Record 09102026 · captured 2026-10-10
The world looked up Nana Patekar. 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.
Vishwanath Patekar, nicknamed Nana, was an Indian actor and filmmaker, mainly working in Hindi and Marathi cinema. Widely regarded as one of the greatest and most influential actors in Indian Cinema, Patekar was recipient of the three National Film Awards, fou
Christa Gail Pike is an American convicted murderer on death row in Tennessee. In 1995, when she was 18, Pike and her boyfriend lured their 19-year-old classmate Colleen Slemmer into a wooded area in Knoxville where they tortured and killed her. Slemmer was be
Anne Patricia Carson is a Canadian academic and writer. Born in Toronto, she graduated from the University of Toronto and starting in 1979 became a professor of classics, comparative literature, and creative writing at several universities. She has taught at t
Nidal Malik Hasan is an American mass murderer, former United States Army major and former physician who perpetrated the 2009 Fort Hood shooting, killing 13 people and injuring 32 others, on November 5, 2009. Hasan, an Army Medical Corps psychiatrist, admitted
Carrie is an American supernatural horror television miniseries developed by Mike Flanagan, and produced by Amazon MGM Studios for Amazon Prime Video. The first series adaptation of the eponymous franchise, it is based on the 1974 novel by Stephen King, and is
Margaret Hamilton (software engineer)
Margaret Elaine Hamilton was an American computer scientist. She directed the Software Engineering Division at the MIT Instrumentation Laboratory, where she led the development of the on-board flight software for NASA's Apollo Guidance Computer for the Apollo
Nana Patekar was an Indian actor, writer, and film maker, mainly working in Hindi and Marathi cinema.
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
Digger is a 2026 American satirical black comedy film directed, co-written, and co-produced by Alejandro González Iñárritu. Tom Cruise, who also produced the film, stars as Digger Rockwell, an aging oil magnate whose drilling sets off an ecological disaster. T
Iraq, officially the Republic of Iraq, is a country in West Asia. Located within the Middle East, it is bordered by Saudi Arabia to the south, Turkey to the north, Iran to the east, the Persian Gulf and Kuwait to the southeast, Jordan to the southwest, and Syr
East of Eden is a 1952 family saga novel by American author and Nobel Prize winner John Steinbeck. Many regard the work as Steinbeck's most ambitious novel, and Steinbeck himself considered it his magnum opus. Steinbeck said of East of Eden, "It has everything
East of Eden (2026 miniseries)
East of Eden is a 2026 American historical drama television limited series created by Zoe Kazan, starring Florence Pugh. It is adapted from John Steinbeck's 1952 novel of the same name and premiered on Netflix on October 1, 2026.
Michael Saul Dell is an American billionaire businessman and investor. He is the founder, chairman, and CEO of Dell Technologies, one of the world's largest technology infrastructure companies, of which he owns a 40% stake in 2026.
Kimberly Ann Guilfoyle is an American diplomat, media personality, and former prosecutor who has served as the United States ambassador to Greece since 2025. She served as an advisor and led the fundraising division of the Donald Trump 2020 presidential campai
Drishyam: The Conclusion is a 2026 Indian Hindi-language crime thriller film co-written, produced, and directed by Abhishek Pathak. Produced by Panorama Studios and Star Studio18, it is a sequel to Drishyam 2 (2022). Unlike its predecessors, the film features
Tanushree Dutta is an Indian former actress, model and beauty pageant titleholder who won Femina Miss India Universe 2004 and then represented her country at Miss Universe 2004 in Ecuador where she placed Top 10.
Myha'la Herrold, known mononymously as Myha'la, is an American actress. She portrays Harper Stern in the British television drama Industry and had roles in the films Bodies Bodies Bodies (2022) and Leave the World Behind (2023).
Lizzie Andrew Borden was an American woman who was tried and acquitted of the murders of her father and stepmother in Fall River, Massachusetts. No other individuals were charged with the murders.
Below is a 2026 Canadian supernatural thriller miniseries created by Jesse McKeown and starring Josh Hartnett, Mackenzie Davis, and Charlie Heaton. It premiered on Netflix on October 8, 2026. The series received generally positive reviews from critics.
Isabella May Ramsey is an English actor. They are known for their breakthrough roles as young noblewoman Lyanna Mormont in the fantasy television series Game of Thrones (2016–2019) and as Ellie in the post-apocalyptic drama series The Last of Us (2023–present)
The T24 Festival, also known as the T24 Social Festival, was an event on September 8, 2012, in the Yangcheon District of Seoul, South Korea, during which former soldier Lee Kwang-nak was challenged to set up a 24-person military tent single-handedly in under t
On January 6, 2023, Abby Zwerner, an American schoolteacher, was seriously injured when a six-year-old male student shot her while she was teaching at Richneck Elementary School in Newport News, Virginia, United States. The boy had brought a 9 mm semi-automati
The World Space Week (WSW) is an annual holiday observed to celebrate outer space activities. It is observed from October 4 to 10 in over 95 nations throughout the world. World Space Week is defined as an international celebration of space science and space te
Other Mommy is a 2026 American supernatural psychological horror film directed by Rob Savage and written by Nathan Elston. The film is based on the 2024 novel Incidents Around the House by Josh Malerman. It stars Jessica Chastain as a mother who must do everyt
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
Ayesha Jhulka is an Indian actress who primarily appears in Hindi films, in addition to Bengali, Kannada and Telugu films. In a career spanning over three decades, she has worked in over 60 films.
Eva Marie Saint was an American actress. In a career spanning nine decades, she received an Academy Award and a Primetime Emmy Award, as well as nominations for two BAFTAs and a Golden Globe. At the time of her death, Saint was one of the earliest and oldest l
On November 5, 2009, a mass shooting took place at Fort Hood near Killeen, Texas, United States. Nidal Hasan, a Palestinian-American U.S. Army major and psychiatrist, shouted Allahu Akbar before fatally shooting 13 people and injuring more than 30 others. It w
Mia Gypsy Mello da Silva Goth is a British actress and model. After modelling as a teenager, Goth made her acting debut in the erotic drama Nymphomaniac (2013). She earned recognition with the films A Cure for Wellness (2016), High Life (2018), Suspiria (2018)
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Lyme disease which is one of the most common infectious vector-borne diseases manifests itself in most cases with erythema migrans (EM) skin lesions. Recent studies show that convolutional neural networks (CNNs) perform well to identify skin lesions from images. Lightweight CNN based pre-scanner applications for resource-constrained mobile devices can help users with early diagnosis of Lyme disease and prevent the tr
Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Physicians rely on patient information about the background of the skin le
Motivated by neural network training in finite-precision arithmetic environments, this work studies the convergence of perturbed iterate SGD using adaptive step sizes in an environment with numerical error. Considering a general stochastic Lipschitz continuous loss function, an asymptotic convergence result to a Clarke stationary point is proven as well as the non-asymptotic convergence to an approximate stationary p
Enabling Quantum Natural Language Processing for Hindi Language
Quantum Natural Language Processing (QNLP) is taking huge leaps in solving the shortcomings of classical Natural Language Processing (NLP) techniques and moving towards a more "Explainable" NLP system. The current literature around QNLP focuses primarily on implementing QNLP techniques in sentences in the English language. In this paper, we propose to enable the QNLP approach to HINDI, which is the third most
This work aims to improve the sample efficiency of parallel large-scale ranking and selection (R&S) problems by leveraging correlation information. We modify the commonly used "divide and conquer" framework in parallel computing by adding a correlation-based clustering step, transforming it into "clustering and conquer". Theoretically, we develop a novel gradient-based analysis framework and show
Machine learning and information theory concepts towards an AI Mathematician
The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning. What could be missing? Can we learn something useful about that gap from how the brains of mathematicians go about their craft? This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities -- which correspond to our int
Accurate and Efficient Object Pose Estimation via the Aggregation of Diffusion Features
Estimating the pose of objects from images is a crucial task of 3D scene understanding, and recent approaches have shown promising results on very large benchmarks. However, these methods experience a significant performance drop when dealing with unseen objects. To address this problem, we have an in-depth analysis on the features of diffusion models, e.g. Stable Diffusion, which hold substantial potential for model
Policy Learning with a Language Bottleneck
Modern AI systems such as self-driving cars and game-playing agents can achieve superhuman performance, but often lack human-like generalization, interpretability, and inter-operability with human users. Inspired by the rich interactions between language and decision-making in humans, we introduce Policy Learning with a Language Bottleneck (PLLB), a framework enabling AI agents to generate linguistic rules that captu
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning
Machine unlearning aims to remove the influence of specified training data or knowledge from a trained model without requiring full retraining. This capability is particularly important for modern image classifiers and large language models (LLMs), where retraining can be computationally expensive. However, machine unlearning on complex data remains difficult because the target information is often distributed across
Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving
Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI. It features a KVCache-centric disaggregated architecture that separates the prefill and decoding clusters. It also leverages the underutilized CPU, DRAM, and SSD resources of the GPU cluster to implement a disaggregated cache of KVCache. The core of Mooncake is its KVCache-centric scheduler, which balances maximizing overall eff
Large language models have demonstrated remarkable capabilities as general-purpose assistants, excelling in a wide range of reasoning tasks and supporting various aspects of daily web usage. This achievement represents a significant step toward achieving artificial general intelligence. Despite these advancements, the effectiveness of large language models often hinges on the specific prompting strategies employed, a
We consider the problem of estimating graph limits, known as graphons, from observations of sequences of sparse finite graphs. In this paper we show a simple method that can shed light on a subset of sparse graphs. The method involves mapping the original graphs to their line graphs. We show that graphs satisfying a particular property, which we call the square-degree property are sparse, but give rise to dense line
Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Review
The integration of Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) in healthcare, through Machine Learning (ML) and Deep Learning (DL) techniques, has advanced the prediction and diagnosis of chronic diseases. AI-driven models such as XGBoost, Random Forest, CNNs, and LSTM RNNs have achieved over 98\% accuracy in predicting heart disease, chronic kidney disease (CKD), Alzheimer's disease, a
Paying attention improves performance, but attention is metabolically costly, so how should a resource-efficient agent allocate it? We study optimal allocation strategies using a normative model of a signal detection task in which attention comes at a cost. The model reveals that optimal attention is temporally structured in one of two patterns depending on task conditions: when attention costs penalize intense focus
Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion Planning
Multi-agent path finding (MAPF) in large networks is computationally challenging. An approach for MAPF is prioritized planning (PP), in which agents plan sequentially according to their priority. Albeit a computationally efficient approach for MAPF, the solution quality strongly depends on the prioritization. Most prioritizations rely either on heuristics, which do not generalize well, or iterate to find adequate pri
BEAT: Balanced Frequency Adaptive Tuning for Long-Term Time-Series Forecasting
Long-term time-series forecasting supports a wide range of applications, including weather prediction and electricity demand planning. Frequency-domain methods address this task by decomposing observations into components that describe temporal variations at different scales. However, separate representations do not by themselves provide an explicit mechanism for adjusting the training emphasis across components. Und
Networks with Finite VC Dimension: Pro and Contra
Approximation and learning of classifiers of large data sets by neural networks in terms of high-dimensional geometry and statistical learning theory are investigated. The influence of the VC dimension of sets of input-output functions of networks on approximation capabilities is compared with its influence on consistency in learning from samples of data. It is shown that, whereas finite VC dimension is desirable for
A Survey on Industrial Anomaly Synthesis
This paper presents a comprehensive review of industrial anomaly synthesis (IAS). Existing surveys on industrial anomalies mainly focus on anomaly detection, while IAS is typically treated as an auxiliary component rather than as an independent topic. However, owing to its increasing importance in data augmentation, downstream model training, and controllable industrial inspection, IAS has become a research direction
C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Models
Pre-trained visual models have become fundamental in computer vision, but they face challenges in continual learning scenarios where data and tasks evolve over time. Low-Rank Adaptation (LoRA) offers efficient fine-tuning capabilities but remains limited for such dynamic environments. Standard LoRA cannot distinguish important subspaces, causing critical knowledge to be overwritten in sequential training. Existing ap
Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning
In self-supervised contrastive learning, negative pairs are typically constructed using an anchor image and a sample drawn from the entire dataset, excluding the anchor. However, this approach can result in the creation of negative pairs with similar semantics, referred to as "false negatives", leading to their embeddings being falsely pushed apart. To address this issue, we introduce GloFND, an optimization-
Boosting the Local Invariance for Better Adversarial Transferability
Transfer-based attacks pose a significant threat to real-world applications by directly targeting victim models with adversarial examples generated on surrogate models. While numerous approaches have been proposed to enhance adversarial transferability, existing works often overlook the intrinsic relationship between adversarial perturbations and input images. In this work, we find that the adversarial perturbations
Sample-Efficient Optimization over Generative Priors via Coarse Learnability
We study zeroth-order optimization where solutions must minimize a cost $d(s)$ while maintaining high probability under a complex generative prior $L(s)$ (e.g., a parameterized model). This reduces to sampling from a target distribution proportional to $L(s) e^{-T \cdot d(s)}$. Since classical model-based optimization (MBO) lacks finite-sample guarantees for expressive approximate learners, we introduce "coarse l
A Survey on Archetypal Analysis
Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure for extracting distinct aspects, so-called archetypes, from observations, with each observational record approximated as a mixture (i.e., convex combination) of these archetypes. AA thereby provides straightforward, interpretable, and explainable representations for feature extraction and dimensionali
InfiFPO: Implicit Model Fusion via Preference Optimization in Large Language Models
Model fusion combines multiple Large Language Models (LLMs) with different strengths into a more powerful, integrated model through lightweight training methods. Existing works on model fusion focus primarily on supervised fine-tuning (SFT), leaving preference alignment (PA) --a critical phase for enhancing LLM performance--largely unexplored. The current few fusion methods on PA phase, like WRPO, simplify the proces
LLM-powered Query Expansion for Enhancing Boundary Prediction in Language-driven Action Localization
Language-driven action localization in videos requires not only semantic alignment between language query and video segment, but also prediction of action boundaries. However, the language query primarily describes the main content of an action and usually lacks specific details of action start and end boundaries, which increases the subjectivity of manual boundary annotation and leads to boundary uncertainty in trai
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 | 13% (130) | 5.1% (51) | 74.55% (82) |
| llms.txt (apex domain) | 10.7% (107) | 4.4% (44) | 60% (66) |
| llms.txt (docs subdomain) | 4.2% (42) | 0.9% (9) | 45.45% (50) |
| .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 | 0.2% (2) | 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.