Record 02022026 · captured 2026-08-25
The world looked up Carlos Alcaraz. 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.
Carlos Alcaraz Garfia is a Spanish professional tennis player. He has been ranked world No. 1 in men's singles by the Association of Tennis Professionals (ATP) for 66 weeks, and finished as the year-end No. 1 in 2022 and 2025. Alcaraz has won 26 ATP Tour–level
Catherine Anne O'Hara was a Canadian and American actress and comedian, whose career spanned over 50 years. O'Hara started in sketch and improvisational comedy in film and television before taking dramatic roles to expand her career. She received various accol
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 2026 Royal Rumble, also promoted as Royal Rumble: Riyadh, was a professional wrestling pay-per-view (PPV) and livestreaming event produced by the American company WWE. It was the 39th annual Royal Rumble and took place on January 31, 2026, at Riyadh Season
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
Novak Djokovic is a Serbian professional tennis player. He has been ranked as the world No. 1 in men's singles by the Association of Tennis Professionals (ATP) for a record 428 weeks, finished as the year-end No. 1 a record eight times, and has been ranked No.
Dhurandhar is a 2025 Indian Hindi-language spy action thriller film written and directed by Aditya Dhar. It is produced by Aditya Dhar, Lokesh Dhar and Jyoti Deshpande under Jio Studios and B62 Studios. The film features an ensemble cast consisting of Ranveer
Iron Lung is a 2026 American independent science fiction horror film starring writer, editor, and director Mark Fischbach in his feature-length directorial debut. It is based on the 2022 video game by David Szymanski. It also stars Caroline Kaplan, Troy Baker,
List of Grand Slam men's singles champions
Many changes in the Grand Slam tennis tournaments have affected the number of titles won by various players during its history. These changes have included the opening of the French national championships to international players in 1925, the elimination of th
Jannik Sinner is an Italian professional tennis player. He is currently ranked world No.1 by Association of Tennis Professionals (ATP), and was the year-end No. 1 in 2024. Sinner has won 30 ATP Tour-level singles titles, including five majors, ten Masters and
The 68th Annual Grammy Awards honored the best recordings, compositions, and artists from August 31, 2024, to August 30, 2025, as chosen by the members of the Recording Academy, on February 1, 2026. In its 23rd year at Crypto.com Arena in Los Angeles and for t
UFC 325: Volkanovski vs. Lopes 2 was a mixed martial arts event produced by the Ultimate Fighting Championship that took place on February 1, 2026, at the Qudos Bank Arena in Sydney, Australia.
Elena Andreyevna Rybakina is a Russian-born Kazakhstani professional tennis player. She is currently ranked world No. 2 in women's singles by the Women's Tennis Association (WTA). Rybakina has won 13 WTA Tour-level singles titles, including two majors at the 2
Alexander Johan Hjalmar Skarsgård is a Swedish actor. A son of actor Stellan Skarsgård, he began acting at the age of seven but quit at thirteen. After serving in the Swedish Navy, Skarsgård returned to acting and gained his first role in the American comedy f
Alexander Volkanovski is an Australian professional mixed martial artist. He currently competes in the Featherweight division of the Ultimate Fighting Championship (UFC), where he is the current and two-time UFC Featherweight Champion. Volkanovski is the first
Grady Demond Wilson was an American actor, best known for his role as Lamont, the titular son in the NBC sitcom Sanford and Son (1972–1977). He later portrayed Oscar Madison on The New Odd Couple (1982–1983) and appeared in the film Me and the Kid (1993).
Excmo. Sr. D. Rafael "Rafa" Nadal Parera, 1st Marquess of Llevant de Mallorca is a Spanish former professional tennis player. He was ranked as the world No. 1 in men's singles by the Association of Tennis Professionals (ATP) for 209 weeks, and finished as the
Hill climbing is a cycling event, as well as a basic skill of the sport. A hill climb is a competition of sustained climbing, that finishes at a higher altitude than the start line. Occasionally featured as stages in major professional races, such as the Tour
Melania is a 2026 American film directed and produced by Brett Ratner. It revolves around the experiences of Melania Trump, the first lady of the United States, in the 20 days before her husband Donald's second presidential inauguration. It was released in the
Send Help is a 2026 American survival horror film directed and co-produced by Sam Raimi and written by Damian Shannon and Mark Swift. The film stars Rachel McAdams and Dylan O'Brien as an employee and her boss, respectively, who become stranded on a desert isl
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.
Bridgerton is an American alternative history, Regency romance television series created by Chris Van Dusen for Netflix. Based on the book series of the same name by Julia Quinn, it is Shondaland's first scripted show for Netflix. The series stars an ensemble
Border 2 is a 2026 Indian Hindi-language epic war film co-written and directed by Anurag Singh. A sequel to J. P. Dutta's 1997 film Border, it was produced by Bhushan Kumar, Krishan Kumar, J. P. Dutta, and Nidhi Dutta under the banners of T-Series Films and J.
Brett Ratner is an American film director and producer. He directed the Rush Hour film series, The Family Man, Red Dragon, X-Men: The Last Stand, Tower Heist, Hercules, and Melania. He is a producer or executive producer of several films, including the Horribl
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
Ash-Shakur Nafi-Shahid Stevenson is an American professional boxer. He has won world championships in four weight classes, from featherweight to junior welterweight. He has held the World Boxing Organization (WBO) and Ring magazine junior welterweight titles s
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
The Night Manager (British TV series)
The Night Manager is a British spy thriller television serial based on the 1993 novel by John le Carré and adapted by David Farr. The six-part first series, directed by Susanne Bier and starring Tom Hiddleston, Hugh Laurie, Olivia Colman, Tom Hollander, David
Moltbook is an internet forum for artificial intelligence agents, launched on January 28, 2026, by Matt Schlicht. It claims to limit posting, commenting, and voting to AI agents authenticated through their owner's "claim" tweet, while human users are restricte
Robert W. "Bo" Welch III is an American production designer, art director, film and television director, and occasional actor. He is best known for his collaborations with filmmakers such as Tim Burton and Barry Sonnenfeld, and for directing 2003's The Cat in
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
A particular challenge for both autonomous and human driving is dealing with risk associated with dynamic occlusion, i.e., occlusion caused by other vehicles in traffic. Based on the theory of hypergames, we develop a novel multi-agent dynamic occlusion risk (DOR) measure for assessing situational risk in dynamic occlusion scenarios. Furthermore, we present a white-box, scenario-based, accelerated safety validation f
Generalized dynamic cognitive hierarchy models for strategic driving behavior
While there has been an increasing focus on the use of game theoretic models for autonomous driving, empirical evidence shows that there are still open questions around dealing with the challenges of common knowledge assumptions as well as modeling bounded rationality. To address some of these practical challenges, we develop a framework of generalized dynamic cognitive hierarchy for both modelling naturalistic human
Three approaches to supervised learning for compositional data with pairwise logratios
The common approach to compositional data analysis is to transform the data by means of logratios. Logratios between pairs of compositional parts (pairwise logratios) are the easiest to interpret in many research problems. When the number of parts is large, some form of logratio selection is a must, for instance by means of an unsupervised learning method based on a stepwise selection of the pairwise logratios that e
Finite-Time Accuracy of Temporal-Difference Learning Under Schur-Stable Recursions
Temporal difference (TD) learning is a cornerstone reinforcement learning (RL) method for policy evaluation, where the goal is to estimate the value function of a Markov decision process under a fixed policy. While a substantial body of work has established its convergence and stability properties, more recent efforts have focused on its statistical efficiency through finite-time error bounds. In this paper, we advan
A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation
Modelling pedestrian behavior is crucial in the development and testing of autonomous vehicles. In this work, we present a hierarchical pedestrian behavior model that generates high-level decisions through the use of behavior trees, in order to produce maneuvers executed by a low-level motion planner using an adapted Social Force model. A full implementation of our work is integrated into GeoScenario Server, a scenar
Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning
Recent works successfully leveraged Large Language Models' (LLM) abilities to capture abstract knowledge about world's physics to solve decision-making problems. Yet, the alignment between LLMs' knowledge and the environment can be wrong and limit functional competence due to lack of grounding. In this paper, we study an approach (named GLAM) to achieve this alignment through functional grounding: we cons
Revealed Multi-Objective Utility Aggregation in Human Driving
A central design problem in game theoretic analysis is the estimation of the players' utilities. In many real-world interactive situations of human decision making, including human driving, the utilities are multi-objective in nature; therefore, estimating the parameters of aggregation, i.e., mapping of multi-objective utilities to a scalar value, becomes an essential part of game construction. However, estimatin
A VAE Approach to Sample Multivariate Extremes
Generating accurate extremes from an observational data set is crucial when seeking to estimate risks associated with the occurrence of future extremes which could be larger than those already observed. Applications range from the occurrence of natural disasters to financial crashes. Generative approaches from the machine learning community do not apply to extreme samples without careful adaptation. Besides, asymptot
On The Relationship Between Continual Learning and Long-Tailed Recognition
Real-world datasets often exhibit long-tailed distributions, where a few dominant "Head" classes have abundant samples while most "Tail" classes are severely underrepresented, leading to biased learning and poor generalization for the Tail. We present a theoretical framework that reveals a previously undescribed connection between Long-Tailed Recognition (LTR) and Continual Learning (CL), the process
Personas are models of users that incorporate motivations, wishes, and objectives; These models are employed in user-centred design to help design better user experiences and have recently been employed in adaptive systems to help tailor the personalized user experience. Designing with personas involves the production of descriptions of fictitious users, which are often based on data from real users. The majority of
Quantifying the perceptual value of lexical and non-lexical channels in speech
Speech is a fundamental means of communication that can be seen to provide two channels for transmitting information: the lexical channel of which words are said, and the non-lexical channel of how they are spoken. Both channels shape listener expectations of upcoming communication; however, directly quantifying their relative effect on expectations is challenging. Previous attempts require spoken variations of lexic
Chatbots are capable of remembering and referencing previous conversations, but does this enhance user engagement or infringe on privacy? To explore this trade-off, we investigated the format of how a chatbot references previous conversations with a user and its effects on a user's perceptions and privacy concerns. In a three-week longitudinal between-subjects study, 169 participants talked about their dental flo
Generative quantum machine learning via denoising diffusion probabilistic models
Deep generative models are key-enabling technology to computer vision, text generation, and large language models. Denoising diffusion probabilistic models (DDPMs) have recently gained much attention due to their ability to generate diverse and high-quality samples in many computer vision tasks, as well as to incorporate flexible model architectures and a relatively simple training scheme. Quantum generative models,
Causal Bayesian Optimization via Exogenous Distribution Learning
Maximizing a target variable as an operational objective within a structural causal model is a fundamental problem. Causal Bayesian Optimization (CBO) approaches typically achieve this either by performing interventions that modify the causal structure to increase the reward or by introducing action nodes to endogenous variables, thereby adjusting the data-generating mechanisms to meet the objective. In this paper, w
XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics
With the rise of complex cyber devices Cyber Forensics (CF) is facing many new challenges. For example, there are dozens of systems running on smartphones, each with more than millions of downloadable applications. Sifting through this large amount of data and making sense requires new techniques, such as from the field of Artificial Intelligence (AI). To apply these techniques successfully in CF, we need to justify
TorchCP: A Python Library for Conformal Prediction
Conformal prediction (CP) is a powerful statistical framework that generates prediction intervals or sets with guaranteed coverage probability. While CP algorithms have evolved beyond traditional classifiers and regressors to sophisticated deep learning models like deep neural networks (DNNs), graph neural networks (GNNs), and large language models (LLMs), existing CP libraries often lack the model support and scalab
OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models
Modern large language models (LLMs) should generally benefit individuals from various cultural backgrounds around the world. However, most recent advanced generative evaluation benchmarks tailed for LLMs mainly focus on English. To this end, we introduce OMGEval, the first Open-source Multilingual Generative test set that can assess the capability of LLMs in different languages. For each language, OMGEval provides 80
Hybrid$^2$ Neural ODE Causal Modeling and an Application to Glycemic Response
Hybrid models composing mechanistic ODE-based dynamics with flexible and expressive neural network components have grown rapidly in popularity, especially in scientific domains where such ODE-based modeling offers important interpretability and validated causal grounding (e.g., for counterfactual reasoning). The incorporation of mechanistic models also provides inductive bias in standard blackbox modeling approaches,
Can Distillation Mitigate Backdoor Attacks in Pre-trained Encoders?
Self-Supervised Learning (SSL) has become a prominent paradigm for pre-training encoders to learning general-purpose representations from unlabeled data and releasing them on third-party platforms for broad downstream deep learning tasks. However, SSL is vulnerable to backdoor attacks, where an adversary may train and distribute poisoned pre-training encoders to contaminate the downstream models. In this paper, we st
FlashFace: Human Image Personalization with High-fidelity Identity Preservation
This work presents FlashFace, a practical tool with which users can easily personalize their own photos on the fly by providing one or a few reference face images and a text prompt. Our approach is distinguishable from existing human photo customization methods by higher-fidelity identity preservation and better instruction following, benefiting from two subtle designs. First, we encode the face identity into a serie
Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation
Variational inference (VI) has emerged as a popular method for approximate inference for high-dimensional Bayesian models. In this paper, we propose a novel VI method that extends the naive mean field via entropic regularization, referred to as $Ξ$-variational inference ($Ξ$-VI). $Ξ$-VI has a close connection to the entropic optimal transport problem and benefits from the computationally efficient Sinkhorn algorithm.
Multivariate Bayesian Last Layer for Regression with Uncertainty Quantification and Decomposition
We present new Bayesian Last Layer neural network models in the setting of multivariate regression under heteroscedastic noise, and propose EM algorithms for parameter learning. Bayesian modeling of a neural network's final layer has the attractive property of uncertainty quantification with a single forward pass. The proposed framework is capable of disentangling the aleatoric and epistemic uncertainty, and can
In dynamic and resource-constrained environments, such as multi-hop wireless mesh networks, traditional routing protocols often falter by relying on predetermined paths that prove ineffective in unpredictable link conditions. Shortest Anypath routing offers a solution by adapting routing decisions based on real-time link conditions. However, the effectiveness of such routing is fundamentally dependent on the quality
Posterior Label Smoothing for Node Classification
Label smoothing is a widely studied regularization technique in machine learning. However, its potential for node classification in graph-structured data, spanning homophilic to heterophilic graphs, remains largely unexplored. We introduce posterior label smoothing, a novel method for transductive node classification that derives soft labels from a posterior distribution conditioned on neighborhood labels. The likeli
Graph Mining under Data scarcity
Multitude of deep learning models have been proposed for node classification in graphs. However, they tend to perform poorly under labeled-data scarcity. Although Few-shot learning for graphs has been introduced to overcome this problem, the existing models are not easily adaptable for generic graph learning frameworks like Graph Neural Networks (GNNs). Our work proposes an Uncertainty Estimator framework that can be
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