Record 09022026 · captured 2026-08-25
The world looked up Lindsey Vonn. 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.
Lindsey Caroline Vonn is an American alpine ski racer. She won four World Cup overall championships with titles in 2008, 2009, 2010, and 2012. Vonn won the gold medal in downhill at the 2010 Winter Olympics, the first one for an American woman. She also won a
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
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 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
Bradley Kirk Arnold was an American singer, songwriter, and musician. In 1996, he co-founded the rock band 3 Doors Down with Todd Harrell and Matt Roberts, serving as its lead vocalist. The band rose to prominence with their 2000 single "Kryptonite", which Arn
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
Super Bowl LX was an American football game played to determine the champion of the National Football League (NFL) for the 2025 season. The National Football Conference (NFC) champion Seattle Seahawks defeated the American Football Conference (AFC) champion Ne
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
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.
Charles Otto Puth Jr. is an American singer-songwriter, musician, and record producer. His initial exposure came through the viral success of his song covers uploaded to YouTube. Puth signed with the record label eleveneleven in 2011.
Brandi Marie Carlile is an American singer-songwriter and producer. Her music spans multiple genres, including folk rock, alternative country, Americana, and classic rock. During her career, she has received eleven Grammy Awards and two Emmy Awards, in additio
Madison Laʻakea Te-Lan Hall Chock is an American ice dancer. Together with her husband and skating partner, Evan Bates, she is a two-time Olympic gold medalist in the team event, the 2026 Winter Olympics silver medalist, a three-time World champion, three-time
Drake Lee Maye is an American professional football quarterback for the New England Patriots of the National Football League (NFL). He played college football for the North Carolina Tar Heels, winning the Shaun Alexander Award and ACC Football Player of the Ye
Green Day is an American rock band formed in Rodeo, California, in 1987 by lead singer and guitarist Billie Joe Armstrong and bassist and backing vocalist Mike Dirnt, with drummer Tré Cool joining in 1990. In 1994, their major-label debut Dookie, released thro
2026 Japanese general election
Early general elections were held in Japan on 8 February 2026 in all constituencies, including proportional blocks, to elect all 465 seats of the House of Representatives, the lower house of the National Diet.
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
Breezy Noble Johnson is an American World Cup alpine ski racer on the U.S. Ski Team. She competes in the speed events of downhill and super-G. A two-time Olympian, she won a gold medal at the 2026 Milano Cortina Games.
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.
3 Doors Down is an American rock band formed in Escatawpa, Mississippi, in 1996. The band's founding members were Brad Arnold, Matt Roberts, and Todd Harrell. The band's music has been described as a mixture of post-grunge, hard rock and alternative rock.
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
List of Super Bowl halftime shows
Halftime shows are common during many American football games. Entertainment during the Super Bowl, the annual championship game of the National Football League (NFL), is one of the more lavish of these performances and is usually very widely watched on televi
Samuel Richard Darnold is an American professional football quarterback for the Seattle Seahawks of the National Football League (NFL). He played college football for the USC Trojans, becoming the first freshman to win the Archie Griffin Award.
Billie Joe Armstrong is an American musician, songwriter and actor. He is best known for being the lead vocalist, guitarist, and primary songwriter of the rock band Green Day, which he co-founded with Mike Dirnt in 1987. He is also a guitarist and vocalist for
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
The Super Bowl is the annual American football game that determines the champion of the National Football League (NFL). The game culminates a season that begins in the previous calendar year, and is the conclusion of the NFL playoffs. The winner receives the V
Morgan James McSweeney is an Irish former political strategist for the British Labour Party. He served as Downing Street Chief of Staff under Prime Minister Keir Starmer from October 2024 until his resignation in February 2026. A close colleague and adviser to
The Super Bowl is the league championship final game of the National Football League (NFL) of the United States. It has served as the final game of every NFL season since 1966 replacing the NFL Championship Game and also served as the final game of every Ameri
Evan Bates is an American ice dancer. With his wife and skating partner, Madison Chock, he is a two-time Olympic gold medalist in the team event, the 2026 Winter Olympics silver medalist, a three-time World champion, three-time Grand Prix Final champion, a thr
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
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
BADet: Boundary-Aware 3D Object Detection from Point Clouds
Currently, existing state-of-the-art 3D object detectors are in two-stage paradigm. These methods typically comprise two steps: 1) Utilize a region proposal network to propose a handful of high-quality proposals in a bottom-up fashion. 2) Resize and pool the semantic features from the proposed regions to summarize RoI-wise representations for further refinement. Note that these RoI-wise representations in step 2) are
When Fair Ranking Meets Uncertain Inference
Existing fair ranking systems, especially those designed to be demographically fair, assume that accurate demographic information about individuals is available to the ranking algorithm. In practice, however, this assumption may not hold -- in real-world contexts like ranking job applicants or credit seekers, social and legal barriers may prevent algorithm operators from collecting peoples' demographic informatio
3D Object Detection for Autonomous Driving: A Survey
Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of perception stack especially for the sake of path planning, motion prediction, and collision avoidance etc. Taking a quick glance at the progress we have made, we attribute challenges to visual appearance recovery in the absence of depth informati
Bayesian Matrix Decomposition and Applications
The sole aim of this book is to give a self-contained introduction to concepts and mathematical tools in Bayesian matrix decomposition in order to seamlessly introduce matrix decomposition techniques and their applications in subsequent sections. However, we clearly realize our inability to cover all the useful and interesting results concerning Bayesian matrix decomposition and given the paucity of scope to present
A computational framework for human values
In the diverse array of work investigating the nature of human values from psychology, philosophy and social sciences, there is a clear consensus that values guide behaviour. More recently, a recognition that values provide a means to engineer ethical AI has emerged. Indeed, Stuart Russell proposed shifting AI's focus away from simply ``intelligence'' towards intelligence ``provably aligned with human val
On CNF formulas irredundant with respect to unit clause propagation
Two CNF formulas are called ucp-equivalent, if they behave in the same way with respect to the unit clause propagation (UCP). A formula is called ucp-irredundant, if removing any clause leads to a formula which is not ucp-equivalent to the original one. As a consequence of known results, the ratio of the size of a ucp-irredundant formula and the size of a smallest ucp-equivalent formula is at most $n^2$, where $n$ is
A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning
Most federated learning (FL) methods use a client-server scheme, where clients communicate only with a central server. However, this scheme is prone to bandwidth bottlenecks at the server and has a single point of failure. In contrast, in a (fully) decentralized approach, clients communicate directly with each other, dispensing with the server and mitigating these issues. Yet, as the client network grows larger and s
Nonparametric Evaluation of Noisy ICA Solutions
Independent Component Analysis (ICA) was introduced in the 1980's as a model for Blind Source Separation (BSS), which refers to the process of recovering the sources underlying a mixture of signals, with little knowledge about the source signals or the mixing process. While there are many sophisticated algorithms for estimation, different methods have different shortcomings. In this paper, we develop a nonparamet
STAG: Structural Test-time Alignment of Gradients for Online Adaptation
Test-Time Adaptation (TTA) adapts pre-trained models using only unlabeled test streams, requiring real-time inference and update without access to source data. We propose StructuralTest-time Alignment of Gradients (STAG), a lightweight plug-in enhancer that exploits an always-available structural signal: the classifier's intrinsic geometry. STAG derives class-wise structural anchors from classifier weights via se
Extracting Manifold Information from Point Clouds
A kernel based method is proposed for the construction of signature (defining) functions of subsets of $\mathbb{R}^d$. The subsets can range from full dimensional manifolds (open subsets) to point clouds (a finite number of points) and include bounded (closed) smooth manifolds of any codimension. The interpolation and analysis of point clouds are the main application. Two extreme cases in terms of regularity are cons
Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows
Alongside optimization-based planners, sampling-based approaches are often used in trajectory planning for autonomous driving due to their simplicity. Model predictive path integral control is a framework that builds upon optimization principles while incorporating stochastic sampling of input trajectories. This paper investigates several sampling approaches for trajectory generation. In this context, normalizing flo
Generalizing the SINDy approach with nested neural networks
Symbolic Regression (SR) is a widely studied field of research that aims to infer symbolic expressions from data. A popular approach for SR is the Sparse Identification of Nonlinear Dynamical Systems (SINDy) framework, which uses sparse regression to identify governing equations from data. This study introduces an enhanced method, Nested SINDy, that aims to increase the expressivity of the SINDy approach thanks to a
Cross-center data heterogeneity and annotation unreliability significantly challenge the intelligent diagnosis of diseases using brain signals. A notable example is the EEG-based diagnosis of neurodegenerative diseases, which features subtler abnormal neural dynamics typically observed in small-group settings. To advance this area, in this work, we introduce a transferable framework employing Manifold Attention and C
ProDAG: Projected Variational Inference for Directed Acyclic Graphs
Directed acyclic graph (DAG) learning is a central task in structure discovery and causal inference. Although the field has witnessed remarkable advances over the past few years, it remains statistically and computationally challenging to learn a single (point estimate) DAG from data, let alone provide uncertainty quantification. We address the difficult task of quantifying graph uncertainty by developing a Bayesian
Training-Conditional Coverage Bounds under Covariate Shift
Conformal prediction methodology has recently been extended to the covariate shift setting, where the distribution of covariates differs between training and test data. While existing results ensure that the prediction sets from these methods achieve marginal coverage above a nominal level, their coverage rate conditional on the training dataset (referred to as training-conditional coverage) remains unexplored. In th
Anonymization Prompt Learning for Facial Privacy-Preserving Text-to-Image Generation
Text-to-image diffusion models, such as Stable Diffusion, generate highly realistic images from text descriptions. However, the generation of certain content at such high quality raises concerns. A prominent issue is the accurate depiction of identifiable facial images, which could lead to malicious deepfake generation and privacy violations. In this paper, we propose Anonymization Prompt Learning (APL) to address th
Predicting the fatigue life of asphalt concrete using neural networks
Asphalt concrete's (AC) durability and maintenance demands are strongly influenced by its fatigue life. Traditional methods for determining this characteristic are both resource-intensive and time-consuming. This study employs artificial neural networks (ANNs) to predict AC fatigue life, focusing on the impact of strain level, binder content, and air-void content. Leveraging a substantial dataset, we tailored our
Science-Informed Design of Deep Learning With Applications to Wireless Systems: A Tutorial
Recent advances in computational infrastructure and large-scale data processing have accelerated the adoption of data-driven inference methods, particularly deep learning (DL), to solve problems in many scientific and engineering domains. In wireless systems, DL has been applied to problems where analytical modeling or optimization is difficult to formulate, relies on oversimplified assumptions, or becomes computatio
Self-Supervised Video Representation Learning in a Heuristic Decoupled Perspective
Video contrastive learning (V-CL) has emerged as a popular framework for unsupervised video representation learning, demonstrating strong results in tasks such as action classification and detection. Yet, to harness these benefits, it is critical for the learned representations to fully capture both static and dynamic semantics. However, our experiments show that existing V-CL methods fail to effectively learn either
IsUMap: Manifold Learning and Data Visualization leveraging Vietoris-Rips filtrations
This work introduces IsUMap, a novel manifold learning technique that enhances data representation by integrating aspects of UMAP and Isomap with Vietoris-Rips filtrations. We present a systematic and detailed construction of a metric representation for locally distorted metric spaces that captures complex data structures more accurately than the previous schemes. Our approach addresses limitations in existing method
High-Precision Edge Detection via Task-Adaptive Texture Handling and Ideal-Prior Guidance
Image edge detection (ED) requires specialized architectures, reliable supervision, and rigorous evaluation criteria to ensure accurate localization. In this work, we present a framework for high-precision ED that jointly addresses architectural design, data supervision, and evaluation consistency. We propose SDPED, a compact ED model built upon Cascaded Skipping Density Blocks (CSDB), motivated by a task-adaptive ar
Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches
3D Content Generation is at the heart of many computer graphics applications, including video gaming, film-making, virtual and augmented reality, etc. This paper proposes a novel deep-learning based approach for automatically generating interactive and playable 3D game scenes, all from the user's casual prompts such as a hand-drawn sketch. Sketch-based input offers a natural, and convenient way to convey the user
Downscaling Neural Network for Coastal Simulations
Learning the fine-scale details of a coastal ocean simulation from a coarse representation is a challenging task. For real-world applications, high-resolution simulations are necessary to advance understanding of many coastal processes, specifically, to predict flooding resulting from tsunamis and storm surges. We propose a Downscaling Neural Network for Coastal Simulation (DNNCS) for spatiotemporal enhancement to le
Hyperbolic Fine-Tuning for Large Language Models
Large language models (LLMs) have demonstrated remarkable performance across various tasks. However, it remains an open question whether the default Euclidean space is the most suitable choice for LLMs. In this study, we investigate the geometric characteristics of LLMs, focusing specifically on tokens and their embeddings. Our findings reveal that token frequency follows a power-law distribution, where high-frequenc
Enhancing Hyperspectral Image Prediction with Contrastive Learning in Low-Label Regime
Self-supervised contrastive learning is an effective approach for addressing the challenge of limited labelled data. This study builds upon the previously established two-stage patch-level, multi-label classification method for hyperspectral remote sensing imagery. We evaluate the method's performance for both the single-label and multi-label classification tasks, particularly under scenarios of limited training
Notable events recorded on this day and month across all years.