Record 23042026 · captured 2026-08-25
The world looked up Nahui Ollin. 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.
Nahui Ollin is a 16th-century concept in Aztec/Mexica cosmology with a variety of meanings. Nahui translates to "four," and Ollin translates to "movement" or "motion." Ollin was primarily portrayed in Aztec codices as two interlaced lines, each portrayed with
David Thomas Mason was an English musician who came to prominence in 1967 as a founding member of the rock band Traffic. He wrote and sang lead vocals on two of the band's best known songs, "Hole in My Shoe" and "Feelin' Alright?" His song "Only You Know and I
Earth Day is an annual event on April 22 to demonstrate support for environmental protection. First held on April 22, 1970, it now includes a wide range of events coordinated globally through earthday.org including 1 billion people in more than 193 countries.
List of highest-grossing Indian films
This ranking lists the highest-grossing Indian films produced by Indian cinema, based on conservative global box office estimates as reported by organisations classified as green by Wikipedia. The figures are not adjusted for inflation. However, there is no of
Michael is a 2026 biographical film directed by Antoine Fuqua and written by John Logan. It follows the early life of the American singer Michael Jackson, from his time with the Jackson 5 in the 1960s to the Bad World Tour in the late 1980s. Jackson is portray
Dhurandhar: The Revenge is a 2026 Indian Hindi-language spy action-thriller film written and directed by Aditya Dhar. It is produced by Dhar, Lokesh Dhar, and Jyoti Deshpande under Jio Studios and B62 Studios. It is a sequel to the 2025 film Dhurandhar and the
David Wilcock was an American paranormal writer, media personality, and YouTuber. He was a significant figure in the disclosure movement and a regular contributor to extraterrestrial influence theories in popular culture media, appearing in productions made by
Liam James Rosenior is an English professional football manager and former player who is the head coach of Ligue 1 club Paris FC.
William Patrick Muldoon III was an American actor, film producer, and musician. He was best known for his roles as Austin Reed on Days of Our Lives and Zander Barcalow on Starship Troopers.
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 fifth and final season of the American satirical superhero television series The Boys, the first series in the franchise based on the comic book series of the same name created by Garth Ennis and Darick Robertson, was developed for television by Eric Kripk
2026 Tamil Nadu Legislative Assembly election
Elections to appoint the 234 members of the 17th Tamil Nadu Legislative Assembly, the highest body of the Government of Tamil Nadu, were held on 23 April 2026. The results were declared on 4 May 2026 by the Election Commission of India. It recorded the highest
Pigeon Creek is a census-designated place (CDP) in Summit County, Ohio, United States. The population was 886 at the 2020 census. It is part of the Akron metropolitan statistical area.
2026 West Bengal Legislative Assembly election
Legislative Assembly elections were held in West Bengal to elect all 294 members of the West Bengal Legislative Assembly in two phases on 23 and 29 April 2026, with the votes counted and results for 293 seats released on 4 May 2026. The election saw the defeat
Michael Joseph Jackson was an American singer, songwriter, dancer, and philanthropist. Dubbed the "King of Pop", he is widely regarded as one of the most culturally significant figures of the 20th century. His musical achievements broke American racial barrier
Claudia Doumit is an Australian actress. She portrayed Jiya Marri in the NBC series Timeless (2016–2018), and is best known for her portrayal of Farah Karim from Call of Duty: Modern Warfare (2019) and Victoria Neuman in the superhero series The Boys (2020-202
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
David Anthony Burke, known professionally as D4vd, is an American singer-songwriter. Born in Queens, New York City, and raised in Houston, Texas, Burke began composing music in 2021 for his Fortnite gameplay montages. He first achieved commercial success with
Clayface is an upcoming American body horror film based on the eponymous character from DC Comics. Directed by James Watkins from a screenplay by Mike Flanagan and Hossein Amini, it will be the third film in the DC Universe (DCU). Tom Rhys Harries stars as Mat
Jaafar Jeremiah Jackson is an American actor and singer. A member of the Jackson family, he released his debut single "Got Me Singing" in 2019 but gained popularity for portraying his uncle Michael Jackson in the record-breaking biographical film Michael (2026
John Ternus is an American engineer and business executive who has been the senior vice president of hardware engineering at Apple Inc. since 2021. On September 1, 2026, he will succeed Tim Cook as the CEO of Apple.
Bhooth Bangla is a 2026 Indian Hindi-language comedy horror film directed by Priyadarshan and produced by Akshay Kumar, Ekta Kapoor and Shobha Kapoor under Balaji Motion Pictures and Cape of Good Films. The film stars Akshay Kumar, Paresh Rawal, Jisshu Sengupt
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").
Beef is an American comedy drama anthology television series created by Lee Sung Jin for Netflix. Season 1 stars Steven Yeun and Ali Wong as Danny Cho and Amy Lau, two strangers whose involvement in a road rage incident escalates into a prolonged feud. Appeari
Euphoria is an American psychological drama television series created and written by Sam Levinson for HBO. Based on the Israeli miniseries of the same name created by Ron Leshem, the series stars Zendaya as drug-addicted teenager Rue Bennett, who also serves a
The Boys is an American satirical superhero streaming television series developed by Eric Kripke for Amazon Prime Video. Based on the comic book series of the same name by Garth Ennis and Darick Robertson, it follows the eponymous team of vigilantes as they co
2026 Virginia redistricting referendum
The 2026 Virginia redistricting referendum appeared on the April 21, 2026, ballot in the state of Virginia as a legislatively referred constitutional amendment. The amendment passed by a slim margin, but it was subsequently struck down by the Supreme Court of
Project Hail Mary is a 2026 American science fiction film produced and directed by Phil Lord and Christopher Miller and written by Drew Goddard, based on the 2021 novel of the same name by Andy Weir. It stars Ryan Gosling, who also produced the film, as Ryland
Anne Jacqueline Hathaway is an American actress. Her accolades include an Academy Award, a British Academy Film Award, a Golden Globe Award, and a Primetime Emmy Award. Her films have grossed over $6.8 billion worldwide.
Jack Henry Quaid is an American actor. The son of actors Meg Ryan and Dennis Quaid, he is also the nephew of character actor Randy Quaid. He made his acting debut with a minor role in the dystopian film The Hunger Games (2012). Quaid was part of the main cast
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Time series clustering based on the characterisation of segment typologies
Time series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each time series into account, which can be used to better compare the time series objects of the dataset
Tropical time series, iterated-sums signatures and quasisymmetric functions
Aiming for a systematic feature-extraction from time series, we introduce the iterated-sums signature over arbitrary commutative semirings. The case of the tropical semiring is a central, and our motivating example. It leads to features of (real-valued) time series that are not easily available using existing signature-type objects. We demonstrate how the signature extracts chronological aspects of a time series, and
This paper and accompanying Python and C++ Framework is the product of the authors perceived problems with narrow (Discrimination based) AI. (Artificial Intelligence) The Framework attempts to develop a genetic transfer of experience through potential structural expressions using a common regulation/exchange value (energy) to create a model whereby neural architecture and all unit processes are co-dependently develop
Diffusion Approximations for Thompson Sampling in the Small Gap Regime
We study the process-level dynamics of Thompson sampling and related sampling-based bandit algorithms in the ``small gap'' regime, where the gaps between the arm means are of order $\sqrtγ$ or smaller and the time horizon is of order $1/γ$, with $γ\downarrow 0$. In this regime, as $γ\downarrow 0$, we show that the process-level dynamics of such algorithms converge weakly to the solutions to certain stochastic
Morphological Classification of Galaxies in S-PLUS using an Ensemble of Convolutional Networks
The universe is composed of galaxies that have diverse shapes. Once the structure of a galaxy is determined, it is possible to obtain important information about its formation and evolution. Morphologically classifying galaxies means cataloging them according to their visual appearance and the classification is linked to the physical properties of the galaxy. A morphological classification made through visual inspect
DetailCLIP: Injecting Image Details into CLIP's Feature Space
Although CLIP-like Visual Language Models provide a functional joint feature space for image and text, due to the limitation of the CILP-like model's image input size (e.g., 224), subtle details are lost in the feature representation if we input high-resolution images (e.g., 2240). Our proposed framework addresses this issue by generating a single feature representation for a high-resolution image that retains im
A Dictionary-based approach to Time Series Ordinal Classification
Time Series Classification (TSC) is an extensively researched field from which a broad range of real-world problems can be addressed obtaining excellent results. One sort of the approaches performing well are the so-called dictionary-based techniques. The Temporal Dictionary Ensemble (TDE) is the current state-of-the-art dictionary-based TSC approach. In many TSC problems we find a natural ordering in the labels asso
Convolutional and Deep Learning based techniques for Time Series Ordinal Classification
Time Series Classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time Series Ordinal Classification (TSOC) is the
Policy Gradient with Kernel Quadrature
Reward evaluation of episodes becomes a bottleneck in a broad range of reinforcement learning tasks. Our aim in this paper is to select a small but representative subset of a large batch of episodes, only on which we actually compute rewards for more efficient policy gradient iterations. We build a Gaussian process modeling of discounted returns or rewards to derive a positive definite kernel on the space of episodes
Community-Informed AI Models for Police Accountability
Face-to-face interactions between police officers and the public affect both individual well-being and democratic legitimacy. Many government-public interactions are captured on video, including interactions between police officers and drivers captured on bodyworn cameras (BWCs). New advances in AI technology enable these interactions to be analyzed at scale, opening promising avenues for improving government transpa
Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with multiple, conflicting objectives, represented by vector-valued rewards. Widely-used scalar RL methods such as Q-learning can be modified to handle multiple objectives by (1) learning vector-valued value functions, and (2) performing action selection using a scalarisation o
Functional linear discriminant analysis (FLDA) is a powerful tool that extends LDA-mediated multiclass classification and dimension reduction to univariate time-series functions. However, in the age of large multivariate and incomplete data, statistical dependencies between features must be estimated in a computationally tractable way, while also dealing with missing data. There is a need for a computationally tracta
Objective: Cytology plays a crucial role in lung cancer diagnosis. Pulmonary cytology involves cell morphological characterization in the specimen and reporting the corresponding findings, which are extremely burdensome tasks. In this study, we propose a technique to generate cytologic findings from for cytologic images to assist in the reporting of pulmonary cytology. Methods: For this study, 801 patch images were r
In the domain of data science, the predictive tasks of classification, regression, and imputation of missing values are commonly encountered challenges associated with tabular data. This research endeavors to apply Large Language Models (LLMs) towards addressing these predictive tasks. Despite their proficiency in comprehending natural language, LLMs fall short in dealing with structured tabular data. This limitation
Deciphering interventional dynamical causality from non-intervention complex systems
Detecting and quantifying causality is a focal topic in the fields of science, engineering, and interdisciplinary studies. However, causal studies on non-intervention systems attract much attention but remain extremely challenging. Delay-embedding technique provides a promising approach. In this study, we propose a framework named Interventional Dynamical Causality (IntDC) in contrast to the traditional Constructive
Towards Certified Unlearning for Deep Neural Networks
In the field of machine unlearning, certified unlearning has been extensively studied in convex machine learning models due to its high efficiency and strong theoretical guarantees. However, its application to deep neural networks (DNNs), known for their highly nonconvex nature, still poses challenges. To bridge the gap between certified unlearning and DNNs, we propose several simple techniques to extend certified un
Verification of Machine Unlearning is Fragile
As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data from machine learning models, following recent legislation. To enhance transparency in machine unlearning and avoid potential dishonesty by model providers, various verification strategies have been proposed. These strategies enable data owners to ascertain whether their ta
Learning Multi-Modal Whole-Body Control for Real-World Humanoid Robots
A major challenge in humanoid robotics is designing a unified interface for commanding diverse whole-body behaviors, from precise footstep sequences to partial-body mimicry and joystick teleoperation. We introduce the Masked Humanoid Controller (MHC), a learned whole-body controller that exposes a simple yet expressive interface: the specification of masked target trajectories over selected subsets of the robot's
Fairness Testing of Large Language Models in Role-Playing
Large Language Models (LLMs) have become foundational in modern language-driven software applications, profoundly influencing daily life. A critical technique in leveraging their potential is role-playing, where LLMs simulate diverse roles to enhance their real-world utility. However, while research has highlighted the presence of social biases in LLM outputs, it remains unclear whether and to what extent these biase
Learn2Synth: Learning Optimal Data Synthesis Using Hypergradients for Brain Image Segmentation
Domain randomization through synthesis is a powerful strategy to train networks that are unbiased with respect to the domain of the input images. Randomization allows networks to see a virtually infinite range of intensities and artifacts during training, thereby minimizing overfitting to appearance and maximizing generalization to unseen data. Although powerful, this approach relies on the accurate tuning of a large
Excretion Detection in Pigsties Using Convolutional and Transformerbased Deep Neural Networks
Animal excretions in form of urine puddles and feces are a significant source of emissions in livestock farming. Automated detection of soiled floor in barns can contribute to improved management processes but also the derived information can be used to model emission dynamics. Previous research approaches to determine the puddle area require manual detection of the puddle in the barn. While humans can detect animal
Large language models (LLMs) increasingly play an important role in a wide range of information processing and management tasks in industry. Many of these tasks are performed in large batches or even offline, and the performance indicator for which is throughput. These tasks usually show the characteristic of prefix sharing, where different prompt input can partially show the common prefix. However, the existing LLM
Splitting criteria for ordinal decision trees: an experimental study
Ordinal Classification (OC) addresses those classification tasks where the labels exhibit a natural order. Unlike nominal classification, which treats all classes as mutually exclusive and unordered, OC takes the ordinal relationship into account, producing more accurate and relevant results. This is particularly critical in applications where the magnitude of classification errors has significant consequences. Despi
Quantization has become one of the most effective methodologies to compress LLMs into smaller size. However, the existing quantization solutions still show limitations of either non-negligible accuracy drop or low system efficiency. In this paper, we propose MixLLM that explores the optimization space of mixed-precision quantization between output features, based on the insight that different features matter differen
This paper introduces a quantum framework for addressing reinforcement learning (RL) tasks, grounded in the quantum principles and leveraging a fully quantum model of the classical Markov decision process (MDP). By employing quantum concepts and a quantum search algorithm, this work presents the implementation and optimization of the agent-environment interactions entirely within the quantum domain, eliminating relia
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