Record 08062026 · captured 2026-08-25
The world looked up Alexander Zverev. 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.
Alexander "Sascha" Zverev is a German professional tennis player. He has a career-high singles ranking of world No. 2 by the ATP achieved in June 2022. Zverev has won 25 ATP Tour singles titles, including the 2026 French Open, a gold medal at the 2020 Tokyo Ol
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
Flavio Cobolli is an Italian professional tennis player. He has a career-high ATP singles ranking of world No. 6 achieved on 24 August 2026 and a doubles ranking of No. 185, achieved on 28 July 2025. Cobolli has won three ATP Tour singles titles and was the ru
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
Backrooms is a 2026 American science fiction psychological horror film directed and co-scored by Kane Parsons, and written by Will Soodik. It is based on Parsons's web series which was inspired by the "Backrooms" creepypasta. In the film, Clark, a furniture st
Masters of the Universe (2026 film)
Masters of the Universe is a 2026 American sword-and-sorcery film based on the media franchise by Mattel. It is the second live-action film adaptation, the 1987 film was the first. It was directed by Travis Knight and written by Chris Butler, Aaron Nee, Adam N
Christian Dannemann Eriksen is a Danish professional footballer who plays as a midfielder for 2. Bundesliga club VfL Wolfsburg and the Denmark national team.
Salim Kumar was an Indian actor, director and writer who worked predominantly in Malayalam cinema. Widely regarded as one of the most prominent and influential actors in the history of Malayalam cinema, he appeared in over 300 films across a career spanning th
Rachel Jane Nickell was a British woman who was stabbed to death on Wimbledon Common in southwest London on 15 July 1992. The initial police investigation of the crime resulted in the arrest in controversial circumstances of an innocent man, who was acquitted.
Scary Movie is a 2026 American parody film directed by Michael Tiddes and written by Marlon Wayans, Shawn Wayans, Keenen Ivory Wayans, Craig Wayans, and Rick Alvarez. It is the sixth installment in the Scary Movie film series and has been referred to as the sp
Anthony Stewart Head was an English actor and singer. Primarily a performer in musical theatre, he rose to fame in the United Kingdom in the 1980s following his role in the Gold Blend couple television advertisements for Nescafé, which led to major roles in se
Peddi is a 2026 Indian Telugu-language sports action drama film written and directed by Buchi Babu Sana. Produced by Venkata Satish Kilaru under Vriddhi Cinemas and co-produced by Ishan Saksena under IVY Entertainment and presented by Mythri Movie Makers and S
Manav Jagdusakumar Suthar is an Indian cricketer who plays for the Indian national team. He is a bowling all-rounder, who bats left-handed and bowls slow left-arm orthodox spin. He represents Rajasthan in domestic cricket and Gujarat Titans in the Indian Premi
Andrea Kimi Antonelli is an Italian racing driver who competes in Formula One for Mercedes. Antonelli has won six Formula One Grands Prix since his debut in 2025.
Ronald Stacey King was an American professional basketball player and sports announcer. He played as a center in the National Basketball Association (NBA) and won three consecutive championships with the Chicago Bulls in 1991, 1992, and 1993. He played college
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
Robert Clive Napper is an English serial killer and rapist. He has been convicted of two murders, one manslaughter, two rapes and two attempted rapes. He was sentenced to indefinite detention at Broadmoor Hospital on 18 December 2008 for the manslaughter of Ra
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
Danielle Fabiola "Inde" Navarrette is an American actress and former online streamer. She began her acting career as a teenager with roles in short films, before landing roles in the Netflix drama series 13 Reasons Why (2020) and The CW's superhero drama serie
Mirra Aleksandrovna Andreeva is a Russian professional tennis player. She has been ranked by the WTA as high as world No. 5 in singles, achieved in July 2025, and No. 12 in doubles, achieved in September 2025. Andreeva has won six WTA Tour–level singles titles
2026 Formula One World Championship
The 2026 FIA Formula One World Championship is a motor racing championship for Formula One cars and the 77th running of the Formula One World Championship. It is recognised by the Fédération Internationale de l'Automobile (FIA), the governing body of internati
The Pa-O National Organisation is a Pa-O political party in Myanmar (Burma). Its armed wing, the Pa-O National Army, has between 400 and 700 active personnel. The PNO administers the Pa-O Self-Administered Zone, which consists of three townships in southern Sh
Peter Mark Andrew Phillips is a British businessman and member of the British royal family. He is the son of Anne, Princess Royal, and Mark Phillips, and a nephew of King Charles III. At his birth, during the reign of his maternal grandmother Queen Elizabeth I
Brett Goldstein is an English actor, comedian, podcaster, producer, and writer. Known for his role as Roy Kent in the Apple TV+ sports comedy-drama series Ted Lasso (2020–present), he received the Primetime Emmy Award for Outstanding Supporting Actor in a Come
On 3 December 2025, Henry Nowak, an 18‑year‑old university student, was murdered in Southampton, Hampshire, England, by 23‑year‑old Vickrum Singh Digwa. Police bodycam footage showing officers arresting Nowak as he lay dying from stab wounds sparked public out
Teach You a Lesson (Korean: 참교육) is a 2026 South Korean action school drama television series written by Lee Nam-kyu, Kim Da-hee, and Moon Jong-ho, directed by Hong Jong-chan, and starring Kim Mu-yeol, Lee Sung-min, Jin Ki-joo, and Pyo Ji-hoon. Based on the Na
Sir Lewis Carl Davidson Hamilton is a British racing driver who competes in Formula One for Ferrari. Hamilton has won a joint-record seven Formula One World Drivers' Championship titles—tied with Michael Schumacher—and holds the records for most wins (106), po
2026 Armenian parliamentary election
Parliamentary elections were held in Armenia on 7 June 2026 to elect members of the 9th convocation of the National Assembly. According to exit polls, incumbent Prime Minister Nikol Pashinyan's party Civil Contract won with 56.7% of the vote, followed by the p
The Backrooms is a fictional location invented in a 2019 thread on the imageboard website 4chan. The Backrooms are usually portrayed as an impossibly large extradimensional complex of empty rooms, accessed by exiting reality. They are one of the best-known exa
Jalen Marquis Brunson, nicknamed "Captain Clutch", and the "King of New York" is an American professional basketball player for the New York Knicks of the National Basketball Association (NBA). The son of former NBA guard Rick Brunson, he played college basket
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Unsupervised Learning Based Focal Stack Camera Depth Estimation
We propose an unsupervised deep learning based method to estimate depth from focal stack camera images. On the NYU-v2 dataset, our method achieves much better depth estimation accuracy compared to single-image based methods.
Characterizing Learning Dynamics under Relative Reparameterization of Singular Models
A common way to analyze learning of statistical models is to consider operations in the models parameter space, however this becomes challenging when there is no one-to-one mapping between the parameter space and the underlying statistical model space. Such ``singular models'' occur frequently and exhibit a characteristic decrease in convergence speed of learning trajectories due to attractor behaviors. In th
Hermes: Accelerating Long-Latency Load Requests via Perceptron-Based Off-Chip Load Prediction
Long-latency load requests continue to limit the performance of high-performance processors. To increase the latency tolerance of a processor, architects have primarily relied on two key techniques: sophisticated data prefetchers and large on-chip caches. In this work, we show that: 1) even a sophisticated state-of-the-art prefetcher can only predict half of the off-chip load requests on average across a wide range o
In earthquake-prone zones, the seismic performance of reinforced concrete cantilever (RCC) retaining walls is significant. In this study, the seismic performance was investigated using horizontal and vertical pseudo-static coefficients. To tackle RCC weights and forces resulting from these earth pressures, 26 constraints for structural strengths and geotechnical stability along with 12 geometric variables are associa
Predicting body fat can provide medical practitioners and users with essential information for preventing and diagnosing heart diseases. Hybrid machine learning models offer better performance than simple regression analysis methods by selecting relevant body measurements and capturing complex nonlinear relationships among selected features in modelling body fat prediction problems. There are, however, some disadvant
A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning
Symmetry is present throughout nature and continues to play an increasingly central role in physics and machine learning. Fundamental symmetries, such as Poincaré invariance, allow physical laws discovered in laboratories on Earth to be extrapolated to the farthest reaches of the universe. Symmetry is essential to achieving this extrapolatory power in machine learning applications. For example, translation invariance
Twin: Tuning Learning Rate and Weight Decay of Deep Homogeneous Classifiers without Validation
We introduce Tune without Validation (Twin), a simple and effective pipeline for tuning learning rate and weight decay of homogeneous classifiers without validation sets, eliminating the need to hold out data and avoiding the two-step process. Twin leverages the margin-maximization dynamics of homogeneous networks and an empirical scaling law that links training and test losses across hyper-parameter configurations.
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in complex environments, such as stabilizing a tokamak fusion reactor or minimizing the drag force on an object in a fluid flow. However, DRL requires an abundance of training examples and may become prohibitively expensive for many applications. In addition, the reliance on deep neural networks
pTNAS: Progressive Neural Architecture Search for Tabular Data
Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost that scales poorly with context size. Deep neural networks remain a highly competitive and more efficient modeling paradigm when equipped with well-designed architectures; however, identifying such architectures in a data-adaptive and budget-aware manner remains challengi
Robustly estimating heterogeneity in factorial data using Rashomon Partitions
In both observational data and randomized control trials, researchers select statistical models to articulate how the outcome of interest varies with combinations of observable covariates. Choosing a model that is too simple can obfuscate important heterogeneity in outcomes between covariate groups, while too much complexity risks identifying spurious patterns. In this paper, we propose a novel Bayesian framework for
T2LM: Long-Term 3D Human Motion Generation from Multiple Sentences
In this paper, we address the challenging problem of long-term 3D human motion generation. Specifically, we aim to generate a long sequence of smoothly connected actions from a stream of multiple sentences (i.e., paragraph). Previous long-term motion generating approaches were mostly based on recurrent methods, using previously generated motion chunks as input for the next step. However, this approach has two drawbac
Certified Robustness to Data Poisoning in Gradient-Based Training
Modern machine learning pipelines leverage large amounts of public data, making it infeasible to guarantee data quality and leaving models open to poisoning and backdoor attacks. Provably bounding model behavior under such attacks remains an open problem. In this work, we address this challenge by developing the first framework providing provable guarantees on the behavior of models trained with potentially manipulat
Electrocardiogram (ECG) recordings have long been vital in diagnosing different cardiac conditions. Recently, research in the field of automatic ECG processing using machine learning methods has gained importance, mainly by utilizing deep learning methods on raw ECG signals. A major advantage of models like convolutional neural networks (CNNs) is their ability to effectively process biomedical imaging or signal data.
Many scientific and engineering problems involve observing a common phenomenon through multiple heterogeneous sensors or measurement modalities. Such observations typically contain both information shared across sensors, reflecting the underlying system, and sensor-specific or extraneous components arising from measurement processes or environmental effects. Disentangling these contributions is essential when sensor-
A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling
Since the various MR contrasts of a given anatomy contain redundant information, one contrast can be used to guide the reconstruction of another undersampled contrast acquired subsequently in the same session. To solve this reconstruction problem leveraging multi-contrast side information, several end-to-end learning-based methods have been proposed. However, a key challenge is the requirement for large paired traini
Graph-Dictionary Signal Model for Sparse Representations of Multivariate Data
Representing and exploiting multivariate signals requires capturing relations between variables, which we can represent by graphs. Graph dictionaries allow to describe complex relational information as a sparse sum of simpler structures, but no prior model exists to infer such underlying structure elements from data. We define a novel Graph-Dictionary signal model, where a finite set of graphs characterizes relations
Modeling AdaGrad, RMSProp, and Adam with Integro-Differential Equations
In this paper, we propose a continuous-time formulation for the AdaGrad, RMSProp, and Adam optimization algorithms by modeling them as first-order integro-differential equations. We perform numerical simulations of these equations, along with stability and convergence analyses, to demonstrate their validity as accurate approximations of the original algorithms. Our results indicate a strong agreement between the beha
The Utility and Complexity of in- and out-of-Distribution Machine Unlearning
Machine unlearning, the process of selectively removing data from trained models, is increasingly crucial for addressing privacy concerns and knowledge gaps post-deployment. Despite this importance, existing approaches are often heuristic and lack formal guarantees. In this paper, we analyze the fundamental utility, time, and space complexity trade-offs of approximate unlearning, providing rigorous certification anal
Training-free Ultra Small Model for Universal Sparse Reconstruction in Compressed Sensing
Pre-trained large models attract widespread attention in recent years, but they face challenges in applications that require high interpretability or have limited resources, such as physical sensing, medical imaging, and bioinformatics. Compressed Sensing (CS) is a well-proved theory that drives many recent breakthroughs in these applications. However, as a typical under-determined linear system, CS suffers from exce
Should You Use Your Large Language Model to Explore or Exploit?
We evaluate the ability of the current generation of large language models (LLMs) to help a decision-making agent facing an exploration-exploitation tradeoff. While previous work has largely study the ability of LLMs to solve combined exploration-exploitation tasks, we take a more systematic approach and use LLMs to explore and exploit in silos in various (contextual) bandit tasks. We find that reasoning models show
The KV cache in large language models is a dominant factor in memory usage, limiting their broader applicability. Quantizing the cache to lower bit widths is an effective way to reduce computational costs; however, previous methods struggle with quantizing key vectors due to outliers, resulting in excessive overhead. We propose a novel quantization approach called PolarQuant, which efficiently addresses the outlier c
Vision-language models (VLMs) have achieved remarkable success in scene understanding and perception tasks, enabling robots to plan and execute actions adaptively in dynamic environments. However, most multimodal large language models lack robust 3D scene localization capabilities, limiting their effectiveness in fine-grained robotic operations. Additionally, challenges such as low recognition accuracy, inefficiency,
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
Ensuring trustworthiness in machine learning (ML) systems is crucial as they become increasingly embedded in high-stakes domains. This paper advocates for integrating causal methods into machine learning to navigate the trade-offs among key principles of trustworthy ML, including fairness, privacy, robustness, accuracy, and explainability. While these objectives should ideally be satisfied simultaneously, they are of
ADAGE: Active Defenses Against GNN Extraction
Graph Neural Networks (GNNs) achieve high performance in various real-world applications, such as drug discovery, traffic states prediction, and recommendation systems. The fact that building powerful GNNs requires a large amount of training data, powerful computing resources, and human expertise turns the models into lucrative targets for model stealing attacks. Prior work has revealed that the threat vector of stea
Chunking the Critic: A Transformer-based Soft Actor-Critic with N-Step Returns
We introduce a sequence-conditioned critic for Soft Actor-Critic (SAC) that models trajectory context with a lightweight Transformer and trains on aggregated $N$-step targets. Unlike prior approaches that (i) score state-action pairs in isolation or (ii) rely on actor-side action chunking to handle long horizons, our method strengthens the critic itself by conditioning on short trajectory segments and integrating mul
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