Record 18092026 · captured 2026-09-19
The world looked up Lizzie Borden. 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.
Lizzie Andrew Borden was an American woman who was tried and acquitted of the August 4, 1892 axe murders of her father and stepmother in Fall River, Massachusetts. No other individuals were charged with the murders.
Resident Evil is a 2026 horror film directed by Zach Cregger and written by Cregger and Shay Hatten, based on the Resident Evil video game franchise by Capcom. It follows Bryan, a medical courier who must fight through an outbreak of infected monsters in Racco
Monster: The Lizzie Borden Story
Monster: The Lizzie Borden Story is the fourth season of the American biographical crime drama anthology television series Monster, created by Ian Brennan for Netflix. The season centers on murder suspect Lizzie Borden who was tried and acquitted of the 1892 a
The Chicxulub crater is an impact crater buried underneath the Yucatán Peninsula in Mexico. The crater is named after the onshore inland community of Chicxulub Pueblo. It was formed slightly over 66 million years ago when an asteroid, about ten kilometers in d
Benjamin Hammond Haggerty, better known by his stage name Macklemore, is an American rapper. A native of Seattle, Washington, he started his career in 2000 as an independent artist releasing Open Your Eyes (2000), The Language of My World (2005), and The Unpla
Hugh Jackman filmography and discography
Australian actor Hugh Jackman is known for his leading man roles on stage and screen. Jackman gained stardom for his portrayal of the Marvel Comics superhero Wolverine from the X-Men film series in 10 films starting with X-Men (2000) to most recently, Deadpool
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
Julia O'Hara Stiles is an American actress and director. Stiles began acting at the age of 11 as part of New York's La MaMa Experimental Theatre Club. Her film debut was a small role at age 15 in I Love You, I Love You Not (1996), followed by a lead role in Wi
Robert Hearth Kenneth Kraft is an American businessman. He is the chairman and chief executive officer (CEO) of the Kraft Group, a diversified holding company with assets in paper and packaging, sports and entertainment, real estate development, and a private
Dancing with the Stars (American TV series) season 35
Season thirty-five of Dancing with the Stars premiered on ABC and Disney+ on September 15, 2026, and is scheduled to conclude on November 24, 2026. This season was the fourth to air live on both networks simultaneously. Season nineteen champion Alfonso Ribeiro
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
Jenna Lee Dewan is an American actress and dancer. She started her career as a backup dancer for Janet Jackson, and later worked with artists including Christina Aguilera, Pink, and Missy Elliott. She is known for her role as Nora Clark in the 2006 film Step U
Edward Christopher Sheeran is an English singer-songwriter. Born in Halifax, West Yorkshire, and raised in Framlingham, Suffolk, he began writing songs around the age of eleven. In early 2011, Sheeran independently released the extended play No.5 Collaboration
Hanuman Ansh is a 2026 Indian Hindi-language biographical film written, directed and produced by Vishal Chaturvedi under his banner, Swambhu Media Network. Serving as the first instalment of a trilogy, it is based on his book Divine Detour: That Changed My Lif
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
Neagley is an American action crime television series created by Nick Santora and Nicholas Wootton for Amazon Prime Video. Based on the character created by Lee Child, it is a spin-off of the television series Reacher. It stars Maria Sten, who reprises her rol
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
Jyothika Saravanan is an Indian actress and producer who primarily works in Tamil and Hindi films. She has received several accolades including a National Film Award, five Filmfare Awards South, six Tamil Nadu State Film Awards and Kalaimamani Award. Jyothika
Widow's Bay is an American supernatural comedy horror television series created by Katie Dippold for Apple TV, and starring Matthew Rhys, Kate O'Flynn, Kevin Carroll, Dale Dickey, Kingston Rumi Southwick, and Stephen Root. The series is set in the fictional Ne
Addison Mitchell McConnell III is an American politician and attorney who has been a United States senator from Kentucky since 1985 and has been Kentucky's senior U.S. senator since 1999. A member of the Republican Party, McConnell is in his seventh Senate ter
The West African CFA franc is the currency used by eight independent states in West Africa which make up the West African Economic and Monetary Union (UEMOA): Benin, Burkina Faso, Côte d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal and Togo. These eight countri
The Campeones Cup is an annual soccer match contested between the reigning MLS Cup champion from Major League Soccer (MLS), representing the United States and Canada, and the Campeón de Campeones winner from Liga MX, representing Mexico. Since its introduction
Ella Beatty is an American actress and the daughter of Annette Bening and Warren Beatty.
Maura Higgins is an Irish television personality, presenter and model. She rose to prominence in 2019, when she was a finalist on the fifth series of ITV2 reality series Love Island. Since then, Higgins has collaborated with numerous fashion and makeup brands
Sydney Bernice Sweeney is an American actress. She gained early recognition for her roles in Everything Sucks!, The Handmaid's Tale, and Sharp Objects in 2018. She received wider acclaim for her performances in the drama series Euphoria (2019–2026) and the fir
Raymond Charles Hörsch was an American erotic photographer, author, and pornographic filmmaker. His 50-year body of work ranged from the poetic to the explicit. His artistic work "often focused on scantily clad women in sadomasochistic settings". He had numero
Joseph Junior Andreou O'Cearuill, commonly known as JJ Gabriel, is an English footballer who plays as a forward for Premier League club Manchester United.
Floyd Samba is a professional footballer who plays as a midfielder for Manchester City. Born in England, he has represented England internationally at youth level and has been called up to represent France and Germany internationally at youth level.
Dancing with the Stars (American TV series)
Dancing with the Stars is an American reality dance competition television series that premiered on ABC on June 1, 2005. It is the American version of the British contest program Strictly Come Dancing and is part of its international franchise. The series pair
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").
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Distribution Matching Losses Can Hallucinate Features in Medical Image Translation
This paper discusses how distribution matching losses, such as those used in CycleGAN, when used to synthesize medical images can lead to mis-diagnosis of medical conditions. It seems appealing to use these new image synthesis methods for translating images from a source to a target domain because they can produce high quality images and some even do not require paired data. However, the basis of how these image tran
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
Millions of blind and visually-impaired (BVI) people navigate urban environments every day, using smartphones for high-level path-planning and white canes or guide dogs for local information. However, many BVI people still struggle to travel to new places. In our endeavor to create a navigation assistant for the BVI, we found that existing Reinforcement Learning (RL) environments were unsuitable for the task. This wo
Cross-Modal Information Maximization for Medical Imaging: CMIM
In hospitals, data are siloed to specific information systems that make the same information available under different modalities such as the different medical imaging exams the patient undergoes (CT scans, MRI, PET, Ultrasound, etc.) and their associated radiology reports. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available
LuvHarris: A Practical Corner Detector for Event-cameras
There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use, for example when a camera is randomly moved in an unconstrained environment. In this paper, we present yet another method to perform corn
How do I update my model? On the resilience of Predictive Process Monitoring models to change
Existing well investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make Predictive Process Monitoring too rigid to deal with the variability of processes working in real environments that
Stochastic tensor space feature theory with applications to robust machine learning
In this paper we develop a Multilevel Orthogonal Subspace (MOS) Karhunen--Loève feature theory based on stochastic tensor spaces, for the construction of robust machine learning features. Training data are treated as instances of a random field within a relevant Bochner space. Our key observation is that separate machine learning classes can reside predominantly in mostly distinct subspaces. Using the Karhunen-Loève
Explainable Predictive Process Monitoring: A User Evaluation
Explainability is motivated by the lack of transparency of black-box Machine Learning approaches, which do not foster trust and acceptance of Machine Learning algorithms. This also happens in the Predictive Process Monitoring field, where predictions, obtained by applying Machine Learning techniques, need to be explained to users, so as to gain their trust and acceptance. In this work, we carry on a user evaluation o
Performance of Machine Learning Classification in Sonomammogram Images using BI-RADS
This research aims to investigate the classification accuracy of various state-of-the-art image classification models across different categories of breast ultrasound images, as defined by the Breast Imaging Reporting and Data System (BI-RADS). To achieve this, we used 2,945 sonomammogram images for training and 936 images for validation, with the source cohort reported as comprising 1,540 patients. In order to condu
ResNLS: An Improved Model for Stock Price Forecasting
Stock prices forecasting has always been a challenging task. Although many research projects try to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper, we introduce a hybrid model that improves the prediction of stock prices by emphasizing the dependencies between adjacent stock prices. The proposed model, ResNLS, is mainly composed of two neural
Efficient generative adversarial networks using linear additive-attention Transformers
Although deep generative models such as Diffusion Models (DMs) and Generative Adversarial Networks (GANs) achieve remarkable image generation performance, they often rely on computationally expensive architectures that limit accessibility and increase training and inference costs. We introduce LadaGAN, a hybrid Transformer-convolutional GAN built around Ladaformer, an efficient linear-attention Transformer block. Its
Large Language Models for the Automated Analysis of Optimization Algorithms
The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs within the realm of optimization algorithms by integrating them into STNWeb. This is a web-based tool for the generation of Search Trajectory Networks (STNs), which are visualizations of optimization algorithm behavior. Although visualizati
Estimation of multiple mean vectors in high dimension
We endeavour to estimate numerous multi-dimensional means of various probability distributions on a common space based on independent samples. Our approach involves forming estimators through convex combinations of empirical means derived from these samples. We introduce two strategies to find appropriate data-dependent convex combination weights: a first one employing a testing procedure to identify neighbouring mea
When fairness metrics fail: A utility-based perspective on $\varepsilon$-fairness
Fairness in decision-making processes is often quantified using probabilistic metrics. However, these metrics need not reflect the consequences of decisions for the affected individuals and groups. We develop a utility-based framework that incorporates these consequences into the assessment of fairness. Our main result shows that a decision-making process can satisfy $\varepsilon$-fairness while nevertheless being ma
A Computational Tropical Geometry Framework for Neural Networks
We propose a computational tropical geometry framework for the symbolic analysis of neural networks with tropical activations. The number of linear regions of a neural network has been actively studied as a measure of the expressivity of a given architecture. To study these, we work in the setting of tropical geometry---a combinatorial and polyhedral variant of algebraic geometry---where there are known connections b
DRL-AdaPart: DRL-Driven Adaptive STAR-RIS Partitioning for Fair and Efficient Resource Utilization
Efficient resource utilization is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) to ensure fair and high data rates. We optimize the number of STAR-RIS elements to be allocated to each user and maximize the sum of the user rates. To promote fairness, we introduce a soft fairness mechanism that guarantees a minimum STAR-RIS element allocation to every user. Subje
SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning
The ability of neural networks to perform robotic perception and control tasks such as depth and optical flow estimation, simultaneous localization and mapping (SLAM), and automatic control has led to their widespread adoption in recent years. Deep Reinforcement Learning has been used extensively in these settings, as it does not have the unsustainable training costs associated with supervised learning. However, Deep
DynaWeightPnP: Toward global real-time 3D-2D solver in PnP without correspondences
This paper addresses a special Perspective-n-Point (PnP) problem: estimating the optimal pose to align 3D and 2D shapes in real-time without correspondences, termed as correspondence-free PnP. While several studies have focused on 3D and 2D shape registration, achieving both real-time and accurate performance remains challenging. This study specifically targets the 3D-2D geometric shape registration tasks, applying t
CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset
Machine learning models are increasingly being deployed in real-world contexts. However, systematic studies on their transferability to specific and critical applications are underrepresented in the research literature. An important example is visual anomaly detection (VAD) for robotic power line inspection. While existing VAD methods perform well in controlled environments, real-world scenarios present diverse and u
Interactive proofs for verifying (quantum) learning and testing
We consider the problem of testing and learning from data in the presence of resource constraints, such as limited memory or weak data access, which place limitations on the efficiency and feasibility of testing or learning. In particular, we ask the following question: Could a resource-constrained learner/tester use interaction with a resource-unconstrained but untrusted party to solve a learning or testing problem
Domain generalisation in computational histopathology is challenging because the images are substantially affected by differences among hospitals due to factors like fixation and staining of tissue and imaging equipment. We hypothesise that focusing on nuclei can improve the out-of-domain (OOD) generalisation in cancer detection. We propose a simple approach to improve OOD generalisation for cancer detection by focus
The fast advancement of Large Vision-Language Models (LVLMs) has shown immense potential. These models are increasingly capable of tackling abstract visual tasks. Geometric structures, particularly graphs with their inherent flexibility and complexity, serve as an excellent benchmark for evaluating these models' predictive capabilities. While human observers can readily identify subtle visual details and perform
Context: Machine Learning (ML) significantly impacts Software Engineering (SE), but studies mainly focus on practitioners, neglecting researchers. This overlooks practices and challenges in teaching, researching, or reviewing ML applications in SE. Objective: This study aims to contribute to the knowledge, about the synergy between ML and SE from the perspective of SE researchers, by providing insights into the pract
ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization
As demand from the film and gaming industries for 3D scenes with target styles grows, the importance of advanced 3D stylization techniques increases. However, recent methods often struggle to maintain local consistency in color and texture throughout stylized scenes, which is essential for maintaining aesthetic coherence. To solve this problem, this paper introduces ArtNVG, an innovative 3D stylization framework that
Kernel selection for regression of physical observables is often heuristic. We investigate a physics-informed strategy in which functional forms and spectral structures associated with Green's functions motivate kernel selection without requiring an exact identification between a machine-learning kernel and a physical propagator. The principal construction is a Jackson-damped Chebyshev kernel inspired by the kern
Information-Geometric Inverse Distillation for Enhancing Adversarial Transferability
Transfer-based adversarial attacks rely on surrogate models to craft perturbations, yet often overfit the surrogate's decision boundary. To address this problem, we propose Inverse Knowledge Distillation (IKD), a simple and attack-agnostic mechanism that maximizes the prediction-distribution discrepancy between benign and adversarial samples on the surrogate model. IKD uses a CE/KL-equivalent soft-label objective
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 | 12.1% (121) | 7.1% (71) | 73.64% (81) |
| llms.txt (apex domain) | 9.9% (99) | 6% (60) | 59.09% (65) |
| llms.txt (docs subdomain) | 3.7% (37) | 1.2% (12) | 45.45% (50) |
| .well-known/mcp.json | 0.6% (6) | 0.3% (3) | 8.18% (9) |
| .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.