Record 18082026 · 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
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
Jansen Rane Panettiere was an American actor, known for his roles in films The Secrets of Jonathan Sperry, The Perfect Game, The Martial Arts Kid, and How High 2. He had also provided the voice roles of Periwinkle in the sixth and final season of the Nick Jr.
Michelle Christine Trachtenberg was an American actress. After beginning her career in commercials at age three, she made her television debut in her first credited role on the Nickelodeon series The Adventures of Pete & Pete (1994–1996) and her feature film d
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
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
Milo Anthony Ventimiglia is an American actor. Making his screen acting debut on The Fresh Prince of Bel-Air in 1995, he portrayed the lead role on the short-lived series Opposite Sex in 2000 before landing his breakthrough role in Gilmore Girls (2001–2007).
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
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
Cardiomegaly is a medical condition in which the heart becomes enlarged. It is more commonly referred to simply as "having an enlarged heart". It is usually the result of underlying conditions that make the heart work harder, such as obesity, heart valve disea
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 (
Heroes is an American superhero drama television series created by Tim Kring that aired on NBC for four seasons from September 25, 2006, to February 8, 2010. The series tells the stories of ordinary people who discover that they have superhuman abilities and h
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
Vitalii Volodymyrovych Klychko, known as Vitali Klitschko, is a Ukrainian politician and former professional boxer who has served as the mayor of Kyiv since 2014. He previously served as the head of the Kyiv City State Administration until the start of the Rus
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.
.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
Ice Princess is a 2005 American teen sports comedy-drama film directed by Tim Fywell, written by Hadley Davis from a story by The Princess Diaries creator Meg Cabot and Davis. It stars Joan Cusack, Kim Cattrall, Michelle Trachtenberg and Hayden Panettiere. The
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
Gaynor Sullivan, known professionally as Bonnie Tyler, was a Welsh singer. Known for her distinctive husky voice, she came to prominence with the release of her debut studio album The World Starts Tonight (1977) and its singles "Lost in France" and "More Than
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
Sleepwalker is a 2026 American psychological thriller film written and directed by Brandon Auman. It stars Hayden Panettiere, Beverly D'Angelo, Justin Chatwin, Mischa Barton, Lori Tan Chinn, and Kea Ho. This film marks Panettiere's final acting performance pri
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
Remember the Titans is a 2000 American biographical sports drama film directed by Boaz Yakin and produced by Jerry Bruckheimer. The screenplay by Gregory Allen Howard is loosely based on the true story of coach Herman Boone, portrayed by Denzel Washington, and
Batwara 1947 is a 2026 Indian Hindi-language period drama film co-written and directed by Rajkumar Santoshi, and produced by Aamir Khan under the banner of Aamir Khan Productions. Set in Lahore against the backdrop of the 1947 Partition of British India and th
Vonnie B'VSean Miller is an American professional football outside linebacker for the Dallas Cowboys of the National Football League (NFL). Miller played college football for the Texas A&M Aggies, where he earned consensus All-American honors and the 2010 Butk
Nashville is an American musical drama television series created by Callie Khouri, who also served as an executive producer of the series along with Steve Buchanan. Other executive producers included Dee Johnson through season four, Connie Britton through seas
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
Thomas Edward John Jr., nicknamed "the Bionic Man", was an American professional baseball pitcher who played in Major League Baseball (MLB) for 26 seasons between 1963 and 1989. He played for the Cleveland Indians, Chicago White Sox, Los Angeles Dodgers, New Y
Kirby Reed is a fictional character in the Scream film series, created by Kevin Williamson and portrayed by the late Hayden Panettiere. She first appeared in Scream 4 (2011) and returns in Scream VI (2023). The character is a target in the fourth killing spree
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Sequential Batch Learning in Finite-Action Linear Contextual Bandits
We study the sequential batch learning problem in linear contextual bandits with finite action sets, where the decision maker is constrained to split incoming individuals into (at most) a fixed number of batches and can only observe outcomes for the individuals within a batch at the batch's end. Compared with both standard online contextual-bandit learning and offline policy learning in contextual bandits, this s
Partial FC: Training 10 Million Identities on a Single Machine
Training face recognition models with millions of identities is challenging because classifier storage, logit memory, and computation grow linearly with the number of classes, eventually making full softmax impractical even when the backbone itself fits comfortably in memory. We present Partial FC (PFC), a scalable approximation to large-class softmax that preserves every positive class center while activating only a
The Less Intelligent the Elements, the More Intelligent the Whole. Or, Possibly Not?
From neural networks to the immune system to agent-based models, connectionism aims at deriving complex behavior from relatively simple components. However, one unsettled question is how ``intelligent'' these components should be, and in what ways their local intelligence relates to the emergence of collective intelligence. I approach this problem by endowing the preys and predators of the Lotka-Volterra mode
Confidence intervals for the random forest generalization error
We show that the byproducts of the standard training process of a random forest yield not only the well known and almost computationally free out-of-bag point estimate of the model generalization error, but also give a direct path to compute confidence intervals for the generalization error which avoids processes of data splitting and model retraining. Besides the low computational cost involved in their construction
Universal distribution of the empirical coverage in split conformal prediction
When split conformal prediction operates in batch mode with exchangeable data, we determine the exact distribution of the empirical coverage of prediction sets produced for a finite batch of future observables, as well as the exact distribution of its almost sure limit when the batch size goes to infinity. Both distributions are universal, being determined solely by the nominal miscoverage level and the calibration s
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning
Achieving optimal statistical performance while ensuring the privacy of personal data is a challenging yet crucial objective in modern data analysis. However, characterizing the optimality, particularly the minimax lower bound, under privacy constraints is technically difficult. To address this issue, we propose a novel approach called the score attack, which provides a lower bound on the differential-privacy-constra
Unsupervised Improvement of Factual Knowledge in Language Models
Masked language modeling (MLM) plays a key role in pretraining large language models. But the MLM objective is often dominated by high-frequency words that are sub-optimal for learning factual knowledge. In this work, we propose an approach for influencing MLM pretraining in a way that can improve language model performance on a variety of knowledge-intensive tasks. We force the language model to prioritize informati
Dynamic Pricing and Advertising with Demand Learning
We consider a novel pricing and advertising framework in which a seller not only sets the product price but also designs flexible advertising schemes to influence customers' valuations of the product. We impose no structural restriction on the seller's feasible advertising strategies and allow her to advertise the product by disclosing or concealing any information. Following the information design literature
Spectral clustering in the Gaussian mixture block model
Gaussian mixture block models are distributions over graphs that strive to model modern networks: to generate a graph from such a model, we associate each vertex $i$ with a latent feature vector $u_i \in \mathbb{R}^d$ sampled from a mixture of Gaussians, and we add edge $(i,j)$ if and only if the feature vectors are sufficiently similar, in that $\langle u_i,u_j \rangle \ge τ$ for a pre-specified threshold $τ$. The d
ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
ODTlearn is an open source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the state-of-the-art mixed-integer optimization (MIO) framework proposed in Aghaei et al. (2025). The current version of the package provides implementations for learning optimal classification trees, optimal fair classification trees, optimal prescriptive tree
ShadowNet for Data-Centric Quantum System Learning
Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality. Statistical learning offers new possibilities in this regime through neural network protocols and classical shadows, while both methods have limitations: the former suffers from incompatible dataset construction rules, resulting in substantial computational demands for data collection when addressing different tasks; the l
Pointer Networks with Q-Learning for Combinatorial Optimization
We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets) to enhance the optimality of attention-based sequence generation, focusing on long-term outcomes. This integration proves particularly effective in solving combinatorial optimization (CO) tasks, especially the Travelling Salesman Problem (TSP), which is the fo
WWW: What, When, Where to Compute-in-Memory
Matrix multiplication is the dominant computation during Machine Learning (ML) inference. To efficiently perform such multiplication operations, Compute-in-memory (CiM) paradigms have emerged as a highly energy efficient solution. However, integrating compute in memory poses key questions, such as 1) What type of CiM to use: Given a multitude of CiM design characteristics, determining their suitability from architect
Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games
Human choice prediction in economic contexts is crucial for applications in marketing, finance, public policy, and more. This task, however, is often constrained by the difficulties in acquiring human choice data. With most experimental economics studies focusing on simple choice settings, the AI community has explored whether LLMs can substitute for humans in these predictions and examined more complex experimental
Online Regularized Statistical Learning in Reproducing Kernel Hilbert Space With Non-Stationary Data
We study recursive regularized learning algorithms in the reproducing kernel Hilbert space (RKHS) with non-stationary online data streams. We introduce the concept of a random Tikhonov regularization path and decompose the tracking error of the algorithm's output for the regularization path into random difference equations in RKHS. We show that the tracking error vanishes in mean square and almost surely if the r
A Bi-directional Multi-solution Scalable Grover Search Algorithm
Grover's search algorithms, including various Partial Grover Searches (PGS), suffer from scaling issues when multiple solutions are sought, as the number of iterations scales with the number of solutions or marked states, making implementation more computationally expensive. Inspired by recent PGS algorithms for multi-solution searchers, this article proposes a scalable Grover quantum search algorithm, referred t
MiniGPT-Reverse-Designing: Predicting Image Adjustments Utilizing MiniGPT-4
Vision-Language Models (VLMs) have recently seen significant advancements through integrating with Large Language Models (LLMs). The VLMs, which process image and text modalities simultaneously, have demonstrated the ability to learn and understand the interaction between images and texts across various multi-modal tasks. Reverse designing, which could be defined as a complex vision-language task, aims to predict the
Mitigating Hallucination in Fictional Character Role-Play
Role-playing has wide-ranging applications in customer support, embodied agents, and computational social science. The influence of parametric world knowledge of large language models (LLMs) often causes role-playing characters to act out of character and to hallucinate about things outside the scope of their knowledge. In this work, we focus on the evaluation and mitigation of hallucination in fictional character ro
Macroeconomic Forecasting with Large Language Models
This paper presents a comparative analysis evaluating the accuracy of Large Language Models (LLMs) against traditional macro time series forecasting approaches. In recent times, LLMs have surged in popularity for forecasting due to their ability to capture intricate patterns in data and quickly adapt across very different domains. However, their effectiveness in forecasting macroeconomic time series data compared to
The projection pursuit (PP) guided tour optimizes a criterion function, known as the PP index, to gradually reveal projections of interest from high-dimensional data through animation. Optimization of some PP indexes can be non-trivial, if they are non-smooth functions, or when the optimum has a small "squint angle", detectable only from close proximity. Here, measures for calculating the smoothness and squin
Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models
Warning: This paper may contain texts with uncomfortable content. Large Language Models (LLMs) have achieved remarkable performance in various tasks, including those involving multimodal data like speech. However, these models often exhibit biases due to the nature of their training data. Recently, more Speech Large Language Models (SLLMs) have emerged, underscoring the urgent need to address these biases. This study
Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation
Many decision-support systems recommend actions by optimizing measurable objectives, even when a human decision-maker retains final authority and considers additional criteria that are difficult to specify in advance. We study how an algorithm should curate a small portfolio of quantitatively strong alternatives in such settings. We introduce generative curation, a framework that learns a recommendation policy to max
Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix
With the advancements in graph neural network, there has been increasing interest in applying this network to ECG signal analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted features and applied a graph neural network to classify arrhythmias. The proposed model was compared with existing approaches from the literature. The results demonstrated that precision and recall for a
MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization
Dimensionality reduction (DR) plays a crucial role in various fields, including data engineering and visualization, by simplifying complex datasets while retaining essential information. However, achieving both high DR accuracy and strong explainability remains a fundamental challenge, especially for users dealing with high-dimensional data. Traditional DR methods often face a trade-off between precision and transpar
Projected random forests and conformal prediction of circular data
We apply conformal prediction techniques to regression problems with circular responses, producing prediction sets with adaptive arc length and finite-sample coverage guarantees for any circular predictive model under the assumption of data exchangeability. Leveraging the high performance of existing predictive models designed for linear responses, we analyze a general projection procedure that converts any linear-re
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