Record 25082026 · captured 2026-08-25
The world looked up Shelley Fabares. 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.
Michele Ann Marie Fabares was an American actress and singer. She was known for her television roles as Francine Webster on One Day at a Time, Mary on the sitcom The Donna Reed Show (1958–1963) and as Christine Armstrong on the sitcom Coach (1989–1997), the la
Enes Kanter Freedom is a Turkish and American human rights activist and former professional basketball player who played 11 seasons in the National Basketball Association (NBA). Born in Switzerland to parents from Turkey, he was raised in Turkey and moved to t
John Edward York is an American businessman who is the principal owner and chief executive officer of the San Francisco 49ers of the National Football League, as well as co-owner of Premier League club Leeds United and Scottish Premiership club Rangers. He is
On July 6, 2024, Sonya Massey, a 36-year-old unarmed Black woman, was murdered by Sean Grayson, a 30-year-old White deputy of the Sangamon County Sheriff's Office, in Woodside Township near Springfield, Illinois, United States.
Michael Joseph Farrell Jr. is an American actor, best known for his role as Captain B.J. Hunnicutt on the television series M*A*S*H (1975–1983). In addition, Farrell was a producer of Patch Adams (1998) starring Robin Williams, and he starred in the television
Spider-Man: Brand New Day is a 2026 American superhero film based on the Marvel Comics character Spider-Man. Produced by Columbia Pictures, Marvel Studios, and Pascal Pictures, and distributed by Sony Pictures Releasing, it is the 38th film in the Marvel Cinem
Hayden Lesley Panettiere was an American actress and singer. She starred as Claire Bennet on the NBC superhero series Heroes (2006–2010), Kirby Reed in the slasher horror franchise Scream (2011–2023), Juliette Barnes in the ABC/CMT musical drama series Nashvil
Lanterns is an American superhero television series created by Chris Mundy, Damon Lindelof, and Tom King for HBO, based on the DC Comics Green Lantern characters Hal Jordan and John Stewart. It is the third television series in the DC Universe (DCU). It featur
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
The Odyssey is a 2026 epic action fantasy film written and directed by Christopher Nolan, who produced it with his wife Emma Thomas. An adaptation of Homer's ancient Greek epic poem the Odyssey, it stars an ensemble cast including Matt Damon, Tom Holland, Anne
Natasha Cloud is an American professional basketball player for the Chicago Sky of the Women's National Basketball Association (WNBA) and for the Phantom of Unrivaled.
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
Lesbian Space Princess is a 2025 Australian adult animated science fiction comedy film written and directed by Emma Hough Hobbs and Leela Varghese in their directorial debuts. It features the voices of Shabana Azeez, Bernie Van Tiel, Gemma Chua-Tran, Richard R
Anna's Archive is an open source search engine for shadow libraries that was launched by the pseudonymous Anna shortly after law enforcement efforts to shut down Z-Library in 2022. The site aggregates records from Z-Library, Sci-Hub, and Library Genesis (LibGe
List of highest-grossing films
Films generate income from several revenue streams, including theatrical exhibition, home video, television broadcast rights, and merchandising. However, theatrical box-office earnings are the primary metric for trade publications in assessing the success of a
Natalie Harp is an American political aide and former television anchor who has served as special assistant and executive assistant to the President of the United States since 2025.
.xyz is a top-level domain name that was proposed in ICANN's new generic top-level domain (gTLD) Program for consisting of the last three letters of the Latin-script alphabet. XYZ.com and CentralNic are the registries for the domain, which was created by entre
Awarapan 2 is a 2026 Indian Hindi-language action thriller film directed by Nitin Kakkar, written by Kakkar, Bilal Siddiqui and Vishesh Bhatt, and produced under his banner Vishesh Films. A sequel to the 2007 film Awarapan, the film stars Emraan Hashmi, Disha
The Manhunters are a fictional race of extraterrestrial robots that appear in titles published by DC Comics.
Shiyazh Tastohyinya Pete is an American professional football player who is an offensive tackle for the Dallas Cowboys of the National Football League (NFL). He played college football for the New Mexico State Aggies and Kentucky Wildcats.
2026 Men's FIH Hockey World Cup
The 2026 Men's FIH Hockey World Cup is the 16th edition of the Men's FIH Hockey World Cup, the quadrennial world championship for men's national field hockey teams organized by the International Hockey Federation. It is being held from 15 to 30 August 2026 in
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").
Lester Louis Adler is an American record and film producer and the co-owner of the Roxy Theatre in West Hollywood, California. He has produced and developed a number of high-profile musical artists including the Grass Roots, Jan and Dean, the Mamas & the Papas
Obsession is a 2025 American supernatural horror film written, directed, and edited by Curry Barker. The film follows Bear, a music store employee who buys a supernatural toy that grants his wish for his friend Nikki to fall in love with him, which makes her b
Glossary of professional wrestling terms
Professional wrestling has accrued a considerable amount of jargon throughout its existence. Much of it stems from the industry's origins in the days of carnivals and circuses. In the past, professional wrestlers used such terms in the presence of fans so as n
Joseph Kevin Keegan was an English football player and manager who played as an attacking midfielder or forward. Nicknamed "King Kev" or "Mighty Mouse", Keegan was recognised for his dribbling ability, finishing and presence in the air, as much as he was for h
Toxic: A Fairy Tale for Grown-Ups is a 2026 Indian gangster film directed by Geetu Mohandas and jointly produced by Venkat K. Narayana and Yash through KVN Productions and Monster Mind Creations LLP respectively. It stars Yash in a dual role, alongside Kiara A
Álvaro Arbeloa Coca is a Spanish professional football manager and former footballer who is the head coach of Premier League club Fulham. He predominantly played as a right-back, and occasionally on the left side.
The United States of America (USA), also known as the United States (U.S.) or America, is a country primarily located in North America. It is a federal republic consisting of 50 states and a federal capital district, Washington, D.C. The 48 contiguous states b
The S&P 500 is a stock market index maintained by S&P Dow Jones Indices. It comprises 503 common stocks which are issued by 500 companies with large market capitalizations, including two share classes of stock from three of its component companies: Alphabet, F
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Kiva is an online non-profit crowdsouring microfinance platform that raises funds for the poor in the third world. The borrowers on Kiva are small business owners and individuals in urgent need of money. To raise funds as fast as possible, they have the option to form groups and post loan requests in the name of their groups. While it is generally believed that group loans pose less risk for investors than individual
Measuring the intelligence of an idealized mechanical knowing agent
We define a notion of the intelligence level of an idealized mechanical knowing agent. This is motivated by efforts within artificial intelligence research to define real-number intelligence levels of complicated intelligent systems. Our agents are more idealized, which allows us to define a much simpler measure of intelligence level for them. In short, we define the intelligence level of a mechanical knowing agent t
Measuring Intelligence and Growth Rate: Variations on Hibbard's Intelligence Measure
In 2011, Hibbard suggested an intelligence measure for agents who compete in an adversarial sequence prediction game. We argue that Hibbard's idea should actually be considered as two separate ideas: first, that the intelligence of such agents can be measured based on the growth rates of the runtimes of the competitors that they defeat; and second, one specific (somewhat arbitrary) method for measuring said growt
Mining Artifacts in Mycelium SEM Micrographs
Mycelium is a promising biomaterial based on fungal mycelium, a highly porous, nanofibrous structure. Scanning electron micrographs are used to characterize its network, but the currently available tools for nanofibrous microstructures do not contemplate the particularities of biomaterials. The adoption of a software for artificial nanofibrous in mycelium characterization adds the uncertainty of imaging artifact form
Unique sparse decomposition of low rank matrices
The problem of finding the unique low dimensional decomposition of a given matrix has been a fundamental and recurrent problem in many areas. In this paper, we study the problem of seeking a unique decomposition of a low rank matrix $Y\in \mathbb{R}^{p\times n}$ that admits a sparse representation. Specifically, we consider $Y = A X\in \mathbb{R}^{p\times n}$ where the matrix $A\in \mathbb{R}^{p\times r}$ has full co
Arbitrarily Shaped Scene Text Detection: A Decade of Advances and Systematic Analysis
Scene text detection has become an important research area in computer vision. However, dynamic changes in scenes and the complex diversity of text appearances make accurate scene text detection highly challenging. Although numerous arbitrary-shaped scene text detection methods have been proposed in recent years, with most claiming state-of-the-art performance, these performance comparisons are often unfair due to va
Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?
Classification outperforms regression across matched machine learning models in portfolio construction. A stacking ensemble of gradient boosted tree, random forest, and neural network yields a value-weighted annualized Sharpe ratio of 1.83 for classification and 1.11 for regression. This outperformance persists in multiclass settings, across subsamples, and after transaction costs. Spanning tests show that classifica
Reward-Punishment Symmetric Universal Intelligence
Can an agent's intelligence level be negative? We extend the Legg-Hutter agent-environment framework to include punishments and argue for an affirmative answer to that question. We show that if the background encodings and Universal Turing Machine (UTM) admit certain Kolmogorov complexity symmetries, then the resulting Legg-Hutter intelligence measure is symmetric about the origin. In particular, this implies rew
Extending Environments To Measure Self-Reflection In Reinforcement Learning
We consider an extended notion of reinforcement learning in which the environment can simulate the agent and base its outputs on the agent's hypothetical behavior. Since good performance usually requires paying attention to whatever things the environment's outputs are based on, we argue that for an agent to achieve on-average good performance across many such extended environments, it is necessary for the ag
Universal Agent Mixtures and the Geometry of Intelligence
Inspired by recent progress in multi-agent Reinforcement Learning (RL), in this work we examine the collective intelligent behaviour of theoretical universal agents by introducing a weighted mixture operation. Given a weighted set of agents, their weighted mixture is a new agent whose expected total reward in any environment is the corresponding weighted average of the original agents' expected total rewards in t
Factor analysis (FA) is a statistical method for explaining how mutually dependent observed variables can be represented in terms of mutually independent latent factors, and it is widely used in the psychological, biological, and physical sciences. We revisit this classic method from the perspective of recent advances in causal structure learning and deep generative models, introducing a framework for Neuro-Causal Fa
Residual-based attention in physics-informed neural networks
Driven by the need for more efficient and seamless integration of physical models and data, physics-informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains a challenge. In this work, we propose an efficient, gradient-less weighting scheme for PINNs that accelerates the convergence of dynamic or static systems. This sim
Learning to Select and Rank from Choice-Based Feedback: A Simple Nested Approach
We study a ranking and selection problem of learning from choice-based feedback with dynamic assortments. In this problem, a company sequentially displays a set of items to a population of customers and collects their choices as feedback. The only information available about the underlying choice model is that the choice probabilities are consistent with some unknown true strict ranking over the items. The objective
Evaluating the Efficacy of LLMs to Emulate Realistic Human Personalities
To enhance immersion and engagement in video games, the design of Affective Non-Player Characters (ANPCs) is a key focus for researchers and practitioners. Affective Computing frameworks improve Non-player characters (NPC) by providing personalities, emotions, and social relations. Large Language Models (LLMs) bring the promise to dynamically enhance character design when coupled with these frameworks, but further re
Approximating invariant functions with the sorting trick is theoretically justified
Many machine learning models leverage group invariance which is enjoyed with a wide-range of applications. For exploiting an invariance structure, one common approach is known as \emph{frame averaging}. One popular example of frame averaging is the \emph{group averaging}, where the entire group is used to symmetrize a function. Another example is the \emph{canonicalization}, where a frame at each point consists of a
A Survey on Human-AI Collaboration with Large Foundation Models
As the capabilities of artificial intelligence (AI) continue to expand rapidly, Human-AI (HAI) Collaboration, combining human intellect and AI systems, has become pivotal for advancing problem-solving and decision-making processes. The advent of Large Foundation Models (LFMs) has greatly expanded its potential, offering unprecedented capabilities by leveraging vast amounts of data to understand and predict complex pa
Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures
Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensional embeddings before partitioning, which embarks unique evaluation challenges. Traditional clustering validation measures, designed for low-dimensional spaces, are problematic for deep clustering for two reasons: 1) the curse of dimensionality when applied to the high-dim
Guessing human intentions to avoid dangerous situations in caregiving robots
For robots to interact socially, they must interpret human intentions and anticipate their potential outcomes accurately. This is particularly important for social robots designed for human care, which may face potentially dangerous situations for people, such as unseen obstacles in their way, that should be avoided. This paper explores the Artificial Theory of Mind (ATM) approach to inferring and interpreting human
Learning in PINNs: Phase transition, diffusion equilibrium, and generalization
We investigate the learning dynamics of fully-connected neural networks through the lens of the neural gradient signal-to-noise ratio (SNR), examining the behavior of first-order optimizers in non-convex objectives. Interpreting the drift/diffusion phases as proposed in the information bottleneck theory, we identify a third phase termed "diffusion equilibrium" (DE), a stable training phase characterized by hi
PU learning refers to the classification problem in which only part of positive samples are labeled. Existing PU learning methods treat unlabeled samples equally. However, in many real tasks, from common sense or domain knowledge, some unlabeled samples are more likely to be positive than others. In this paper, we propose soft label PU learning, in which unlabeled data are assigned soft labels according to their prob
On the Expressive Power of Sparse Geometric MPNNs
Motivated by applications in chemistry and other sciences, we study the expressive power of message-passing neural networks for geometric graphs, whose node features correspond to 3-dimensional positions. Recent work has shown that such models can separate generic pairs of non-isomorphic geometric graphs, though they may fail to separate some rare and complicated instances. However, these results assume a fully conne
Image-Conditional Diffusion Transformer for Underwater Image Enhancement
Underwater image enhancement (UIE) has attracted much attention owing to its importance for underwater operation and marine engineering. Motivated by the recent advance in generative models, we propose a novel UIE method based on image-conditional diffusion transformer (ICDT). Our method takes the degraded underwater image as the conditional input and converts it into latent space where ICDT is applied. ICDT replaces
Exploring learning environments for label\-efficient cancer diagnosis
Despite significant research efforts and advancements, cancer remains a leading cause of mortality. Early cancer prediction has become a crucial focus in cancer research to streamline patient care and improve treatment outcomes. Manual tumor detection by histopathologists can be time consuming, prompting the need for computerized methods to expedite treatment planning. Traditional approaches to tumor detection rely o
As Artificial Intelligence applications expand, the evaluation of models faces heightened scrutiny. Ensuring public readiness requires evaluation datasets, which differ from training data by being disjoint and ethically sourced in compliance with privacy regulations. The performance and fairness of face recognition systems depend significantly on the quality and representativeness of these evaluation datasets. This d
Expert-level vision-language foundation model for real-world radiology and comprehensive evaluation
Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in medicine have shown potential in processing multimodal information, offering a unified solution for various radiology tasks. However, existing studies either pre-trained VL models on natural data or did not fully integrate vision-language architecture and pretraining, often
Origins publishing files written for machines rather than people. Measured against a fixed cohort — a reading that cannot be reconstructed later.
| 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 | 10.8% (108) | 5.8% (58) | 74.55% (82) |
| llms.txt (apex domain) | 8.8% (88) | 4.9% (49) | 58.18% (64) |
| llms.txt (docs subdomain) | 3.4% (34) | 1% (10) | 43.64% (48) |
| .well-known/mcp.json | 0.5% (5) | 0.3% (3) | 7.27% (8) |
| .well-known/agents.txt | 0.2% (2) | 0.2% (2) | 0% (0) |
| ai.txt | 0.2% (2) | 0.1% (1) | 0% (0) |
| ai-plugin.json (deprecated) | 0.6% (6) | 0.3% (3) | 0.91% (1) |
Notable events recorded on this day and month across all years.