Record 22052026 · captured 2026-08-25
The world looked up Kyle Busch. 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.
Kyle Thomas Busch, nicknamed "Rowdy", was an American professional stock car racing driver and racing team owner who competed from 2001 until his death in 2026. Throughout his career, Busch raced under several car numbers, though he was most prominently identi
The Cockroach Janta Party, also known as the Cockroach movement, is an Indian youth-based satirical political movement founded on 16 May 2026 by Abhijeet Dipke, a political communications strategist and activist. The CJP harnessed widespread political and econ
Dota: Dragon's Blood is a 2021 adult animated epic fantasy television series. It is based on the 2013 video game Dota 2 by Valve. The show was produced by Studio Mir in association with Ashley Edward Miller's company Kaiju Boulevard. The series premiered on Ne
Obsession is a 2025 American supernatural horror film written, directed, and edited by Curry Barker. The film follows Bear, a music store employee who buys a supernatural toy that grants his wish for his friend Nikki to fall in love with him, which makes her b
Murder of Dominic Russo and Davion Flanagan
The murder of Dominic Russo and Davion Flanagan occurred during the early morning hours of July 31, 2022, when Mackenzie Shirilla intentionally crashed her vehicle into a brick wall in Strongsville, Ohio, United States, killing two passengers: her boyfriend, D
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
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
.xyz is a top-level domain name that was proposed in ICANN's new generic top-level domain (gTLD) Program for consisting of the last three letters of the Latin-script alphabet. XYZ.com and CentralNic are the registries for the domain, which was created by entre
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
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
Giorgia Meloni is an Italian politician who has served as Prime Minister of Italy since October 2022. She is the first woman to hold the office and the head of the second-longest government in the history of the Italian Republic. A member of the Chamber of Dep
Drishyam 3 is a 2026 Indian Malayalam-language crime thriller film written and directed by Jeethu Joseph. Produced by Antony Perumbavoor for Aashirvad Cinemas, it is a sequel to Drishyam 2 (2021) and the third installment in the Drishyam film series. The film
Unai Emery Etxegoien is a Spanish football manager and former player who is the manager of Premier League club Aston Villa. Since 2021, he has been the majority shareholder of fourth-tier Spanish club Real Unión. One of the most successful managers in the hist
Windsor Lancers men's ice hockey
The Windsor Lancers men's ice hockey team is an active ice hockey program representing the Windsor Lancers athletic department of the University of Windsor. The team has been active since the early 1960s and is currently a member of the Ontario University Athl
The 2026 FIFA World Cup was the 23rd FIFA World Cup, the quadrennial international men's soccer championship contested by the national teams of the member associations of FIFA. The tournament began on June 11, 2026, and concluded on July 19 with Spain winning
Off Campus is an American romantic drama television series created by Louisa Levy for Amazon Prime Video. It is based on the Off-Campus book series by Elle Kennedy. The series premiered on May 13, 2026 and received positive reviews. In February 2026, ahead of
Star Wars: The Mandalorian and Grogu is a 2026 American science fiction film directed by Jon Favreau, who co-wrote the film with Dave Filoni and Noah Kloor. Produced by Lucasfilm and Fairview Entertainment, and distributed by Walt Disney Studios Motion Picture
Karuppu (transl. Black) is a 2026 Indian Tamil-language fantasy action drama film directed by RJ Balaji from a screenplay he co-wrote with Ashwin Ravichandran, Rahul Raj, T. S. Gopi Krishnan and Karan Aravind Kumar. Produced by Dream Warrior Pictures, the film
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
Black Rose is the sixth studio album by American singer Tyrese Gibson. It was released independently on July 10, 2015, through his label Voltron Recordz, while distribution was handled by Caroline Records. The production on the album was handled by Warryn Camp
Victor Wembanyama, nicknamed "Wemby" and "the Alien", is a French professional basketball player for the San Antonio Spurs of the National Basketball Association (NBA). He was selected first overall by the Spurs in the 2023 NBA draft and is considered one of t
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
Vanessa Kay is an American model. She is the ex-wife of Donald Trump Jr. They were married from 2005 to 2018, and had five children.
Robert Stiles Harward Jr. is a retired United States Navy SEAL and a former Deputy Commander of the United States Central Command, under the leadership command of General James Mattis. After working as a chief executive at Lockheed Martin's Middle East busines
The Boys is an American satirical superhero television series developed by showrunner Eric Kripke that premiered on July 26, 2019, on the streaming service Amazon Prime Video. Kripke also serves as an executive producer, alongside Evan Goldberg, Neal H. Moritz
N. Marie Wilson is an Indian politician from Tamil Nadu. He is a member of the Tamil Nadu Legislative Assembly, elected from the Radhakrishnan Nagar Assembly constituency in Chennai district in the 2026 Tamil Nadu Legislative Assembly election, representing th
Kylie Ann Minogue is an Australian singer, songwriter, and actress. Frequently referred to as the "Princess of Pop", she has achieved recognition in both the music industry and the fashion world as a major style icon. Her accolades include two Grammy Awards, f
Aston Villa Football Club is a professional football club based in Aston, Birmingham, England. Founded in 1874, it competes in the Premier League, the top tier of English football, and has played at its home ground, Villa Park, since 1897. Aston Villa is one o
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
Cristiano Ronaldo dos Santos Aveiro is a Portuguese professional footballer who plays as a forward for and captains the Saudi Pro League club Al-Nassr and the Portugal national team. Nicknamed CR7, he is widely regarded as one of the greatest players in histor
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
La representación de la variación contextual mediante definiciones terminológicas flexibles
In this doctoral thesis, we apply premises of cognitive linguistics to terminological definitions and present a proposal called the flexible terminological definition. This consists of a set of definitions of the same concept made up of a general definition (in this case, one encompassing the entire environmental domain) along with additional definitions describing the concept from the perspective of the subdomains i
Do Vision Models Encode Object-Level Semantic Relatedness? A Cognitive Psychology-Inspired Benchmark
Modern vision models have achieved strong object-recognition performance, yet it remains unclear whether their representations encode object-level semantic relatedness, the meaningful connection between object concepts that supports human visual cognition. Existing benchmarks predominantly target category prediction or rely on image--text matching, leaving the visual representation itself underexamined. Drawing on co
Spiking neural networks (SNNs) have attracted much attention for their high energy efficiency and recent advances in classification performance. However, unlike traditional deep learning approaches, the study of SNN robustness to adversarial examples remains relatively underdeveloped. In this work, we advance the adversarial attack side of SNNs through three contributions. First, we show that successful white-box adv
Exploring Code Analysis: Zero-Shot Insights on Syntax and Semantics with LLMs
Code analysis is fundamental in Software Engineering, supporting debugging, optimization, and security assessment. Human developers approach it through syntax parsing, static semantics inference, and dynamic reasoning. Traditional tools are effective but limited by language specificity and weak cross-language generalization. Large language models (LLMs) are promising for code tasks, yet their capabilities for fundame
TreeDQN: Sample-Efficient Off-Policy Reinforcement Learning for Combinatorial Optimization
A convenient approach to optimally solving combinatorial optimization tasks is the Branch-and-Bound method. Its branching heuristic can be learned to solve a large set of similar tasks. The promising results here are achieved by the recently appeared on-policy reinforcement learning method based on the tree Markov Decision Process. To overcome its main disadvantages, namely, very large training time and unstable trai
Cumulative Reasoning with Large Language Models
Recent advancements in large language models (LLMs) have shown remarkable progress, yet their ability to solve complex problems remains limited. In this work, we introduce Cumulative Reasoning (CR), a structured framework that enhances LLM problem-solving by emulating human-like iterative and cumulative thought processes. CR orchestrates LLMs in three distinct roles: Proposer, Verifier(s), and Reporter, to systematic
Improved DDIM Sampling with Moment Matching Gaussian Mixtures
We propose using a Gaussian Mixture Model (GMM) as reverse transition operator (kernel) within the Denoising Diffusion Implicit Models (DDIM) framework, which is one of the most widely used approaches for accelerated sampling from pre-trained Denoising Diffusion Probabilistic Models (DDPM). Specifically we match the first and second order central moments of the DDPM forward marginals by constraining the parameters of
This paper introduces a causal attribution model to enhance the interpretability of large language models (LLMs) and improve their causal reasoning abilities via precise fine-tuning. Despite LLMs' proficiency in diverse tasks, their reasoning processes often remain black box, and thus restrict targeted enhancement. We propose a novel causal attribution model that utilizes "do-operators" for constructing i
Decoding News Narratives: A Critical Analysis of Large Language Models in Framing Detection
The growing complexity and diversity of news coverage have made framing analysis a crucial yet challenging task in computational social science. Traditional approaches, including manual annotation and fine-tuned models, remain limited by high annotation costs, domain specificity, and inconsistent generalisation. Instruction-based large language models (LLMs) offer a promising alternative, yet their reliability for fr
This paper explores the transformative potential of computer-assisted textual analysis in enhancing instructional quality through in-depth insights from educational artifacts. We integrate Richard Elmore's Instructional Core Framework to examine how artificial intelligence (AI) and machine learning (ML) methods, particularly natural language processing (NLP), can analyze educational content, teacher discourse, an
QKFormer: Hierarchical Spiking Transformer using Q-K Attention
Spiking Transformers, which integrate Spiking Neural Networks (SNNs) with Transformer architectures, have attracted significant attention due to their potential for energy efficiency and high performance. However, existing models in this domain still suffer from suboptimal performance. We introduce several innovations to improve the performance: i) We propose a novel spike-form Q-K attention mechanism, tailored for S
4D Radar Semantic Segmentation of People in Field Conditions Using Temporal Multi-View Networks
Reliable people detection is crucial for the safe autonomy of mobile robots and heavy vehicles, both on roads and in industrial settings like mining and construction. However, common sensors like cameras or lidars are prone to failure in adverse conditions such as dust, fog, or smoke, which limits their use in real-world robotic systems. Radar, on the other hand, delivers robust measurements in a wide range of enviro
Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
Causal machine learning holds promise for estimating individual treatment effects from complex data. For successful real-world applications of machine learning methods, it is of paramount importance to obtain reliable insights into which variables drive heterogeneity in the response to treatment. We propose PermuCATE, an algorithm based on the Conditional Permutation Importance (CPI) method, for statistically rigorou
ImProver: Agent-Based Automated Proof Optimization
Large language models (LLMs) have been used to generate formal proofs of mathematical theorems in proofs assistants such as Lean. However, we often want to optimize a formal proof with respect to various criteria, depending on its downstream use. For example, we may want a proof to adhere to a certain style, or to be readable, concise, or modularly structured. Having suitably optimized proofs is also important for le
Music102: An $D_{12}$-equivariant transformer for chord progression accompaniment
We present Music102, an advanced model aimed at enhancing chord progression accompaniment through a $D_{12}$-equivariant transformer. Inspired by group theory and symbolic music structures, Music102 leverages musical symmetry--such as transposition and reflection operations--integrating these properties into the transformer architecture. By encoding prior music knowledge, the model maintains equivariance across both
The Leaf Area Index (LAI) is a critical parameter to understand ecosystem health and vegetation dynamics. In this paper, we propose a novel method for pixel-wise LAI prediction by leveraging the complementary information from Sentinel 1 radar data and Sentinel 2 multi-spectral data at multiple timestamps. Our approach uses a deep neural network based on multiple U-nets tailored specifically to this task. To handle th
A Mechanistic Explanatory Strategy for XAI
Despite significant advancements in XAI, scholars note a persistent lack of solid conceptual foundations and integration with broader scientific discourse on explanation. In response, emerging research draws on explanatory strategies from various sciences and the philosophy of science literature to fill these gaps. This paper outlines a mechanistic strategy for explaining the functional organization of deep learning
Deep learning-based modularized loading protocol for parameter estimation of Bouc-Wen class models
This study proposes a modularized deep learning-based loading protocol for optimal parameter estimation of Bouc-Wen (BW) class models. The protocol consists of two key components: optimal loading history construction and CNN-based rapid parameter estimation. Each component is decomposed into independent sub-modules tailored to distinct hysteretic behaviors-basic hysteresis, structural degradation, and pinching effect
Sparse Orthogonal Parameters Tuning for Continual Learning
Continual learning methods based on pre-trained models (PTM) have recently gained attention which adapt to successive downstream tasks without catastrophic forgetting. These methods typically refrain from updating the pre-trained parameters and instead employ additional adapters, prompts, and classifiers. In this paper, we from a novel perspective investigate the benefit of sparse orthogonal parameters for continual
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery
Robust matrix completion (RMC) is a widely used machine learning tool that simultaneously tackles two critical issues in low-rank data analysis: missing data entries and extreme outliers. This paper proposes a novel scalable and learnable non-convex approach, coined Learned Robust Matrix Completion (LRMC), for large-scale RMC problems. LRMC enjoys low computational complexity with linear convergence. Motivated by the
Neuro-Symbolic AI for Analytical Solutions of Differential Equations
Analytical solutions to differential equations offer exact, interpretable insight but are rarely available because discovering them requires expert intuition or exhaustive search of combinatorial spaces. We introduce SIGS, a neuro-symbolic framework for equation-driven closed-form solution discovery. SIGS uses a context-free grammar to generate mathematically valid and physically meaningful building blocks, with a us
We establish novel and general high-dimensional concentration inequalities and Berry-Esseen bounds for vector-valued martingales induced by Markov chains. We apply these results to analyze the performance of the Temporal Difference (TD) learning algorithm with linear function approximations, a widely used method for policy evaluation in Reinforcement Learning (RL), obtaining a sharp high-probability consistency guara
Prior shift estimation for positive unlabeled data through the lens of kernel embedding
We study estimation of a class prior for unlabeled target samples which possibly differs from that of source population. Moreover, it is assumed that the source data is partially observable: only samples from the positive class and from the whole population are available (PU learning scenario). We introduce a novel direct estimator of a class prior which avoids estimation of posterior probabilities in both population
LFX: Towards Unified Light Field Dense Semantic Segmentation and Salient Object Detection
Light field cameras capture multi-view observations within a single exposure. However, existing studies are typically tailored to specific LF representations, leaving the field without a unified learning framework. To bridge this gap, we present LFX, the first unified framework for LF perception. LFX establishes a representation-invariant feature modulation space, enabling it to adapt to heterogeneous LF representati
Large Language Models (LLMs) have gained traction in educational settings, often framed as virtual tutors or teaching assistants. Following early skepticism and bans, many schools and universities have begun integrating these systems into curricula. Yet decisions about whether and how to deploy LLM-based tools are frequently made without systematic engagement with the full range of stakeholders they affect. In this p
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