Record 10022026 · captured 2026-08-25
The world looked up Bad Bunny. 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.
Benito Antonio Martínez Ocasio, known professionally as Bad Bunny, is a Puerto Rican rapper, singer and record producer. Dubbed the "King of Latin Trap", he is widely credited with helping Spanish-language rap reach mainstream global popularity and is consider
List of Super Bowl halftime shows
Halftime shows are common during many American football games. Entertainment during the Super Bowl, the annual championship game of the National Football League (NFL), is one of the more lavish of these performances and is usually very widely watched on televi
Jeffrey Edward Epstein was an American financier and child sex offender. He began his career as a math teacher at the Dalton School in New York City, before entering the banking and finance sector. Over several decades, he made much of his fortune providing ta
Super Bowl LX was an American football game played to determine the champion of the National Football League (NFL) for the 2025 season. The National Football Conference (NFC) champion Seattle Seahawks defeated the American Football Conference (AFC) champion Ne
Samuel Richard Darnold is an American professional football quarterback for the Seattle Seahawks of the National Football League (NFL). He played college football for the USC Trojans, becoming the first freshman to win the Archie Griffin Award.
Ilia Malinin is an American figure skater. He is a 2026 Olympic Games team event gold medalist, three-time World champion, three-time Grand Prix Final champion, seven-time Grand Prix gold medalist, four-time Challenger Series gold medalist, and four-time U.S.
The Epstein files are a partially released collection of millions of documents, images, videos, and emails related to the activities of American financier and convicted child sex offender Jeffrey Epstein, including his social circle of public figures, politici
Kenneth Walker III is an American professional football running back for the Kansas City Chiefs of the National Football League (NFL). He played college football for the Wake Forest Demon Deacons and Michigan State Spartans, winning the Walter Camp and Doak Wa
Drake Lee Maye is an American professional football quarterback for the New England Patriots of the National Football League (NFL). He played college football for the North Carolina Tar Heels, winning the Shaun Alexander Award and ACC Football Player of the Ye
Michael Macdonald is an American professional football coach who is the head coach for the Seattle Seahawks of the National Football League (NFL). He began his career with the Baltimore Ravens in 2014, serving as a defensive assistant. In 2021, Macdonald left
The Seattle Seahawks are a professional American football team based in Seattle. The Seahawks compete in the National Football League (NFL) as a member of the National Football Conference (NFC) West division. They have played their home games at Lumen Field in
The Super Bowl is the annual American football game that determines the champion of the National Football League (NFL). The game culminates a season that begins in the previous calendar year, and is the conclusion of the NFL playoffs. The winner receives the V
Robert James Ritchie, known professionally as Kid Rock, is an American musician, singer, rapper, and songwriter. After establishing himself in the Detroit hip-hop scene, he broke through into mainstream success with a rap rock sound before shifting his perform
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
Lindsey Caroline Vonn is an American alpine ski racer. She won four World Cup overall championships with titles in 2008, 2009, 2010, and 2012. Vonn won the gold medal in downhill at the 2010 Winter Olympics, the first one for an American woman. She also won a
Enrique "Ricky" Martín Morales is a Puerto Rican singer and songwriter. He is known for his musical versatility, with his discography incorporating a wide variety of many elements, such as Latin pop, dance, reggaeton, salsa, and other genres. Dubbed the "King
The 2026 Winter Olympics, officially the XXV Olympic Winter Games and commonly known as Milano Cortina 2026, were an international winter multi-sport event held from 6 to 22 February 2026, at multiple sites across Lombardy, Veneto and Trentino-Alto Adige/Südti
The Super Bowl is the league championship final game of the National Football League (NFL) of the United States. It has served as the final game of every NFL season since 1966 replacing the NFL Championship Game and also served as the final game of every Ameri
The Super Bowl LX halftime show, officially known as the Apple Music Super Bowl LX Halftime Show for sponsorship purposes, was the halftime entertainment of Super Bowl LX, which took place on February 8, 2026, at Levi's Stadium in Santa Clara, California, Unit
Puerto Rico, officially the Commonwealth of Puerto Rico, is a self-governing Caribbean archipelago and island organized as an unincorporated territory of the United States under the designation of commonwealth. Located about 1,000 miles (1,600 km) southeast of
Jessica Marie Alba is an American actress and businesswoman. She rose to prominence at age 19 for portraying Max Guevara, the lead character in the television series Dark Angel (2000–2002), for which she received a Golden Globe nomination. Her cinematic breakt
Ghislaine Noelle Marion Maxwell is a British convicted child sex trafficker and former socialite. In 2021, she was convicted of child sex trafficking, and in 2022 was sentenced to 20 years in prison.
Savannah Clark Guthrie is an Australian-American broadcast journalist and attorney. She is a main co-anchor of the NBC News morning show Today, a position she has held since July 2012.
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.
Eileen Feng Gu, also known by her Chinese name Gu Ailing (谷爱凌), is a Chinese-American freestyle skier and model. She has represented China in halfpipe, slopestyle, and big air events since the 2018–19 season. With three gold and three silver medals, Gu is the
The New England Patriots are a professional American football team based in the Greater Boston area. The Patriots compete in the National Football League (NFL) as a member of the American Football Conference (AFC) East division. The team plays its home games a
2026 Japanese general election
Early general elections were held in Japan on 8 February 2026 in all constituencies, including proportional blocks, to elect all 465 seats of the House of Representatives, the lower house of the National Diet.
Disappearance of Nancy Guthrie
On February 1, 2026, Nancy Guthrie (née Long), the American 84‑year‑old mother of NBC News journalist and Today co-anchor Savannah Guthrie, was kidnapped from her home in Catalina Foothills, a suburb of Tucson, Arizona. Evidence recovered at the residence indi
Jmail is a browser-based archive of the Epstein files, which were released by the United States House Committee on Oversight and Government Reform under the Epstein Files Transparency Act (EFTA). The website was initially stylized in a Gmail-based interface, a
Jo Lynn "Jody" Allen is an American businesswoman, entrepreneur, and philanthropist. She is the younger sister of Microsoft co-founder Paul Allen, and served as the chief executive officer of his investment and project management company, Vulcan Inc., from its
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
BERT Learns (and Teaches) Chemistry
Modern computational organic chemistry is becoming increasingly data-driven. There remain a large number of important unsolved problems in this area such as product prediction given reactants, drug discovery, and metric-optimized molecule synthesis, but efforts to solve these problems using machine learning have also increased in recent years. In this work, we propose the use of attention to study functional groups a
TextVQA requires models to read and reason about text in images to answer questions about them. Specifically, models need to incorporate a new modality of text present in the images and reason over it to answer TextVQA questions. In this challenge, we use generative model T5 for TextVQA task. Based on pre-trained checkpoint T5-3B from HuggingFace repository, two other pre-training tasks including masked language mode
Detecting fake accounts through Generative Adversarial Network in online social media
Online social media is integral to human life, facilitating messaging, information sharing, and confidential communication while preserving privacy. Platforms like Twitter, Instagram, and Facebook exemplify this phenomenon. However, users face challenges due to network anomalies, often stemming from malicious activities such as identity theft for financial gain or harm. This paper proposes a novel method using user s
PC-SNN: Predictive Coding-based Local Hebbian Plasticity Learning in Spiking Neural Networks
Spiking Neural Networks (SNNs), regarded as the third generation of neural networks, emulate the brain's information processing with unparalleled biological plausibility compared to traditional neural networks. However, their non-linear, event-driven dynamics pose significant challenges for training, and existing methods often deviate from neuroscientific principles of cortical learning. Drawing inspiration from
Distributed sequential federated learning
The analysis of data stored in multiple sites has become more popular, raising new concerns about the security of data storage and communication. Federated learning, which does not require centralizing data, is a common approach to preventing heavy data transportation, securing valued data, and protecting personal information protection. Therefore, determining how to aggregate the information obtained from the analys
Cross-Modal Retrieval for Motion and Text via DropTriple Loss
Cross-modal retrieval of image-text and video-text is a prominent research area in computer vision and natural language processing. However, there has been insufficient attention given to cross-modal retrieval between human motion and text, despite its wide-ranging applicability. To address this gap, we utilize a concise yet effective dual-unimodal transformer encoder for tackling this task. Recognizing that overlapp
To transfer or not transfer: Unified transferability metric and analysis
In transfer learning, transferability is one of the most fundamental problems, which aims to evaluate the effectiveness of arbitrary transfer tasks. Existing research focuses on classification tasks and neglects domain or task differences. More importantly, there is a lack of research to determine whether to transfer or not. To address these, we propose a new analytical approach and metric, Wasserstein Distance based
Fast Image-based Neural Relighting with Translucency-Reflection Modeling
Image-based lighting (IBL) is a widely used technique that renders objects using a high dynamic range image or environment map. However, aggregating the irradiance at the object's surface is computationally expensive, in particular for non-opaque, translucent materials that require volumetric rendering techniques. In this paper we present a fast neural 3D reconstruction and relighting model that extends volumetri
Estimating the Value of Evidence-Based Decision Making
In an era of data abundance, statistical evidence is increasingly critical for business and policy decisions. Yet, organizations lack empirical tools to assess the value of evidence-based decision making (EBDM), optimize statistical precision, and balance the costs of evidence-gathering strategies against their benefits. To tackle these challenges, this article introduces an empirical framework to estimate the value
LBL: Logarithmic Barrier Loss Function for One-class Classification
One-class classification (OCC) aims to train a classifier only with the target class data and attracts great attention for its strong applicability in real-world application. Despite a lot of advances have been made in OCC, it still lacks the effective OCC loss functions for deep learning. In this paper, a novel logarithmic barrier function based OCC loss (LBL) that assigns large gradients to the margin samples and t
Probabilistic Phase Labeling and Lattice Refinement for Autonomous Material Research
X-ray diffraction (XRD) is an essential technique to determine a material's crystal structure in high-throughput experimentation, and has recently been incorporated in artificially intelligent agents in autonomous scientific discovery processes. However, rapid, automated and reliable analysis method of XRD data matching the incoming data rate remains a major challenge. To address these issues, we present CrystalS
YaRN: Efficient Context Window Extension of Large Language Models
Rotary Position Embeddings (RoPE) have been shown to effectively encode positional information in transformer-based language models. However, these models fail to generalize past the sequence length they were trained on. We present YaRN (Yet another RoPE extensioN method), a compute-efficient method to extend the context window of such models, requiring 10x less tokens and 2.5x less training steps than previous metho
DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided Image Editing
Text-guided image editing faces significant challenges when considering training and inference flexibility. Much literature collects large amounts of annotated image-text pairs to train text-conditioned generative models from scratch, which is expensive and not efficient. After that, some approaches that leverage pre-trained vision-language models have been proposed to avoid data collection, but they are limited by e
Cognitive Edge Device (CED) for Real-Time Environmental Monitoring in Aquatic Ecosystems
Invasive signal crayfish have a detrimental impact on ecosystems. They spread the fungal-type crayfish plague disease (Aphanomyces astaci) that is lethal to the native white clawed crayfish, the only native crayfish species in Britain. Invasive signal crayfish extensively burrow, causing habitat destruction, erosion of river banks and adverse changes in water quality, while also competing with native species for reso
A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models
We develop a novel deep learning approach for pricing European basket options written on assets that follow jump-diffusion dynamics. The option pricing problem is formulated as a partial integro-differential equation, which is approximated via a new implicit-explicit minimizing movement time-stepping approach, involving approximation by deep, residual-type Artificial Neural Networks (ANNs) for each time step. The int
Manual operations remain essential in industrial production because of their flexibility and low implementation cost. However, ensuring their quality and monitoring execution in real time remains a challenge, especially under conditions of high variability and human-induced errors. In this paper, we present an AI-based control system for tracking manual assembly and propose a novel methodology to evaluate its overall
We prove a non-asymptotic central limit theorem for vector-valued martingale differences using Stein's method, and use Poisson's equation to extend the result to functions of Markov Chains. We then show that these results can be applied to establish a non-asymptotic central limit theorem for Temporal Difference (TD) learning with averaging.
TSJNet: A Multi-modality Target and Semantic Awareness Joint-driven Image Fusion Network
This study aims to address the problem of incomplete information in unimodal images for semantic segmentation and object detection tasks. Existing multimodal fusion methods suffer from limited capability in discriminative modeling of multi-scale semantic structures and salient target regions, which further restricts the effective fusion of task-related semantic details and target information across modalities. To tac
cmaes: A Simple yet Practical Python Library for CMA-ES
The covariance matrix adaptation evolution strategy (CMA-ES) has been highly effective in black-box continuous optimization, as demonstrated by its success in both benchmark problems and various real-world applications. To address the need for an accessible and powerful tool in this domain, we developed cmaes, a simple and practical Python library for CMA-ES. cmaes is characterized by its simplicity, offering intuiti
Knowledge-Centric Metacognitive Learning
Interactions are central to intelligent reasoning and learning abilities, with the interpretation of abstract knowledge guiding meaningful interaction with objects in the environment. While humans readily adapt to novel situations by leveraging abstract knowledge acquired over time, artificial intelligence systems lack principled mechanisms for incorporating abstract knowledge into learning, leading to fundamental ch
Analysis of singular subspaces under random perturbations
We present a comprehensive analysis of singular vector and singular subspace perturbations in the signal-plus-noise matrix model with random Gaussian noise. Assuming a low-rank signal matrix, we extend the Davis-Kahan-Wedin theorem in a fully generalized manner, applicable to any unitarily invariant matrix norm, building on previous results by O'Rourke, Vu, and the author. Our analysis provides fine-grained insig
View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV
In this paper, we address the challenge of Multi-Object Tracking (MOT) in moving Unmanned Aerial Vehicle (UAV) scenarios, where irregular flight trajectories, such as hovering, turning left/right, and moving up/down, lead to significantly greater complexity compared to fixed-camera MOT. Specifically, changes in the scene background not only render traditional frame-to-frame object IoU association methods ineffective
Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography
Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3D medical images with corresponding textual reports. CT-RATE comprises 25,692 non-contrast 3D chest CT scans from 21,304 unique patients. Each scan is accompanied by its corresponding radiology report. Leveraging CT-RATE, we develop CT-CLIP
Spectral Convolutional Conditional Neural Processes
Neural Processes (NPs) are meta-learning models that learn to map sets of observations to approximations of the corresponding posterior predictive distributions. By accommodating variable-sized, unstructured collections of observations and enabling probabilistic predictions at arbitrary query points, NPs provide a flexible framework for modeling functions over continuous domains. Since their introduction, numerous va
Robust Hyperbolic Learning with Curvature-Aware Optimization
Hyperbolic deep learning has become a growing research direction in computer vision due to the unique properties afforded by the alternate embedding space. The negative curvature and exponentially growing distance metric provide a natural framework for capturing hierarchical relationships between datapoints and allowing for finer separability between their embeddings. However, current hyperbolic learning approaches a
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