Record 12022026 · captured 2026-08-25
The world looked up James Van Der Beek. 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.
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
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
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.
Tumbler Ridge is a district municipality in the foothills of the B.C. Rockies in northeastern British Columbia, Canada, and a member municipality of the Peace River Regional District. With a population of 2,399 (2021) living in a townsite, the municipality enc
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
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.
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.
Heather McComb is an American actress. She is best known for her roles as Maggie on Party of Five (1998–1999) and Frances Malone in Profiler (1997–1998).
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
Maxim Naumov is an American figure skater. He is the 2026 U.S. national bronze medalist, three-time U.S. national pewter medalist, and the 2020 U.S. junior national champion. Naumov finished within the top five at the 2020 World Junior Championships.
On February 10, 2026, a mass shooting occurred in Tumbler Ridge, British Columbia, Canada. On that afternoon, Jesse Van Rootselaar killed her mother and half-brother at their home before going to Tumbler Ridge Secondary School, where she killed six people and
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
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.
Leslie Herbert Wexner is an American billionaire businessman and political activist. He is the co-founder and chair emeritus of Bath & Body Works, Inc. He has been the principal in Abercrombie & Fitch, Victoria's Secret and La Senza, amongst several other reta
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 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
Lilah Fear is a British ice dancer. Representing Great Britain with her skating partner, Lewis Gibson, she is the 2025 World bronze medalist; theirs was the first World medal for Britain in 41 years. Fear is also a four-time European medalist, a two-time Grand
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
Colorectal cancer, also known as bowel cancer, colon cancer, intestinal cancer, or rectal cancer, is the development of cancer from the colon or rectum, in which uncontrolled growth of colon cells that can invade/spread to other parts of the body takes place.
Laurence Fournier Beaudry is a Canadian and French ice dancer. Representing France with partner Guillaume Cizeron, she is the 2026 Olympic champion, the 2026 World champion, the 2026 European champion, the 2025–26 Grand Prix Final silver medalist, a two-time G
Mikaela Pauline Shiffrin is an American alpine skier. Shiffrin is the most decorated American alpine skier in World Championships history. She has the most World Cup wins of any alpine skier in history and is the only one to have reached the milestone of 100 v
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
Ice hockey at the 2026 Winter Olympics
The ice hockey competitions of the 2026 Winter Olympics was played at two venues located in the Milan cluster: the PalaItalia and one of the Fiera Milano pavilions.
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
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
Dawson's Creek is an American teen drama television series about the lives of a close-knit group of friends in the fictional town of Capeside, Massachusetts, beginning in high school and continuing into college. It aired from January 20, 1998, to May 14, 2003,
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
List of school shootings in Canada
This chronological list of school shootings in Canada includes any school shootings in Canada that occurred at primary and secondary public or private schools, as well as colleges and universities, and on school buses. A "school shooting" is defined by this li
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.
Classification of high-dimensional data with spiked covariance matrix structure
We study the classification problem for high-dimensional data with $n$ observations on $p$ features where the $p \times p$ covariance matrix $Σ$ exhibits a spiked eigenvalue structure and the vector $ζ$, given by the difference between the {\em whitened} mean vectors, is sparse. We analyze an adaptive classifier (adaptive with respect to the sparsity $s$) that first performs dimension reduction on the feature vectors
MITI: SLAM Benchmark for Laparoscopic Surgery
We propose a new benchmark for evaluating stereoscopic visual-inertial computer vision algorithms (SLAM/ SfM/ 3D Reconstruction/ Visual-Inertial Odometry) for minimally invasive surgical (MIS) interventions in the abdomen. Our MITI Dataset available at [https://mediatum.ub.tum.de/1621941] provides all the necessary data by a complete recording of a handheld surgical intervention at Research Hospital Rechts der Isar o
Neural Score Matching for High-Dimensional Causal Inference
Traditional methods for matching in causal inference are impractical for high-dimensional datasets. They suffer from the curse of dimensionality: exact matching and coarsened exact matching find exponentially fewer matches as the input dimension grows, and propensity score matching may match highly unrelated units together. To overcome this problem, we develop theoretical results which motivate the use of neural netw
Airway Tree Modeling Using Dual-channel 3D UNet 3+ with Vesselness Prior
The lung airway tree modeling is essential to work for the diagnosis of pulmonary diseases, especially for X-Ray computed tomography (CT). The airway tree modeling on CT images can provide the experts with 3-dimension measurements like wall thickness, etc. This information can tremendously aid the diagnosis of pulmonary diseases like chronic obstructive pulmonary disease [1-4]. Many scholars have attempted various wa
We analyze the mixing time of Metropolized Hamiltonian Monte Carlo (HMC) with the leapfrog integrator to sample from a distribution on $\mathbb{R}^d$ whose log-density is smooth, has Lipschitz Hessian in Frobenius norm and satisfies isoperimetry. We bound the gradient complexity to reach $ε$ error in total variation distance from a warm start by $\tilde O(d^{1/4}\text{polylog}(1/ε))$ and demonstrate the benefit of ch
Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection is crucial for improving patient outcomes. Nevertheless, early diagnosis of cancer is a major challenge, particularly in low-resource settings where access to medical resources and trained radiologists is limited. The objective of this study is to propose an automated end-to-end deep learning-based framework for the early detection
Structured Sentiment Analysis as Transition-based Dependency Graph Parsing
Structured sentiment analysis (SSA) aims to automatically extract people's opinions from a text in natural language and adequately represent that information in a graph structure. One of the most accurate methods for performing SSA was recently proposed and consists of approaching it as a dependency graph parsing task. Although we can find in the literature how transition-based algorithms excel in different depen
Fine-grained Analysis of Non-parametric Estimation for Pairwise Learning
In this paper, we are concerned with the generalization performance of non-parametric estimation for pairwise learning. Most of the existing work requires the hypothesis space to be convex or a VC-class, and the loss to be convex. However, these restrictive assumptions limit the applicability of the results in studying many popular methods, especially kernel methods and neural networks. We significantly relax these r
Brain2Music: Reconstructing Music from Human Brain Activity
The process of reconstructing experiences from human brain activity offers a unique lens into how the brain interprets and represents the world. In this paper, we introduce a method for reconstructing music from brain activity, captured using functional magnetic resonance imaging (fMRI). Our approach uses either music retrieval or the MusicLM music generation model conditioned on embeddings derived from fMRI data. Th
Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images
Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to generate synthetic images that incorporate a diverse range of features to accurately represent the distribution of features present in the training imagery. Furthermore, th
Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data
Characterizing the relationship between neural population activity and behavioral data is a central goal of neuroscience. While latent variable models (LVMs) are successful in describing high-dimensional time-series data, they are typically only designed for a single type of data, making it difficult to identify structure shared across different experimental data modalities. Here, we address this shortcoming by propo
LLMs as Hackers: Autonomous Linux Privilege Escalation Attacks
Penetration-testing is crucial for identifying system vulnerabilities, with privilege-escalation being a critical subtask to gain elevated access to protected resources. Language Models (LLMs) presents new avenues for automating these security practices by emulating human behavior. However, a comprehensive understanding of LLMs' efficacy and limitations in performing autonomous Linux privilege-escalation attacks
Agricultural management, with a particular focus on fertilization strategies, holds a central role in shaping crop yield, economic profitability, and environmental sustainability. While conventional guidelines offer valuable insights, their efficacy diminishes when confronted with extreme weather conditions, such as heatwaves and droughts. In this study, we introduce an innovative framework that integrates Deep Reinf
Federated Learning (FL) commonly relies on a central server to coordinate training across distributed clients. While effective, this paradigm suffers from significant communication overhead, impacting overall training efficiency. To mitigate this, prior work has explored compression techniques such as quantization. However, in heterogeneous FL settings, clients may employ different quantization levels based on their
Provable Emergence of Deep Neural Collapse and Low-Rank Bias in $L^2$-Regularized Nonlinear Networks
We present a unified theoretical framework connecting the first property of Deep Neural Collapse (DNC1) to the emergence of implicit low-rank bias in nonlinear networks trained with $L^2$ weight decay regularization. Our main contributions are threefold. First, we derive a quantitative relation between the Total Cluster Variation (TCV) of intermediate embeddings and the numerical rank of stationary weight matrices. I
Goal-Conditioned Reinforcement Learning from Sub-Optimal Data on Metric Spaces
We study the problem of learning optimal behavior from sub-optimal datasets for goal-conditioned offline reinforcement learning under sparse rewards, invertible actions and deterministic transitions. To mitigate the effects of \emph{distribution shift}, we propose MetricRL, a method that combines metric learning for value function approximation with weighted imitation learning for policy estimation. MetricRL avoids c
Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?
Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training. However, collecting such large-scale densely-supervised datasets is notoriously costly. To reduce the cumbersome data annotation process, we propose a novel sparsely-annotated framework, in which we just annotate one 3D object per scene. Such a sparse annotation strategy could signific
Diffusion posterior sampling for simulation-based inference in tall data settings
Identifying the parameters of a non-linear model that best explain observed data is a core task across scientific fields. When such models rely on complex simulators, evaluating the likelihood is typically intractable, making traditional inference methods such as MCMC inapplicable. Simulation-based inference (SBI) addresses this by training deep generative models to approximate the posterior distribution over paramet
Kernel-based Optimally Weighted Conformal Time-Series Prediction
In this work, we present a novel conformal prediction method for time-series, which we call Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI). Specifically, KOWCPI adapts the classic Reweighted Nadaraya-Watson (RNW) estimator for quantile regression on dependent data and learns optimal data-adaptive weights. Theoretically, we tackle the challenge of establishing a conditional coverage guarantee
Games with Payments between Learning Agents
In repeated games, such as auctions, players rely on autonomous learning agents to choose their actions. We study settings in which players have their agents make monetary transfers to other agents during play at their own expense, in order to influence learning dynamics in their favor. Our goal is to understand when players have incentives to use such payments, how payments between agents affect learning outcomes, a
Tensor learning with orthogonal, Lorentz, and symplectic symmetries
Tensors are a fundamental data structure for many scientific contexts, such as time series analysis, materials science, and physics, among many others. Improving our ability to produce and handle tensors is essential to efficiently address problems in these domains. In this paper, we show how to exploit the underlying symmetries of functions that map tensors to tensors. More concretely, we develop universally express
Towards Better Code Understanding in Decoder-Only Models with Contrastive Learning
Recent advances in large-scale code generation models have led to remarkable progress in producing high-quality code. These models are trained in a self-supervised manner on extensive unlabeled code corpora using a decoder-only architecture. However, despite their generative strength, decoder-only models often exhibit limited performance on code understanding tasks such as code search and clone detection, primarily d
Exponential time differencing for matrix-valued dynamical systems
Matrix evolution equations occur in many applications, such as dynamical Lyapunov/Sylvester systems or Riccati equations in optimization and stochastic control, machine learning or data assimilation. In many such problems, the dominant stability restriction is imposed by a stiff linear term, making standard explicit integrators impractical. Exponential time differencing (ETD) is known to produce highly stable numeric
Implicit Probabilistic Reasoning Does Not Reflect Explicit Answers in Large Language Models
The handling of probabilities in the form of uncertainty or partial information is an essential task for LLMs in many settings and applications. A common approach to evaluate an LLM's probabilistic reasoning capabilities is to assess its ability to answer questions pertaining to probability through the use of multiple-choice questions (MCQs). However, this paradigm, which we refer to as explicit probabilistic rea
Synthetic data: How could it be used for infectious disease research?
Over the last three to five years, it has become possible to generate machine learning synthetic data for healthcare-related uses. However, concerns have been raised about potential negative factors associated with the possibilities of artificial dataset generation. These include the potential misuse of generative artificial intelligence (AI) in fields such as cybercrime, the use of deepfakes and fake news to deceive
Notable events recorded on this day and month across all years.