Record 21042026 · captured 2026-08-25
The world looked up Nahui Ollin. 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.
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
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
William Patrick Muldoon III was an American actor, film producer, and musician. He was best known for his roles as Austin Reed on Days of Our Lives and Zander Barcalow on Starship Troopers.
John Ternus is an American engineer and business executive who has been the senior vice president of hardware engineering at Apple Inc. since 2021. On September 1, 2026, he will succeed Tim Cook as the CEO of Apple.
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
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
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
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
David Anthony Burke, known professionally as D4vd, is an American singer-songwriter. Born in Queens, New York City, and raised in Houston, Texas, Burke began composing music in 2021 for his Fortnite gameplay montages. He first achieved commercial success with
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
Brock Edward Lesnar is an American retired professional wrestler, former mixed martial artist, amateur wrestler, and professional American football player. He is best known for his tenures in WWE between 2000 and 2026. Lesnar is the only person to have won 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
Records of men who have sex with men in Japan date back to ancient times. Western scholars have identified these as evidence of homosexuality in Japan. Though these relations had existed in Japan for millennia, they became most apparent to scholars during the
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
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
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").
The Day Shall Dawn is a 1959 Pakistani Bengali-Urdu language drama film directed by A. J. Kardar. The film was selected as the Pakistani entry for the Best Foreign Language Film at the 32nd Academy Awards, but was not accepted as a nominee. It was also entered
Koje Unscreened is a journalistic booklet published in 1953 and jointly written by Wilfred Burchett and Alan Winnington, the only two native English speaking journalists to cover the Korean War from the northern side of the conflict.
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,
Leati Joseph Anoaʻi, better known by his ring name Roman Reigns, is an American professional wrestler, actor, and former football player. As a wrestler, he has been signed to WWE since 2010, where he performs on the Raw brand and is the current World Heavyweig
The Sarcophagus of Berardo Maggi is a sculptural work made of ammonitic red (121×197×101.5 cm) within the first quarter of the 14th century and preserved in the old cathedral of Brescia.
Stan Moody is an English professional snooker player from Halifax, West Yorkshire. In February 2023 he won the WSF World Junior Championship, and turned professional that year. Competing on the World Snooker Tour he was a two-time quarter-finalist during the 2
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
Timothy Donald Cook is an American business executive who has served as the chief executive officer (CEO) of Apple since 2011. He had previously been the company's chief operating officer under its co-founder Steve Jobs. Cook joined Apple in March 1998 as a se
Torioluwa Oshiumi Isaac Odugbesan is a Nigerian professional wrestler. He is signed to WWE, where he performs on the Raw brand under the ring name Oba Femi and is the incumbent King of the Ring. He is a former two-time NXT Champion, and a former one-time NXT N
2026 West Bengal Legislative Assembly election
Legislative Assembly elections were held in West Bengal to elect all 294 members of the West Bengal Legislative Assembly in two phases on 23 and 29 April 2026, with the votes counted and results for 293 seats released on 4 May 2026. The election saw the defeat
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
Killing of Celeste Rivas Hernandez
On September 8, 2025, the remains of 14-year-old Celeste Abigail Rivas Hernandez were discovered in Los Angeles, California, inside the front trunk of an impounded Tesla Model X registered to David Burke, a musician known professionally as D4vd. In April 2026,
2026 Tamil Nadu Legislative Assembly election
Elections to appoint the 234 members of the 17th Tamil Nadu Legislative Assembly, the highest body of the Government of Tamil Nadu, were held on 23 April 2026. The results were declared on 4 May 2026 by the Election Commission of India. It recorded the highest
Since 28 February 2026, the United States and Israel have been at war with Iran and its regional allies. Hostilities broke out after US–Israeli airstrikes killed several Iranian officials, including Supreme Leader Ali Khamenei. The strikes were launched amid o
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis
Deep learning fostered a leap ahead in automated skin lesion analysis in the last two years. Those models are expensive to train and difficult to parameterize. Objective: We investigate methodological issues for designing and evaluating deep learning models for skin lesion analysis. We explore 10 choices faced by researchers: use of transfer learning, model architecture, train dataset, image resolution, type of data
(De)Constructing Bias on Skin Lesion Datasets
Melanoma is the deadliest form of skin cancer. Automated skin lesion analysis plays an important role for early detection. Nowadays, the ISIC Archive and the Atlas of Dermoscopy dataset are the most employed skin lesion sources to benchmark deep-learning based tools. However, all datasets contain biases, often unintentional, due to how they were acquired and annotated. Those biases distort the performance of machine-
Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons
Watching cartoons can be useful for children's intellectual, social and emotional development. However, the most popular video sharing platform today provides many videos with Elsagate content. Elsagate is a phenomenon that depicts childhood characters in disturbing circumstances (e.g., gore, toilet humor, drinking urine, stealing). Even with this threat easily available for children, there is no work in the lite
Bayesian Neural Networks: An Introduction and Survey
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language processing. Despite their success, they are often implemented in a frequentist scheme, meaning they are unable to reason about uncertainty in their predictions. This article introduces Ba
#PraCegoVer: A Large Dataset for Image Captioning in Portuguese
Automatically describing images using natural sentences is an important task to support visually impaired people's inclusion onto the Internet. It is still a big challenge that requires understanding the relation of the objects present in the image and their attributes and actions they are involved in. Then, visual interpretation methods are needed, but linguistic models are also necessary to verbally describe th
Citrus juices and fruits are commodities with great economic potential in the international market, but productivity losses caused by mites and other pests are still far from being a good mark. Despite the integrated pest mechanical aspect, only a few works on automatic classification have handled images with orange mite characteristics, which means tiny and noisy regions of interest. On the computational side, atten
Graph Neural Networks for Graphs with Heterophily: A Survey
Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications. Most GNNs rely on the homophily assumption that nodes belonging to the same class are more likely to be connected. However, as a ubiquitous graph property in numerous real-world scenarios, heterophily, i.e., nodes with different labels tend to be linked, significantly limits
PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming
In deterministic optimization, it is typically assumed that all problem parameters are fixed and known. In practice, however, some parameters may be a priori unknown but can be estimated from contextual information. A typical predict-then-optimize approach separates predictions and optimization into two distinct stages. Recently, end-to-end predict-then-optimize has emerged as an attractive alternative. This work int
Numerical approximations of partial differential equations (PDEs) are routinely employed to formulate the solution of physics, engineering, and mathematical problems involving functions of several variables, such as the propagation of heat or sound, fluid flow, elasticity, electrostatics, electrodynamics, and more. While this has led to solving many complex phenomena, there are some limitations. Conventional approach
User Simulation for Evaluating Information Access Systems
Information access systems, such as search engines, recommender systems, and conversational assistants, have become integral to our daily lives as they help us satisfy our information needs. However, evaluating the effectiveness of these systems presents a long-standing and complex scientific challenge. This challenge is rooted in the difficulty of assessing a system's overall effectiveness in assisting users to
A Machine Learning Approach to Two-Stage Adaptive Robust Optimization
We propose an approach based on machine learning to solve two-stage linear adaptive robust optimization (ARO) problems with binary here-and-now variables and polyhedral uncertainty sets. We encode the optimal here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the optimal wait-and-see decisions into what we denote as the strategy. We solve multiple similar ARO inst
Assessing the Generalizability of Deep Neural Networks-Based Models for Black Skin Lesions
Melanoma is the most severe type of skin cancer due to its ability to cause metastasis. It is more common in black people, often affecting acral regions: palms, soles, and nails. Deep neural networks have shown tremendous potential for improving clinical care and skin cancer diagnosis. Nevertheless, prevailing studies predominantly rely on datasets of white skin tones, neglecting to report diagnostic outcomes for div
Biomarker detection is an indispensable part of the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection methods rely on expensive and complex molecular genetic testing, for which professionals are required to analyze the results, and intra-rater variability is often reported. To overcome these challenges, we propose an interpretable deep learning pipeline, named Multi-Biomarker
In areas that are inaccessible to humans, such as the lunar surface and landslide sites, there is a need for multiple autonomous mobile robot systems that can replace human workers. In particular, at landslide sites such as river channel blockages, robots are required to remove water and sediment from the site as soon as possible. Conventionally, several construction machines have been deployed to the site for civil
Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning
Metamaterials with functional responses can exhibit varying properties under different conditions (e.g., wave-based responses or deformation-induced property variation). This work addresses the rapid inverse design of such metamaterials to meet target qualitative functional behaviors, a challenge due to its intractability and non-unique solutions. Unlike data-intensive and non-interpretable deep-learning-based method
Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach
Multimodal sentiment analysis aims to identify the emotions expressed by individuals through visual, language, and acoustic cues. However, most existing research assume that all modalities are available during both training and testing, which makes their algorithms susceptible to the missing-modality scenarios. In this paper, we propose a novel knowledge-transfer network to translate between different modalities to r
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
Diffusion models have become a leading paradigm in generative AI, with score estimation via denoising score matching as a central component. While recent theory provides strong statistical guarantees, it typically relies on algorithm-agnostic assumptions and treats empirical risk minimization as if it were solved exactly. In practice, however, score functions are parameterized by highly nonconvex neural networks and
Unraveling the Key of Machine Learning-based Android Malware Detection
With the rapid advancement of machine learning (ML), ML-based Android malware detection has gained significant popularity due to its ability to automatically learn malicious patterns from Android apps. However, the lack of an in-depth and systematic analysis of existing research makes it difficult to obtain a holistic understanding of the state of the art in this field. In this work, we present the most comprehensive
AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions. This growing presence means that humans are now constantly interacting with machine learning-based systems, training and using models everyday. Several different techniques in computer science literature account for the human interaction with machine learning systems, but their classification is sparse and
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of planning make it challenging. Existing learning-based planning methods follow a deterministic paradigm to directly regress the action, failing to cope with the uncertainty problem. In this work, we propose a probabilistic planning model for end-to-end autonomous driving, terme
CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks
Numerous roadside perception datasets have been introduced to propel advancements in autonomous driving and intelligent transportation systems research and development. However, it has been observed that the majority of their concentrates is on urban arterial roads, inadvertently overlooking residential areas such as parks and campuses that exhibit entirely distinct characteristics. In light of this gap, we propose C
A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting
Recent industrial credit scoring models remain heavily reliant on manually tuned statistical learning methods. Despite their potential, deep learning architectures have struggled to consistently outperform traditional statistical models in industrial credit scoring, largely due to the complexity of heterogeneous financial data and the challenge of modeling evolving creditworthiness. To bridge this gap, we introduce F
Machine Unlearning: A Comprehensive Survey
As the right to be forgotten has been legislated worldwide, many studies attempt to design unlearning mechanisms to protect users' privacy when they want to leave machine learning service platforms. Specifically, machine unlearning is to make a trained model to remove the contribution of an erased subset of the training dataset. This survey aims to systematically classify a wide range of machine unlearning and di
Uncovering Logit Suppression Vulnerabilities in LLM Safety Alignment
Large language models (LLMs) have revolutionized various applications, making robust safety alignment essential to prevent harmful outputs. Current safety alignment techniques, however, harbor inherent vulnerabilities due to their reliance on logit suppression. In this work, we identify critical logit-level vulnerabilities by introducing Semantic-sensitive Alignment and Generation (SSAG), a method designed to systema
Network Interdiction Goes Neural
Network interdiction problems are combinatorial optimization problems involving two players: one aims to solve an optimization problem on a network, while the other seeks to modify the network to thwart the first player's objectives. Such problems typically emerge in an attacker-defender context, encompassing areas such as military operations, disease spread analysis, and communication network management. The pri
Notable events recorded on this day and month across all years.