Record 25022026 · 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.
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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
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
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
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
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
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
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
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
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 Carradine family is an American family of actors. Its family patriarch was the minister Beverly Carradine. His grandson, the actor John Carradine, had five sons, four of whom also became actors.
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
John Michael Gaudreau was an American professional ice hockey player. A left winger, he played 11 seasons in the National Hockey League (NHL). He played college ice hockey for the Boston College Eagles in NCAA Division I for three seasons beginning in 2011 and
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
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
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 Jalisco New Generation Cartel, also known as CJNG, is a Mexican criminal syndicate based in Jalisco founded and headed by Nemesio Oseguera Cervantes, commonly known as El Mencho, until he was killed by the Mexican Army in 2026. The cartel has been characte
Linus Williams Ifejirika, popularly known as Blord, is a Nigerian cryptocurrency entrepreneur and the founder of Blord Group. His business interests span fintech, digital payments, and real estate, with companies such as Blord Real Estate Ltd., Blord Jetpay Lt
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.
Paradise is an American post-apocalyptic political thriller television series created by Dan Fogelman and starring Sterling K. Brown, Julianne Nicholson, and James Marsden. It was released on Hulu in the United States on January 26, 2025. The series has receiv
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
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
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
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
Rosalinda González Valencia is a Mexican businesswoman and convicted money launderer of the Jalisco New Generation Cartel (CJNG), a criminal group based in Jalisco. She has also been known by her alias "La Jefa". She was married to El Mencho, once one of Mexic
Rubén Oseguera González, commonly referred to by his alias El Menchito, is a Mexican-American convicted drug lord and former high-ranking member of the Jalisco New Generation Cartel (CJNG), a criminal group based in Jalisco. He is the son of Nemesio Oseguera C
On 22 February 2026, the Mexican Armed Forces conducted an operation which killed El Mencho, the leader of the Jalisco New Generation Cartel (CJNG), and six others in Tapalpa, Jalisco, Mexico. The operation sparked clashes in the area, resulting in shootouts,
Jane Dawn Elizabeth Andrews was a royal dresser for Sarah, Duchess of York. Andrews was imprisoned in 2001 for murdering her lover, and released from prison in 2019.
John Craig Davidson is a Scottish activist and campaigner for Tourette syndrome. At age 16, Davidson was the subject of the BBC television documentary John's Not Mad (1989) about the manifestations of his Tourette's. He has also appeared in several other BBC d
I Swear is a 2025 British biographical drama film directed, written, and produced by Kirk Jones. It is based on the true life story of John Davidson, a Scottish man with severe Tourette syndrome who, as a teenager, was the subject of the 1989 television docume
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Autonomous driving has made great progress and been introduced in practical use step by step. On the other hand, the concept of personal mobility is also getting popular, and its autonomous driving specialized for individual drivers is expected for a new step. However, it is difficult to collect a large driving dataset, which is basically required for the learning of autonomous driving, from the individual driver of
Usability Study of Security Features in Programmable Logic Controllers
Programmable Logic Controllers (PLCs) drive industrial processes critical to society, for example, water treatment and distribution, electricity and fuel networks. Search engines, e.g., Shodan, have highlighted that PLCs are often left exposed to the Internet, one of the main reasons being the misconfigurations of security settings. This leads to the question - why do these misconfigurations occur and, specifically,
ShaRP: Shape-Regularized Multidimensional Projections
Projections, or dimensionality reduction methods, are techniques of choice for the visual exploration of high-dimensional data. Many such techniques exist, each one of them having a distinct visual signature - i.e., a recognizable way to arrange points in the resulting scatterplot. Such signatures are implicit consequences of algorithm design, such as whether the method focuses on local vs global data pattern preserv
Robot control using reinforcement learning has become popular, but its learning process generally terminates halfway through an episode for safety and time-saving reasons. This study addresses the problem of the most popular exception handling that temporal-difference (TD) learning performs at such termination. That is, by forcibly assuming zero value after termination, unintentionally implicit underestimation or ove
Towards Attributions of Input Variables in a Coalition
This paper focuses on the fundamental challenge of partitioning input variables in attribution methods for Explainable AI, particularly in Shapley value-based approaches. Previous methods always compute attributions given a predefined partition but lack theoretical guidance on how to form meaningful variable partitions. We identify that attribution conflicts arise when the attribution of a coalition differs from the
Interpretable Medical Image Classification using Prototype Learning and Privileged Information
Interpretability is often an essential requirement in medical imaging. Advanced deep learning methods are required to address this need for explainability and high performance. In this work, we investigate whether additional information available during the training process can be used to create an understandable and powerful model. We propose an innovative solution called Proto-Caps that leverages the benefits of ca
Augmenting Lateral Thinking in Language Models with Humor and Riddle Data for the BRAINTEASER Task
The SemEval 2024 BRAINTEASER task challenges language models to perform lateral thinking -- a form of creative, non-linear reasoning that remains underexplored in NLP. The task comprises two subtasks, Sentence Puzzle and Word Puzzle, requiring models to defy conventional commonsense associations. We present a system that fine-tunes DeBERTaV3 using HuggingFace's AutoModelForMultipleChoice architecture. We augment
Variational Markov chain mixtures with automatic component selection
Markov state modeling has gained popularity in various scientific fields since it reduces complex time-series data sets into transitions between a few states. Yet common Markov state modeling frameworks assume a single Markov chain describes the data, so they suffer from an inability to discern heterogeneities. As an alternative, this paper models time-series data using a mixture of Markov chains, and it automaticall
Learning to Control Unknown Strongly Monotone Games
Consider a strongly monotone game where the players' utility functions include a reward function and a linear term for each dimension, with coefficients that are controlled by the manager. Gradient play converges to a unique Nash equilibrium (NE) that does not optimize the global objective. The global performance at NE can be improved by imposing linear constraints on the NE, also known as a generalized Nash equi
Coherent and Multi-modality Image Inpainting via Latent Space Optimization
With the advancements in denoising diffusion probabilistic models (DDPMs), image inpainting has significantly evolved from merely filling information based on nearby regions to generating content conditioned on various prompts such as text, exemplar images, and sketches. However, existing methods, such as model fine-tuning and simple concatenation of latent vectors, often result in generation failures due to overfitt
ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth Estimation
Estimating depth from a single image is a challenging visual task. Compared to relative depth estimation, metric depth estimation attracts more attention due to its practical physical significance and critical applications in real-life scenarios. However, existing metric depth estimation methods are typically trained on specific datasets with similar scenes, facing challenges in generalizing across scenes with signif
Single-pixel cameras are an effective solution for imaging outside the visible spectrum, where traditional CMOS/CCD cameras have challenges. When combined with machine learning, they can analyze images quickly enough for practical applications. Solving the problem of high-dimensional single-pixel visualization can potentially be accelerated via quantum machine learning, thereby expanding the range of practical proble
Rethinking Disentanglement under Dependent Factors of Variation
Representation learning is an approach that allows to discover and extract the factors of variation from the data. Intuitively, a representation is said to be disentangled if it separates the different factors of variation in a way that is understandable to humans. Definitions of disentanglement and metrics to measure it usually assume that the factors of variation are independent of each other. However, this is gene
DreamBarbie: Text to Barbie-Style 3D Avatars
To integrate digital humans into everyday life, there is a strong demand for generating high-quality, fine-grained disentangled 3D avatars that support expressive animation and simulation capabilities, ideally from low-cost textual inputs. Although text-driven 3D avatar generation has made significant progress by leveraging 2D generative priors, existing methods still struggle to fulfill all these requirements simult
Ultrawide-field fluorescein angiography (UWF-FA) facilitates diabetic retinopathy (DR) detection by providing a clear visualization of peripheral retinal lesions. However, the intravenous dye injection with potential risks hamper its application. We aim to acquire dye-free UWF-FA images from noninvasive UWF retinal imaging (UWF-RI) using generative artificial intelligence (GenAI) and evaluate its effectiveness in DR
RegTrack: Simplicity Beneath Complexity in Robust Multi-Modal 3D Multi-Object Tracking
Existing 3D multi-object tracking (MOT) methods often sacrifice efficiency and generalizability for robustness, largely relying on complex association metrics derived from multi-modal architectures and class-specific motion priors. Challenging the rooted belief that greater complexity necessarily yields greater robustness, we propose a robust, efficient, and generalizable method for multi-modal 3D MOT, dubbed RegTrac
Implementation of neural network operators with applications to remote sensing data
In this paper, we provide two algorithms based on the theory of multidimensional neural network (NN) operators activated by hyperbolic tangent sigmoidal functions. Theoretical results are recalled to justify the performance of the here implemented algorithms. Specifically, the first algorithm models multidimensional signals (such as digital images), while the second one addresses the problem of rescaling and enhancem
Two Models for Surface Segmentation using the Total Variation of the Normal Vector
We consider the problem of surface segmentation, where the goal is to partition a surface represented by a triangular mesh. The segmentation is based on the similarity of the normal vector field to a given set of label vectors. We propose a variational approach and compare two different regularizers, both based on a total variation measure. The first regularizer penalizes the total variation of the assignment functio
Predicting Subway Passenger Flows under Incident Situation with Causality
In the context of rail transit operations, real-time passenger flow prediction is essential; however, most models primarily focus on normal conditions, with limited research addressing incident situations. There are several intrinsic challenges associated with prediction during incidents, such as a lack of interpretability and data scarcity. To address these challenges, we propose a two-stage method that separates pr
Weber-Fechner Law in Temporal Difference learning derived from Control as Inference
This paper investigates a novel nonlinear update rule based on temporal difference (TD) errors in reinforcement learning (RL). The update rule in the standard RL states that the TD error is linearly proportional to the degree of updates, treating all rewards equally without no bias. On the other hand, the recent biological studies revealed that there are nonlinearities in the TD error and the degree of updates, biasi
As economics scales, a key bottleneck is representing what papers claim in a comparable, aggregable form. We introduce evidence-annotated claim graphs that map each paper into a directed network of standardized economic concepts (nodes) and stated relationships (edges), with each edge labeled by evidentiary basis, including whether it is supported by causal inference designs or by non-causal evidence. Using a structu
Characterizing LLM Inference Energy-Performance Tradeoffs across Workloads and GPU Scaling
LLM inference exhibits substantial variability across queries and execution phases, yet inference configurations are often applied uniformly. We present a measurement-driven characterization of workload heterogeneity and energy-performance behavior of LLM inference under GPU dynamic voltage and frequency scaling (DVFS). We evaluate five decoder-only LLMs (1B-32B parameters) across four NLP benchmarks using a controll
Safe Reinforcement Learning for Real-World Engine Control
This work introduces a toolchain for applying Reinforcement Learning (RL), specifically the Deep Deterministic Policy Gradient (DDPG) algorithm, in safety-critical real-world environments. As an exemplary application, transient load control is demonstrated on a single-cylinder internal combustion engine testbench in Homogeneous Charge Compression Ignition (HCCI) mode, that offers high thermal efficiency and low emiss
Evidence on the Regularisation Properties of Maximum-Entropy Reinforcement Learning
The generalisation and robustness properties of policies learnt through Maximum-Entropy Reinforcement Learning are investigated on chaotic dynamical systems with Gaussian noise on the observable. First, the robustness under noise contamination of the agent's observation of entropy regularised policies is observed. Second, notions of statistical learning theory, such as complexity measures on the learnt model, are
Increasing Information for Model Predictive Control with Semi-Markov Decision Processes
Recent works in Learning-Based Model Predictive Control of dynamical systems show impressive sample complexity performances using criteria from Information Theory to accelerate the learning procedure. However, the sequential exploration opportunities are limited by the system local state, restraining the amount of information of the observations from the current exploration trajectory. This article resolves this limi
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