Record 26022026 · captured 2026-08-25
The world looked up Robert Carradine. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
Complete record JSON
What the most people looked up, ranked by Wikipedia pageviews for that day.
Robert Reed Carradine was an American actor. He made his first appearances in television Western series, such as Bonanza and his brother David's television series, Kung Fu. Carradine starred as Lewis Skolnick in teen comedy film series Revenge of the Nerds and
Martin Hayter Short is a Canadian comedian, actor, and writer. He is known as an energetic comedian who gained prominence for his roles in sketch comedy. He has also acted in numerous films and television shows. His awards include two Primetime Emmy Awards, tw
Nemesio Rubén Oseguera Cervantes, commonly referred to by his alias "El Mencho", was a Mexican drug lord and head of the Jalisco New Generation Cartel (CJNG), an organized crime group based in Jalisco. He was the most wanted person in Mexico and one of the mos
Alysa Liu is an American figure skater. She is the 2026 Olympic champion in both the women's singles and team events, the 2025 World champion, the 2022 World bronze medalist, the 2025–26 Grand Prix Final champion, a two-time Grand Prix medalist, a four-time Ch
Erotic photography is a style of art photography of an erotic, sexually suggestive or sexually provocative nature. It is a type of erotic art.
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
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
David Carradine was an American actor and director, whose career included over 200 major and minor roles in film, television and on stage. He was widely known to television audiences as the star of the series Kung Fu (1972–1975), playing Kwai Chang Caine, a pe
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
Alexander N. Green is an American lawyer and politician serving as the U.S. representative for Texas's 9th congressional district since 2005. A member of the Democratic Party, Green served as the justice of the peace of Harris County, Texas from 1977 to 2004.
Keith Ian Carradine is an American actor. In film, he is known for his roles as Tom Frank in Robert Altman's Nashville, E. J. Bellocq in Louis Malle's Pretty Baby, and Mickey in Alan Rudolph's Choose Me. On television, he is known for his roles as Wild Bill Hi
Ever Carradine is an American actress. She is known for her roles as Tiffany Porter and Kelly Ludlow on the ABC television series Once and Again and Commander in Chief, and as Naomi Putnam and Janet Stein on the Hulu original series The Handmaid's Tale and Run
Ilhan Abdullahi Omar is an American politician serving as the U.S. representative for Minnesota's 5th congressional district since 2019. The district includes all of Minneapolis and some of its first-ring suburbs. From 2017 to 2019, Omar served in the Minnesot
José Pedro Balmaceda Pascal is a Chilean and American actor. Known for his portrayals of parental figures on screen and stage, he has received numerous accolades including an Actor Award, in addition to nominations for a Golden Globe Award and four Primetime E
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
Abigail Anne Spanberger is an American politician and former intelligence officer serving since 2026 as the 75th governor of Virginia. A member of the Democratic Party, she served from 2019 to 2025 as the U.S. representative for Virginia's 7th congressional di
Connor Charles Hellebuyck is an American professional ice hockey player who is a goaltender for the Winnipeg Jets of the National Hockey League (NHL).
The 2025–26 UEFA Champions League was the 71st season of Europe's premier club football tournament organised by UEFA, and the 34th season since it was rebranded from the European Cup to the UEFA Champions League.
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 State of the Union address is an annual message delivered by the president of the United States to a joint session of the United States Congress near the beginning of most calendar years on the current condition of the nation. The speech generally includes
John Carradine was an American character actor, who was considered one of the greatest in American cinema. He was a member of Cecil B. DeMille's stock company and later John Ford's company, known for his roles in horror films, Westerns, and Shakespearean theat
Donald John Trump is an American politician, media personality, and businessman who is the 47th president of the United States. A member of the Republican Party, he served as the 45th president from 2017 to 2021.
Eric William Dane was an American actor. After multiple television roles in the 1990s and 2000s, including his recurring role as Jason Dean on Charmed, he was cast as Dr. Mark Sloan on the ABC medical drama Grey's Anatomy. He went on to appear in films such as
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.
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
Elmer Royce Williams is a retired United States Navy (USN) naval aviator and Medal of Honor recipient. He is known for his solo dogfight with seven Soviet pilots during the Korean War in 1952, which military experts have called "one of the greatest feats in av
Martha Plimpton is an American actress. She started her career as a teen actress in film before transitioning to adult roles on stage and screen. She has received several awards including a Primetime Emmy Award as well as nominations for three Tony Awards. Her
Ramasamy Nallakannu was an Indian politician. He was a senior leader of the Communist Party of India (CPI), who served as the State Secretary of the Communist Party of India of Tamil Nadu from 1992 to 2005.
Jack Rowden Hughes is an American professional ice hockey player who is a center and alternate captain for the New Jersey Devils of the National Hockey League (NHL). A product of the U.S. National Development Team, Hughes was drafted first overall by the Devil
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reveals that such customer-centric de
Private Blind Model Averaging - Distributed, Non-interactive, and Convergent
Distributed differentially private learning techniques enable a large number of users to jointly learn a model without having to first centrally collect the training data. At the same time, neither the communication between the users nor the resulting model shall leak information about the training data. This kind of learning technique can be deployed to edge devices if it can be scaled up to a large number of users,
Motivated by applications such as machine repair, project monitoring, and anti-poaching patrol scheduling, we study intervention planning of stochastic processes under resource constraints. This planning problem has previously been modeled as restless multi-armed bandits (RMAB), where each arm is an intervention-dependent Markov Decision Process. However, the existing literature assumes all intervention resources bel
The Dark Side of ChatGPT: Legal and Ethical Challenges from Stochastic Parrots and Hallucination
With the launch of ChatGPT, Large Language Models (LLMs) are shaking up our whole society, rapidly altering the way we think, create and live. For instance, the GPT integration in Bing has altered our approach to online searching. While nascent LLMs have many advantages, new legal and ethical risks are also emerging, stemming in particular from stochastic parrots and hallucination. The EU is the first and foremost ju
Emergence of a phonological bias in ChatGPT
Current large language models, such as OpenAI's ChatGPT, have captured the public's attention because how remarkable they are in the use of language. Here, I demonstrate that ChatGPT displays phonological biases that are a hallmark of human language processing. More concretely, just like humans, ChatGPT has a consonant bias. That is, the chatbot has a tendency to use consonants over vowels to identify words.
Improving Denoising Diffusion Models via Simultaneous Estimation of Image and Noise
This paper introduces two key contributions aimed at improving the speed and quality of images generated through inverse diffusion processes. The first contribution involves reparameterizing the diffusion process in terms of the angle on a quarter-circular arc between the image and noise, specifically setting the conventional $\displaystyle \sqrt{\barα}=\cos(η)$. This reparameterization eliminates two singularities a
Oscillatory combustion in aero engines and modern gas turbines often has significant adverse effects on their operation, and accurately recognizing various oscillation modes is the prerequisite for understanding and controlling combustion instability. However, the high-dimensional spatial-temporal data of a complex combustion system typically poses considerable challenges to the dynamical mode recognition. Based on a
Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration
Synthetic control methods (SCMs) are a canonical approach used to estimate treatment effects from panel data in the internet economy. We shed light on a frequently overlooked but ubiquitous assumption made in SCMs of "overlap": a treated unit can be written as some combination -- typically, convex or linear -- of the units that remain under control. We show that if units select their own interventions, and th
Life-cycle management of large-scale transportation systems requires determining a sequence of inspection and maintenance decisions to minimize long-term risks and costs while dealing with multiple uncertainties and constraints that lie in high-dimensional spaces. Traditional approaches have been widely applied but often suffer from limitations related to optimality, scalability, and the ability to properly handle un
Breaking the HISCO Barrier: Automatic Occupational Standardization with OccCANINE
This paper introduces OccCANINE, an open-source tool that maps occupational descriptions to HISCO codes. Manual coding is slow and error-prone; OccCANINE replaces weeks of work with results in minutes. We fine-tune CANINE on 15.8 million description-code pairs from 29 sources in 13 languages. The model achieves 96 percent accuracy, precision, and recall. We also show that the approach generalizes to three systems - O
In this paper, we consider the problem of reference tracking in uncertain nonlinear systems. A neural State-Space Model (NSSM) is used to approximate the nonlinear system, where a deep encoder network learns the nonlinearity from data, and a state-space component captures the temporal relationship. This transforms the nonlinear system into a linear system in a latent space, enabling the application of model predictiv
Real-Time Motion Detection Using Dynamic Mode Decomposition
Dynamic Mode Decomposition (DMD) is a numerical method that seeks to fit timeseries data to a linear dynamical system. In doing so, DMD decomposes dynamic data into spatially coherent modes that evolve in time according to exponential growth/decay or with a fixed frequency of oscillation. A prolific application of DMD has been to video, where one interprets the high-dimensional pixel space evolving through time as th
A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning
Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent. To foster future advancements, we provide a detailed overview of the UIE task from several perspectives. Firstly, we introduce the physical models, data construction processes, evaluation metrics, and loss functions.
Multi-Head RAG: Solving Multi-Aspect Problems with LLMs
Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by retrieving supporting documents into the prompt, but existing methods do not explicitly target queries that require fetching multiple documents with substantially different content. Such multi-aspect queries are challenging because relevant documents can be far apart in embedding space, making joint retrieval difficult. We introduce Multi-H
A Problem-Oriented Perspective and Anchor Verification for Code Optimization
Large Language Models (LLMs) have shown remarkable capabilities in solving various programming tasks, such as code generation. However, their potential for code optimization, particularly in performance enhancement, remains largely unexplored. This paper investigates the capabilities of LLMs in optimizing code for minimal execution time, addressing a critical gap in current research. The recently proposed code optimi
Despite the outstanding performance in multimodal tasks, Large Vision-Language Models (LVLMs) have been plagued by the issue of hallucination, i.e., generating content that is inconsistent with the corresponding visual inputs. While previous works have proposed various benchmarks to evaluate this issue, the quality of these evaluations remains unverified. We observe that some of these benchmarks may produce inconsist
When Can Transformers Count to n?
Large language models based on the transformer architecture can solve highly complex tasks, yet their fundamental limitations on simple algorithmic problems remain poorly understood. In this work, we focus on basic counting tasks and investigate how the difficulty of these tasks scales with the transformer embedding dimension, the context length, and the vocabulary size. We reveal a sharp theoretical phase transition
Shapley Value Computation in Ontology-Mediated Query Answering
The Shapley value was originally introduced in cooperative game theory as a wealth distribution mechanism. It has since found use in knowledge representation and databases for the purpose of assigning scores to formulas and database tuples based upon their contribution to obtaining a query result or inconsistency. The application of the Shapley value outside of its original setting relies upon defining a numeric weal
Temporal Knowledge-Graph Memory in a Partially Observable Environment
Agents in partially observable environments require persistent memory to integrate observations over time. While KGs (knowledge graphs) provide a natural representation for such evolving state, existing benchmarks rarely expose agents to environments where both the world dynamics and the agent's memory are explicitly graph-shaped. We introduce the Room Environment v3, a configurable environment whose hidden state
PACE: Procedural Abstractions for Communicating Efficiently
A central but unresolved aspect of problem-solving in AI is the capability to introduce and use abstractions, something humans excel at. Work in cognitive science has demonstrated that humans tend towards higher levels of abstraction when engaged in collaborative task-oriented communication, enabling gradually shorter and more information-efficient utterances. Several computational methods have attempted to replicate
PoseAdapt: Sustainable Human Pose Estimation via Continual Learning Benchmarks and Toolkit
Human pose estimators are typically retrained from scratch or naively fine-tuned whenever keypoint sets, sensing modalities, or deployment domains change--an inefficient, compute-intensive practice that rarely matches field constraints. We present PoseAdapt, an open-source framework and benchmark suite for continual pose model adaptation. PoseAdapt defines domain-incremental and class-incremental tracks that simulate
Learning Partial Graph Matching via Optimal Partial Transport
Partial graph matching extends traditional graph matching by allowing some nodes to remain unmatched, enabling applications in more complex scenarios. However, this flexibility introduces additional complexity, as both the subset of nodes to match and the optimal mapping must be determined. While recent studies have explored deep learning techniques for partial graph matching, a significant limitation remains: the ab
A Causal Graph-Enhanced Gaussian Process Regression for Modeling Engine-out NOx
The stringent regulatory requirements on nitrogen oxides (NOx) emissions from diesel compression ignition engines require accurate and reliable models for real time monitoring and diagnostics. Although traditional methods such as physical sensors and virtual engine control module (ECM) sensors provide essential data, they are only used for estimation. Ubiquitous literature primarily focuses on deterministic models wi
Modular Deep Learning for Multivariate Time-Series: Decoupling Imputation and Downstream Tasks
Missing values are pervasive in large-scale time-series data, posing challenges for reliable analysis and decision-making. Many neural architectures have been designed to model and impute the complex and heterogeneous missingness patterns of such data. Most existing methods are end-to-end, rendering imputation tightly coupled with downstream predictive tasks and leading to limited reusability of the trained model, re
LLM2CLIP: Powerful Language Model Unlocks Richer Cross-Modality Representation
CLIP is a seminal multimodal model that maps images and text into a shared representation space through contrastive learning on billions of image-caption pairs. Inspired by the rapid progress of large language models (LLMs), we investigate how the superior linguistic understanding and broad world knowledge of LLMs can further strengthen CLIP, particularly in handling long and complex captions. We introduce an efficie
Notable events recorded on this day and month across all years.