Record 23102026 · captured 2026-10-24
The world looked up Tom Skerritt. 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.
Thomas Roy Skerritt was an American actor and director who appeared in over 170 film and television productions between 1962 and 2025. The beginning of his film career coincided with the New Hollywood movement, with a breakthrough role as Duke Forrest in Rober
John David Anderson was an American country singer. Starting in 1977 with the release of his first single, "I've Got a Feelin' ", Anderson charted more than 40 singles on the Billboard country music charts, including five number ones: "Wild and Blue", "Swingin
Jailer 2 is a 2026 Indian Tamil-language action comedy film written and directed by Nelson Dilipkumar. It was produced by Kalanithi Maran through Sun Pictures. A sequel to Jailer (2023), the film stars Rajinikanth, Ramya Krishnan, Yogi Babu, Mirnaa and Rithvik
Elizabeth Anne Holmes is an American businesswoman convicted of fraud in connection with her health technology company Theranos. Holmes founded Theranos in 2003, and its valuation soared in the early 2010s after it claimed to have revolutionized blood testing
Clayface is a 2026 American body horror psychological thriller film based on the eponymous character from DC Comics. Directed by James Watkins from a screenplay by Mike Flanagan and Hossein Amini, it is the third film in the DC Universe (DCU). Tom Rhys Harries
PHP2Shell is a pair of high-impact computer vulnerabilities in PHP versions 5.4–8.5 that allow remote code execution (RCE), potentially allowing a malicious user to gain remote control of the website host. Their existence was publicly announced on 20 October 2
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
Jerry Faye Hall is an American model and actress. She began modeling in the 1970s and became one of the most sought-after models in the world. She transitioned into acting, appearing in the 1989 film Batman. Hall was the long-term partner of Rolling Stones fro
You Can See Everything is a 2026 American meta-documentary film directed by Nathan Fielder and Lance Oppenheim. It follows Elizabeth Holmes, the founder of the defunct health technology company Theranos, and her partner Billy Evans, who invited a film crew to
Amy Leigh Acton is an American physician and researcher who is the Democratic nominee for governor of Ohio in the 2026 election.
BioWare was a Canadian video game developer based in Edmonton, Alberta. It was founded in 1995 by newly graduated medical doctors Ray Muzyka, Greg Zeschuk and Augustine Yip. The company was acquired by American publisher Electronic Arts in 2007.
Vivek Ganapathy Ramaswamy is an American entrepreneur and politician. He founded Roivant Sciences, a biotechnological pharmaceutical company, in 2014 and was its chief executive officer until 2021. Ramaswamy entered national politics as the youngest presidenti
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
Ronald Duguay was a Canadian professional ice hockey player and coach who played 12 seasons in the National Hockey League (NHL) from 1977 through 1989, and served four seasons as a minor league coach. As a player, he played in the 1979 Stanley Cup Final with t
Heath Andrew Ledger was an Australian actor. Known for his versatility across independent and major studio films, his work consisted of 20 films in a variety of genres. His accolades included an Academy Award, an Actor Award, a BAFTA Award, and a Golden Globe
PHP is a general-purpose scripting language geared toward web development. It was created by Danish-Canadian programmer Rasmus Lerdorf in 1993 and released in 1995. The PHP reference implementation is now produced by the PHP Group. PHP was originally an abbrev
Yaxel Okari Lendeborg is an American-Dominican basketball player for the Golden State Warriors of the National Basketball Association (NBA). He was picked 11th overall in the 2026 NBA draft. Lendeborg played college basketball for the Arizona Western Matadors,
VisionQuest is an American television miniseries created by Terry Matalas for the streaming service Disney+, based on Marvel Comics featuring the character Vision. A spin-off from the miniseries WandaVision (2021), it is the 18th television series in the Marve
The Aberfan disaster was the catastrophic collapse of a colliery spoil tip on 21 October 1966. The tip had been created on a mountain slope above the Welsh village of Aberfan, near Merthyr Tydfil, and overlaid a natural spring. Heavy rain led to a build-up of
Limonene is a slightly yellow-green liquid aliphatic hydrocarbon classified as a cyclic monoterpene, and is the major component in the fragrance and essential oil of citrus fruit peels, taking its name from Italian limone 'lemon'.
Abdulrahman Mohamed El-Sayed is an American epidemiologist, public health administrator, academic and politician who is the Democratic nominee in the 2026 United States Senate election in Michigan.
Street Fighter is a 2026 martial arts film directed by Kitao Sakurai and written by Sakurai and T. J. Fixman, with a story by Dalan Musson and Gary Dauberman. It is the third live-action feature-length film based on the Street Fighter video game series by Capc
Slow Horses is a British spy thriller television series based on the Slough House novels by Mick Herron, and adapted for television by the English comedian and writer Will Smith. It follows Slough House, an MI5 unit where disgraced or failed agents are consign
Neatsville is an unincorporated community in Adair County, located 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
Nathan Joseph Fielder is a Canadian and American filmmaker, actor, and comedian. He is known for his awkward, deadpan persona and his use of satire and surreal humor.
India at the 2026 Asian Para Games
The Republic of India competed in the 2026 Asian Para Games, held in Nagoya, Aichi Prefecture, Japan, from 18 to 24 October 2026. This was India's fifth appearance at the Games. A total of 167 athletes competed across 11 disciplines. Pramod Bhagat (badminton)
Kim Jong Un is a North Korean politician and dictator who has been serving as the supreme leader of North Korea since 2011, following the death of his father, Kim Jong Il. He is the third ruler from the Kim family to lead the country, which was founded by his
Walker Ross Kessler is an American professional basketball player for the Los Angeles Lakers of the National Basketball Association (NBA). He played college basketball for the North Carolina Tar Heels and Auburn Tigers.
Ansel Easton Adams was an American landscape photographer and environmentalist known for his black-and-white images of the American West. He helped found Group f/64, an association of photographers advocating "pure" photography which favored sharp focus and th
Jeffrey Preston Bezos is an American businessman, and the founder, executive chairman, and former president and CEO of Amazon, the world's largest e-commerce and cloud computing company. According to the Bloomberg Billionaires Index and Forbes, he was the worl
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
On Sex, Evolution, and the Multiplicative Weights Update Algorithm
We consider a recent innovative theory by Chastain et al. on the role of sex in evolution [PNAS'14]. In short, the theory suggests that the evolutionary process of gene recombination implements the celebrated multiplicative weights updates algorithm (MWUA). They prove that the population dynamics induced by sexual reproduction can be precisely modeled by genes that use MWUA as their learning strategy in a particu
Artificial Intelligence (AI) started out with the ambition to reproduce the human mind, but as soon as the sheer scale of that ambition became apparent, it retreated into either studying specialized intelligent behaviours or proposing overarching architectural concepts for interfacing specialized intelligent components---conceived of as agents acting through organizational structures. The agent-based modelling paradi
Learning to Schedule in Parallel-Server Queues with Stochastic Bilinear Rewards
We consider the problem of scheduling in multi-class, parallel-server queuing systems with uncertain rewards from job-server assignments. In this scenario, jobs incur holding costs while awaiting completion, and job-server assignments yield observable stochastic rewards with unknown mean values. The mean rewards for job-server assignments are assumed to follow a bilinear model with respect to features that characteri
What's in the laundromat? Mapping and characterising offshore owned domestic property in London
The UK, particularly London, is a global hub for money laundering, a significant portion of which uses domestic property. However, understanding the distribution and characteristics of offshore domestic property in the UK is challenging due to data availability. This paper attempts to remedy that situation by enhancing a publicly available dataset of UK property owned by offshore companies. We create a data processin
Evolutionary Deep Nets for Non-Intrusive Load Monitoring
Compact appliance classifiers can support local electricity-load monitoring where memory and computation are constrained. We evaluate adaptive sparse evolutionary training and warm-start compression using synthetic aggregate windows derived from household appliance recordings. A completed convolutional network study trains 120 models over four targets and three seeds with validation-only model selection. Two analyses
Optimal Clustering of Multi-layer Networks under Mixture Block Models
We study clustering for multi-layer networks, where each observation is an undirected network measured on the same set of vertices. Under the mixture multi-layer stochastic block model (MMSBM), each network is generated from one of two stochastic block models, and the goal is to recover the latent network labels. We establish the minimax optimal network clustering error rate, which takes an exponential form and is ch
Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data
We present a method to create storytelling visualization with time series data. Many personal decisions nowadays rely on access to dynamic data regularly, as we have seen during the COVID-19 pandemic. It is thus desirable to construct storytelling visualization for dynamic data that is selected by an individual for a specific context. Because of the need to tell data-dependent stories, predefined storyboards based on
Hearing the shape of an arena with spectral swarm robotics
Swarm robotics relies on robots with minimal sensing and computation that achieve self-organization at the level of the collective to address a variety of tasks. Yet swarm robotics still struggles with global tasks that require understanding the broader spatial context in which robots operate, such as identifying the shape of the arena in which they are embedded. Here, we introduce spectral swarm robotics, a framewor
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
As with most other data domains, EEG data analysis relies on rich domain-specific preprocessing. Beyond such preprocessing, machine learners would hope to deal with such data as with any other time series data. For EEG classification many models have been developed with layer types and architectures we typically do not see in time series classification. Furthermore, typically separate models for each individual subje
Social Life Simulation for Non-Cognitive Skills Learning
Non-cognitive skills are crucial for personal and social life well-being, and such skill development can be supported by narrative-based (e.g., storytelling) technologies. While generative AI enables interactive and role-playing storytelling, little is known about how users engage with and perceive the use of AI in social life simulation for non-cognitive skills learning. Additionally, the benefits of AI mentorship o
Scrambled text: training Language Models to correct OCR errors using synthetic data
OCR errors are common in digitised historical archives significantly affecting their usability and value. Generative Language Models (LMs) have shown potential for correcting these errors using the context provided by the corrupted text and the broader socio-cultural context, a process called Context Leveraging OCR Correction (CLOCR-C). However, getting sufficient training data for fine-tuning such models can prove c
Risk-Aware Alignment Must Incorporate Reward Uncertainty
We consider the problem of policy optimization with noisy rewards, where the policy is constrained to be in the neighborhood of a reference policy.We study the risk of the policy optimization process, defined to be the chance of obtaining a policy worse off than the reference policy. In an idealized multivariate Gaussian model for the noisy reward function, we show that the regularizer achieving optimal risk in a sui
Ray Data: A dynamic and scalable system for heterogeneous data processing
While ML model training and inference are both GPU-intensive, CPU-based data processing is often the bottleneck. Existing distributed data processing systems assume homogeneous resource requirements. They excel at CPU-based computation but either under-utilize heterogeneous resources or impose high overheads on failure and reconfiguration. We introduce Ray Data, a scalable system for heterogeneous workloads spanning
Image Velocimetry using Direct Displacement Field estimation with Neural Networks for Fluids
An important tool for experimental fluids mechanics research is Particle Image Velocimetry (PIV). Several robust methodologies have been proposed to perform the estimation of velocity field from the images, however, alternative methods are still needed to increase the spatial resolution of the results. This work presents a novel approach for estimating fluid flow fields using neural networks and the optical flow equa
The Fenchel-Young Generalization of Bayesian Principles
Conjugate Bayes plays a key role in machine learning, ensuring computationally simple posterior updates. We generalize the variational principle behind this computation by using the Fenchel-Young (FY) inequality, which lets us define a generalized evidence lower bound beyond the KL geometry, and at equality, characterizes new generalized posteriors. A generalized notion of conjugacy follows, defined as closure of pri
Electron flow matching for generative reaction mechanism prediction obeying conservation laws
Central to our understanding of chemical reactivity is the principle of mass conservation, which is fundamental for ensuring physical consistency, balancing equations, and guiding reaction design. However, data-driven computational models for tasks such as reaction product prediction rarely abide by this most basic constraint. In this work, we recast the problem of reaction prediction as a problem of electron redistr
Fragility-aware Classification for Risk Characterization and Robust Learning
Classification models play a central role in data-driven decision-making applications such as medical diagnosis, recommendation systems, and risk assessment. Traditional performance metrics, such as accuracy and AUC, quantify classification errors and pairwise ranking performance but do not account for the magnitude of errors, i.e., the risk of confident misjudgments. This limitation is particularly consequential in
Oscar Wilde said, "The difference between literature and journalism is that journalism is unreadable, and literature is not read." Unfortunately, The digitally archived journalism of Oscar Wilde's 19th century often has no or poor quality Optical Character Recognition (OCR), reducing the accessibility of these archives and making them unreadable both figuratively and literally. This paper helps address th
Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity. In practice, many modern applications provide multiple related views of the same system, which has rarely been considered for causal discovery. Here, we leverage this multi-view structure to achieve causal discovery with weak assumptions. We propose a multi-view linear Structural
ACTIVA: Amortized Causal Effect Estimation via Variational Autoencoders
Predicting post-intervention distributions from observational data is central to many scientific and decision-making problems, but remains challenging due to causal ambiguity, restrictive modeling assumptions, and the lack of amortization across tasks. We introduce ACTIVA, a transformer-based conditional variational autoencoder for amortized estimation of full interventional distributions from observational data and
We give a nonasymptotic generalization theory for multilayer fully connected neural networks trained with path regularization, for general Lipschitz losses and without assuming the loss bounded, the architecture sparse, or any width or depth limit. Approximation error is modelled explicitly, and along the way we settle an open problem of E et al. on approximation rates in generalized Barron spaces. We treat the under
Variational quantum algorithms (VQAs) are a promising approach for leveraging near-term quantum computers, but trainability and quantum-resource costs remain major challenges. Here we address these challenges with the \textbf{H}ardware \textbf{E}fficient and dynamical \textbf{LI}e algebra supported \textbf{A}nsatz (HELIA) and two training methods combining the classical-enhanced \g method with the quantum-based Param
Analysis of On-policy Policy Gradient Methods under the Distribution Mismatch
Policy gradient methods are among the most successful approaches for solving challenging reinforcement learning problems. Despite their empirical success, many state-of-the-art policy gradient algorithms for discounted problems estimate update directions using a sampling distribution that differs from the discounted state distribution required by the policy gradient theorem. In this work, we analyze the impact of thi
Information Thermodynamics of Agents: The Work Capacity of Channels with Memory
Predicting future observations plays a central role in machine learning, biology, economics, and many other fields. It lies at the heart of organizational principles such as the variational free energy principle and, based on the second law of thermodynamics, has even been shown to be necessary for reaching the fundamental energetic limits of information processing on a tape. While the usefulness of the predictive pa
Kongzi: A Historical Large Language Model with Fact Enhancement
Large Language Models (LLMs) can exhibit factual inaccuracies during long-chain reasoning or under misleading prompts, limiting their reliability in historical analysis. We propose Kongzi, a language model for historical understanding and reasoning that combines continued pre-training on historical corpora, supervised fine-tuning, and fact-enhanced reinforcement learning. Its factuality reward compares entities and r
Origins publishing files written for machines rather than people. Measured against a frozen cohort, so a change in the number means a change in adoption.
| Signal | Web head Tranco top 1,000 n=1,000 | Web tail sampled to rank 100k n=1,000 | AI-native model & dev platforms n=110 |
|---|---|---|---|
| Any agent-facing signal | 12.7% (127) | 5.2% (52) | 74.55% (82) |
| llms.txt (apex domain) | 10.7% (107) | 4.4% (44) | 60.91% (67) |
| llms.txt (docs subdomain) | 4.1% (41) | 0.8% (8) | 46.36% (51) |
| .well-known/mcp.json | 0.6% (6) | 0.3% (3) | 9.09% (10) |
| .well-known/agents.txt | 0.3% (3) | 0.1% (1) | 0% (0) |
| ai.txt | 0.3% (3) | 0.1% (1) | 0% (0) |
| ai-plugin.json (deprecated) | 0.6% (6) | 0.2% (2) | 0.91% (1) |
Notable events recorded on this day and month across all years.