Record 16022026 · captured 2026-08-25
The world looked up Jeffrey Epstein. 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.
Jeffrey Edward Epstein was an American financier and child sex offender. He began his career as a math teacher at the Dalton School in New York City, before entering the banking and finance sector. Over several decades, he made much of his fortune providing ta
Wuthering Heights is the only novel by the English author Emily Brontë, initially published in 1847 under her pen name Ellis Bell. It concerns two extensive upland estates and their landowning families on the West Yorkshire moors, the Earnshaws and the Lintons
Wuthering Heights is a 2026 romantic period drama film produced, written and directed by Emerald Fennell. Loosely based on the 1847 novel by Emily Brontë, the film is a reinterpretation intended by Fennell to "recreate the feeling of a teenage girl reading thi
The 2026 Winter Olympics, officially the XXV Olympic Winter Games and commonly known as Milano Cortina 2026, were an international winter multi-sport event held from 6 to 22 February 2026, at multiple sites across Lombardy, Veneto and Trentino-Alto Adige/Südti
1989 Tiananmen Square protests and massacre
Protests led by students and workers, known in China as the June Fourth Incident, were held in Tiananmen Square in Beijing, China, from 15 April to 4 June 1989. After weeks of unsuccessful attempts between the demonstrators and the Chinese government to find a
Lucas Pinheiro Braathen is a Norwegian and Brazilian alpine ski racer who specializes in the technical events of slalom and giant slalom. He is the men's giant slalom champion at the 2026 Winter Olympics, becoming the first Brazilian and South American in hist
Ice hockey at the 2026 Winter Olympics – Men's tournament
The men's tournament in ice hockey at the 2026 Winter Olympics took place in Milan, Italy, between 11 and 22 February 2026. Twelve countries qualified for the tournament; eight via ranking by the IIHF, three via qualification tournaments, and Italy as hosts. R
Anthony Ha-Jin Kim is an American professional golfer who plays on the LIV Golf League. He previously played on the PGA Tour, where he won three events, and appeared in the 2008 Ryder Cup and the 2009 Presidents Cup. He suffered an Achilles injury in 2012 and
The Epstein files are a partially released collection of millions of documents, images, videos, and emails related to the activities of American financier and convicted child sex offender Jeffrey Epstein, including his social circle of public figures, politici
John Fitzgerald Kennedy Jr., also referred to as JFK Jr., was an American businessman, attorney, magazine publisher, and journalist. He was the son of the 35th U.S. president John F. Kennedy, and First Lady Jacqueline Kennedy.
James David Van Der Beek was an American actor. Known for his portrayal of Dawson Leery on The WB's Dawson's Creek (1998–2003), he also played a fictionalized version of himself on the cult ABC sitcom Don't Trust the B---- in Apartment 23 (2012–2013), starred
Carolyn Jeanne Bessette-Kennedy was an American fashion publicist. Raised in Greenwich, Connecticut, she graduated from Boston University and joined Calvin Klein, where she rose from a sales position in Boston to publicity and show-production roles in New York
Ilia Malinin is an American figure skater. He is a 2026 Olympic Games team event gold medalist, three-time World champion, three-time Grand Prix Final champion, seven-time Grand Prix gold medalist, four-time Challenger Series gold medalist, and four-time U.S.
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
List of people named in the Epstein files
The Epstein files comprise over six million pages of documents detailing the activities of American financier and convicted child sex offender Jeffrey Epstein. So far about three and a half million files have been made public with redactions, among them 180,00
Maha Shivaratri is a Hindu festival celebrated annually to worship the deity Shiva, between February and March. According to the Hindu calendar, The festival is observed on the caturdaśī of kṛṣṇapakṣa of the month of Māgha or Phālguṇa. The discrepancy is only
Jacqueline Lee Kennedy Onassis was the first lady of the United States from 1961 to 1963, as the wife of John F. Kennedy, the 35th president of the United States. She redefined the previously mostly ceremonial role into a platform for arts and culture by hosti
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 ICC Men's T20 World Cup, formerly the ICC World Twenty20, is a biennial world cup for cricket in Twenty20 International (T20I) format, organised by the International Cricket Council (ICC). It was held in every odd year from 2007 to 2009, and since 2010 has
Pamela Jo Bondi is an American attorney and politician who served as the 87th United States attorney general from 2025 to 2026. A member of the Republican Party, she served as the 37th attorney general of Florida from 2011 to 2019.
A Knight of the Seven Kingdoms (TV series)
A Knight of the Seven Kingdoms is an American fantasy drama television series created by Ira Parker and George R. R. Martin. A prequel to Game of Thrones (2011–2019), it is the third television series in Martin's A Song of Ice and Fire franchise and is an adap
India–Pakistan cricket rivalry
The India–Pakistan cricket rivalry is one of the most intense sports rivalries in the world. Matches between the teams are considered some of the biggest in the world and are among the most-viewed in all of sports.
O'Romeo is a 2026 Indian Hindi-language romantic action thriller film written and directed by Vishal Bhardwaj. Produced by Sajid Nadiadwala for Nadiadwala Grandson Entertainment, the film is based on the non-fiction book Mafia Queens of Mumbai by Hussain Zaidi
Ghislaine Noelle Marion Maxwell is a British convicted child sex trafficker and former socialite. In 2021, she was convicted of child sex trafficking, and in 2022 was sentenced to 20 years in prison.
Eileen Feng Gu, also known by her Chinese name Gu Ailing (谷爱凌), is a Chinese-American freestyle skier and model. She has represented China in halfpipe, slopestyle, and big air events since the 2018–19 season. With three gold and three silver medals, Gu is the
The 2026 ICC Men's T20 World Cup was the tenth edition of the ICC Men's T20 World Cup, co-hosted by Board of Control for Cricket in India and Sri Lanka Cricket from 7 February to 8 March 2026. Sri Lanka had previously hosted the competition in 2012 and India i
Jacob Nathaniel Elordi is an Australian actor. His accolades include a Critics' Choice Award and three AACTA Awards, in addition to nominations for an Academy Award, three British Academy Film Awards and two Golden Globe Awards.
Bruce Lee was a Hong Kong and American martial artist, actor, and filmmaker. He was the founder of Jeet Kune Do, a hybrid martial arts philosophy, which was formed from his experiences in unarmed fighting and self-defense—as well as eclectic, Zen Buddhist, and
Curling is a sport in which players slide stones on a sheet of ice towards a target area that is segmented into four concentric circles. It is related to bowls, boules, and shuffleboard. Two teams, each with four players, take turns sliding heavy, polished gra
The Housemaid is a 2025 American erotic psychological thriller film directed by Paul Feig and written by Rebecca Sonnenshine. It is based on the 2022 novel by Freida McFadden, and stars Sydney Sweeney and Amanda Seyfried. In the film, Millie Calloway, a young
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
DART: aDaptive Accept RejecT for non-linear top-K subset identification
We consider the bandit problem of selecting $K$ out of $N$ arms at each time step. The reward can be a non-linear function of the rewards of the selected individual arms. The direct use of a multi-armed bandit algorithm requires choosing among $\binom{N}{K}$ options, making the action space large. To simplify the problem, existing works on combinatorial bandits {typically} assume feedback as a linear function of indi
AutoLL: Automatic Linear Layout of Graphs based on Deep Neural Network
Linear layouts are a graph visualization method that can be used to capture an entry pattern in an adjacency matrix of a given graph. By reordering the node indices of the original adjacency matrix, linear layouts provide knowledge of latent graph structures. Conventional linear layout methods commonly aim to find an optimal reordering solution based on predefined features of a given matrix and loss function. However
Data distribution shift is a common problem in machine learning-powered smart city applications where the test data differs from the training data. Augmenting smart city applications with online machine learning models can handle this issue at test time, albeit with high cost and unreliable performance. To overcome this limitation, we propose to endow test-time adaptation with a systematic active fine-tuning (SAF) la
Land-use monitoring is fundamental for spatial planning, particularly in view of compound impacts of growing global populations and climate change. Despite existing applications of deep learning in land use monitoring, standard convolutional kernels in deep neural networks limit the applications of these networks to the Euclidean domain only. Considering the geodesic nature of the measurement of the earth's surfa
The Hard-Constraint PINNs for Interface Optimal Control Problems
We show that the physics-informed neural networks (PINNs), in combination with some recently developed discontinuity capturing neural networks, can be applied to solve optimal control problems subject to partial differential equations (PDEs) with interfaces and some control constraints. The resulting algorithm is mesh-free and scalable to different PDEs, and it ensures the control constraints rigorously. Since the bo
Contemporary applications, such as recommendation systems and mobile health monitoring, require real-time processing and analysis of sequentially arriving high-dimensional tensor data. Traditional offline learning, involving the storage and utilization of all data in each computational iteration, becomes impractical for these tasks. Furthermore, existing low-rank tensor methods lack the capability for online statisti
This work aims to improve the sample efficiency of parallel large-scale ranking and selection (R&S) problems by leveraging correlation information. We modify the commonly used "divide and conquer" framework in parallel computing by adding a correlation-based clustering step, transforming it into "clustering and conquer". Analytical results under a symmetric benchmark scenario show that this seemin
Multi-Scale Texture Loss for CT denoising with GANs
Generative Adversarial Networks (GANs) have proved as a powerful framework for denoising applications in medical imaging. However, GAN-based denoising algorithms still suffer from limitations in capturing complex relationships within the images. In this regard, the loss function plays a crucial role in guiding the image generation process, encompassing how much a synthetic image differs from a real image. To grasp hi
CATP: Cross-Attention Token Pruning for Accuracy Preserved Multimodal Model Inference
In response to the rising interest in large multimodal models, we introduce Cross-Attention Token Pruning (CATP), a precision-focused token pruning method. Our approach leverages cross-attention layers in multimodal models, exemplified by BLIP-2, to extract valuable information for token importance determination. CATP employs a refined voting strategy across model heads and layers. In evaluations, CATP achieves up to
Low-Rank Online Dynamic Assortment with Dual Contextual Information
As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to continuously optimize assortments over time. In this paper, we consider the dynamic assortment problem with dual contexts -- user and item features. In hi
Multi-Dataset Multi-Task Learning for COVID-19 Prognosis
In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim. The challenge, however, lies in the scarcity of large, labeled datasets with compatible tasks for training deep learning models without leading to overfitting. Addressing this issue, we introduce a novel multi-dataset multi-task training fra
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
While overparameterization in machine learning models offers great benefits in terms of optimization and generalization, it also leads to increased computational requirements as model sizes grow. In this work, we show that by leveraging the inherent low-dimensional structures of data and compressible dynamics within the model parameters, we can reap the benefits of overparameterization without the computational burde
LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting
Time Series Forecasting (TSF) has long been a challenge in time series analysis. Inspired by the success of Large Language Models (LLMs), researchers are now developing Large Time Series Models (LTSMs)-universal transformer-based models that use autoregressive prediction-to improve TSF. However, training LTSMs on heterogeneous time series data poses unique challenges, including diverse frequencies, dimensions, and pa
Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across Heads
Vision Transformers and their variants have achieved remarkable success in diverse visual perception tasks. Despite their effectiveness, they suffer from two significant limitations. First, the quadratic computational complexity of multi-head self-attention (MHSA), which restricts scalability to large token counts, and second, a high dependency on large-scale training data to attain competitive performance. In this p
Continuous-time q-Learning for Jump-Diffusion Models under Tsallis Entropy
This paper studies the continuous-time reinforcement learning in jump-diffusion models by featuring the q-learning (the continuous-time counterpart of Q-learning) under Tsallis entropy regularization. Contrary to the Shannon entropy, the general form of Tsallis entropy renders the optimal policy not necessarily a Gibbs measure. Herein, the Lagrange multiplier and KKT condition are needed to ensure that the learned po
MaskInversion: Localized Embeddings via Optimization of Explainability Maps
Vision-language foundation models such as CLIP have achieved tremendous results in global vision-language alignment, but still show some limitations in creating representations for specific image regions. % To address this problem, we propose MaskInversion, a method that leverages the feature representations of pre-trained foundation models, such as CLIP, to generate a context-aware embedding for a query image region
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks
Recent advancements in image synthesis, particularly with the advent of GAN and Diffusion models, have amplified public concerns regarding the dissemination of disinformation. To address such concerns, numerous AI-generated Image (AIGI) Detectors have been proposed and achieved promising performance in identifying fake images. However, there still lacks a systematic understanding of the adversarial robustness of AIGI
Generating Physical Dynamics under Priors
Generating physically feasible dynamics in a data-driven context is challenging, especially when adhering to physical priors expressed in specific equations or formulas. Existing methodologies often overlook the integration of physical priors, resulting in violation of basic physical laws and suboptimal performance. In this paper, we introduce a novel framework that seamlessly incorporates physical priors into diffus
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference
Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do not incorporate outcome information; prominent examples include prospect
Total Variation Denoising (TVD) is a fundamental denoising and smoothing method. In this article, we identify a new local minmax/maxmin formula producing two estimators which sandwich the univariate TVD estimator at every point. Operationally, this formula gives a local definition of TVD as a minmax/maxmin of a simple function of local averages. Moreover we find that this minmax/maxmin formula is generalizeable and c
Convolutional Neural Networks (CNNs) excel in many visual tasks but remain susceptible to adversarial attacks-imperceptible perturbations that degrade performance. Prior research reveals that brain-inspired regularizers, derived from neural recordings, can bolster CNN robustness; however, reliance on specialized data limits practical adoption. We revisit a regularizer proposed by Li et al. (2019) that aligns CNN repr
Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection
Alzheimer's disease (AD) poses significant global health challenges due to its increasing prevalence and associated societal costs. Early detection and diagnosis of AD are critical for delaying progression and improving patient outcomes. Traditional diagnostic methods and single-modality data often fall short in identifying early-stage AD and distinguishing it from Mild Cognitive Impairment (MCI). This study addr
SaVe-TAG: LLM-based Interpolation for Long-Tailed Text-Attributed Graphs
Real-world graph data often follows long-tailed distributions, making it difficult for Graph Neural Networks (GNNs) to generalize well across both head and tail classes. Recent advances in Vicinal Risk Minimization (VRM) have shown promise in mitigating class imbalance with numeric interpolation; however, existing approaches largely rely on embedding-space arithmetic, which fails to capture the rich semantics inheren
Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detection
Monocular 3D object detection has attracted great attention due to simplicity and low cost. Existing methods typically follow conventional 2D detection paradigms, first locating object centers and then predicting 3D attributes via neighboring features. However, these methods predominantly rely on progressive cross-scale feature aggregation and focus solely on local information, which may result in a lack of global aw
Arbor is a software library designed for efficient simulation of large-scale networks of biological neurons with detailed morphological structures. It combines customizable neuronal and synaptic mechanisms with high-performance computing, supporting multi-core CPU and GPU systems. In humans and other animals, synaptic plasticity processes play a vital role in cognitive functions, including learning and memory. Recent
Notable events recorded on this day and month across all years.