Record 11092026 · captured 2026-09-12
The world looked up Barry Melrose. 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.
Barry James Melrose was a Canadian and American professional ice hockey player, coach and broadcaster. He played in the World Hockey Association (WHA) and National Hockey League (NHL). After retiring from playing, he became a head coach for the Los Angeles Kin
Paul Andrew Lock is an American professional football quarterback for the Seattle Seahawks of the National Football League (NFL). He played college football for the Missouri Tigers and was selected by the Denver Broncos in the second round of the 2019 NFL draf
Charles James Kirk was an American right-wing political activist, entrepreneur, and media personality. He co‑founded the conservative student organization Turning Point USA (TPUSA) in 2012 and served as its executive director until his assassination in 2025. A
Killing of the Clancy children
On January 24, 2023, Lindsay Clancy fatally strangled her three children—five-year-old Cora, three-year-old Dawson, and eight-month-old Callan—at the family's home in Duxbury, Massachusetts, United States. She then attempted suicide by cutting her wrists and n
On September 10, 2025, Charlie Kirk, an American right-wing political activist, was assassinated at Utah Valley University in Orem, Utah, while speaking at an outdoor campus debate planned by Turning Point USA, the conservative youth organization he co-founded
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
The September 11 attacks, colloquially known as 9/11, were a coordinated series of suicide attacks perpetrated by the Islamic terrorist organization al-Qaeda against the United States in 2001. A total of 19 hijackers commandeered four commercial passenger airl
Michael McFarland was an American voice actor, ADR director, script writer and line producer who worked on English dubs of Japanese anime. He was known as the original English voice of Master Roshi and Yajirobe in Funimation's dubs of Dragon Ball and Dragon Ba
Sabah Futbol Klubu is a professional football club based in Masazir, Azerbaijan. The team has played in the Azerbaijan Premier League, the top tier of Azerbaijani football league system, since the 2018–19 season. Its home ground is the Bank Respublika Arena in
List of songs recorded by Bruce Springsteen
Bruce Springsteen is an American singer-songwriter who has recorded almost 400 songs over a career lasting six decades. He began his career in the 1960s with local New Jersey bands the Castiles, Earth, and Steel Mill before embarking on a solo career and signi
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
.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
Hanuman Ansh is a 2026 Indian Hindi-language biographical devotional drama film written, directed, and produced by Dr Vishal Chaturvedi under his banner, Swambhu Media Network. The film is the first instalment of the film trilogy based on his book "Divine Deto
Benjamin Todd Shelton is an American professional tennis player. He has been ranked world No. 5 in men's singles by the Association of Tennis Professionals (ATP), achieved in November 2025. Shelton has won seven ATP Tour singles titles, including two Masters 1
Mirzapur: The Movie is a 2026 Indian Hindi-language action crime thriller film directed by Gurmeet Singh and written by Puneet Krishna. Produced by Ritesh Sidhwani and Farhan Akhtar under Excel Entertainment, the film stars Pankaj Tripathi, Ali Fazal, Divyennd
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
The Gentlemen (2024 TV series)
The Gentlemen is a black comedy crime drama television series created by Guy Ritchie for Netflix. It is a spin-off of Ritchie's 2019 film of the same name, taking place in its fictional universe and sharing thematic connections but following a standalone story
Sandra Annette Bullock is an American and German actress and film producer. The world's highest-paid actress of 2010 and 2014, Bullock's filmography spans both comedy and drama, and her accolades include an Academy Award, a Golden Globe Award, two Screen Actor
The Bayeux Tapestry is an embroidered cloth nearly 70 metres long and 50 centimetres tall that depicts the events leading up to the Norman conquest of England, led by William, Duke of Normandy, and the Battle of Hastings in 1066. It has been known by several n
John Patrick Ternus is an American engineer and business executive who has been the chief executive officer (CEO) of Apple since September 1, 2026. He is also a member of the company's board of directors. Ternus joined Apple's product design team in 2001 and w
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
Shailene Diann Woodley is an American actress. Known for her work on screen and stage, her accolades include a Primetime Emmy Award and a Trophée Chopard, in addition to nominations for two Actor Awards, a British Academy Film Award, and two Golden Globes.
Drake Lee Maye is an American professional football quarterback for the New England Patriots of the National Football League (NFL). He played college football for the North Carolina Tar Heels, winning the Shaun Alexander Award and ACC Football Player of the Ye
Toxic: A Fairy Tale for Grown-Ups is a 2026 Indian psychological thriller gangster film directed by Geetu Mohandas and jointly produced by Venkat K. Narayana and Yash through KVN Productions and Monster Mind Creations respectively. It stars Yash, Kiara Advani,
The Navier–Stokes equations describe the motion of viscous fluids. This system of partial differential equations was named after Claude-Louis Navier and George Gabriel Stokes, who developed them over a few decades of progressive work, from 1822 (Navier) to 184
Gopalaswamy Doraiswamy Naidu was an Indian innovator, inventor, industrialist, and educator. He redesigned and transformed imported technologies into practical and affordable innovations for India, and is widely regarded as a versatile genius. His contribution
Theodore Peter James Kinnaird Taptiklis is an English actor and producer. He gained recognition for playing Tobias Eaton in The Divergent Series (2014–2016). He has starred in the horror films Underworld: Awakening (2012) and Underworld: Blood Wars (2016), the
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
Jacob Coxon is a British technology researcher who previously worked at artificial intelligence companies OpenAI and Anthropic. In September 2026, Coxon resigned from Anthropic, citing fears that artificial intelligence poses an existential threat to humanity.
Aryna Siarhiejeŭna Sabalenka is a Belarusian professional tennis player. She is the current world No. 1 in women's singles by the WTA and is a former No. 1 in doubles. Sabalenka has won 24 career singles titles, including four majors—two each at the Australian
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Time-Varying Graph Learning with Constraints on Graph Temporal Variation
We propose a novel framework for learning time-varying graphs from spatiotemporal measurements. Given an appropriate prior on the temporal behavior of signals, our proposed method can estimate time-varying graphs from a small number of available measurements. To achieve this, we introduce three regularization terms in convex optimization problems that constrain the sparseness of temporal variations of the time-varyin
A Short Survey of Viewing Large Language Models in Legal Aspect
Large language models (LLMs) have transformed many fields, including natural language processing, computer vision, and reinforcement learning. These models have also made a significant impact in the field of law, where they are being increasingly utilized to automate various legal tasks, such as legal judgement prediction, legal document analysis, and legal document writing. However, the integration of LLMs into the
Optimizing Three Critical Factors for Practical and Effective OOD Detection Fine-Tuning
In out-of-distribution (OOD) detection, fine-tuning with auxiliary outlier data often improves detection performance at the cost of classification accuracy. This trade-off stems from the loss of the original in-distribution (ID) distribution during fine-tuning. To establish a more practical and effective paradigm, we optimize three critical factors: model reminder, data sampling, and representation learning. We propo
Label Differential Privacy via Aggregation
This paper explores the use of linear aggregation to protect the privacy of sensitive training labels through the concept of \emph{label differential privacy} (label-DP) while maintaining regression task utility. Our key finding is that weighted linear aggregation of training instances with i.i.d. $N(0, 1)$ weights can achieve $(\varepsilon, δ)$-label-DP with $m = O\left(n/(\log(1/δ))\right)$. Unlike prior methods, o
Customized large language models can outperform Community Notes in correcting misinformation
Addressing misinformation in real-world settings is challenging: content is often multimodal; factuality judgments are nuanced and context-dependent; new events emerge rapidly across domains; corrections must be timely, trustworthy, and politically impartial; and multidimensional, multistakeholder frameworks remain lacking. Crowdsourced fact-checking systems such as Community Notes have gained broad adoption, but tim
Fisher-Rao Gradient Flows of Linear Programs and State-Action Natural Policy Gradients
Kakade's natural policy gradient method has been studied extensively in recent years, showing linear convergence with and without regularization. We study another natural gradient method based on the Fisher information matrix of the state-action distributions which has received little attention from the theoretical side. Here, the state-action distributions follow the Fisher-Rao gradient flow inside the state-act
DNA: Differentially private Neural Augmentation for contact tracing
The COVID19 pandemic had enormous economic and societal consequences. Contact tracing is an effective way to reduce infection rates by detecting potential virus carriers early. However, this was not generally adopted in the recent pandemic, and privacy concerns are cited as the most important reason. We substantially improve the privacy guarantees of the current state of the art in decentralized contact tracing. Wher
Optimal Rates of Convergence for Entropy Regularization in Discounted Markov Decision Processes
We study the error introduced by entropy regularization in infinite-horizon discrete discounted Markov decision processes. We show that this error decreases exponentially in the inverse regularization strength, both in a weighted KL-divergence and in value with a problem-specific exponent. This is in contrast to previously known estimates, of the order $O(τ)$, where $τ$ is the regularization strength. We provide a lo
Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems
In this paper, we introduce novel gradient-based optimization methods for state-based potential games (SbPGs) within self-learning distributed production systems. SbPGs are recognised for their efficacy in enabling self-optimizing distributed multi-agent systems and offer a proven convergence guarantee, which facilitates collaborative player efforts towards global objectives. Our study strives to replace conventional
Oracle Bone Inscriptions Multi-modal Dataset
Oracle bone inscriptions(OBI) is the earliest developed writing system in China, bearing invaluable written exemplifications of early Shang history and paleography. However, the task of deciphering OBI, in the current climate of the scholarship, can prove extremely challenging. Out of the 4,500 oracle bone characters excavated, only a third have been successfully identified. Therefore, leveraging the advantages of ad
No Screening is More Efficient with Multiple Objects
We study the welfare-maximizing allocation of heterogeneous objects when screening uses costly effort rather than monetary transfers. No-screening mechanisms perform well as object variety increases. In a symmetric continuous market with i.i.d. values whose CDF is log-concave, the multidimensional problem reduces exactly to a single-dimensional problem in agents' best-option values. More options make low best-opt
Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg Games
In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarchical structure assigns more important modules of the manufa
Explainable few-shot learning workflow for detecting invasive and exotic tree species
Deep Learning methods are notorious for relying on extensive labeled datasets to train and assess their performance. This can cause difficulties in practical situations where models should be trained for new applications for which very little data is available. While few-shot learning algorithms can address the first problem, they still lack sufficient explanations for the results. This research presents a workflow t
Hyperparameter tuning remains a significant challenge in the training of deep neural networks (DNNs), requiring manual search or time-intensive grid searches that increase resource costs and limit the accessibility of machine learning. The global initial learning rate is among the most consequential of these hyperparameters. Adaptive and scheduling-based methods manage the learning rate during training but still requ
Mapping Seven Decades of Philosophy in Colombia: Dynamic Topic Modelling of Ideas y Valores
Data-driven approaches to philosophy have emerged as a valuable tool for studying the history of the discipline. However, most studies in this area have focused on a limited number of journals from specific regions and subfields. We expand the scope of this research by applying dynamic topic modelling techniques to explore the history of philosophy in Colombia and Latin America. Our study examines the Colombian philo
SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data
Improving the reliability and completeness of colonoscopic inspection is critical for reducing missed lesions and improving colorectal cancer prevention. Reliable scene understanding is essential for navigation, reconstruction, and assessment of inspection completeness. Anatomical structures such as mucosal folds provide stable geometric cues for endoscope localization, while surgical instruments introduce dynamic oc
SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies
Hepatic vessels in computed tomography scans often suffer from image fragmentation and noise interference, making it difficult to maintain vessel integrity and posing significant challenges for vessel segmentation. To address this issue, we propose an innovative model: SegKAN. First, we improve the conventional embedding module by adopting a novel convolutional network structure for image embedding, which smooths out
Gaussian Mixture Models (GMMs) range among the most frequently used models in machine learning. However, training large, general GMMs becomes computationally prohibitive for data sets that have many data points $N$ of high-dimensionality $D$. For GMMs with arbitrary covariances, we here derive a highly efficient variational approximation, which is then integrated with mixtures of factor analyzers (MFAs). For GMMs wit
The observational partial order of causal structures with latent variables
For two causal structures with the same set of visible variables, one is said to observationally dominate the other if the set of distributions over the visible variables realizable by the first contains the set of distributions over the visible variables realizable by the second. Knowing such dominance relations is useful for adjudicating between these structures given observational data. Here, we consider the probl
Quantum State Preparation with the QNN-based SRBB Algorithm
In this work, a novel algorithm structured on Lie algebras for the approximate quantum state preparation problem is proposed, addressing a challenge of fundamental importance in many areas of quantum computing. The algorithm uses a variational quantum circuit designed on the Standard Recursive Block Basis (SRBB), a hierarchical construction for the matrix algebra of the $SU(2^n)$ group, which is capable of linking th
PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images
Breast cancer survival prediction in computational pathology presents a remarkable challenge due to tumor heterogeneity. For instance, different regions of the same tumor in the pathology image can show distinct morphological and molecular characteristics. This makes it difficult to extract representative features from whole slide images (WSIs) that truly reflect the tumor's aggressive potential and likely surviv
Ensuring high accuracy and efficiency of predictive models is paramount in the aerospace industry, particularly in the context of multidisciplinary design and optimization processes. These processes often require numerous evaluations of complex objective functions, which can be computationally expensive and time-consuming. To build efficient and accurate predictive models, we propose a new approach that leverages Bay
Exploring Multimodal Prompt for Visualization Authoring with Large Language Models
Recent advances in large language models (LLMs) have shown great potential in automating the process of visualization authoring through simple natural language utterances. However, instructing LLMs using natural language is limited in precision and expressiveness for conveying visualization intent, leading to misinterpretation and time-consuming iterations. To address these limitations, we conduct an empirical study
This paper proposes a novel interdisciplinary framework for analyzing police body-worn camera (BWC) footage from the Rochester Police Department (RPD) using advanced artificial intelligence (AI) and statistical machine learning (ML) techniques. Our goal is to detect, classify, and analyze patterns of interaction between police officers and civilians to identify key behavioral dynamics, such as respect, disrespect, es
Balancing Safety and Optimality in Robot Path Planning: Algorithm and Metric
Path planning for autonomous robots faces a fundamental trade-off between path length and obstacle clearance. While existing algorithms typically prioritize a single objective, we introduce the Unified Path Planner (UPP), a graph-search algorithm that dynamically balances safety and optimality via adaptive heuristic weighting. UPP employs a local inverse-distance safety field and auto-tunes its parameters based on re
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 | 11.5% (115) | 6.6% (66) | 75.45% (83) |
| llms.txt (apex domain) | 9.1% (91) | 5.5% (55) | 60.91% (67) |
| llms.txt (docs subdomain) | 3.7% (37) | 1.2% (12) | 44.55% (49) |
| .well-known/mcp.json | 0.6% (6) | 0.3% (3) | 7.27% (8) |
| .well-known/agents.txt | 0.3% (3) | 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.