Record 21082026 · captured 2026-08-25
The world looked up Hayden Panettiere. 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.
Hayden Lesley Panettiere was an American actress and singer. She starred as Claire Bennet on the NBC superhero series Heroes (2006–2010), Kirby Reed in the slasher horror franchise Scream (2011–2023), Juliette Barnes in the ABC/CMT musical drama series Nashvil
Natalie Harp is an American political aide and former television anchor who has served as special assistant and executive assistant to the President of the United States since 2025.
Spider-Man: Brand New Day is a 2026 American superhero film based on the Marvel Comics character Spider-Man. Produced by Columbia Pictures, Marvel Studios, and Pascal Pictures, and distributed by Sony Pictures Releasing, it is the 38th film in the Marvel Cinem
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
The Odyssey is a 2026 epic action fantasy film written and directed by Christopher Nolan, who produced it with his wife Emma Thomas. An adaptation of Homer's ancient Greek epic poem the Odyssey, it stars an ensemble cast including Matt Damon, Tom Holland, Anne
Google LLC is an American multinational technology corporation focused on information technology, online advertising, search engine technology, email, cloud computing, software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (
Awarapan 2 is a 2026 Indian Hindi-language action thriller film directed by Nitin Kakkar, written by Kakkar, Bilal Siddiqui and Vishesh Bhatt, and produced under his banner Vishesh Films. A sequel to the 2007 film Awarapan, the film stars Emraan Hashmi, Disha
Niu Lai is a 2026 Chinese animated film directed by Xin Yumeng and written by Sun Lifang. It was produced by Dalian Jingyuan Culture Film and Television Media, formerly an interior design and decoration company, and released by Tianjin Yamazaki Film.
2026 Men's FIH Hockey World Cup
The 2026 Men's FIH Hockey World Cup is the 16th edition of the Men's FIH Hockey World Cup, the quadrennial world championship for men's national field hockey teams organized by the International Hockey Federation. It is being held from 15 to 30 August 2026 in
Jason Atta Kwei Arday was a British academic who was a professor of sociology of education at the University of Cambridge from 2023 to 2026. Arday received international attention and resigned amid accusations of plagiarism, false claims in his research, and f
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
The Vulture and the Little Girl
The Vulture and the Little Girl, also known as The Struggling Girl, is a photograph by Kevin Carter which first appeared in The New York Times on March 26, 1993. It is a photograph of a frail famine-stricken boy, mistakenly believed to be a girl until 2011, wh
Omayra Sánchez Garzón was a Colombian girl trapped and killed by a landslide when she was 13 years old.
Lanterns is an American superhero television series created by Chris Mundy, Damon Lindelof, and Tom King for HBO, based on the DC Comics Green Lantern characters Hal Jordan and John Stewart. It is the third television series in the DC Universe (DCU). It featur
Wladimir Klitschko is a Ukrainian former professional boxer who competed from 1996 to 2017. He held multiple heavyweight world championships between 2000 and 2015, including unified titles between 2008 and 2015. During this time he also held the International
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").
Anna's Archive is an open source search engine for shadow libraries that was launched by the pseudonymous Anna shortly after law enforcement efforts to shut down Z-Library in 2022. The site aggregates records from Z-Library, Sci-Hub, and Library Genesis (LibGe
List of highest-grossing films
Films generate income from several revenue streams, including theatrical exhibition, home video, television broadcast rights, and merchandising. However, theatrical box-office earnings are the primary metric for trade publications in assessing the success of a
The End of Oak Street is a 2026 American science fiction survival film written, co-produced, and directed by David Robert Mitchell. It stars Anne Hathaway, Ewan McGregor, Maisy Stella and Christian Convery as a family whose suburban neighborhood has been trans
Nathan Cofnas is an American postdoctoral researcher at Ghent University in Belgium. He is known for controversies surrounding his advocacy of scientific racism.
Thomas Jonathan Ossoff is an American politician who has served as the senior United States senator from Georgia since 2021. He is the youngest incumbent U.S. senator, and a member of the Democratic Party. Ossoff is also the youngest senior senator of the Unit
Danielle Fabiola "Inde" Navarrette is an American actress and former online streamer. She began her acting career as a teenager with roles in short films, before landing roles in the Netflix drama series 13 Reasons Why (2020) and The CW's superhero drama serie
Eric Michael Swalwell is an American former politician who served as a U.S. representative from California from 2013 to 2026. A member of the Democratic Party, Swalwell previously served on the city council for Dublin, California from 2010 to 2013.
The ʻokina is the letter that transcribes the glottal stop consonant in Hawaiian. It does not have distinct uppercase and lowercase forms, and is represented electronically by the modifier letter turned comma: ʻ.
Vishwanath & Sons is a 2026 Indian Tamil language romance and family drama film written and directed by Venky Atluri. Produced by Sithara Entertainments and Fortune Four Cinemas, the film stars Suriya, alongside Mamitha Baiju, Raadhika Sarathkumar, and Raveena
List of Marvel Cinematic Universe films
The Marvel Cinematic Universe (MCU) centers on American superhero films produced by Marvel Studios, based on characters that appear in publications by Marvel Comics. The MCU is the shared universe in which all of the films are set. Marvel Studios has released
The United States of America (USA), also known as the United States (U.S.) or America, is a country primarily located in North America. It is a federal republic consisting of 50 states and a federal capital district, Washington, D.C. The 48 contiguous states b
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
Reacher is an American action crime television series developed by Nick Santora for Amazon Prime Video. Based on the Jack Reacher novel series by Lee Child, it stars Alan Ritchson as the title character, a self-proclaimed drifter and former U.S. Army military
Lizzie Andrew Borden was an American woman who was tried and acquitted of the August 4, 1892, axe murders of her father and stepmother in Fall River, Massachusetts. No one else was charged in the murders, and Borden spent the remainder of her life in Fall Rive
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
On the convergence of optimistic policy iteration for stochastic shortest path problem
In this paper, we prove some convergence results of a special case of optimistic policy iteration algorithm for stochastic shortest path problem. We consider both Monte Carlo and $TD(λ)$ methods for the policy evaluation step under the condition that the termination state will eventually be reached almost surely.
Graph convolution network based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing. Each relationship represents a kind of spatial dependency, like region-wise distance or functional similarity. To incorporate multiple relationships into spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph c
Efficient adjustment sets in causal graphical models with hidden variables
We study the selection of covariate adjustment sets for estimating the value of point exposure dynamic policies, also known as dynamic treatment regimes, assuming a non-parametric causal graphical model with hidden variables, in which at least one adjustment set is fully observable. We show that recently developed criteria, for graphs without hidden variables, to compare the asymptotic variance of non-parametric esti
Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity
In the standard data analysis framework, data is collected (once and for all), and then data analysis is carried out. However, with the advancement of digital technology, decision-makers constantly analyze past data and generate new data through their decisions. We model this as a Markov decision process and show that the dynamic interaction between data generation and data analysis leads to a new type of bias -- rei
Economic model predictive control (EMPC) is a promising methodology for optimal operation of dynamical processes that has been shown to improve process economics considerably. However, EMPC performance relies heavily on the accuracy of the process model used. As an alternative to model-based control strategies, reinforcement learning (RL) has been investigated as a model-free control methodology, but issues regarding
Learning-Based Speed Estimation from Accelerometer-Only Inertial Sensing
The proposed model, CarSpeedNet, estimates scalar vehicle speed from a window of three-axis smartphone acceleration, without gyroscope, wheel-odometry, vehicle-bus, or positioning input at inference. The reported experiment comprises 13.2 hours of on-road driving. Beyond the network comparison, a finite-context analysis treats window length as part of the sensing problem. For nested histories, the minimum Bayes mean-
KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge
For Large Language Models (LLMs) to be effectively deployed in a specific country, they must possess an understanding of the nation's culture and basic knowledge. To this end, we introduce National Alignment, which measures an alignment between an LLM and a targeted country from two aspects: social value alignment and common knowledge alignment. Social value alignment evaluates how well the model understands nati
Knowledge distillation (KD) has been widely used to improve lightweight AI models by transferring soft-label knowledge from a large teacher model to a student model. However, existing KD methods are primarily designed for the image domain rather than lightweight IoT devices, and they often struggle to maintain well-separated feature representations for different attack types, especially as the number of classes incre
Towards Better Understanding of Cybercrime: The Role of Fine-Tuned LLMs in Translation
Understanding cybercrime communications is paramount for cybersecurity defence. This often involves translating communications into English for processing, interpreting, and generating timely intelligence. The problem is that translation is hard. Human translation is slow, expensive, and scarce. Machine translation is inaccurate and biased. We propose using fine-tuned Large Language Models (LLM) to generate translati
This work investigates efficient score-based black-box adversarial attacks that achieve a high Attack Success Rate (ASR) and good generalization ability. We propose a novel attack framework, termed DifAttack++, which operates in a hierarchical disentangled feature space and significantly differs from existing methods that manipulate the entire feature space. Specifically, DifAttack++ firstly disentangles an image'
Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion
Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost of training and evaluating models. Efficient Pareto front approximation of large models enables multi-objective optimization for various tasks such as multi-task learning and trade-off analysis. Existing algorithms for learning Pareto set,
Determination of the Number of Topics Intrinsically: Is It Possible?
The number of topics might be the most important parameter of a topic model. The topic modelling community has developed a set of various procedures to estimate the number of topics in a dataset, but there has not yet been a sufficiently complete comparison of existing practices. This study attempts to partially fill this gap by investigating the performance of various methods applied to several topic models on a num
This paper presents a novel anomaly and outlier detection algorithm from the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This algorithm leverages the concept of similarity and higher-order interactions across multiple subspaces to identify outliers. A comprehensive set of experiments was conducted to evaluate the performance of SPINEX. This algorithm was examined against 21 co
This paper presents a novel clustering algorithm from the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) algorithmic family. The newly proposed clustering variant leverages the concept of similarity and higher-order interactions across multiple subspaces to group data into clusters. To showcase the merit of SPINEX, a thorough set of benchmarking experiments was carried out against 13 alg
Prompt-based continual learning has shown strong performance in rehearsal-free class-incremental learning by adapting learnable prompts while freezing a pre-trained Vision Transformer (ViT) backbone. However, the effect of backbone scale remains underexplored. We observe that larger ViT backbones consistently yield better continual learning performance, which motivates us to study how to transfer such capability from
This paper introduces a new addition to the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family, tailored specifically for time series and forecasting analysis. This new algorithm leverages the concept of similarity and higher-order temporal interactions across multiple time scales to enhance predictive accuracy and interpretability in forecasting. To evaluate the effectiveness of SPIN
Iterative Improvement of an Additively Regularized Topic Model
Topic modelling is fundamentally a soft clustering problem (of known objects -- documents, over unknown clusters -- topics). That is, the task is incorrectly posed. In particular, the topic models are unstable and incomplete. All this leads to the fact that the process of finding a good topic model (repeated hyperparameter selection, model training, and topic quality assessment) can be particularly long and labor-int
Your Turn: At Home Turning Angle Estimation for Parkinson's Disease Severity Assessment
People with Parkinson's Disease (PD) often experience progressively worsening gait, including changes in how they turn around, as the disease progresses. Existing clinical rating tools are not capable of capturing hour-by-hour variations of PD symptoms, as they are confined to brief assessments within clinic settings. Measuring gait turning angles continuously and passively is a component step towards using gait
ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting
Time series forecasting, particularly in few-shot learning scenarios, is challenging due to the limited availability of high-quality training data. To address this, we present a pilot study on using reinforcement learning (RL) for time series data augmentation. Our method, ReAugment, tackles three critical questions: which parts of the training set should be augmented, how the augmentation should be performed, and wh
Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks
Image processing is one of the most promising applications for quantum machine learning (QML). Quanvolutional Neural Networks with non-trainable parameters are the preferred solution to run on current and near future quantum devices. The typical input preprocessing pipeline for quanvolutional layers comprises of four steps: optional input binary quantization, encoding classical data into quantum states, processing th
This article introduces a new symbolic regression algorithm based on the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This new algorithm (SPINEX_SymbolicRegression) adopts a similarity-based approach to identifying high-merit expressions that satisfy accuracy- and structural similarity metrics. We conducted extensive benchmarking tests comparing SPINEX_SymbolicRegression to ove
Virtual Sensing to Enable Real-Time Monitoring of Inaccessible Locations & Unmeasurable Parameters
Real-time monitoring of safety-critical interior states is an open problem across energy, environmental and industrial systems where direct instrumentation is infeasible. Approaches based on governing equations, discrete state vectors or fixed sensor locations cannot deliver mesh-independent, field-level reconstruction at arbitrary interior coordinates in real time. Here we introduce neural operator-based virtual sen
Deep tabular models should ideally balance predictive performance, parameter efficiency, and robustness to imperfect learning signals---properties that are rarely considered jointly. We present Table2Image, a lightweight tabular learning model built around a learned generation pathway that maps tabular inputs into intermediate, structured proxy representations. We additionally examine a variant with variance inflatio
Graph convolutional neural networks (GCNs) have shown tremendous promise in addressing data-intensive challenges in recent years. In particular, some attempts have been made to improve predictions of Susceptible-Infected-Recovered (SIR) models by incorporating human mobility between metapopulations and using graph approaches to estimate corresponding hyperparameters. Recently, researchers have found that a hybrid GCN
The widespread acceptance of empirically derived codal provisions and equations in civil engineering stands in stark contrast to the skepticism facing machine learning (ML) models, despite their shared statistical foundations. This paper examines this philosophical tension through the lens of structural engineering and explores how integrating ML challenges traditional engineering philosophies and professional identi
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