of Deep Neural Networks (DNNs) to realistic problems, recently, some empirical studies of DNNs reported a universal phenomenon of Frequency Principle (F-Principle): a DNN tends to learn a target function from low to high frequencies during the training. The F-Principle has been very useful in providing both qualitative and quantitative understandings of DNNs. In this paper, we rigorou
The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decision rule is deployed. However, in a number of settings, we may be concerned that our training sample is biased in the sense that some groups (characterized by either observable or unobservable attributes) may be under- or over-represented re
NervePool: A Simplicial Pooling Layer
For deep learning problems on graph-structured data, pooling layers are important for down sampling, reducing computational cost, and to minimize overfitting. We define a pooling layer, nervePool, for data structured as simplicial complexes, which are generalizations of graphs that include higher-dimensional simplices beyond vertices and edges; this structure allows for greater flexibility in modeling higher-order re
Understanding the Initial Condensation of Convolutional Neural Networks
Previous research has shown that fully-connected networks with small initialization and gradient-based training methods exhibit a phenomenon known as condensation during training. This phenomenon refers to the input weights of hidden neurons condensing into isolated orientations during training, revealing an implicit bias towards simple solutions in the parameter space. However, the impact of neural network structure
Consistent Distributed Ranking of Generative Models via Kernel Distances
Ranking generative models based on the fidelity and diversity of their outputs is required to identify the best generator in a group of candidate generative AI models. To rank a group of models in a conventional centralized setting, a standard score is commonly evaluated for each involved model. The selection and design of reference-based evaluation scores have been extensively studied in centralized settings, where
Techniques for supercharging academic writing with generative AI
Academic writing is an indispensable yet laborious part of the research enterprise. This Perspective maps out principles and methods for using generative artificial intelligence (AI), specifically large language models (LLMs), to elevate the quality and efficiency of academic writing. We introduce a human-AI collaborative framework that delineates the rationale (why), process (how), and nature (what) of AI engagement
Gradient Testing and Estimation by Comparisons
We study gradient testing and gradient estimation of smooth functions using only a comparison oracle that, given two points, indicates which one has the larger function value. For any smooth $f\colon\mathbb R^n\to\mathbb R$, $\mathbf{x}\in\mathbb R^n$, and $\varepsilon>0$, we design a gradient testing algorithm that determines whether the normalized gradient $\nabla f(\mathbf{x})/\|\nabla f(\mathbf{x})\|$ is $\var
This study employs cutting-edge wearable monitoring technology to conduct high-precision, high-temporal-resolution (1-second interval) cognitive load assessment on electroencephalogram (EEG) data from the FP1 channel and heart rate variability (HRV) data of secondary vocational students. By jointly analyzing these two critical physiological indicators, the research delves into their application value in assessing cog
Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization. However, having a large parameter space is considered one of the main suspects of the neural networks' vulnerability to adversarial example -- input samples crafted ad-hoc to induce a desired misclassification. Relevant literature has claimed contradictory remarks in support of and a
We present a finite-sample analysis of decentralized learning in two-player zero-sum matrix games and stochastic games, with a focus on best-response-based learning algorithms. In matrix games, the learning algorithm is payoff-based and symmetric: each player updates its policy using only its own payoff observations, incrementally moving toward an estimated smoothed best response to the opponent's latest policy.
HELIOT: LLM-Based CDSS for Adverse Drug Reaction Management
Medication errors significantly threaten patient safety, leading to adverse drug events and substantial economic burdens on healthcare systems. Clinical Decision Support Systems (CDSSs) aimed at mitigating these errors often face limitations when processing unstructured clinical data, including reliance on static databases and rule-based algorithms, frequently generating excessive alerts that lead to alert fatigue am
Byzantine-Robust Aggregation for Securing Decentralized Federated Learning
Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices. Decentralized Federated Learning (DFL) extends the FL paradigm by eliminating the central server, thereby enhancing scalability and robustness through the avoidance of a single point of failure. However, DFL faces significant challenges in optimizing security, as most By
Adversarial Robustness of AI-Generated Image Detectors in the Real World
The rapid advancement of Generative Artificial Intelligence (GenAI) capabilities is accompanied by a concerning rise in its misuse. In particular the generation of credible misinformation in the form of images poses a significant threat to the public trust in democratic processes. Consequently, there is an urgent need to develop tools to reliably distinguish between authentic and AI-generated content. The majority of
Tuning Language Models by Mixture-of-Depths Ensemble
Transformer-based Large Language Models (LLMs) traditionally rely on final-layer loss for finetuning and final-layer representations for predictions, potentially overlooking the predictive power embedded in late layers. Interpretability tools such as the logit lens show that late-layer representations already carry largely formed, task-relevant predictions; here we ask whether that observation can be turned into an a
DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning
In reinforcement learning (RL), temporal difference (TD) error is known to be related to the firing rate of dopamine neurons. It has been observed that each dopamine neuron does not behave uniformly, but each responds to the TD error in an optimistic or pessimistic manner, interpreted as a kind of distributional RL. To explain such a biological data, a heuristic model has also been introduced with learning rates asym
Towards Consistent and Efficient Dataset Distillation via Diffusion-Driven Selection
Dataset distillation provides an effective approach to reduce memory and computational costs by optimizing a compact dataset that achieves performance comparable to the full original. However, for large-scale datasets and complex deep networks (e.g., ImageNet-1K with ResNet-101), the vast optimization space hinders distillation effectiveness, limiting practical applications. Recent methods leverage pre-trained diffus
Communication-Efficient, 2D Parallel Stochastic Gradient Descent for Distributed-Memory Optimization
Distributed-memory implementations of numerical optimization algorithm, such as stochastic gradient descent (SGD), require interprocessor communication at every iteration of the algorithm. On modern distributed-memory clusters where communication is more expensive than computation, the scalability and performance of these algorithms are limited by communication cost. This work generalizes prior work on 1D $s$-step SG
Guided Persona-based AI Surveys: Can we replicate personal mobility preferences at scale using LLMs?
This study explores the potential of Large Language Models (LLMs) to generate artificial surveys, with a focus on personal mobility preferences in Germany. By leveraging LLMs for synthetic data creation, we aim to address the limitations of traditional survey methods, such as high costs, inefficiency and scalability challenges. A novel approach incorporating "Personas" - combinations of demographic and behavi
LLMs for Drug-Drug Interaction Prediction: A Comprehensive Comparison
The increasing volume of drug combinations in modern therapeutic regimens needs reliable methods for predicting drug-drug interactions (DDIs). While Large Language Models (LLMs) have revolutionized various domains, their potential in pharmaceutical research, particularly in DDI prediction, remains largely unexplored. This study thoroughly investigates LLMs' capabilities in predicting DDIs by uniquely processing m
A Systematic Survey of Semantic Role Labeling in the Era of Pretrained Language Models
Semantic role labeling (SRL) is a central natural language processing task for understanding predicate-argument structures within texts and enabling downstream applications. Despite extensive research, comprehensive surveys that critically synthesize the field from a unified perspective remain lacking. This survey makes several contributions beyond organizing existing work. We propose a unified four-dimensional taxon
Learning to Explain Air Traffic Situation
Understanding how air traffic controllers construct a mental 'picture' of complex air traffic situations is crucial but remains a challenge due to the inherently intricate, high-dimensional interactions between aircraft, pilots, and controllers. Previous work on modeling the strategies of air traffic controllers and their mental image of traffic situations often centers on specific air traffic control tasks o
Bayesian Optimization for General Reaction Conditions
General chemical reaction conditions that achieve consistently high performance across multiple substrates are important for practical applications such as library synthesis and high-throughput experimentation. However, identifying such conditions efficiently has been a longstanding challenge, as it requires decision making under uncertainty with respect to both conditions and substrates, while minimizing the number
Text-and-Image-To-Image (TI2I), an extension of Text-To-Image (T2I), integrates image inputs with textual instructions to enhance image generation. Existing methods often partially utilize image inputs, focusing on specific elements like objects or styles, or they experience a decline in generation quality with complex, multi-image instructions. To overcome these challenges, we introduce Training-Free Text-and-Image-
Circular Quasiconformal Deturbulence: Geometry-Based Restoration from Multiple Turbulent Frames
Imaging through inhomogeneous media often results in severe distortions, posing significant challenges to downstream image-processing tasks. The lack of clean paired images makes supervised learning impractical, motivating unsupervised restoration approaches. In this work, we propose the Circular Quasi-Conformal Deturbulence (CQCD) framework, an unsupervised approach that reconstructs distortion-free images from mult
Chisme: Heterogeneity-Aware Gossip Learning
As end-user device capability increases and demand for intelligent services at the Internet's edge rises, distributed learning has emerged as a key enabling technology for the intelligent edge. Existing approaches like federated learning (FL) and decentralized FL (DFL) enable privacy-preserving distributed learning among clients, while gossip learning (GL) approaches have emerged to address the potential challeng
Notable events recorded on this day and month across all years.