Record 01072026 · 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
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
.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
Daveigh Elizabeth Chase was an American actress.
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
Antonio Eromonsele Nordby Nusa is a Norwegian professional footballer who plays as a winger or attacking midfielder for Bundesliga club RB Leipzig and the Norway national team. He has been described by many as one of the most exciting young prospects in world
Maya Joint is an Australian professional tennis player. She has a career-high singles ranking of world No. 28 by the WTA, achieved on 16 February 2026, and a best doubles ranking of No. 33, set on the same date. Joint has won four WTA Tour titles, two in singl
Serena Jameka Williams is an American professional tennis player. She was ranked as the world No. 1 in women's singles by the Women's Tennis Association (WTA) for 319 weeks, and finished as the year-end No. 1 five times. Williams won 73 WTA Tour–level singles
Issa Laye Lucas Jean Diop is a professional footballer who plays as a centre-back for Premier League club Ipswich Town. Born in France, and a former France youth international, he plays for the Morocco national team.
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
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
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
Cody Mathès Gakpo is a Dutch professional footballer who plays as a left winger or centre forward for Premier League club Liverpool and the Netherlands national team.
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 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
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
Ivory Coast, also known as Côte d'Ivoire and officially as the Republic of Côte d'Ivoire, is a country on the southern coast of West Africa. Its capital city of Yamoussoukro is located in the centre of the country, while its largest city and economic centre is
Ismael Saibari Ben El Basra is a professional footballer who plays as an attacking midfielder or forward for Bundesliga club Bayern Munich. Born in Spain, he represents the Morocco national team.
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
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
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
Morocco national football team
The Morocco national football team has represented Morocco in men's international football since their first international match in 1957. It is controlled by the Royal Moroccan Football Federation (FRMF), the governing body for football in Morocco. It has been
Achraf Hakimi Mouh is a professional footballer who plays as a right-back for Ligue 1 club Paris Saint-Germain and captains the Morocco national team. Known for his attacking prowess and strong defensive contribution, he is widely regarded as one of the best r
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
Morocco, officially the Kingdom of Morocco, is a country in the Maghreb region of North Africa. It has coastlines on the Mediterranean Sea to the north and the Atlantic Ocean to the west, and has land borders with Algeria to the east; the Spanish exclaves of C
The 2022 FIFA World Cup was the 22nd FIFA World Cup, the quadrennial world championship for national football teams organised by FIFA. It took place in Qatar from 20 November to 18 December 2022, after the country was awarded the hosting rights in 2010. It was
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
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
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.
Yassine Bounou, also known mononymously as Bono, is a Moroccan professional footballer who plays as a goalkeeper for Saudi Pro League club Al Hilal and the Morocco national team.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Monte Carlo Information Geometry: The dually flat case
Exponential families and mixture families are parametric probability models that can be geometrically studied as smooth statistical manifolds with respect to any statistical divergence like the Kullback-Leibler (KL) divergence or the Hellinger divergence. When equipping a statistical manifold with the KL divergence, the induced manifold structure is dually flat, and the KL divergence between distributions amounts to
Multiple Testing of Linear Forms for Noisy Matrix Completion
Many important tasks of large-scale recommender systems can be naturally cast as testing multiple linear forms for noisy matrix completion. These problems, however, present unique challenges because of the subtle bias-and-variance tradeoff of and an intricate dependence among the estimated entries induced by the low-rank structure. In this paper, we develop a general approach to overcome these difficulties by introdu
Corruption Robust Offline Reinforcement Learning with Human Feedback
We study data corruption robustness for reinforcement learning with human feedback (RLHF) in an offline setting. Given an offline dataset of pairs of trajectories along with feedback about human preferences, an $\varepsilon$-fraction of the pairs is corrupted (e.g., feedback flipped or trajectory features manipulated), capturing an adversarial attack or noisy human preferences. We aim to design algorithms that identi
ROSpace: Intrusion Detection Dataset for a ROS2-Based Cyber-Physical System
Most of the intrusion detection datasets to research machine learning-based intrusion detection systems (IDSs) are devoted to cyber-only systems, and they typically collect data from one architectural layer. Additionally, often the attacks are generated in dedicated attack sessions, without reproducing the realistic alternation and overlap of normal and attack actions. We present a dataset for intrusion detection by
Detection Latencies of Anomaly Detectors: An Overlooked Perspective ?
The ever-evolving landscape of attacks, coupled with the growing complexity of ICT systems, makes crafting anomaly-based intrusion detectors (ID) and error detectors (ED) a difficult task: they must accurately detect attacks, and they should promptly perform detections. Although improving and comparing the detection capability is the focus of most research works, the timeliness of the detection is less considered and
Perturbation Effects on Robustness and Individual Fairness
Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings. However, existing evaluation protocols typically assess these dimensions in isolation, thereby obscuring critical failure modes. To bridge this gap, we formalize Robust Individual Fairness (RIF): under semantic-preserving (truth-condition-p
A Realistic Protocol for Evaluation of Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) allows training deep learning models for classification and localization (LOC) using only global class-level labels. The absence of bounding box (bbox) supervision during training raises challenges in the literature for hyper-parameter tuning, model selection, and evaluation. WSOL methods rely on a validation set with bbox annotations for model selection, and a test set wi
PoseGravity: Pose Estimation from Points and Lines with Axis Prior
This paper presents a new algorithm to estimate absolute camera pose given an axis of the camera's rotation matrix. Current algorithms solve the problem via algebraic solutions on limited input domains. This paper shows that the problem can be solved efficiently by finding the intersection points of a hyperbola and the unit circle. The solution can flexibly accommodate combinations of point and line features in m
Despite their empirical success across a wide range of generative tasks, the fundamental principles underlying the ability of diffusion models to learn data distributions are poorly understood. In this work, we develop a new mathematical framework that explains how diffusion models can effectively learn low-dimensional distributions from a finite number of training samples without suffering from the curse of dimensio
Quality Perceptions and Intended Engagement in Response to AI-Generated and AI-Assisted News
The increasing use of artificial intelligence (AI) in news production raises important questions about how audiences perceive and respond to AI-generated journalism. This preregistered survey experiment (N = 599, German-speaking Switzerland) examines (i) perceptions of article quality (measured as credibility, readability, and expertise) across news excerpts that were human-written, AI-assisted, or fully AI-generated
Detailed and photorealistic 3D human modeling is essential for various applications and has seen tremendous progress. However, full-body reconstruction from a monocular RGB image remains challenging due to the ill-posed nature of the problem and sophisticated clothing topology with self-occlusions. In this paper, we propose PSHuman, a novel framework that explicitly reconstructs human meshes utilizing priors from the
Collaborative Knowledge Distillation via a Learning-by-Education Node Community
A novel Learning-by-Education Node Community framework (LENC) for Collaborative Knowledge Distillation (CKD) is presented, which facilitates continual collective learning through effective knowledge exchanges among diverse deployed Deep Neural Network (DNN) peer nodes. These DNNs dynamically and autonomously adopt either the role of a student, seeking knowledge, or that of a teacher, imparting knowledge, fostering a
Automated Discovery of Operable Dynamics from Videos
Dynamical systems form the foundation of scientific discovery, traditionally modeled with predefined state variables such as the angle and angular velocity, and differential equations such as the equation of motion for a single pendulum. We introduce a framework that automatically discovers a low-dimensional and operable representation of system dynamics, including a set of compact state variables that preserve the s
Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models
As AI-generated content increasingly populates the web, generative AI models are at growing risk of being trained on their own outputs, a process known as AI autophagy. This feedback loop has been shown to induce model collapse, typically characterized by a loss of diversity in generated content. However, existing work offers a limited understanding of this phenomenon and relies on mitigation strategies that assume a
Stealthy Multi-Task Adversarial Attacks
Deep neural networks are highly vulnerable to adversarial perturbations, raising serious safety concerns in the real-world systems. While prior work mainly explores single-task attacks or jointly degrading all tasks in multi-task models, practical scenarios often demand more selective and stealthy attack strategies. To address this challenge, we propose Stealthy Multi-Task Adversarial Attack (SMTA$^{2}$), a novel fra
Filterless Snapshot Hyperspectral Imaging using Guided Patch Diffusion
We consider the problem of reconstructing a HxWx31 hyperspectral image from a $H\times W$ grayscale snapshot measurement that is captured using only a single diffractive lens and a filterless panchromatic photosensor. This problem is severely ill-posed, but we present a model that produces high-quality results in simulation and experiment. We make efficient use of limited training data by creating a conditional denoi
Sampling Out-of-Distribution Chemical Spaces via Bayesian Flow
Generating novel molecules with higher properties than the training space, namely the out-of-distribution generation, is important for de novo drug design. However, it is not easy for distribution learning-based models, for example diffusion models, to solve this challenge as these methods are designed to fit the distribution of training data as close as possible. In this paper, we show that Bayesian flow network, es
As increasingly capable large language models (LLMs) emerge, researchers have begun exploring their potential for subjective tasks. While recent work demonstrates that LLMs can be aligned with diverse human perspectives, evaluating this alignment on downstream tasks (e.g., hate speech detection) remains challenging due to the use of inconsistent datasets across studies. To address this issue, in this resource paper w
Joint Optimization for 4D Human-Scene Reconstruction in the Wild
Reconstructing human motion and its surrounding environment is crucial for understanding human-scene interaction and predicting human movements in the scene. While much progress has been made in capturing human-scene interaction in constrained environments, those prior methods can hardly reconstruct the natural and diverse human motion and scene context from web videos. In this work, we propose JOSH, a novel optimiza
A Statistical Learning Approach to Mediterranean Cyclones
Mediterranean cyclones are extreme meteorological events of which much less is known compared to their tropical, oceanic counterparts. The raising interest in such phenomena is due to their impact on a region increasingly more affected by climate change, but a precise characterization remains a non trivial task. In this work we showcase how a Bayesian algorithm (Latent Dirichlet Allocation) can classify Mediterranean
Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition
Emotions play a crucial role in human behavior and decision-making, making emotion recognition a key area of interest in human-computer interaction (HCI). This study addresses the challenges of emotion recognition by integrating facial expression analysis with electroencephalogram (EEG) signals, introducing a novel multimodal framework-Milmer. The proposed framework employs a transformer-based fusion approach to effe
Compositional Concept-Based Neuron-Level Interpretability for Deep Reinforcement Learning
Deep reinforcement learning (DRL) has successfully addressed many complex control problems. However, the neural networks representing policies or values remain opaque, undermining trust in high-stakes applications. While concept-based methods have shown promise in deciphering internal representations in computer vision, applying them to DRL is impeded by the absence of pre-defined semantic concepts in continuous stat
Deep learning methods have been widely used for Human Activity Recognition (HAR) using recorded signals from Iner-tial Measurement Units (IMUs) sensors that are installed on various parts of the human body. For this type of HAR, sev-eral challenges exist, the most significant of which is the analysis of multivarious IMU sensors data. Here, we introduce a Hierarchically Unsupervised Fusion (HUF) model designed to extr
Mantis: Lightweight Foundation Model for Time Series Classification
While foundation models have revolutionized various domains, their application to time series classification remains rather under-explored, with existing literature predominantly focused on forecasting. To bridge this gap, we introduce \textbf{Mantis}, a transformer-based foundation model pre-trained exclusively on synthetic data via self-supervised contrastive learning. We demonstrate that effective tokenization is
Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection
Large Language Models (LLMs) often hallucinate, limiting their reliability in sensitive applications. In black-box settings, several self-consistency-based techniques have been proposed for hallucination detection. We empirically show that these methods perform nearly as well as a supervised (black-box) oracle, leaving limited room for further gains within this paradigm. To address this limitation, we explore cross-m
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