Record 06022026 · 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
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
Savannah Clark Guthrie is an Australian-American broadcast journalist and attorney. She is a main co-anchor of the NBC News morning show Today, a position she has held since July 2012.
Disappearance of Nancy Guthrie
On February 1, 2026, Nancy Guthrie (née Long), the American 84‑year‑old mother of NBC News journalist and Today co-anchor Savannah Guthrie, was kidnapped from her home in Catalina Foothills, a suburb of Tucson, Arizona. Evidence recovered at the residence indi
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
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.
Jmail is a browser-based archive of the Epstein files, which were released by the United States House Committee on Oversight and Government Reform under the Epstein Files Transparency Act (EFTA). The website was initially stylized in a Gmail-based interface, a
Peter Benjamin Mandelson, Baron Mandelson, is a British former Labour Party politician, lobbyist and diplomat. He was the Member of Parliament (MP) for Hartlepool from 1992 to 2004. He served in Tony Blair and Gordon Brown's cabinets as Minister without portfo
Lucy Letby is a British former NHS neonatal nurse convicted of murdering seven babies and attempting to murder seven others at the Countess of Chester Hospital in Chester between June 2015 and June 2016. She was investigated after an unusual cluster of deaths
Michael Feldman is an American public relations and communications consultant and a former Democratic political adviser. Feldman was Vice President Al Gore’s traveling chief of staff during the 2000 presidential election campaign. He is a founding partner and
Iron Lung is a 2026 American independent science fiction horror film starring writer, editor, and director Mark Fischbach in his feature-length directorial debut. It is based on the 2022 video game by David Szymanski. It also stars Caroline Kaplan, Troy Baker,
On August 10, 2019, Jeffrey Epstein, an American financier and a child sex offender, was found unresponsive in his jail cell at 6:30 a.m, at the Metropolitan Correctional Center in New York City, hanging off the side of his cell's bed, where he was awaiting tr
Benito Antonio Martínez Ocasio, known professionally as Bad Bunny, is a Puerto Rican rapper, singer and record producer. Dubbed the "King of Latin Trap", he is widely credited with helping Spanish-language rap reach mainstream global popularity and is consider
Women's Premier League (cricket)
The Women's Premier League (WPL) is a professional women's Twenty20 (T20) cricket league in India, organised by the Board of Control for Cricket in India (BCCI). Founded in 2022, it features five city-based franchise teams. The WPL is one of the fastest-growin
Little Saint James, nicknamed Epstein Island, is a small private island in the United States Virgin Islands southeast of Saint Thomas. It was owned by American financier and convicted child sex offender Jeffrey Epstein from 1998 until his death in 2019.
Virginia Lee Roberts Giuffre was an American and Australian advocate for survivors of sex trafficking and one of the most prominent accusers of Jeffrey Epstein. Giuffre provided detailed allegations to media outlets about Epstein and Ghislaine Maxwell. She all
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
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 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
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
Dhurandhar is a 2025 Indian Hindi-language spy action thriller film written and directed by Aditya Dhar. It is produced by Aditya Dhar, Lokesh Dhar and Jyoti Deshpande under Jio Studios and B62 Studios. The film features an ensemble cast consisting of Ranveer
"Pizzagate" is a conspiracy theory that went viral during the 2016 United States presidential election cycle, falsely claiming that the New York City Police Department (NYPD) had discovered a pedophile ring linked to members of the Democratic Party while searc
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
A website, or web site, is any web page whose content is identified by a common domain name and is published on at least one web server. Websites are typically dedicated to a particular topic or purpose, such as news, education, commerce, entertainment, or soc
Catherine Anne O'Hara was a Canadian and American actress and comedian, whose career spanned over 50 years. O'Hara started in sketch and improvisational comedy in film and television before taking dramatic roles to expand her career. She received various accol
Mark Epstein (property developer)
Mark Lawrence Epstein, nicknamed "Puggy", is an American property developer and real estate investor. The brother of convicted sex offender and financier Jeffrey Epstein, he has been active in real estate since the 1990s, founding and leading several companies
Traci Elizabeth Lords is an American actress, singer, and former adult film actress. Following her departure from the adult film industry, she pursued an acting career, appearing in Cry-Baby (1990) and later in films, including Skinner (1993), Virtuosity (1995
Border 2 is a 2026 Indian Hindi-language epic war film co-written and directed by Anurag Singh. A sequel to J. P. Dutta's 1997 film Border, it was produced by Bhushan Kumar, Krishan Kumar, J. P. Dutta, and Nidhi Dutta under the banners of T-Series Films and J.
Jeffrey Preston Bezos is an American businessman, and the founder, executive chairman, and former president and CEO of Amazon, the world's largest e-commerce and cloud computing company. According to the Bloomberg Billionaires Index and Forbes, he was the worl
Donald John Trump is an American politician, media personality, and businessman who is the 47th president of the United States. A member of the Republican Party, he served as the 45th president from 2017 to 2021.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction
Complex deep learning models show high prediction tasks in various clinical prediction tasks but their inherent complexity makes it more challenging to explain model predictions for clinicians and healthcare providers. Existing research on explainability of deep learning models in healthcare have two major limitations: using post-hoc explanations and using raw clinical variables as units of explanation, both of which
Normative modelling is an emerging method for understanding the underlying heterogeneity within brain disorders like Alzheimer Disease (AD) by quantifying how each patient deviates from the expected normative pattern that has been learned from a healthy control distribution. Since AD is a multifactorial disease with more than one biological pathways, multimodal magnetic resonance imaging (MRI) neuroimaging data can p
ThreshNet: An Efficient DenseNet Using Threshold Mechanism to Reduce Connections
With the continuous development of neural networks for computer vision tasks, more and more network architectures have achieved outstanding success. As one of the most advanced neural network architectures, DenseNet shortcuts all feature maps to solve the model depth problem. Although this network architecture has excellent accuracy with low parameters, it requires an excessive inference time. To solve this problem,
Identifying Dementia Subtypes with Electronic Health Records
Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogeneous with a variety of different symptoms that progress at different rates. In this work, we used an unsupervised data-driven K-Means clustering approach on the component scores of the Clinical Dementia Rating (CDR) s
Efficient Convolutional Neural Networks on Raspberry Pi for Image Classification
With the good performance of deep learning algorithms in the field of computer vision (CV), the convolutional neural network (CNN) architecture has become a main backbone of the computer vision task. With the widespread use of mobile devices, neural network models based on platforms with low computing power are gradually being paid attention. However, due to the limitation of computing power, deep learning algorithms
PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking
This paper describes the PASH participation in TREC 2021 Deep Learning Track. In the recall stage, we adopt a scheme combining sparse and dense retrieval method. In the multi-stage ranking phase, point-wise and pair-wise ranking strategies are used one after another based on model continual pre-trained on general knowledge and document-level data. Compared to TREC 2020 Deep Learning Track, we have additionally introd
The efficient extraction of text information from the background in degraded color document images is an important challenge in the preservation of ancient manuscripts. The imperfect preservation of ancient manuscripts has led to different types of degradation over time, such as page yellowing, staining, and ink bleeding, seriously affecting the results of document image binarization. This work proposes an effective
The Transformer-based method has demonstrated remarkable performance for image super-resolution in comparison to the method based on the convolutional neural networks (CNNs). However, using the self-attention mechanism like SwinIR (Image Restoration Using Swin Transformer) to extract feature information from images needs a significant amount of computational resources, which limits its application on low computing po
Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm
Hospital emergency departments frequently receive lots of bone fracture cases, with pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons perform surgery, they need to ask patients how the fracture occurred and analyze the fracture situation by interpreting X-ray images. The interpretation of X-ray images often requires a combination of techniques from radiologists and surgeon
LLT: An R package for Linear Law-based Feature Space Transformation
The goal of the linear law-based feature space transformation (LLT) algorithm is to assist with the classification of univariate and multivariate time series. The presented R package, called LLT, implements this algorithm in a flexible yet user-friendly way. This package first splits the instances into training and test sets. It then utilizes time-delay embedding and spectral decomposition techniques to identify the
In recent years, numerous screening methods have been published for ultrahigh-dimensional data that contain hundreds of thousands of features; however, most of these features cannot handle data with thousands of classes. Prediction models built to authenticate users based on multichannel biometric data result in this type of problem. In this study, we present a novel method known as random forest-based multiround scr
Learning ECG Signal Features Without Backpropagation Using Linear Laws
This paper introduces LLT-ECG, a novel method for electrocardiogram (ECG) signal classification that leverages concepts from theoretical physics to automatically generate features from time series data. Unlike traditional deep learning approaches, LLT-ECG operates in a forward manner, eliminating the need for backpropagation and hyperparameter tuning. By identifying linear laws that capture shared patterns within spe
SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents
Deep Reinforcement Learning (DRL) has made significant advancements in various fields, such as autonomous driving, healthcare, and robotics, by enabling agents to learn optimal policies through interactions with their environments. However, the application of DRL in safety-critical domains presents challenges, particularly concerning the safety of the learned policies. DRL agents, which are focused on maximizing rewa
We introduce Transductive Local Complexity (TLC) to extend the classical Local Rademacher Complexity (LRC) to the transductive setting, incorporating substantial and novel components. Although LRC has been used to obtain sharp generalization bounds and minimax rates for inductive tasks such as classification and nonparametric regression, it has remained an open problem whether a localized Rademacher complexity framew
ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning
Long-term time series forecasting (LTSF) is important for various domains but is confronted by challenges in handling the complex temporal-contextual relationships. As multivariate input models underperforming some recent univariate counterparts, we posit that the issue lies in the inefficiency of existing multivariate LTSF Transformers to model series-wise relationships: the characteristic differences between series
Normative models in neuroimaging learn the brain patterns of healthy population distribution and estimate how disease subjects like Alzheimer's Disease (AD) deviate from the norm. Existing variational autoencoder (VAE)-based normative models using multimodal neuroimaging data aggregate information from multiple modalities by estimating product or averaging of unimodal latent posteriors. This can often lead to uni
YOLOv8-AM: YOLOv8 Based on Effective Attention Mechanisms for Pediatric Wrist Fracture Detection
Wrist trauma and even fractures occur frequently in daily life, particularly among children who account for a significant proportion of fracture cases. Before performing surgery, surgeons often request patients to undergo X-ray imaging first and prepare for it based on the analysis of the radiologist. With the development of neural networks, You Only Look Once (YOLO) series models have been widely used in fracture de
Can Poverty Be Reduced by Acting on Discrimination? An Agent-based Model for Policy Making
In the last decades, there has been a deceleration in the rates of poverty reduction, suggesting that traditional redistributive approaches to poverty mitigation could be losing effectiveness, and alternative insights to advance the number one UN Sustainable Development Goal are required. The criminalization of poor people has been denounced by several NGOs, and an increasing number of voices suggest that discriminat
For Multivariate Time Series Forecasting (MTSF), recent deep learning applications show that univariate models frequently outperform multivariate ones. To address the difficiency in multivariate models, we introduce a method to Construct Auxiliary Time Series (CATS) that functions like a 2D temporal-contextual attention mechanism, which generates Auxiliary Time Series (ATS) from Original Time Series (OTS) to effectiv
YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images
The introduction of YOLOv9, the latest version of the You Only Look Once (YOLO) series, has led to its widespread adoption across various scenarios. This paper is the first to apply the YOLOv9 algorithm model to the fracture detection task as computer-assisted diagnosis (CAD) to help radiologists and surgeons to interpret X-ray images. Specifically, this paper trained the model on the GRAZPEDWRI-DX dataset and extend
Imperceptible Protection against Style Imitation from Diffusion Models
Recent progress in diffusion models has profoundly enhanced the fidelity of image generation, but it has raised concerns about copyright infringements. While prior methods have introduced adversarial perturbations to prevent style imitation, most are accompanied by the degradation of artworks' visual quality. Recognizing the importance of maintaining this, we introduce a visually improved protection method while
Objective: We aimed to develop and validate a novel multimodal framework HiMAL (Hierarchical, Multi-task Auxiliary Learning) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer Disease (AD). Methods: HiMAL utilized multimodal longitudinal visit data including imaging features, cognitive assessmen
INTRODUCTION: Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. METHODS: We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with availabl
A Differential and Pointwise Control Approach to Reinforcement Learning
Reinforcement learning (RL) in continuous state-action spaces remains challenging in scientific computing due to poor sample efficiency and lack of pathwise physical consistency. We introduce Differential Reinforcement Learning (Differential RL), a novel framework that reformulates RL from a continuous-time control perspective via a differential dual formulation. This induces a Hamiltonian structure that embeds physi
In-context Time Series Predictor
Recent Transformer-based large language models (LLMs) demonstrate in-context learning ability to perform various functions based solely on the provided context, without updating model parameters. To fully utilize the in-context capabilities in time series forecasting (TSF) problems, unlike previous Transformer-based or LLM-based time series forecasting methods, we reformulate "time series forecasting tasks" a
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