Record 17022026 · captured 2026-08-25
The world looked up Robert Duvall. 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.
Robert Selden Duvall was an American actor and filmmaker, best known for his roles in films of the later 20th century. Duvall began acting professionally on stage in 1952, performing in summer plays at the Gateway Playhouse in Bellport on Long Island until 195
Luciana Pedraza is an Argentine actress and director. She was married to American actor Robert Duvall from 2005 until his death in 2026. She is the granddaughter of Argentine aviation pioneer Susana Ferrari Billinghurst.
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
American actor, director, and producer Robert Duvall had an extensive career in film and television, first appearing on an episode of Armstrong Circle Theatre in 1959. His television work during the 1960s includes Route 66 (1961), Alfred Hitchcock Presents (19
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
Eileen Feng Gu, also known by her Chinese name Gu Ailing (谷爱凌), is a Chinese-American freestyle skier and model. She has represented China in halfpipe, slopestyle, and big air events since the 2018–19 season. With three gold and three silver medals, Gu is the
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
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
Wuthering Heights is a 2026 romantic period drama film produced, written and directed by Emerald Fennell. Loosely based on the 1847 novel by Emily Brontë, the film is a reinterpretation intended by Fennell to "recreate the feeling of a teenage girl reading thi
1989 Tiananmen Square protests and massacre
Protests led by students and workers, known in China as the June Fourth Incident, were held in Tiananmen Square in Beijing, China, from 15 April to 4 June 1989. After weeks of unsuccessful attempts between the demonstrators and the Chinese government to find a
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
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
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.
John Fitzgerald Kennedy Jr., also referred to as JFK Jr., was an American businessman, attorney, magazine publisher, and journalist. He was the son of the 35th U.S. president John F. Kennedy, and First Lady Jacqueline Kennedy.
Elana Meyers Taylor is an American Olympic bobsledder who has competed since 2007. Born in Oceanside, California, Meyers Taylor was raised in Douglasville, Georgia and is a graduate of George Washington University, where she was a member of the softball team.
The 2026 ICC Men's T20 World Cup was the tenth edition of the ICC Men's T20 World Cup, co-hosted by Board of Control for Cricket in India and Sri Lanka Cricket from 7 February to 8 March 2026. Sri Lanka had previously hosted the competition in 2012 and India i
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
Christian Lee Hutson is an American singer, musician and songwriter. He began his career as a member of The Driftwood Singers, before signing to ANTI- as a solo artist in 2019. He has released three full-length albums: Beginners (2020), Quitters (2022) and Par
Carolyn Jeanne Bessette-Kennedy was an American fashion publicist. Raised in Greenwich, Connecticut, she graduated from Boston University and joined Calvin Klein, where she rose from a sales position in Boston to publicity and show-production roles in New York
Maya Ray Thurman Hawke is an American actress and singer-songwriter. The daughter of Ethan Hawke and Uma Thurman, she began her career in modeling and subsequently made her screen debut as Jo March in the 2017 BBC adaptation of Little Women. She gained interna
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
Bat Pussy is an American pornographic film, believed to have been produced in the early 1970s. Ostensibly a spoof of the 1966–1968 Batman television series, it has been cited as the earliest example of a pornographic parody film and more infamously considered
Jacqueline Lee Kennedy Onassis was the first lady of the United States from 1961 to 1963, as the wife of John F. Kennedy, the 35th president of the United States. She redefined the previously mostly ceremonial role into a platform for arts and culture by hosti
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.
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
Presidents' Day, officially Washington's Birthday at the federal governmental level, is a holiday in the United States celebrated on the third Monday of February. It is often celebrated to honor all those who served as presidents of the United States and, sinc
O'Romeo is a 2026 Indian Hindi-language romantic action thriller film written and directed by Vishal Bhardwaj. Produced by Sajid Nadiadwala for Nadiadwala Grandson Entertainment, the film is based on the non-fiction book Mafia Queens of Mumbai by Hussain Zaidi
Ice hockey at the 2026 Winter Olympics – Men's tournament
The men's tournament in ice hockey at the 2026 Winter Olympics took place in Milan, Italy, between 11 and 22 February 2026. Twelve countries qualified for the tournament; eight via ranking by the IIHF, three via qualification tournaments, and Italy as hosts. R
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
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.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
An RL-Based Adaptive Detection Strategy to Secure Cyber-Physical Systems
Increased dependence on networked, software based control has escalated the vulnerabilities of Cyber Physical Systems (CPSs). Detection and monitoring components developed leveraging dynamical systems theory are often employed as lightweight security measures for protecting such safety critical CPSs against false data injection attacks. However, existing approaches do not correlate attack scenarios with parameters of
Deep Two-Way Matrix Reordering for Relational Data Analysis
Matrix reordering is a task to permute the rows and columns of a given observed matrix such that the resulting reordered matrix shows meaningful or interpretable structural patterns. Most existing matrix reordering techniques share the common processes of extracting some feature representations from an observed matrix in a predefined manner, and applying matrix reordering based on it. However, in some practical cases
Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation
Natural policy gradient (NPG) methods with entropy regularization achieve impressive empirical success in reinforcement learning problems with large state-action spaces. However, their convergence properties and the impact of entropy regularization remain elusive in the function approximation regime. In this paper, we establish finite-time convergence analyses of entropy-regularized NPG with linear function approxima
We propose a new regression algorithm that learns from a set of input-output pairs. Our algorithm is designed for populations where the relation between the input variables and the output variable exhibits a heterogeneous behavior across the predictor space. The algorithm starts with generating subsets that are concentrated around random points in the input space. This is followed by training a local predictor for ea
Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one of the most serious dangers to human health, and the number of such patients is still growing. Early and accurate detection plays a key role in successful treatment and intervention. Electrocardiogram (ECG) is the gold standard for identifying a variety of cardiovascular abnormalities. In clinical practices and most of the curr
Realtime Data-Efficient Portrait Stylization Based On Geometric Alignment
Portrait Stylization aims to imbue portrait photos with vivid artistic effects drawn from style examples. Despite the availability of enormous training datasets and large network weights, existing methods struggle to maintain geometric consistency and achieve satisfactory stylization effects due to the disparity in facial feature distributions between facial photographs and stylized images, limiting the application o
TKN: Transformer-based Keypoint Prediction Network For Real-time Video Prediction
Video prediction is a complex time-series forecasting task with great potential in many use cases. However, traditional methods prioritize accuracy and overlook slow prediction speeds due to complex model structures, redundant information, and excessive GPU memory consumption. These methods often predict frames sequentially, making acceleration difficult and limiting their applicability in real-time scenarios like da
This study examines the relationship between student perceptions and their intention to use generative AI in higher education. Drawing on Expectancy-Value Theory (EVT), a questionnaire was developed to measure students' knowledge of generative AI, perceived value, and perceived cost. A sample of 405 students participated in the study, and confirmatory factor analysis was used to validate the constructs. The resul
Inference for relative sparsity
In healthcare, there is much interest in estimating policies, or mappings from covariates to treatment decisions. Recently, there is also interest in constraining these estimated policies to the standard of care, which generated the observed data. A relative sparsity penalty was proposed to derive policies that have sparse, explainable differences from the standard of care, facilitating justification of the new polic
A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot
Generative modeling in machine learning aims to synthesize new data samples that are statistically similar to those observed during training. While conventional generative models such as GANs and diffusion models typically assume access to large and diverse datasets, many real-world applications (e.g. in medicine, satellite imaging, and artistic domains) operate under limited data availability and strict constraints.
When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
Offline reinforcement learning algorithms often require careful hyperparameter tuning. Before deployment, we need to select amongst a set of candidate policies. However, there is limited understanding about the fundamental limits of this offline policy selection (OPS) problem. In this work we provide clarity on when sample efficient OPS is possible, primarily by connecting OPS to off-policy policy evaluation (OPE) an
Data-Driven Merton's Strategies via Policy Randomization
We study Merton's expected utility maximization problem in an incomplete market, characterized by a factor process in addition to the stock price process, where all the model primitives are unknown. The agent under consideration is a price taker who has access only to the stock and factor value processes and the instantaneous volatility. We propose an auxiliary problem in which the agent can invoke policy randomi
Permutation-based Inference for Variational Learning of Directed Acyclic Graphs
Estimating the structure of Bayesian networks as directed acyclic graphs (DAGs) from observational data is a fundamental challenge, particularly in causal discovery. Bayesian approaches excel by quantifying uncertainty and addressing identifiability, but key obstacles remain: (i) representing distributions over DAGs and (ii) estimating a posterior in the underlying combinatorial space. We introduce PIVID, a method th
Fully autonomous tuning of a spin qubit
Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor quantum circuits is still limited by challenges in efficiently tuning and operating these circuits. Identifying optimal operating conditions for these qubits is complex, involving the exploration of vast parameter spaces. This presents a re
Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning
While fine-tuning large language models (LLMs) for specific tasks often yields impressive results, it comes at the cost of memory inefficiency due to back-propagation in gradient-based training. Memory-efficient Zeroth-order (MeZO) optimizers, recently proposed to address this issue, only require forward passes during training, making them more memory-friendly. However, compared with exact gradients, ZO-based gradien
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization
Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Generalization (FedDG) attempts to train a global model using collaborative clients in a privacy-preserving manner that can generalize well to unseen clients possibly with domain shift. However, most existing FedDG methods either cause additional privacy ris
When Attention Collapses: How Degenerate Layers in LLMs Enable Smaller, Stronger Models
Large Language Models (LLMs) are known for their performance, but we uncover a significant structural inefficiency: a phenomenon we term attention collapse. In many pre-trained decoder-style LLMs, the attention matrices in deeper layers degenerate, collapsing to near rank-one structures. These underutilized layers, which we call lazy layers, are redundant and impair model efficiency. To address this, we introduce Inh
Optimal Design for Human Preference Elicitation
Learning of preference models from human feedback has been central to recent advances in artificial intelligence. Motivated by the cost of obtaining high-quality human annotations, we study efficient human preference elicitation for learning preference models. The key idea in our work is to generalize optimal designs, an approach to computing optimal information-gathering policies, to lists of items that represent po
Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer health is critical to maintain grid stability and performance. Thermal insulation ageing is a key transformer failure mode, which is generally tracked by monitoring the hotspot temperature (HST). However, HST measurement is complex, costly, an
DEPTH: Discourse Education through Pre-Training Hierarchically
Language Models (LMs) struggle with linguistic understanding at the discourse level, even though discourse patterns such as coherence, cohesion, and narrative flow are prevalent in their pre-training data. To improve the discourse capabilities of LMs already at the pre-training stage, we introduce DEPTH, an encoder-decoder model that learns latent representations for sentences using a discourse-oriented pre-training
Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios
Deploying robots in human-shared environments requires a deep understanding of how nearby agents and objects interact. Employing causal inference to model cause-and-effect relationships facilitates the prediction of human behaviours and enables the anticipation of robot interventions. However, a significant challenge arises due to the absence of implementation of existing causal discovery methods within the ROS ecosy
This paper presents the design and evaluation of a novel multi-level LLM interface for supermarket robots to assist customers. The proposed interface allows customers to convey their needs through both generic and specific queries. While state-of-the-art systems like OpenAI's GPTs are highly adaptable and easy to build and deploy, they still face challenges such as increased response times and limitations in stra
Synergizing Foundation Models and Federated Learning: A Survey
Over the past few years, the landscape of Artificial Intelligence (AI) has been reshaped by the emergence of Foundation Models (FMs). Pre-trained on massive datasets, these models exhibit exceptional performance across diverse downstream tasks through adaptation techniques like fine-tuning and prompt learning. More recently, the synergy of FMs and Federated Learning (FL) has emerged as a promising paradigm, often ter
Paraphrase Types Elicit Prompt Engineering Capabilities
Much of the success of modern language models depends on finding a suitable prompt to instruct the model. Until now, it has been largely unknown how variations in the linguistic expression of prompts affect these models. This study systematically and empirically evaluates which linguistic features influence models through paraphrase types, i.e., different linguistic changes at particular positions. We measure behavio
Recent Advancements and Challenges of Turkic Central Asian Language Processing
Research in NLP for Central Asian Turkic languages - Kazakh, Uzbek, Kyrgyz, and Turkmen - faces typical low-resource language challenges like data scarcity, limited linguistic resources and technology development. However, recent advancements have included the collection of language-specific datasets and the development of models for downstream tasks. Thus, this paper aims to summarize recent progress and identify fu
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