Record 20042026 · captured 2026-08-25
The world looked up WrestleMania 42. 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.
WrestleMania 42, also promoted as WrestleMania Vegas, was a 2026 professional wrestling pay-per-view (PPV) and livestreaming event produced by WWE. It was the 42nd annual WrestleMania and took place as a two-night event on Saturday, April 18 and Sunday, April
Nahui Ollin is a 16th-century concept in Aztec/Mexica cosmology with a variety of meanings. Nahui translates to "four," and Ollin translates to "movement" or "motion." Ollin was primarily portrayed in Aztec codices as two interlaced lines, each portrayed with
Dhurandhar: The Revenge is a 2026 Indian Hindi-language spy action-thriller film written and directed by Aditya Dhar. It is produced by Dhar, Lokesh Dhar, and Jyoti Deshpande under Jio Studios and B62 Studios. It is a sequel to the 2025 film Dhurandhar and the
Bhooth Bangla is a 2026 Indian Hindi-language comedy horror film directed by Priyadarshan and produced by Akshay Kumar, Ekta Kapoor and Shobha Kapoor under Balaji Motion Pictures and Cape of Good Films. The film stars Akshay Kumar, Paresh Rawal, Jisshu Sengupt
List of highest-grossing Indian films
This ranking lists the highest-grossing Indian films produced by Indian cinema, based on conservative global box office estimates as reported by organisations classified as green by Wikipedia. The figures are not adjusted for inflation. However, there is no of
Beef is an American comedy drama anthology television series created by Lee Sung Jin for Netflix. Season 1 stars Steven Yeun and Ali Wong as Danny Cho and Amy Lau, two strangers whose involvement in a road rage incident escalates into a prolonged feud. Appeari
Limonene is a slightly yellow-green liquid aliphatic hydrocarbon classified as a cyclic monoterpene, and is the major component in the fragrance and essential oil of citrus fruit peels, taking its name from Italian limone ("lemon").
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
Lee Cronin's The Mummy is a 2026 supernatural horror film written and directed by Lee Cronin. A reimagining of The Mummy franchise based around the Nasmaranian, an ancient Egyptian demon that possesses victims with exorcism themes, the film stars Jack Reynor,
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
Saraya-Jade Bevis is an English professional wrestler and actress. As of April 2026, she is signed to WWE, where she performs under the ring name Paige. She is also known for her tenure in All Elite Wrestling (AEW) from 2022 to 2025, where she performed under
2026 Bulgarian parliamentary election
Parliamentary elections were held in Bulgaria on 19 April 2026 to elect 240 members of the National Assembly. The vote was triggered by the resignation of the Zhelyazkov government on 11 December 2025 following widespread anti-corruption protests. It marked th
Project Hail Mary is a 2026 American science fiction film produced and directed by Phil Lord and Christopher Miller and written by Drew Goddard, based on the 2021 novel of the same name by Andy Weir. It stars Ryan Gosling, who also produced the film, as Ryland
Ranjana Kumari is an Indian social activist, writer, and academic. She is the director of the Centre for Social Research in Delhi and chairwoman of Women Power Connect, a national organization of women's groups.
A diglyceride, or diacylglycerol (DAG), is a glyceride consisting of two fatty acid chains covalently bonded to a glycerol molecule through ester linkages. Two possible forms exist, 1,2-diacylglycerols and 1,3-diacylglycerols. Diglycerides are natural componen
Charlize Theron is a South African and American actress and producer. One of the world's highest-paid actresses, her accolades include an Academy Award and a Golden Globe Award, in addition to nominations for three BAFTAs and two Emmy Awards.
John Alan Robinson was a philosopher, mathematician, and computer scientist. He was a professor emeritus at Syracuse University.
Youth is a 2026 Indian Tamil-language coming of age romantic comedy film written and directed by Ken Karunas, in his directorial debut, who also enacts the lead role. The cast also includes Suraj Venjaramoodu, Devadarshini, Anishma Anilkumar, Meenakshi Dinesh
Matthew Thomas Fitzpatrick is an English professional golfer. After winning the 2013 U.S. Amateur, he later won his first professional tournament at the 2015 British Masters. He has five wins on the PGA Tour as of 2026, including a major championship at the 20
Mathis Rayan Cherki is a French professional footballer who plays as an attacking midfielder or winger for Premier League club Manchester City and the France national team.
Luke Douglas Kennard is an American professional basketball player for the Phoenix Suns of the National Basketball Association (NBA). He played college basketball for the Duke Blue Devils and was drafted by the Detroit Pistons with the 12th pick in the 2017 NB
Nadia Farès was a Moroccan-French actress and singer.
Justin Drew Bieber is a Canadian singer. Regarded as a prominent figure in contemporary popular music, he rose to fame in the late 2000s after being discovered by American talent manager Scooter Braun, who signed him to Raymond Braun Media Group (RBMG). Bieber
Akśaya Tṛtīyā, also known as Ākhā Tīja, is an annual Jaina and Hindu spring festival. It falls on the third tithi of the bright half of the Hindu month of Vaiśākha.
Deepika Prakash Padukone is an Indian actress who works predominantly in Hindi films. Her accolades include three Filmfare Awards. Time named her one of the 100 most influential people in the world in 2018 and awarded her the Time100 Impact Award in 2022.
Dexter Lawrence II, nicknamed "Sexy Dexy", is an American professional football nose tackle for the Cincinnati Bengals of the National Football League (NFL). He played college football for the Clemson Tigers and was selected by the New York Giants in the first
The stance of a vehicle is a term that describes a vehicle's suspension height and the fitment of the wheels in the fender arches. It may refer to any vehicle, including sports cars, pickup trucks and off-road vehicles. However, it is mostly associated with lo
Euphoria is an American psychological drama television series created and written by Sam Levinson for HBO. Based on the Israeli miniseries of the same name created by Ron Leshem, the series stars Zendaya as drug-addicted teenager Rue Bennett, who also serves a
The Pitt is an American medical drama television series created by R. Scott Gemmill and executive produced by John Wells and Noah Wyle. It is Gemmill, Wells, and Wyle's second collaboration; they previously worked together on ER (1994–2009). It stars Wyle, Tra
Roommates is a 2026 American black comedy film directed by Chandler Levack and written by Jimmy Fowlie and Ceara O'Sullivan. The film stars Sadie Sandler and Chloe East.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Automatic Modeling of Social Concepts Evoked by Art Images as Multimodal Frames
Social concepts referring to non-physical objects--such as revolution, violence, or friendship--are powerful tools to describe, index, and query the content of visual data, including ever-growing collections of art images from the Cultural Heritage (CH) field. While much progress has been made towards complete image understanding in computer vision, automatic detection of social concepts evoked by images is still a c
Revisiting 16-bit Neural Network Training: A Practical Approach for Resource-Limited Learning
With the increasing complexity of machine learning models, managing computational resources like memory and processing power has become a critical concern. Mixed precision techniques, which leverage different numerical precisions during model training and inference to optimize resource usage, have been widely adopted. However, access to hardware that supports lower precision formats (e.g., FP8 or FP4) remains limited
Seeing the Intangible: Survey of Image Classification into High-Level and Abstract Categories
The field of Computer Vision (CV) is increasingly shifting towards ``high-level'' visual sensemaking tasks, yet the exact nature of these tasks remains unclear and tacit. This survey paper addresses this ambiguity by systematically reviewing research on high-level visual understanding, focusing particularly on Abstract Concepts (ACs) in automatic image classification. Our survey contributes in three main ways
Adaptive Spatio-temporal Estimation on the Graph Edges via Line Graph Transformation
Spatial-temporal estimation of signals on graph edges is challenging because most conventional Graph Signal Processing techniques are defined on the graph nodes. Leveraging the Line Graph transform, the Line Graph Least Mean Square (LGLMS) algorithm unifies the Line Graph transformation with classical adaptive filters, reinterpreting online estimation techniques for time-varying signals on graph edges. LGLMS leverage
Deep Learning Based Amharic Chatbot for FAQs in Universities
University students often spend a considerable amount of time seeking answers to common questions from administrators or teachers. This can become tedious for both parties, leading to a need for a solution. In response, this paper proposes a chatbot model that utilizes natural language processing and deep learning techniques to answer frequently asked questions (FAQs) in the Amharic language. Chatbots are computer pr
Automatic Combination of Sample Selection Strategies for Few-Shot Learning
In few-shot learning, the selection of samples has a significant impact on the performance of the model. While effective sample selection strategies are well-established in supervised settings, research on large language models largely overlooks them, favouring strategies specifically tailored to individual in-context learning settings. In this paper, we propose a new method for Automatic Combination of SamplE Select
The increasing demand for automatic high-level image understanding, particularly in detecting abstract concepts (AC) within images, underscores the necessity for innovative and more interpretable approaches. These approaches need to harmonize traditional deep vision methods with the nuanced, context-dependent knowledge humans employ to interpret images at intricate semantic levels. In this work, we leverage situated
The study of associations between an individual's age and imaging and non-imaging data is an active research area that attempts to aid understanding of the effects and patterns of aging. In this work we have conducted a supervoxel-wise association study between both volumetric and tissue density features in coronary computed tomography angiograms and the chronological age of a subject, to understand the localized
Internet memes, channels for humor, social commentary, and cultural expression, are increasingly used to spread toxic messages. Studies on the computational analyses of toxic memes have significantly grown over the past five years, and the only three surveys on computational toxic meme analysis cover only work published until 2022, leading to inconsistent terminology and unexplored trends. Our work fills this gap by
Citizen reporting platforms help the public and authorities stay informed about sexual harassment incidents. However, the high volume of data shared on these platforms makes reviewing each individual case challenging. Therefore, a summarization algorithm capable of processing and understanding various code-mixed languages is essential. In recent years, Large Language Models (LLMs) have shown exceptional performance i
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback
As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge. Traditional alignment methods, relying on human or LLM annotated datasets, are limited by their resource-intensive nature, inherent subjectivity, misalignment with real-world user preferences, and the risk of feedback loops that amplify model biases. To overcome these limitations, we
Estimating Joint Interventional Distributions from Marginal Interventional Data
In this paper we show how to exploit interventional data to acquire the joint conditional distribution of all the variables using the Maximum Entropy principle. To this end, we extend the Causal Maximum Entropy method to make use of interventional data in addition to observational data. Using Lagrange duality, we prove that the solution to the Causal Maximum Entropy problem with interventional constraints lies in the
Goal-based Neural Physics Vehicle Trajectory Prediction Model
Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, thereby influencing traffic safety and efficiency. Numerous studies have been conducted to predict short-term vehicle trajectories in the immediate future. However, long-term trajectory prediction remains a major challenge due to accumulated
Resource-efficient equivariant quantum convolutional neural networks
Equivariant quantum neural networks (QNNs) are promising variational models that exploit symmetries to improve machine learning capabilities. Despite theoretical developments in equivariant QNNs, their implementation on near-term quantum devices remains challenging due to limited computational resources. This study proposes a resource-efficient model of equivariant quantum convolutional neural networks (QCNNs) called
Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All
We study the design of iterative combinatorial auctions (ICAs). The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, recent work has proposed machine learning (ML)-based preference elicitation algorithms that aim to elicit only the most critical information from bidders to maximize efficiency. However, while the SOTA ML-based algorithms elicit bidders
VeriGraph: Scene Graphs for Execution Verifiable Robot Planning
Recent progress in vision-language models (VLMs) has opened new possibilities for robot task planning, but these models often produce incorrect action sequences. To address these limitations, we propose VeriGraph, a novel framework that integrates VLMs for robotic planning while verifying action feasibility. VeriGraph uses scene graphs as an intermediate representation to capture key objects and spatial relationships
Transformer Neural Processes - Kernel Regression
Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic processes. Originally developed as a scalable alternative to Gaussian Processes (GPs), which are limited by $O(n^3)$ runtime complexity, the most accurate modern NPs can often rival GPs but still suffer from an $O(n^2)$ bottleneck due to their attention mechanism. We introduce t
EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond
Event-based Action Recognition (EAR) possesses the advantages of high-temporal resolution capturing and privacy preservation compared with traditional action recognition. Current leading EAR solutions typically follow two regimes: project unconstructed event streams into dense constructed event frames and adopt powerful frame-specific networks, or employ lightweight point-specific networks to handle sparse unconstruc
Evaluating the quality of synthesized images remains a significant challenge in the development of text-to-image (T2I) generation. Most existing studies in this area primarily focus on evaluating text-image alignment, image quality, and object composition capabilities, with comparatively fewer studies addressing the evaluation of the factuality of T2I models, particularly when the concepts involved are knowledge-inte
Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity. Despite their significance, these languages face critical challenges, including data scarcity and technological limitations, which hinder their comprehensive study and preservation. Recent advancements in large language models (LLMs) offer transformative opportunities for addressing these
Large Language Models for Market Research: A Data-augmentation Approach
Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Their ability to generate human-like text has opened new possibilities for market research, particularly in conjoint analysis, where understanding consumer preferences is essential but often resource-intensive. Traditional survey-based methods face limitations in scalability and cost, makin
Continuous monitoring of glacier calving fronts is essential for sea level rise projections. This study benchmarks Deep Learning systems for front delineation in Synthetic Aperture Radar imagery. While Deep Learning systems exhibit errors up to 221 m, human annotators deviate by only 38 m, underscoring the need for further research.
Analytic Personalized Federated Meta-Learning
Analytic Federated Learning (AFL) is an enhanced gradient-free federated learning (FL) paradigm designed to accelerate training by updating the global model in a single step with closed-form least-square (LS) solutions. However, the obtained global model suffers performance degradation across clients with heterogeneous data distribution. Meta-learning is a common approach to tackle this problem by delivering personal
When Cultures Meet: Multicultural Text-to-Image Generation
Text-to-image generation models have achieved strong performance in culturally homogeneous settings, yet their ability to generate multicultural scenes, where people and landmarks originate from different cultures, remains largely unexplored. We introduce multicultural text-to-image generation as a new task and present the first benchmark designed to study this setting. Our dataset contains 9,000 images spanning five
FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users
Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curation. Inspired by the strong in-context capabilities of LLMs, we propose few-shot preference optimization (FSPO), an algorithm for LLM personalization that reframes reward modeling as a meta-learning problem. Under FSPO, an LLM learns to quickly infer a personalized reward functi
Notable events recorded on this day and month across all years.