Record 04022026 · 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.
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,
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
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.
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
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
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
Olivia Lauryn Dean is an English singer and songwriter. Her accolades include four Brit Awards and the Grammy Award for Best New Artist.
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.
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 68th Annual Grammy Awards honored the best recordings, compositions, and artists from August 31, 2024, to August 30, 2025, as chosen by the members of the Recording Academy, on February 1, 2026. In its 23rd year at Crypto.com Arena in Los Angeles and for t
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
Charles Negron II was an American singer-songwriter. He was best known as a founding member and lead vocalist of the rock band Three Dog Night.
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
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
"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
Billie Eilish Pirate Baird O'Connell is an American singer-songwriter. Known for her distinctive musical sound and vocal style, Eilish is a prominent figure in 2020s pop culture. She first gained public attention in 2015 with her debut single "Ocean Eyes" whic
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.
Kayleigh Rose Amstutz, known professionally as Chappell Roan, is an American singer and songwriter. She is known for her camp and drag queen–influenced style.
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
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
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
Josh D'Amaro is an American business executive who has been the chief executive officer of the Walt Disney Company since 2026, succeeding Bob Iger. D'Amaro has been employed by the Walt Disney Company for 27 years, specializing in its resorts sector, being pre
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
Send Help is a 2026 American survival horror film directed and co-produced by Sam Raimi and written by Damian Shannon and Mark Swift. The film stars Rachel McAdams and Dylan O'Brien as an employee and her boss, respectively, who become stranded on a desert isl
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
A four-minute mile is the completion of a mile run (1.609 km) in four minutes or less. It translates to an average speed of 15 miles per hour (24.1 km/h). It is a standard for male professional middle-distance runners in several countries.
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Experience replay is a core ingredient of modern deep reinforcement learning, yet its benefits in policy optimization are poorly understood beyond empirical heuristics. This paper develops a novel theoretical framework for experience replay in modern policy gradient methods, where two sources of dependence fundamentally complicate analysis: Markovian correlations along trajectories and policy drift across optimizatio
The lack of efficient segmentation methods and fully-labeled datasets limits the comprehensive assessment of optical coherence tomography angiography (OCTA) microstructures like retinal vessel network (RVN) and foveal avascular zone (FAZ), which are of great value in ophthalmic and systematic diseases evaluation. Here, we introduce an innovative OCTA microstructure segmentation network (OMSN) by combining an encoder-
Discrete Latent Structure in Neural Networks
Many types of data from fields including natural language processing, computer vision, and bioinformatics, are well represented by discrete, compositional structures such as trees, sequences, or matchings. Latent structure models are a powerful tool for learning to extract such representations, offering a way to incorporate structural bias, discover insight about the data, and interpret decisions. However, effective
Contextual Causal Bayesian Optimisation
We introduce a unified framework for contextual and causal Bayesian optimisation, which aims to design intervention policies maximising the expectation of a target variable. Our approach leverages both observed contextual information and known causal graph structures to guide the search. Within this framework, we propose a novel algorithm that jointly optimises over policies and the sets of variables on which these p
Regulatory Markets: The Future of AI Governance
Appropriately regulating artificial intelligence is an increasingly urgent and widespread policy challenge. We identify two primary, competing problem. First is a technical deficit: Legislatures and regulatory face significant challenges in rapidly translating conventional command-and-control legal requirements into technical requirements. Second is a democratic deficit: Over-reliance on industry to provide technical
Attention is the core mechanism of today's most used architectures for natural language processing and has been analyzed from many perspectives, including its effectiveness for machine translation-related tasks. Among these studies, attention resulted to be a useful source of information to get insights about word alignment also when the input text is substituted with audio segments, as in the case of the speech
Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains
The computational efficiency of many neural operators, widely used for learning solutions of PDEs, relies on the fast Fourier transform (FFT) for performing spectral computations. As the FFT is limited to equispaced (rectangular) grids, this limits the efficiency of such neural operators when applied to problems where the input and output functions need to be processed on general non-equispaced point distributions. L
Quantum Architecture Search with Unsupervised Representation Learning
Unsupervised representation learning presents new opportunities for advancing Quantum Architecture Search (QAS) on Noisy Intermediate-Scale Quantum (NISQ) devices. QAS is designed to optimize quantum circuits for Variational Quantum Algorithms (VQAs). Most QAS algorithms tightly couple the search space and search algorithm, typically requiring the evaluation of numerous quantum circuits, resulting in high computation
Panoptic maps enable robots to reason about both geometry and semantics. However, open-vocabulary models repeatedly produce closely related labels that split panoptic entities and degrade volumetric consistency. The proposed UPPM advances open-world scene understanding by leveraging foundation models to introduce a panoptic Dynamic Descriptor that reconciles open-vocabulary labels with unified category structure and
Scene Text Recognition (STR) is challenging in extracting effective character representations from visual data when text is unreadable. Permutation language modeling (PLM) is introduced to refine character predictions by jointly capturing contextual and visual information. However, in PLM, the use of random permutations causes training fit oscillation, and the iterative refinement (IR) operation also introduces addit
Sparse maximal update parameterization: A holistic approach to sparse training dynamics
Several challenges make it difficult for sparse neural networks to compete with dense models. First, setting a large fraction of weights to zero impairs forward and gradient signal propagation. Second, sparse studies often need to test multiple sparsity levels, while also introducing new hyperparameters (HPs), leading to prohibitive tuning costs. Indeed, the standard practice is to re-use the learning HPs originally
ME-IGM: Individual-Global-Max in Maximum Entropy Multi-Agent Reinforcement Learning
Multi-agent credit assignment is a fundamental challenge for cooperative multi-agent reinforcement learning (MARL), where a team of agents learn from shared reward signals. The Individual-Global-Max (IGM) condition is a widely used principle for multi-agent credit assignment, requiring that the joint action determined by individual Q-functions maximizes the global Q-value. Meanwhile, the principle of maximum entropy
Mixed-type time series (MTTS) is a bimodal data type that is common in many domains, such as healthcare, finance, environmental monitoring, and social media. It consists of regularly sampled continuous time series and irregularly sampled categorical event sequences. The integration of both modalities through multimodal fusion is a promising approach for processing MTTS. However, the question of how to effectively fus
A Syntax-Injected Approach for Faster and More Accurate Sentiment Analysis
Sentiment Analysis (SA) is a crucial aspect of Natural Language Processing (NLP), focusing on identifying and interpreting subjective assessments in textual content. Syntactic parsing is useful in SA as it improves accuracy and provides explainability; however, it often becomes a computational bottleneck due to slow parsing algorithms. This article proposes a solution to this bottleneck by using a Sequence Labeling S
Conformal Prediction for Causal Effects of Continuous Treatments
Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which has several practical benefits due to model-agnostic finite-sample guarantees. Yet, existing methods for conformal prediction of causal effects are limited to binary/discrete treatments and make highly restrictive assumptions such as known
Hyper-Compression: Model Compression via Hyperfunction
The rapid growth of large models' size has far outpaced that of computing resources. To bridge this gap, encouraged by the parsimonious relationship between genotype and phenotype in the brain's growth and development, we propose the so-called Hyper-Compression that turns the model compression into the issue of parameter representation via a hyperfunction. Specifically, it is known that the trajectory of some
We address the fundamental task of inferring cross-document coreference and hierarchy in scientific texts, which has important applications in knowledge graph construction, search, recommendation and discovery. Large Language Models (LLMs) can struggle when faced with many long-tail technical concepts with nuanced variations. We present a novel method which generates context-dependent definitions of concept mentions
Saliency-Guided DETR for Moment Retrieval and Highlight Detection
Existing approaches for video moment retrieval and highlight detection are not able to align text and video features efficiently, resulting in unsatisfying performance and limited production usage. To address this, we propose a novel architecture that utilizes recent foundational video models designed for such alignment. Combined with the introduced Saliency-Guided Cross Attention mechanism and a hybrid DETR architec
Fast Training of Sinusoidal Neural Fields via Scaling Initialization
Neural fields are an emerging paradigm that represent data as continuous functions parameterized by neural networks. Despite many advantages, neural fields often have a high training cost, which prevents a broader adoption. In this paper, we focus on a popular family of neural fields, called sinusoidal neural fields (SNFs), and study how it should be initialized to maximize the training speed. We find that the standa
Individual Regret in Cooperative Stochastic Multi-Armed Bandits
We study the regret in stochastic Multi-Armed Bandits (MAB) with multiple agents that communicate over an arbitrary connected communication graph. We analyzed a variant of Cooperative Successive Elimination algorithm, COOP-SE, and show an individual regret bound of $O(R/ m + A^2 + A \sqrt{\log T})$ and a nearly matching lower bound. Here $A$ is the number of actions, $T$ the time horizon, $m$ the number of agents, an
MemoryFormer: Minimize Transformer Computation by Removing Fully-Connected Layers
In order to reduce the computational complexity of large language models, great efforts have been made to to improve the efficiency of transformer models such as linear attention and flash-attention. However, the model size and corresponding computational complexity are constantly scaled up in pursuit of higher performance. In this work, we present MemoryFormer, a novel transformer architecture which significantly re
Agnostic Learning of Arbitrary ReLU Activation under Gaussian Marginals
We consider the problem of learning an arbitrarily-biased ReLU activation (or neuron) over Gaussian marginals with the squared loss objective. Despite the ReLU neuron being the basic building block of modern neural networks, we still do not understand the basic algorithmic question of whether one arbitrary ReLU neuron is learnable in the non-realizable setting. In particular, all existing polynomial time algorithms o
Decoding fairness: a reinforcement learning perspective
Behavioral experiments on the ultimatum game (UG) reveal that we humans prefer fair acts, which contradicts the prediction made in orthodox Economics. Existing explanations, however, are mostly attributed to exogenous factors within the imitation learning framework. Here, we adopt the reinforcement learning paradigm, where individuals make their moves aiming to maximize their accumulated rewards. Specifically, we app
Multimodal learning has been demonstrated to enhance performance across various clinical tasks, owing to the diverse perspectives offered by different modalities of data. However, existing multimodal segmentation methods rely on well-registered multimodal data, which is unrealistic for real-world clinical images, particularly for indistinct and diffuse regions such as liver tumors. In this paper, we introduce Diff4MM
Integrators at War: Mediating in AI-assisted Resort-to-Force Decisions
The integration of AI systems into the military domain is changing the way war-related decisions are made. It binds together three disparate groups of actors - developers, integrators, users - and creates a relationship between these groups and the machine, embedded in the (pre-)existing organisational and system structures. In this article, we focus on the important, but often neglected, group of integrators within
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