Record 14092026 · captured 2026-09-15
The world looked up Sitee. 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.
Sitee is a 1993 Maldivian drama film written and directed by Ahmed Nimal. Produced by Aslam Rasheed under Slam Studio in association with Kid Productions, the film stars Nimal and Fathimath Rameeza in pivotal roles.
Alexander "Sascha" Zverev is a German professional tennis player. He has a career-high singles ranking of world No. 2 by the ATP, first achieved in June 2022. Zverev has won 26 ATP Tour singles titles, including two majors at the 2026 French Open and 2026 US O
Elena Andreyevna Rybakina is a Russian-born Kazakhstani professional tennis player. She is currently ranked world No. 1 in singles by the Women's Tennis Association (WTA). Rybakina has won 14 WTA Tour-level singles titles, including three majors, two WTA 1000
Benjamin Todd Shelton is an American professional tennis player. He has been ranked world No. 4 in men's singles by the Association of Tennis Professionals (ATP), achieved in September 2026. Shelton has won seven ATP Tour singles titles, including two Masters
Ryan Garcia is an American professional boxer. He has held the World Boxing Council (WBC) welterweight title since 2026. He previously held the WBC interim lightweight title in 2021.
Archibald Charles Manning is an American college football quarterback for the Texas Longhorns. He is a member of the Manning family of football players.
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
General elections were held in Sweden on 13 September 2026 to elect the 349 members of the Riksdag. They in turn will elect the prime minister. Local elections were also held on the same day. The preliminary count on election night indicated that the left-wing
Aryna Siarhiejeŭna Sabalenka is a Belarusian professional tennis player. She has been ranked the world No. 1 in women's singles by the Women's Tennis Association (WTA) for a total of 107 weeks, and finished as the year-end No. 1 twice. Sabalenka has won 24 car
John Davis Tuggle was an American professional football running back who played in the National Football League (NFL) for one season with the New York Giants. He played college football for the California Golden Bears and was selected by the Giants as the fina
Conor Nigel Benn is a British professional boxer. He has challenged once for the WBC welterweight title in September 2026. He is the son of former two-division world champion boxer Nigel Benn.
Vishwanath & Sons is a 2026 Indian Tamil-language romantic family drama film written and directed by Venky Atluri. Produced by Sithara Entertainments and Fortune Four Cinemas, the film stars Suriya, alongside Mamitha Baiju, Radhika Sarathkumar, and Raveena Tan
Hanuman Ansh is a 2026 Indian Hindi-language biographical devotional drama 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: T
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
Bryan Shelton is an American former professional tennis player and coach. During his playing career, he won two singles and two doubles ATP tour titles, and reached the mixed doubles final at the 1992 French Open, partnering with Lori McNeil. Shelton played co
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
This is a list of characters in the Garfield comic strip, created by Jim Davis, organized by category and date of first appearance.
Trinity Rain Moyer-Rodman is an American professional soccer player who plays as a forward for the Washington Spirit of the National Women's Soccer League (NWSL) and the United States national team.
Cooper Archibald Manning is an American entrepreneur and television personality who is the host of the television show The Manning Hour for Fox Sports as well as principal and senior managing director of investor relations for AJ Capital Partners. He is the el
United Airlines Flight 93 was a domestic scheduled passenger flight that was hijacked by four al-Qaeda terrorists on the morning of Tuesday, September 11, 2001, as part of the September 11 attacks. The hijackers planned to deliberately crash the plane into a f
Alan Michael Ritchson is an American actor. He made his acting debut as Arthur Curry / Aquaman on The CW superhero series Smallville (2005–10), where he appeared as a guest star between the fifth and tenth seasons. He subsequently had a starring role in the Sp
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
Michael Polansky is an American entrepreneur, venture capitalist, music producer and songwriter. He is engaged to Lady Gaga and has co-written various songs with her.
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
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
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
Céline Marie Claudette Dion is a Canadian singer, entrepreneur and philanthropist. Dubbed the "Queen of Power Ballads", she is known for her impact on popular music through powerful, technically skilled vocals and commercially successful works. With over 200 m
Stefani Joanne Angelina Germanotta, known professionally as Lady Gaga, is an American singer, songwriter, and actress. An influential figure in popular music, she is known for her image reinventions, flamboyant fashion, and versatility across the entertainment
Practical Magic 2 is a 2026 American romantic fantasy film directed by Susanne Bier from a screenplay by Akiva Goldsman, Georgia Pritchett, and Kelly Marcel, based on the 2021 novel The Book of Magic by Alice Hoffman. The sequel to Practical Magic (1998), the
Dario Amodei is an American artificial intelligence (AI) researcher and entrepreneur. In 2021, he and his sister Daniela Amodei co-founded Anthropic, the company behind the large language model series Claude. Before that, he was the vice president of research
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Bayesian Prediction for Artificial Intelligence
This paper shows that the common method used for making predictions under uncertainty in A1 and science is in error. This method is to use currently available data to select the best model from a given class of models-this process is called abduction-and then to use this model to make predictions about future data. The correct method requires averaging over all the models to make a prediction-we call this method tran
Why we need an AI-resilient society- Profiling Large Language Models
Three generations of software have transformed the role of artificial intelligence in society. In the first, programmers wrote explicit logic. In the second, neural networks learned programs from data. In the third, large language models turn natural language itself into a programming interface. These shifts reach far beyond computer science, reshaping how societies generate knowledge, make decisions, and govern them
The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. Apart from the global health crises, the pandemic has also caused significant economic and financial difficulties and socio-physiological implications. Effective screening, triage, treatment planning, and prognostication of outcome plays a key role in controlling the pandemic. Recent studies have highli
AbductionRules: Training Transformers to Explain Unexpected Inputs
Transformers have recently been shown to be capable of reliably performing logical reasoning over facts and rules expressed in natural language, but abductive reasoning - inference to the best explanation of an unexpected observation - has been underexplored despite significant applications to scientific discovery, common-sense reasoning, and model interpretability. We present AbductionRules, a group of natural langu
Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects
We propose a generalization of the synthetic control methods to the setting with dynamic treatment effects, in which each unit receives multiple treatments sequentially, according to an adaptive policy that depends on a latent, endogenously time-varying confounding state. Under a low-rank latent factor model assumption, which admits linear time-varying and time-invariant dynamic triangular systems as special cases, w
Computing linear sections of varieties: quantum entanglement, tensor decompositions and beyond
We study the problem of finding elements in the intersection of an arbitrary conic variety in $\mathbb{F}^n$ with a given linear subspace (where $\mathbb{F}$ can be the real or complex field). This problem captures a rich family of algorithmic problems under different choices of the variety. The special case of the variety consisting of rank-1 matrices already has strong connections to central problems in different a
Guided Adversarial Robust Transfer Learning with Source Mixing
Transfer learning is a critical technique that enables the application of knowledge gained from existing tasks or domains to improve performance on a new one, reducing the need for extensive data and training in each new context. Many existing transfer learning methods rely on leveraging information from source populations closely resembling the target population. However, this approach often overlooks valuable knowl
Protect Your Score: Contact Tracing With Differential Privacy Guarantees
The pandemic in 2020 and 2021 had enormous economic and societal consequences, and studies show that contact tracing algorithms can be key in the early containment of the virus. While large strides have been made towards more effective contact tracing algorithms, we argue that privacy concerns currently hold deployment back. The essence of a contact tracing algorithm constitutes the communication of a risk score. Yet
In human-centric Cyber-Physical Systems (CPS), personalized Deep Reinforcement Learning (DRL) agents must share fine-grained control actions with cloud services, exposing sensitive private states to inference attacks by honest-but-curious adversaries. Static privacy models fail to address the dynamic nature of human interactions. This paper introduces PEARL (Personalized Early-exit Adaptive Reinforcement Learning), a
Satisficing Regret Minimization in Bandits: Constant Rate and Light-Tailed Distribution
Motivated by the concept of satisficing in decision-making, we consider the problem of satisficing regret minimization in bandit optimization. In this setting, the learner aims at selecting satisficing arms (arms with mean reward exceeding a certain threshold value) as frequently as possible. The performance is measured by satisficing regret, which is the cumulative deficit of the chosen arm's mean reward compare
De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation
The universality of deep neural networks across different modalities and their generalization capabilities to unseen domains play an essential role in medical image segmentation. The recent segment anything model (SAM) has demonstrated strong adaptability across diverse natural scenarios. However, the huge computational costs, demand for manual annotations as prompts and conflict-prone decoding process of SAM degrade
Transformers As Approximations of Solomonoff Induction
Solomonoff Induction is an optimal-in-the-limit unbounded algorithm for sequence prediction, representing a Bayesian mixture of every computable probability distribution and performing close to optimally in predicting any computable sequence. Being an optimal form of computational sequence prediction, it seems plausible that it may be used as a model against which other methods of sequence prediction might be compare
All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing
Due to their capacity to acquire world knowledge from large corpora, pre-trained language models (PLMs) are extensively used in ultra-fine entity typing tasks where the space of labels is extremely large. In this work, we explore the limitations of the knowledge acquired by PLMs by proposing a novel heuristic to approximate the pre-training distribution of entities when the pre-training data is unknown. Then, we syst
In Magnetic Resonance Imaging (MRI), high temporal-resolved motion can be useful for image acquisition and reconstruction, MR-guided radiotherapy, dynamic contrast-enhancement, flow and perfusion imaging, and functional assessment of motion patterns in cardiovascular, abdominal, peristaltic, fetal, or musculoskeletal imaging. Conventionally, these motion estimates are derived through image-based registration, a parti
Distributionally Robust Optimization
Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a family of probability distributions consistent with any available structural or statistical information. DRO seeks decisions that perform best under the worst distribut
From Bench-to-Bedside: A Review of Clinical Trials in Drug Discovery and Development
Clinical trials bridge basic research and clinical application, serving as essential steps in drug development. This review examines clinical trial phases (Phase I [safety assessment], Phase II [efficacy evaluation], Phase III [large-scale validation], and Phase IV [post-marketing surveillance]), highlighting the distinct characteristics and interconnections. Major challenges are identified, including ethical complia
A Training-free Method for LLM Text Attribution
Verifying the provenance of text is increasingly important for firms, educational institutions, and online platforms as Large Language Models (LLMs) produce output that is nearly indistinguishable from human-generated content. We study the problem of determining whether a given text was generated by a particular LLM while controlling the false positive rate. We model LLM-generated text as a sequential stochastic proc
Modern artificial intelligence is supported by machine learning models (e.g., foundation models) that are pretrained on a massive data corpus and then adapted to solve a variety of downstream tasks. To summarize performance across multiple tasks, evaluation metrics are often aggregated into a summary metric, e.g., average accuracy across 10 question-answering tasks. When aggregating evaluation metrics, it is useful t
Driven by the need to address labor shortages and meet the demands of a rapidly growing population, robotic automation has become a critical component in precision agriculture. Leaf-level hyperspectral spectroscopy is shown to be a powerful tool for phenotyping, monitoring crop health, identifying essential nutrients within plants as well as detecting diseases and water stress. This work introduces RoMu4o, a robotic
Modern machine learning models excel at detecting individual actions, sounds, or scene attributes from short, localized observations. However, many real-world tasks, such as in smart cities and healthcare, require reasoning over high-level complex events (CEs): spatiotemporal, rule-governed patterns of short-term atomic events (AEs). Complex event detection (CED) is challenging due to long temporal dependencies, gene
Benchmarking Self-Supervised Learning Methods for Accelerated MRI Reconstruction
Reconstructing MRI from highly undersampled measurements is crucial for accelerating medical imaging, but is challenging due to the ill-posedness of the inverse problem. While supervised deep learning (DL) approaches have shown remarkable success, they traditionally rely on fully-sampled ground truth (GT) images, which are expensive or impossible to obtain in real scenarios. This problem has created a recent surge in
Safe Learning Under Irreversible Dynamics via Asking for Help
Most learning algorithms with formal regret guarantees essentially rely on trying all possible behaviors, which is problematic when some errors cannot be recovered from. Instead, we allow the learning agent to ask for help from a mentor and to transfer knowledge between similar states. We show that this combination enables the agent to learn both safely and effectively. Under standard online learning assumptions, we
Statistical Mechanics of Semantic Compression
The basic problem of semantic compression is to minimize the length of a message while preserving its meaning. This differs from classical notions of compression in that the distortion is not measured directly at the level of bits, but rather in an abstract semantic space. In order to make this precise, we take inspiration from cognitive neuroscience and machine learning and model semantic space as a continuous Eucli
FLOAT Drone: A Fully-actuated Coaxial Aerial Robot for Close-Proximity Operations
How to endow aerial robots with the ability to operate in close proximity remains an open problem. The core challenges lie in the propulsion system's dual-task requirement: generating manipulation forces while simultaneously counteracting gravity. These competing demands create dynamic coupling effects during physical interactions. Furthermore, rotor-induced airflow disturbances critically undermine operational r
Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision
We present Lunima-OmniLV (abbreviated as OmniLV), a universal multimodal multi-task framework for low-level vision that addresses over 100 sub-tasks across four major categories: image restoration, image enhancement, weak-semantic dense prediction, and stylization. OmniLV leverages both textual and visual prompts to offer flexible and user-friendly interactions. Built on Diffusion Transformer (DiT)-based generative p
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% (120) | 6% (60) | 75.45% (83) |
| llms.txt (apex domain) | 9.7% (97) | 5.2% (52) | 60.91% (67) |
| llms.txt (docs subdomain) | 3.9% (39) | 0.8% (8) | 43.64% (48) |
| .well-known/mcp.json | 0.6% (6) | 0.3% (3) | 7.27% (8) |
| .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) | 0.7% (7) | 0.2% (2) | 0.91% (1) |
Notable events recorded on this day and month across all years.