Record 13022026 · 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
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.
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
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).
2026 Bangladeshi general election
General elections were held in Bangladesh on 12 February 2026 to elect members of the Jatiya Sangsad. It was the first general election since the July Uprising of 2024 that ended the 15-year-long rule of Sheikh Hasina. The Bangladesh Nationalist Party (BNP), l
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
Chloe Kim is an American professional snowboarder and two-time Olympic gold medalist. At the 2018 Winter Olympics, she became the youngest woman to win an Olympic snowboarding gold medal when she won gold in the women's snowboard halfpipe at 17 years old.
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
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
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
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,
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.
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 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
Ron Lester was an American actor. He was best known for his roles in the films Varsity Blues, Not Another Teen Movie, Good Burger, and the television series Popular.
Walter Edward Cox, known professionally as Bud Cort, was an American actor known for his unorthodox starring roles in Robert Altman's Brewster McCloud (1970), for which he was nominated for a Golden Laurel Award, and Hal Ashby's Harold and Maude (1971), for wh
Namibia, officially the Republic of Namibia, is a country in Southern Africa. It borders the Atlantic Ocean to the west, Zambia and Angola to the north, Botswana to the east, and South Africa to the south; in the northeast, approximating a quadripoint, Zimbabw
Guillaume Cizeron is a French ice dancer. With current partner Laurence Fournier Beaudry, he is the 2026 Olympic champion, the 2026 World champion, the 2026 European champion, the 2025–26 Grand Prix Final silver medalist, a two-time Grand Prix champion, and th
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
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
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
Stacy Ann-Marie Keibler is an American retired professional wrestler, entertainer, and model. She is best known for her tenures in World Championship Wrestling (WCW) and World Wrestling Entertainment (WWE).
Sir James Arthur Ratcliffe is a British billionaire, chemical engineer, and businessman. Ratcliffe is the chairman and chief executive officer (CEO) of the INEOS chemicals group, which he founded in 1998.
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.
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.
Nikolaj Sørensen is a Danish-Canadian ice dancer. Most recently competing for Canada with Laurence Fournier Beaudry, he is a two-time Four Continents silver medalist, an eight-time Grand Prix medallist, a five-time Challenger medallist, and the 2023 Canadian n
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
The ICC Men's T20 World Cup, formerly the ICC World Twenty20, is a biennial world cup for cricket in Twenty20 International (T20I) format, organised by the International Cricket Council (ICC). It was held in every odd year from 2007 to 2009, and since 2010 has
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
This work proposes a rapid algorithm, BM-Global, for nuclear-norm-regularized convex and low-rank matrix optimization problems. BM-Global efficiently decreases the objective value via low-cost steps leveraging the nonconvex but smooth Burer-Monteiro (BM) decomposition, while effectively escapes saddle points and spurious local minima ubiquitous in the BM form to obtain guarantees of fast convergence rates to the glob
Minimax Optimal Estimation of Stability Under Distribution Shift
The performance of decision policies and prediction models often deteriorates when applied to environments different from the ones seen during training. To ensure reliable operation, we analyze the stability of a system under distribution shift, which is defined as the smallest change in the underlying environment that causes the system's performance to deteriorate beyond a permissible threshold. In contrast to s
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
Magnetic resonance imaging (MRI) is a powerful medical imaging modality, but long acquisition times limit throughput, patient comfort, and clinical accessibility. Diffusion-based generative models serve as strong image priors for reducing scan-time with accelerated MRI reconstruction and offer robustness across variations in the acquisition model. However, most existing diffusion-based approaches do not exploit the u
Diffusion models, emerging as powerful deep generative tools, excel in various applications. They operate through a two-steps process: introducing noise into training samples and then employing a model to convert random noise into new samples (e.g., images). However, their remarkable generative performance is hindered by slow training and sampling. This is due to the necessity of tracking extensive forward and revers
Compiling High-Level Neural Network Specifications into VNN-LIB Queries
The formal verification of traditional software has been revolutionised by verification-orientated languages such as Dafny and F* which enable developers to write high-level specifications that are automatically compiled down to low-level SMT-LIB queries. In contrast, neural network verification currently lacks such infrastructure, often requiring users to express requirements in formats close to the low-level VNN-LI
Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement
Underwater visuals undergo various complex degradations, inevitably influencing the efficiency of underwater vision tasks. Recently, diffusion models were employed to underwater image enhancement (UIE) tasks, and gained SOTA performance. However, these methods fail to consider the physical properties and underwater imaging mechanisms in the diffusion process, limiting information completion capacity of diffusion mode
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation
Despite the significant success achieved by deep learning methods in medical image segmentation, researchers still struggle in the computer-aided diagnosis of abdominal lymph nodes due to the complex abdominal environment, small and indistinguishable lesions, and limited annotated data. To address these problems, we present a pipeline that integrates the conditional diffusion model for lymph node generation and the n
Warped Time Series Anomaly Detection
This paper addresses the problem of detecting time series outliers, focusing on systems with repetitive behavior, such as industrial robots operating on production lines.Notable challenges arise from the fact that a task performed multiple times may exhibit different duration in each repetition and that the time series reported by the sensors are irregularly sampled because of data gaps. The anomaly detection approac
Are Biological Systems More Intelligent Than Artificial Intelligence?
Are biological self-organising systems more ``intelligent'' than artificial intelligence (AI)? If so, why? I address this question using a mathematical framework that defines intelligence in terms of adaptability. Systems are modelled as stacks of abstraction layers (\emph{Stack Theory}) and compared by how effectively they delegate agentic control down their stacks. I illustrate this using computational, bio
Feature-Based Interpretable Surrogates for Optimization
For optimization models to be used in practice, it is crucial that users trust the results. A key factor in this aspect is the interpretability of the solution process. A previous framework for inherently interpretable optimization models used decision trees to map instances to solutions of the underlying optimization model. Based on this work, we investigate how we can use more general optimization rules to further
Prevailing Research Areas for Music AI in the Era of Foundation Models
Parallel to rapid advancements in foundation model research, the past few years have witnessed a surge in music AI applications. As AI-generated and AI-augmented music become increasingly mainstream, many researchers in the music AI community may wonder: what research frontiers remain unexplored? This paper outlines several key areas within music AI research that present significant opportunities for further investig
Spoken language is often, if not always, understood in a context formed by the identity of the speaker. For example, we can easily make sense of an utterance such as "I'm going to have a manicure this weekend" or "The first time I got pregnant I had a hard time" when spoken by a woman, but it would be harder to understand when it is spoken by a man. Previous ERP studies have shown mixed results re
Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods
Physics-informed deep learning often faces optimization challenges due to the complexity of solving partial differential equations (PDEs), which involve exploring large solution spaces, require numerous iterations, and can lead to unstable training. These challenges arise particularly from the ill-conditioning of the optimization problem caused by the differential terms in the loss function. To address these issues,
LabSafety Bench: Benchmarking LLMs on Safety Issues in Scientific Labs
Artificial Intelligence (AI) is revolutionizing scientific research, yet its growing integration into laboratory environments presents critical safety challenges. Large language models (LLMs) and vision language models (VLMs) now assist in experiment design and procedural guidance, yet their "illusion of understanding" may lead researchers to overtrust unsafe outputs. Here we show that current models remain f
The widespread use of AI-generated content from diffusion models has raised significant concerns regarding misinformation and copyright infringement. Watermarking is a crucial technique for identifying these AI-generated images and preventing their misuse. In this paper, we introduce Shallow Diffuse, a new watermarking technique that embeds robust and invisible watermarks into diffusion model outputs. Unlike existing
Supervised Transfer Learning Framework for Fault Diagnosis in Wind Turbines
Common challenges in fault diagnosis include the lack of labeled data and the need to build models for each domain, resulting in many models that require supervision. Transfer learning can help tackle these challenges by learning cross-domain knowledge. Many approaches still require at least some labeled data in the target domain, and often provide unexplainable results. To this end, we propose a supervised transfer
Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment
Human visual perception naturally evaluates image quality across multiple scales, a hierarchical process that existing blind image quality assessment (BIQA) algorithms struggle to replicate effectively. This limitation stems from a fundamental misunderstanding: current multi-scale approaches fail to recognize that quality perception varies dramatically between scales -- what appears degraded when viewed closely may l
Indoor SLAM often suffers from issues such as scene drifting, double walls, and blind spots, particularly in confined spaces with objects close to the sensors (e.g. LiDAR and cameras) in reconstruction tasks. Real-time visualization of point cloud registration during data collection may help mitigate these issues, but a significant limitation remains in the inability to in-depth compare the scanned data with actual p
NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap via Informational Interviews
Large Language Models (LLMs) have demonstrated impressive capabilities in generating coherent text but often struggle with grounding language and strategic dialogue. To address this gap, we focus on journalistic interviews, a domain rich in grounding communication and abundant in data. We curate a dataset of 40,000 two-person informational interviews from NPR and CNN, and reveal that LLMs are significantly less likel
PBP: Post-training Backdoor Purification for Malware Classifiers
In recent years, the rise of machine learning (ML) in cybersecurity has brought new challenges, including the increasing threat of backdoor poisoning attacks on ML malware classifiers. For instance, adversaries could inject malicious samples into public malware repositories, contaminating the training data and potentially misclassifying malware by the ML model. Current countermeasures predominantly focus on detecting
Controlling Dynamical Systems into Unseen Target States Using Machine Learning
We present a novel, model-free, and data-driven methodology for controlling complex dynamical systems into previously unseen target states, including those with significantly different and complex dynamics. Leveraging a parameter-aware realization of next-generation reservoir computing (NGRC), our approach accurately predicts system behavior in unobserved parameter regimes, enabling control over transitions to arbitr
In domains such as finance, healthcare, and robotics, managing worst-case scenarios is critical, as failure to do so can lead to catastrophic outcomes. Distributional Reinforcement Learning (DRL) provides a natural framework to incorporate risk sensitivity into decision-making processes. However, existing approaches face two key limitations: (1) the use of fixed risk measures at each decision step often results in ov
FaceQSORT: a Multi-Face Tracking Method based on Biometric and Appearance Features
In this work, a novel multi-face tracking method named FaceQSORT is proposed. To mitigate multi-face tracking challenges (e.g., partially occluded or lateral faces), FaceQSORT combines biometric and visual appearance features (extracted from the same image (face) patch) for association. The Q in FaceQSORT refers to the scenario for which FaceQSORT is desinged, i.e. tracking people's faces as they move towards a g
The rapid advancement of Generative Adversarial Networks (GANs) and diffusion models has enabled the creation of highly realistic synthetic images, presenting significant societal risks, such as misinformation and deception. As a result, detecting AI-generated images has emerged as a critical challenge. Existing researches emphasize extracting fine-grained features to enhance detector generalization, yet they often l
Chain-of-Thought (CoT) training has markedly advanced the reasoning capabilities of large language models (LLMs), yet the mechanisms by which CoT training enhances generalization remain inadequately understood. In this work, we demonstrate that compositional generalization is fundamental: models systematically combine simpler learned skills during CoT training to address novel and more complex problems. Through a the
Notable events recorded on this day and month across all years.