Record 11022026 · 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.
Complete record JSON
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
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
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
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.
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.
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.
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
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
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
Jutta Monica Leerdam is a Dutch speed skater specializing in long-track sprint events. She won the gold medal in the 1000 m event at the 2026 Milano Cortina Olympics, setting an Olympic record. She also won the silver medal in the 500 m event at the 2026 Olymp
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
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
Jake Joseph Paul is an American professional boxer, influencer, and former actor. He began his career posting videos on Vine in September 2013 and had amassed 5.3 million followers and 2 billion views before the app was discontinued. He launched his YouTube ch
Leslie Herbert Wexner is an American billionaire businessman and political activist. He is the co-founder and chair emeritus of Bath & Body Works, Inc. He has been the principal in Abercrombie & Fitch, Victoria's Secret and La Senza, amongst several other reta
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
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
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
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
Madison Laʻakea Te-Lan Hall Chock is an American ice dancer. Together with her husband and skating partner, Evan Bates, she is a two-time Olympic gold medalist in the team event, the 2026 Winter Olympics silver medalist, a three-time World champion, three-time
Lucy Letby is a British former NHS neonatal nurse convicted of murdering seven babies and attempting to murder seven others at the Countess of Chester Hospital in Chester between June 2015 and June 2016. She was investigated after an unusual cluster of deaths
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
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
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
A Knight of the Seven Kingdoms (TV series)
A Knight of the Seven Kingdoms is an American fantasy drama television series created by Ira Parker and George R. R. Martin. A prequel to Game of Thrones (2011–2019), it is the third television series in Martin's A Song of Ice and Fire franchise and is an adap
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
List of people named in the Epstein files
The Epstein files comprise over six million pages of documents detailing the activities of American financier and convicted child sex offender Jeffrey Epstein. So far about three and a half million files have been made public with redactions, among them 180,00
Wuthering Heights is the only novel by the English author Emily Brontë, initially published in 1847 under her pen name Ellis Bell. It concerns two extensive upland estates and their landowning families on the West Yorkshire moors, the Earnshaws and the Lintons
Maxim Naumov is an American figure skater. He is the 2026 U.S. national bronze medalist, three-time U.S. national pewter medalist, and the 2020 U.S. junior national champion. Naumov finished within the top five at the 2020 World Junior Championships.
Sultan Ahmed bin Sulayem is an Emirati businessman. He was the chairman and chief executive officer (CEO) of DP World until 13 February 2026, and the chairman of the Ports, Customs & Free Zone Corporation until February 2026. In the February 2026 public releas
Bruce Lee was a Hong Kong and American martial artist, actor, and filmmaker. He was the founder of Jeet Kune Do, a hybrid martial arts philosophy, which was formed from his experiences in unarmed fighting and self-defense—as well as eclectic, Zen Buddhist, and
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
The Bayesian Method of Tensor Networks
Bayesian learning is a powerful learning framework which combines the external information of the data (background information) with the internal information (training data) in a logically consistent way in inference and prediction. By Bayes rule, the external information (prior distribution) and the internal information (training data likelihood) are combined coherently, and the posterior distribution and the poster
This article presents an artificial intelligence (AI) architecture intended to simulate the iterative updating of the human working memory system. It features several interconnected neural networks designed to emulate the specialized modules of the cerebral cortex. These are structured hierarchically and integrated into a global workspace. They are capable of temporarily maintaining high-level representational patter
Universal Neural Optimal Transport
Optimal Transport (OT) problems are a cornerstone of many applications, but solving them is computationally expensive. To address this problem, we propose UNOT (Universal Neural Optimal Transport), a novel framework capable of accurately predicting (entropic) OT distances and plans between discrete measures for a given cost function. UNOT builds on Fourier Neural Operators, a universal class of neural networks that m
Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits
Data encoding remains a fundamental bottleneck in quantum machine learning, where amplitude encoding of high-dimensional classical vectors into quantum states incurs exponential cost. In this work, we propose a pre-trained tensor-train (TT) encoding network (Pre-TT-Encoder) that significantly reduces the computational complexity of amplitude encoding while preserving essential data structure. The Pre-TT-Encoder explo
Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular samples, missing data, or unaligned measurements from multiple sensors. Our method relies on a continuous-time-dependent model of the series' evolution dynamics. It leverages adaptations of conditional, implicit neural representations for seq
Efficient HDR Reconstruction from Real-World Raw Images
The growing prevalence of high-resolution displays on edge devices has created a pressing need for efficient high dynamic range (HDR) imaging algorithms. However, most existing HDR methods either struggle to deliver satisfactory visual quality or incur high computational and memory costs, limiting their applicability to high-resolution inputs (typically exceeding 12 megapixels). Furthermore, current HDR dataset colle
On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling
On-policy reinforcement learning (RL) algorithms are typically characterized as algorithms that perform policy updates using i.i.d. trajectories collected by the agent's current policy. However, after observing only a finite number of trajectories, such on-policy sampling may produce data that fails to match the expected on-policy data distribution. This sampling error leads to high-variance gradient estimates th
MToP: A MATLAB Benchmarking Platform for Evolutionary Multitasking
Evolutionary multitasking (EMT) has emerged as a popular topic of evolutionary computation over the past decade. It aims to concurrently address multiple optimization tasks within limited computing resources, leveraging inter-task knowledge transfer techniques. Despite the abundance of multitask evolutionary algorithms (MTEAs) proposed for multitask optimization (MTO), there remains a need for a comprehensive softwar
ZS-SRT: An Efficient Zero-Shot Super-Resolution Training Method for Neural Radiance Fields
Neural Radiance Fields (NeRF) have achieved great success in the task of synthesizing novel views that preserve the same resolution as the training views. However, it is challenging for NeRF to synthesize high-quality high-resolution novel views with low-resolution training data. To solve this problem, we propose a zero-shot super-resolution training framework for NeRF. This framework aims to guide the NeRF model to
Predicting the nonlinear evolution of cosmic structure from initial conditions is typically approached using Lagrangian, particle-based methods. These techniques excel in terms of tracking individual trajectories, but they might not be suitable for applications where point-based information is unavailable or impractical. In this work, we explore an alternative, field-based approach using Eulerian inputs. Specifically
Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents
Growing concerns about safety and alignment of AI systems highlight the importance of embedding moral capabilities in artificial agents: a promising solution is the use of learning from experience, i.e., Reinforcement Learning. In multi-agent (social) environments, complex population-level phenomena may emerge from interactions between individual learning agents. Many of the existing studies rely on simulated social
Deep Learning-Based Object Pose Estimation: A Comprehensive Survey
Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled traini
Analysis of the Geometric Structure of Neural Networks and Neural ODEs via Morse Functions
Besides classical feed-forward neural networks such as multilayer perceptrons, also neural ordinary differential equations (neural ODEs) have gained particular interest in recent years. Neural ODEs can be interpreted as an infinite depth limit of feed-forward or residual neural networks. We study the input-output dynamics of finite and infinite depth neural networks with scalar output. In the finite depth case, the i
EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition
The task of Visual Place Recognition (VPR) is to predict the location of a query image from a database of geo-tagged images. Recent studies in VPR have highlighted the significant advantage of employing pre-trained foundation models like DINOv2 for the VPR task. However, these models are often deemed inadequate for VPR without further fine-tuning on VPR-specific data. In this paper, we present an effective approach t
A Point-Neighborhood Learning Framework for Nasal Endoscope Image Segmentation
Lesion segmentation on nasal endoscopic images is challenging due to its complex lesion features. Fully-supervised deep learning methods achieve promising performance with pixel-level annotations but impose a significant annotation burden on experts. Although weakly supervised or semi-supervised methods can reduce the labelling burden, their performance is still limited. Some weakly semi-supervised methods employ a n
Monkeypox virus (MPXV) is a zoonotic virus that poses a significant threat to public health, particularly in remote parts of Central and West Africa. Early detection of monkeypox lesions is crucial for effective treatment. However, due to its similarity with other skin diseases, monkeypox lesion detection is a challenging task. To detect monkeypox, many researchers used various deep-learning models such as MobileNetv
A Generalized Version of Chung's Lemma and its Applications
Chung's Lemma is a classical tool for establishing asymptotic convergence rates of (stochastic) optimization methods under strong convexity-type assumptions and appropriate polynomial diminishing step sizes. In this work, we develop a generalized version of Chung's Lemma, which provides a simple non-asymptotic convergence framework for a more general family of step size rules. We demonstrate broad applicabili
Evaluating lightweight unsupervised online IDS for masquerade attacks in CAN
Vehicular controller area networks (CANs) are susceptible to masquerade attacks by malicious adversaries. In masquerade attacks, adversaries silence a targeted ID and then send malicious frames with forged content at the expected timing of benign frames. As masquerade attacks could seriously harm vehicle functionality and are the stealthiest attacks to detect in CAN, recent work has devoted attention to compare frame
General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design
Structure-based drug design (SBDD) aims to generate ligands that bind strongly and specifically to target protein pockets. Recent diffusion models have advanced SBDD by capturing the distributions of atomic positions and types, yet they often underemphasize binding affinity control during generation. To address this limitation, we introduce \textbf{\textnormal{\textbf{BADGER}}}, a general \textbf{binding-affinity gui
Decomposed Direct Preference Optimization for Structure-Based Drug Design
Diffusion models have achieved promising results for Structure-Based Drug Design (SBDD). Nevertheless, high-quality protein subpocket and ligand data are relatively scarce, which hinders the models' generation capabilities. Recently, Direct Preference Optimization (DPO) has emerged as a pivotal tool for aligning generative models with human preferences. In this paper, we propose DecompDPO, a structure-based optim
Aggregation Models with Optimal Weights for Distributed Gaussian Processes
Gaussian process (GP) models have received increasing attention in recent years due to their superb prediction accuracy and modeling flexibility. To address the computational burdens of GP models for large-scale datasets, distributed learning for GPs are often adopted. Current aggregation models for distributed GPs is not time-efficient when incorporating correlations between GP experts. In this work, we propose a no
Inverse materials design has proven successful in accelerating novel material discovery. Many inverse materials design methods use unsupervised learning where a latent space is learned to offer a compact description of materials representations. A latent space learned this way is likely to be entangled, in terms of the target property and other properties of the materials. This makes the inverse design process ambigu
LoRA Provides Differential Privacy by Design via Random Sketching
Low-rank adaptation of language models has been proposed to reduce the computational and memory overhead of fine-tuning pre-trained language models. LoRA incorporates trainable low-rank matrices into some parameters of the pre-trained model, called adapters. In this work, we show theoretically that the low-rank adaptation mechanism of LoRA is equivalent to fine-tuning adapters with noisy batch gradients, with the noi
Models initialized from self-supervised pretraining may suffer from poor alignment with downstream tasks, reducing the extent to which subsequent fine-tuning can adapt pretrained features toward downstream objectives. To mitigate this, we introduce BiSSL, a novel bilevel training framework that enhances the alignment of self-supervised pretrained models with downstream tasks prior to fine-tuning. BiSSL acts as an int
Measuring and Controlling Solution Degeneracy across Task-Trained Recurrent Neural Networks
Task-trained recurrent neural networks (RNNs) are widely used in neuroscience and machine learning to model dynamical computations. To gain mechanistic insight into how neural systems solve tasks, prior work often reverse-engineers individual trained networks. However, different RNNs trained on the same task and achieving similar performance can exhibit strikingly different internal solutions, a phenomenon known as s
Notable events recorded on this day and month across all years.