Record 24042026 · 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
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
2021 Tamil Nadu Legislative Assembly election
Tamil Nadu legislative assembly election was held on 6 April 2020 to elect the representatives of the 16th Tamil Nadu assembly. Elections were held for all the 234 constituencies in the assembly. The Election Commission of India announced the schedule for the
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 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
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
Hung Cao is an American politician and former military officer who has served as the acting United States secretary of the navy since April 2026 and as the 35th United States under secretary of the navy from October 2025. He ran as the Republican nominee for V
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
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
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
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
Hell's Kitchen (American TV series) season 9
Season 9 of the American competitive reality television series Hell's Kitchen premiered on July 18, 2011, on Fox and concluded on September 19, 2011, with a two-hour season finale. Gordon Ramsay returned as host and head chef, while Scott Leibfried and Andi Va
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
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
Dianna Marie Russini is an American former sports journalist who worked as a National Football League (NFL) reporter and insider.
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
Randy C. Alcorn is an American Christian author who has written over sixty books, including both fiction and non-fiction. They have sold over 12 million copies and been translated into 70 languages. His influences include CS Lewis, A.W. Tozer and Francis Schae
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
John Cartwright Phelan is an American businessman and political donor who served as the United States secretary of the Navy from 2025 to 2026. Phelan worked for several firms during his career in finance before co-founding MSD Capital. He then founded Rugger M
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").
Clayface is an alias used by several characters appearing in American comic books published by DC Comics. Most incarnations of the character possess clay-like bodies and shapeshifting abilities, and all of them are adversaries of the superhero Batman. In 2009,
2021 West Bengal Legislative Assembly election
Legislative Assembly elections were held in West Bengal, to elect all 294 members of West Bengal Legislative Assembly. This electoral process of 292 seats unfolded between 27 March to 29 April 2021, taking place in eight phases. Voting for the two remaining co
Michael George Vrabel is an American professional football coach and former linebacker who is the head coach for the New England Patriots of the National Football League (NFL). Vrabel previously played in the NFL for 14 seasons, most notably with the Patriots.
Saint George's Day is the feast day of Saint George, celebrated by Christian churches, countries, regions, and cities from which he is the main patron saint, including England, Ethiopia, Georgia, Catalonia, Aragon, Palestine, Rio de Janeiro, Alcoi, and Genoa,
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
My Neighbor Totoro is a 1988 Japanese animated fantasy film written and directed by Hayao Miyazaki and animated by Studio Ghibli for Tokuma Shoten. It stars the voices of Noriko Hidaka, Chika Sakamoto and Hitoshi Takagi, and focuses on two young sisters who, a
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
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
Survivor 50: In the Hands of the Fans
Survivor 50: In the Hands of the Fans is the 50th season of the American competitive reality television series Survivor. It premiered on February 25, 2026, on CBS in the United States, and it is the eighteenth consecutive season to be filmed in the Mamanuca Is
Saint George, also George of Lydda, was an early Christian martyr who is venerated as a saint in Christianity. According to holy tradition, he was a soldier in the Roman army. Of Cappadocian Greek origin, he became a member of the Praetorian Guard for Roman em
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Deep Reinforcement Learning reaches a superhuman level of play in many complete information games. The state of the art algorithm for learning with zero knowledge is AlphaZero. We take another approach, Athénan, which uses a different, Minimax-based, search algorithm called Descent, as well as different learning targets and that does not use a policy. We show that for multiple games it is much more efficient than the
Principled Evaluation with Human Labels: One Rater at a Time and Rater Equivalence
In many classification tasks, there is no definitive ground truth, only human judgments that may disagree. We address two challenges that arise in such settings: (1) how to use human raters to score classifiers, and (2) how to use them for comparison benchmarks. For the first, the common practice is to score classifiers against the majority vote of an evaluation panel of several human raters. We argue that this is no
Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation
Sampling from Gibbs distributions and computing their log-partition function are fundamental tasks in statistics, machine learning, and statistical physics. While efficient algorithms are known for log-concave densities, the worst-case non-log-concave setting necessarily suffers from the curse of dimensionality. For many numerical problems, the curse of dimensionality can be alleviated when the target function is smo
Grid-SD2E: A General Grid-Feedback in a System for Cognitive Learning
Comprehending how the brain interacts with the external world through generated neural data is crucial for determining its working mechanism, treating brain diseases, and understanding intelligence. Although many theoretical models have been proposed, they have thus far been difficult to integrate and develop. In this study, we were inspired in part by grid cells in creating a more general and robust grid module and
Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks
Computational models of syntax are predominantly text-based. Here we propose that the most basic first step in the evolution of syntax can be modeled directly from raw speech in a fully unsupervised way. We focus on one of the most ubiquitous and elementary suboperations of syntax -- concatenation. We introduce \textit{spontaneous concatenation}: a phenomenon where a ciwGAN/fiwGAN models (based on convolutional neura
Mind the Gap: Optimal and Equitable Encouragement Policies
In consequential domains, it is often impossible to compel individuals to take treatment, so that optimal policy rules are merely suggestions in the presence of human non-adherence to treatment recommendations. We study personalized decision problems in which the planner controls recommendations into treatment rather than treatment itself. Under a covariate-conditional no-direct-effect model of encouragement, policy
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own
Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorithms directly in the real world. For one thing, RL is data-intensive and typically requires millions of interactions with environments, which are impractical in real scenarios. For another, it is necessary to make heavy engineering efforts to design reward functions manually. To
Is K-fold cross validation the best model selection method for Machine Learning?
As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference. K-fold cross-validation (CV) is the most common approach to ascertaining the likelihood that a machine learning outcome is generated by chance, and it frequently outperforms conventional hypothesis testing. This improvement uses measures directly obtained from machine learning classificat
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
This work presents the first large-scale neutral benchmark experiment focused on single-event, right-censored, low-dimensional survival data. Benchmark experiments are essential in methodological research to scientifically compare new and existing model classes through proper empirical evaluation. Existing benchmarks in the survival literature are smaller in scale regarding the number of used datasets and extent of e
Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression
Humans can retain old knowledge while learning new information, but Large Language Models (LLMs) often suffer from catastrophic forgetting when post-pretrained or supervised fine-tuned (SFT) on domain-specific data. Moreover, for Multimodal Large Language Models (MLLMs) which are composed of the LLM base and visual projector (e.g. LLaVA), a significant decline in performance on language benchmarks was observed compar
Adaptive Soft Error Protection for Neural Network Processing
Previous research on selective protection for neural network components typically exploits only static vulnerability differences. Although these methods improve upon classical modular redundancy, they still incur substantial overhead for neural network workloads that are both memory-intensive and compute-intensive. In this work, we observe that neural network vulnerability is also input-dependent and varies dynamical
Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model
A common challenge towards the adaptability of Large Language Models (LLMs) is their ability to learn new languages over time without hampering the model's performance on languages in which the model is already proficient (usually English). Continual fine-tuning (CFT) is the process of sequentially fine-tuning an LLM to enable the model to adapt to downstream tasks with varying data distributions and time shifts.
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation
Dimensionality reduction (DR) offers a useful representation of complex high-dimensional data. Recent DR methods focus on hyperbolic geometry to derive a faithful low-dimensional representation of hierarchical data. However, existing methods are based on neighbor embedding, frequently ruining the continual relation of the hierarchies. This paper presents hyperboloid Gaussian process (GP) latent variable models (hGP-L
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
To avoid myopic behavior, multi-step lookahead Bayesian optimization (BO) algorithms consider the sequential nature of BO and have demonstrated promising results in recent years. However, owing to the curse of dimensionality, most of these methods make significant approximations or suffer scalability issues. This paper presents a novel reinforcement learning (RL)-based framework for multi-step lookahead BO in high-di
Federated Co-tuning Framework for Large and Small Language Models
By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the capabilities of LLMs. Nonetheless, a gap persists in achieving simultaneous mutual enhancement between the server's LLM and the downstream clients' Small Language Models (SLMs). To address this, we propose FedCoLLM, a novel and parameter-efficient federated framework des
Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators
A major challenge in extracting information from current and upcoming surveys of cosmological Large-Scale Structure (LSS) is the limited availability of computationally expensive high-fidelity simulations. We introduce calibrated Neural Quantile Estimation (NQE), a new Simulation-Based Inference (SBI) method that leverages a large number of approximate simulations for training and a small number of high-fidelity simu
VidHal: Benchmarking Temporal Hallucinations in Vision LLMs
Vision Large Language Models (VLLMs) are widely acknowledged to be prone to hallucinations. Existing research addressing this problem has primarily been confined to image inputs, with limited exploration of video-based hallucinations. Furthermore, current evaluation methods fail to capture nuanced errors in generated responses, which are often exacerbated by the rich spatiotemporal dynamics of videos. To address this
SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation
The Vision Transformer (ViT) has achieved notable success in computer vision, with its variants widely validated across various downstream tasks, including semantic segmentation. However, as general-purpose visual encoders, ViT backbones often do not fully address the specific requirements of task decoders, highlighting opportunities for designing decoders optimized for efficient semantic segmentation. This paper pro
A Systems Thinking Approach to Algorithmic Fairness
Systems thinking provides us with a way to model the algorithmic fairness problem by allowing us to encode prior knowledge and assumptions about where we believe bias might exist in the data generating process. We can then encode these beliefs as a series of causal graphs, enabling us to link AI/ML systems to politics and the law. This allows us to combine techniques from machine learning, causal inference, and syste
DepthMaster: Taming Diffusion Models for Monocular Depth Estimation
Monocular depth estimation within the diffusion-denoising paradigm demonstrates impressive generalization ability but suffers from low inference speed. Recent methods adopt a single-step deterministic paradigm to improve inference efficiency while maintaining comparable performance. However, they overlook the gap between generative and discriminative features, leading to suboptimal results. In this work, we propose D
Higher Order Approximation Rates for ReLU CNNs in Korobov Spaces
This paper investigates the $L_p$ approximation error for higher order Korobov functions using deep convolutional neural networks (CNNs) with ReLU activation. For target functions having a mixed derivative of order m+1 in each direction, we improve classical approximation rate of second order to (m+1)-th order (modulo a logarithmic factor) in terms of the depth of CNNs. The key ingredient in our analysis is approxima
Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis
Scaling large language models (LLMs) improves performance but significantly increases inference costs, with feed-forward networks (FFNs) consuming the majority of computational resources. While Mixture-of-Experts (MoE) architectures can reduce this cost through sparse activation, restructuring existing dense models into MoEs typically requires extensive retraining on hundreds of billions of tokens. We propose an anal
Planetary exploration using aerial assets has the potential for unprecedented scientific discoveries on Mars. While NASA's Mars helicopter Ingenuity proved flight in Martian atmosphere is possible, future Mars rotorcraft will require advanced navigation capabilities for long-range flights. One such critical capability is Map-based Localization (MbL) which registers an onboard image to a reference map during fligh
Weighted quantization using MMD: From mean field to mean shift via gradient flows
Approximating a probability distribution using a set of particles is a fundamental problem in machine learning and statistics, with applications including clustering and quantization. Formally, we seek a weighted mixture of Dirac measures that best approximates the target distribution. While much existing work relies on the Wasserstein distance to quantify approximation errors, maximum mean discrepancy (MMD) has rece
Anomaly detection in smart power grids is a critical challenge due to the complexity, heterogeneity, and dynamic nature of sensor data streams. Existing one-class classification methods, particularly Subspace Support Vector Data Description (SVDD), have been extended to multimodal scenarios but often fail to fully exploit the structural dependencies across modalities, limiting their robustness in real-world applicati
Notable events recorded on this day and month across all years.