Record 05022026 · 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.
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.
John Trevor Virgo was an English professional snooker player and broadcaster. After achieving success as an amateur, Virgo turned professional in 1976 at age 30 and won four professional titles during his career, including the 1979 UK Championship, where he de
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,
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
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
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
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
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.
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
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
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
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
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
"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
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
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
James Edward Harden Jr. is an American professional basketball player who most recently played for the Cleveland Cavaliers of the National Basketball Association (NBA). He is widely regarded as one of the greatest shooting guards and scorers in NBA history. In
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
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
Fallout is an American post-apocalyptic drama television series created by Graham Wagner and Geneva Robertson-Dworet for Amazon Prime Video. Based on the role-playing video game franchise created by Tim Cain and Leonard Boyarsky, the series is set two centurie
The second season of the American post-apocalyptic drama television series Fallout premiered on December 16, 2025 on Amazon Prime Video, with the remaining episodes released weekly through February 3, 2026. Based on the role-playing video game franchise create
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.
XXX may refer to:
William Henry Gates III is an American businessman and philanthropist. A pioneer of the microcomputer revolution of the 1970s and 1980s, he co-founded the software company Microsoft in 1975 with his childhood friend Paul Allen. Following Microsoft's initial pu
Melinda Ann French Gates is an American philanthropist. Born and raised in Dallas, Texas, she attended Duke University, where she earned a bachelor's degree in computer science and economics and an MBA. She joined Microsoft in 1987 as a multimedia product deve
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Offensive speech detection is a key component of content moderation. However, what is offensive can be highly subjective. This paper investigates how machine and human moderators disagree on what is offensive when it comes to real-world social web political discourse. We show that (1) there is extensive disagreement among the moderators (humans and machines); and (2) human and large-language-model classifiers are una
The Exact Sample Complexity Gain from Invariances for Kernel Regression
In practice, encoding invariances into models improves sample complexity. In this work, we study this phenomenon from a theoretical perspective. In particular, we provide minimax optimal rates for kernel ridge regression on compact manifolds, with a target function that is invariant to a group action on the manifold. Our results hold for any smooth compact Lie group action, even groups of positive dimension. For a fi
Dictionary Learning under Symmetries via Group Representations
The dictionary learning problem can be viewed as a data-driven process to learn a suitable transformation so that data is sparsely represented directly from example data. In this paper, we examine the problem of learning a dictionary that is invariant under a pre-specified group of transformations. Natural settings include Cryo-EM, multi-object tracking, synchronization, pose estimation, etc. We specifically study th
P-Tensors: a General Formalism for Constructing Higher Order Message Passing Networks
Several recent papers have proposed increasing the expressive power of graph neural networks by exploiting subgraphs or other topological structures. In parallel, researchers have investigated higher order permutation equivariant networks. In this paper we tie these two threads together by providing a general framework for higher order permutation equivariant message passing in subgraph neural networks. In this paper
SingFake: Singing Voice Deepfake Detection
The rise of singing voice synthesis presents critical challenges to artists and industry stakeholders over unauthorized voice usage. Unlike synthesized speech, synthesized singing voices are typically released in songs containing strong background music that may hide synthesis artifacts. Additionally, singing voices present different acoustic and linguistic characteristics from speech utterances. These unique propert
Sample Complexity Bounds for Estimating Probability Divergences under Invariances
Group-invariant probability distributions appear in many data-generative models in machine learning, such as graphs, point clouds, and images. In practice, one often needs to estimate divergences between such distributions. In this work, we study how the inherent invariances, with respect to any smooth action of a Lie group on a manifold, improve sample complexity when estimating the 1-Wasserstein distance, the Sobol
ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs
Methods for finetuning generative models for concept-driven personalization generally achieve strong results for subject-driven or style-driven generation. Recently, low-rank adaptations (LoRA) have been proposed as a parameter-efficient way of achieving concept-driven personalization. While recent work explores the combination of separate LoRAs to achieve joint generation of learned styles and subjects, existing tec
Unlocking Past Information: Temporal Embeddings in Cooperative Bird's Eye View Prediction
Accurate and comprehensive semantic segmentation of Bird's Eye View (BEV) is essential for ensuring safe and proactive navigation in autonomous driving. Although cooperative perception has exceeded the detection capabilities of single-agent systems, prevalent camera-based algorithms in cooperative perception neglect valuable information derived from historical observations. This limitation becomes critical during
Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias
With the impressive progress of deep learning, applications relying on machine learning are increasingly being integrated into daily life. However, most deep learning models have an opaque, oracle-like nature making it difficult to interpret and understand their decisions. This problem led to the development of the field known as eXplainable Artificial Intelligence (XAI). One method in this field known as Projective
Bootstrapping Cognitive Agents with a Large Language Model
Large language models contain noisy general knowledge of the world, yet are hard to train or fine-tune. On the other hand cognitive architectures have excellent interpretability and are flexible to update but require a lot of manual work to instantiate. In this work, we combine the best of both worlds: bootstrapping a cognitive-based model with the noisy knowledge encoded in large language models. Through an embodied
Policy Learning with a Language Bottleneck
Modern AI systems such as self-driving cars and game-playing agents achieve superhuman performance, but often lack human-like generalization, interpretability, and inter-operability with human users. Inspired by the rich interactions between language and decision-making in humans, we introduce Policy Learning with a Language Bottleneck (PLLB), a framework enabling AI agents to generate linguistic rules that capture t
Data-driven Error Estimation: Excess Risk Bounds without Class Complexity as Input
Constructing confidence intervals that are simultaneously valid across a class of estimates is central to tasks such as multiple mean estimation, generalization guarantees, and adaptive experimental design. We frame this as an ``error estimation problem," where the goal is to determine a high-probability upper bound on the maximum error for a class of estimates. We propose an entirely data-driven approach that de
Scalable physical source-to-field inference with hypernetworks
We present a generative model that amortises computation for the field and potential around e.g.~gravitational or electromagnetic sources. Exact numerical calculation has either computational complexity $\mathcal{O}(M\times{}N)$ in the number of sources $M$ and evaluation points $N$, or requires a fixed evaluation grid to exploit fast Fourier transforms. Using an architecture where a hypernetwork produces an implicit
Merged ChemProt-DrugProt for Relation Extraction from Biomedical Literature
The extraction of chemical-gene relations plays a pivotal role in understanding the intricate interactions between chemical compounds and genes, with significant implications for drug discovery, disease understanding, and biomedical research. This paper presents a data set created by merging the ChemProt and DrugProt datasets to augment sample counts and improve model accuracy. We evaluate the merged dataset using tw
A Universal Class of Sharpness-Aware Minimization Algorithms
Recently, there has been a surge in interest in developing optimization algorithms for overparameterized models as achieving generalization is believed to require algorithms with suitable biases. This interest centers on minimizing sharpness of the original loss function; the Sharpness-Aware Minimization (SAM) algorithm has proven effective. However, most literature only considers a few sharpness measures, such as th
CoSQA+: Pioneering the Multi-Choice Code Search Benchmark with Test-Driven Agents
Semantic code search, retrieving code that matches a given natural language query, is an important task to improve productivity in software engineering. Existing code search datasets face limitations: they rely on human annotators who assess code primarily through semantic understanding rather than functional verification, leading to potential inaccuracies and scalability issues. Additionally, current evaluation metr
Fake News Detection: It's All in the Data!
This comprehensive survey serves as an indispensable resource for researchers embarking on the journey of fake news detection. By highlighting the pivotal role of dataset quality and diversity, it underscores the significance of these elements in the effectiveness and robustness of detection models. The survey meticulously outlines the key features of datasets, various labeling systems employed, and prevalent biases
IE-NeRF: Inpainting Enhanced Neural Radiance Fields in the Wild
We present a novel approach for synthesizing realistic novel views using Neural Radiance Fields (NeRF) with uncontrolled photos in the wild. While NeRF has shown impressive results in controlled settings, it struggles with transient objects commonly found in dynamic and time-varying scenes. Our framework called \textit{Inpainting Enhanced NeRF}, or \ours, enhances the conventional NeRF by drawing inspiration from the
Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMM
We study the problem of learning a partially observed matrix under the low rank assumption in the presence of fully observed side information that depends linearly on the true underlying matrix. This problem consists of an important generalization of the Matrix Completion problem, a central problem in Statistics, Operations Research and Machine Learning, that arises in applications such as recommendation systems, sig
High-resolution climate simulations are valuable for understanding climate change impacts. This has motivated use of regional convection-permitting climate models (CPMs), but these are very computationally expensive. We present a convection-permitting model generative emulator (CPMGEM), to skilfully emulate precipitation simulations by a 2.2km-resolution regional CPM at much lower cost. This utilises a generative mac
No Screening is More Efficient with Multiple Objects
We study efficient mechanism design for allocating multiple heterogeneous objects. The aim is to maximize the residual surplus, the total value generated from an allocation minus the costs of screening. We discover a robust trend indicating that no-screening mechanisms, such as serial dictatorship with exogenous priority order, tend to perform better as the variety of goods increases. We analyze the underlying reason
Revisiting 360 Depth Estimation with PanoGabor: A New Fusion Perspective
Depth estimation from a monocular 360 image is important to the perception of the entire 3D environment. However, the inherent distortion and large field of view (FoV) in 360 images pose great challenges for this task. To this end, existing mainstream solutions typically introduce additional perspective-based 360 representations ({e.g., Cubemap) to achieve effective feature extraction. Nevertheless, regardless of the
STAND: Self-Aware Precondition Induction for Interactive Task Learning
In interactive task learning (ITL), AI agents learn new capabilities from limited human instruction provided during task execution. STAND is a new method of data-efficient rule precondition induction specifically designed for these human-in-the-loop training scenarios. A key feature of STAND is its self-awareness of its own learning -- it can provide accurate metrics of training progress back to users. STAND beats po
Deep Multimodal Learning with Missing Modality: A Survey
During multimodal model training and testing, certain data modalities may be absent due to sensor limitations, cost constraints, privacy concerns, or data loss, negatively affecting performance. Multimodal learning techniques designed to handle missing modalities can mitigate this by ensuring model robustness even when some modalities are unavailable. This survey reviews recent progress in Multimodal Learning with Mi
PersoBench: Benchmarking Personalized Response Generation in Large Language Models
While large language models (LLMs) have exhibited impressive conversational capabilities, their proficiency in delivering personalized responses remains unclear. Although recent benchmarks automatically evaluate persona consistency in role-playing contexts using LLM-based judgment, the evaluation of personalization in response generation remains underexplored. To address this gap, we present an automated benchmarking
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