Record 02072026 · 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
.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
Harry Edward Kane is an English professional footballer who plays as a striker for Bundesliga club Bayern Munich and captains the England national team. He is regarded as one of the best players in the world, one of the best strikers of his generation, and one
Julián Andrés Quiñones Quiñones is a professional footballer who plays as a forward or winger for Saudi Pro League club Al-Qadsiah. Born in Colombia, he represents the Mexico national team.
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
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
Democratic Republic of the Congo
The Democratic Republic of the Congo (DRC), also known as the DR Congo, Congo-Kinshasa, or simply the Congo, and formerly named Zaire, is a country in Central Africa. By land area, it is the second-largest country in Africa and the eleventh-largest in the worl
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
Michael Akpovie Olise is a professional footballer who plays as a winger or attacking midfielder for Bundesliga club Bayern Munich and the France national team. Widely regarded as one of the best players in the world, he is known for his creative playmaking, t
DR Congo national football team
The DR Congo national football team, recognised by FIFA as Congo DR and by CAF as DR Congo, represents the Democratic Republic of the Congo in men's international football. It is controlled by the Congolese Association Football Federation. They are nicknamed L
Melat Asfawosen Kiros is an American lawyer, graduate student, and politician. A member of the Democratic Party and the Democratic Socialists of America, she is the Democratic nominee for Colorado's 1st congressional district in 2026, having defeated 15-term i
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
Victor Edward Willis was an American singer-songwriter, who co-founded the disco group Village People. He performed as their lead singer and was co-songwriter for all of their most successful singles. In the group, Willis performed costumed as a policeman or n
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
Romelu Lukaku Bolingoli is a Belgian professional footballer who plays as a striker for Süper Lig club Fenerbahçe and the Belgium national team. Lukaku ranks second for the all-time European men's top goalscorers in international football, with 93 goals.
Lionel Mpasi Nzau is a professional footballer who plays as goalkeeper for Ligue 1 club Le Havre. Born in France, he plays for the DR Congo national team.
Senegal, officially the Republic of Senegal, is the westernmost country of mainland West Africa, situated along the Atlantic Ocean coast. It borders Mauritania to the north, Mali to the east, Guinea to the southeast and Guinea-Bissau to the southwest. Senegal
Aaron Wan-Bissaka is a professional footballer who plays as a right-back for Premier League club Aston Villa, on loan from EFL Championship club West Ham United. Born in England, he plays for the DR Congo national team.
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
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
Sebastián Andrés Beccacece is an Argentine professional football manager. He last served as the head coach of the Ecuador national team.
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
Daveigh Elizabeth Chase was an American actress.
Bosnia and Herzegovina, often referred to as Bosnia-Herzegovina or simply Bosnia, is a country in Southeast Europe. Situated on the Balkan Peninsula, it borders Serbia to the east, Montenegro to the southeast, and Croatia to the north and southwest, with a 20-
Raúl Alonso Jiménez Rodríguez is a Mexican professional footballer who plays as a striker for EFL Championship club Wolverhampton Wanderers and the Mexico national team.
LeBron Raymone James Sr. is an American professional basketball player for the Philadelphia 76ers of the National Basketball Association (NBA). Nicknamed "King James", he is the NBA's all-time leading scorer and has won four NBA championships from 10 NBA Final
Gilberto Rafael Mora Zambrano is a Mexican professional footballer who plays as an attacking midfielder for Liga MX club Xolos de Tijuana and the Mexico 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
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 Mexico national football team represents Mexico in men's international football, which is governed by the Mexican Football Federation founded in 1927. It has been an affiliate member of FIFA since 1929 and a founding affiliate member of CONCACAF since 1961
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
We present a new high-level synthesis methodology for using large language model tools to generate hardware designs. The methodology uses exclusively open-source tools excluding the large language model. As a case study, we use our methodology to generate a permuted congruential random number generator design with a wishbone interface. We verify the functionality and quality of the random number generator design usin
Semi-Bandit Learning for Monotone Stochastic Optimization
Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area. However, a significant limitation of this approach is that it requires full knowledge of the underlying probability distributions. Can we still get good (approximation)
DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis
Real-world time series analysis faces significant challenges when dealing with irregular and incomplete data. While Neural Differential Equation (NDE) based methods have shown promise, they struggle with limited expressiveness, scalability issues, and stability concerns. Conversely, Neural Flows offer stability but falter with irregular data. We introduce 'DualDynamics', a novel framework that synergistically
Prediction of Cellular Identities from Trajectory and Cell Fate Information
Determining cell identities in imaging sequences is an important yet challenging task. The conventional method for cell identification is via cell tracking, which is complex and can be time-consuming. In this study, we propose an innovative approach to cell identification during early $\textit{C. elegans}$ embryogenesis using machine learning. Cell identification during $\textit{C. elegans}$ embryogenesis would provi
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
Irregular sampling intervals and missing values in real-world time series data present challenges for conventional methods that assume consistent intervals and complete data. Neural Ordinary Differential Equations (Neural ODEs) offer an alternative approach, utilizing neural networks combined with ODE solvers to learn continuous latent representations through parameterized vector fields. Neural Stochastic Differentia
Learn Once, Edit Anywhere: Visual Direction Transfer for Diffusion Models
The rapid advancement of diffusion models has enabled the generation of high-fidelity images from textual prompts, yet achieving precise, disentangled control over specific attributes remains a significant challenge. A fundamental limitation arises because visual differences between images are often far more descriptive and nuanced than what can be captured through human-crafted text descriptions, which frequently fa
Neural Dynamic Data Valuation via Stochastic State-Adjoint Trajectories
Classical data valuation defines a data point's value through the finite marginal contribution $U(C\cup\{i\})-U(C)$, but estimating this quantity over coalitions requires repeated training and does not describe the contribution made along a stochastic training path. We ask whether marginal contributions of data points can be estimated from one coupled trajectory while retaining a verifiable relation to coalition-
We study Generated Contents Enrichment (GCE), a conditional image-generation task in which a sparse scene description is first enriched through an explicit scene representation and then rendered into semantically richer visual content. Conventional image-generation systems can produce visually realistic outputs from limited scene descriptions, but the added content is usually implicit in the generator rather than rep
Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique
Growing concerns over the theft and misuse of Large Language Models (LLMs) underscore the need for effective fingerprinting to link a model to its original version and detect misuse. We define five essential properties for a successful fingerprint: Transparency, Efficiency, Persistence, Robustness, and Unforgeability. We present a novel fingerprinting framework that provides verifiable proof of ownership while preser
Industry is moving toward autonomous, network-connected machines that detect and adapt to changing conditions, including hardware faults. Conventional fault-tolerant design duplicates hardware and reroutes control logic; reinforcement learning (RL) offers a learning-based alternative. This paper presents the first systematic comparison of two RL algorithms -- Proximal Policy Optimization (PPO) and Soft Actor-Critic (
Path Following and Stabilisation of a Bicycle Model using a Reinforcement Learning Approach
Over the years, complex control approaches have been developed to control the motion of a bicycle. Reinforcement Learning (RL), a branch of machine learning, promises easy deployment of so-called agents. Deployed agents are increasingly considered as an alternative to controllers for mechanical systems. The present work introduces an RL approach to do path following with a virtual bicycle model while simultaneously s
Kant's Critique of Pure Reason, a major contribution to the history of epistemology, proposes a table of categories to elucidate the structure of the a priori principles underlying human judgment. Artificial intelligence (AI) technology claims to simulate or replicate human judgment. To evaluate this claim, it is necessary to examine whether AI judgments exhibit the essential characteristics of human judgment. Th
Novel adaptation of video segmentation to 3D MRI: efficient zero-shot knee segmentation with SAM2
Intelligent medical image segmentation methods are rapidly evolving and being increasingly applied, yet they face the challenge of domain transfer, where algorithm performance degrades due to different data distributions between source and target domains. To address this, we introduce a method for zero-shot, single-prompt segmentation of 3D knee MRI by adapting Segment Anything Model 2 (SAM2), a general-purpose segme
2DGH: 2D Gaussian-Hermite Splatting for High-quality Rendering and Better Geometry Features
2D Gaussian Splatting has recently emerged as a significant method in 3D reconstruction, enabling novel view synthesis and geometry reconstruction simultaneously. While the well-known Gaussian kernel is broadly used, its lack of anisotropy and deformation ability leads to dim and vague edges at object silhouettes, limiting the reconstruction quality of current Gaussian splatting methods. To enhance the representation
From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems
To effectively address potential harms from Artificial Intelligence (AI) systems, it is essential to identify and mitigate system-level hazards. Current analysis approaches focus on individual components of an AI system, like training data or models, in isolation, overlooking hazards from component interactions or how they are situated within a company's development process. To this end, we draw from the establis
Continuous Speculative Decoding for Autoregressive Image Generation
Continuous visual autoregressive (AR) models have demonstrated promising performance in image generation, but their inherently sequential nature results in slow inference speed. Speculative decoding, a successful acceleration technique for large language models (LLMs), has effectively accelerated discrete visual AR models. However, the absence of an analogous theory for continuous distributions precludes its use in a
Histopathology Multi-modal Embedding for Pathology Composed Retrieval
To overcome the black-box nature of predictive AI and the hallucination risks of generative models, retrieval-based models offer an interpretable, evidence-based paradigm for pathology clinical workflow. However, real-world clinical queries are inherently interleaved (e.g., pathology images and text). Current dual-encoders suffer from an \textbf{Architectural Mismatch}, lacking the mechanism to fuse such composed que
Reinforcement learning (RL) has successfully solved various deterministic and stochastic planning problems. However, conventional RL struggles with complex real-world constraints, particularly when feasibility is explicit and depends on the current state or trajectory. In this work, we address stochastic sequential decision-making with state-dependent constraints through a real-world case study of the master stowage
Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels and avoid dysglycemia, including hyperglycemia and hypoglycemia. Despite advances in automated insulin delivery (AID) systems, achieving optimal glycemic control remains challenging. These systems integrate data from wearable devices such as insulin pumps and continuous glucose monitors (CGMs), helpi
ForAug: Mitigating Biases in Image Classification via Controlled Image Compositions
Large-scale image classification datasets exhibit strong compositional biases: objects tend to be centered, appear at characteristic scales, and co-occur with class-specific context. By exploiting such biases, models attain high in-distribution accuracy but remain fragile under distribution shifts. To address this issue, we introduce ForAug, a controlled composition augmentation scheme that factorizes each training i
Verbosity Tradeoffs and the Impact of Scale on the Faithfulness of LLM Self-Explanations
When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans. But are these explanations faithful, i.e. do they convey the factors actually responsible for the decision? In this work, we analyse counterfactual faithfulness across 75 models from 13 families. We analyze the tradeoff between conciseness and comprehensiveness, how correlational faithfulness metrics assess this t
Optimization on the Oblique Manifold for Sparse Simplex Constraints via Multiplicative Updates
Low-rank optimization problems with sparse simplex constraints involve variables that must satisfy nonnegativity, sparsity, and sum-to-1 conditions, making their optimization particularly challenging due to the interplay between low-rank structures and constraints. These problems arise in various applications, including machine learning, signal processing, environmental fields, and computational biology. In this work
Proteins move and deform to ensure their biological functions. Despite significant progress in protein structure prediction, approximating conformational ensembles at physiological conditions remains a fundamental open problem. This paper presents a novel perspective on the problem by directly targeting continuous compact representations of protein motions inferred from sparse experimental observations. We develop a
Explainability in mulimodal deep transformation models for stroke outcome prediction
Multimodal prediction models based on imaging and clinical data are increasingly used for clinical decision support, yet their interpretability remains limited. We present multimodal Deep Transformation Models (DTMs) combining statistical approaches and neural networks to achieve strong predictive performance while preserving interpretability for tabular data. A key contribution of this work is the adaption of the xA
Towards Weaker Variance Assumptions for Stochastic Optimization
We revisit a classical assumption for analyzing stochastic gradient algorithms where the squared norm of the stochastic subgradient (or the variance for smooth problems) is allowed to grow as fast as the squared norm of the optimization variable. We contextualize this assumption in view of its inception in the 1960s, its seemingly independent appearance in the recent literature, its relationship to weakest-known vari
Notable events recorded on this day and month across all years.