Record 03042026 · captured 2026-08-25
The world looked up Artemis II. 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.
Artemis II was a crewed flyby of the Moon. It is currently the only crewed flight beyond low Earth orbit since Apollo 17 in 1972. It was the first crewed flight of the NASA-led Artemis program, the first crewed flight of the Space Launch System (SLS), and the
Pamela Jo Bondi is an American attorney and politician who served as the 87th United States attorney general from 2025 to 2026. A member of the Republican Party, she served as the 37th attorney general of Florida from 2011 to 2019.
Dhurandhar: The Revenge is a 2026 Indian Hindi-language spy action-thriller film written and directed by Aditya Dhar. It is produced by Dhar, Lokesh Dhar, and Jyoti Deshpande under Jio Studios and B62 Studios. It is a sequel to the 2025 film Dhurandhar and the
Todd Wallace Blanche is an American attorney and former prosecutor who is the 88th United States attorney general, having served in an acting capacity from April 2026 until his confirmation in August. Blanche has also served as the 40th deputy U.S. attorney ge
The Super Mario Galaxy Movie is a 2026 American animated adventure comedy film based on Nintendo's Mario video game franchise. Directed by Aaron Horvath and Michael Jelenic and written by Matthew Fogel, it is the sequel to The Super Mario Bros. Movie (2023). C
Christina Hammock Koch is an American engineer and NASA astronaut. On her mission to the International Space Station in 2019–20 she was part of the first all‑female spacewalk and set the record for the longest spaceflight by a woman. On the Artemis II lunar fl
List of highest-grossing Indian films
This ranking lists the highest-grossing Indian films produced by Indian cinema, based on conservative global box office estimates as reported by organisations classified as green by Wikipedia. The figures are not adjusted for inflation. However, there is no of
Lee Michael Zeldin is an American politician and lawyer serving as the 17th administrator of the Environmental Protection Agency (EPA) since January 2025. Previously, he served in the United States House of Representatives, representing New York's 1st congress
Lamar Joseph Odom is an American former professional basketball player who played for four teams during his 14-year career in the National Basketball Association (NBA), and won back-to-back championships in 2009 and 2010 with the Los Angeles Lakers. He was als
Gregory Reid Wiseman is a United States Navy captain, test pilot, and NASA astronaut. He was the commander of the 2026 Artemis II lunar flyby mission, the first crewed flight around the Moon since Apollo 17 in 1972. He served as the 17th chief of the Astronaut
Since 28 February 2026, the United States and Israel have been at war with Iran and its regional allies. Hostilities broke out after US–Israeli airstrikes killed several Iranian officials, including Supreme Leader Ali Khamenei. The strikes were launched amid o
Theodore Robert Bundy was an American serial killer who kidnapped, raped and murdered dozens of young women and girls between 1974 and 1978. His modus operandi typically consisted of convincing his target that he was in need of assistance or duping them into b
Jeremy Roger Hansen is a Royal Canadian Air Force colonel and CSA astronaut. As a mission specialist on Artemis II in April 2026, he became the only person not from the United States to travel beyond low Earth orbit and to travel to the vicinity of the Moon. H
Maundy Thursday, also referred to as Holy Thursday, or Thursday of the Lord's Supper, among other names, is a Christian feast during Holy Week that marks the beginning of the Paschal Triduum, and commemorates the Washing of the Feet (Maundy) and Last Supper of
The Artemis program is a Moon exploration program led by the United States' National Aeronautics and Space Administration (NASA), aimed at returning humans to the Moon for the first time since the Apollo program and building a permanent lunar base. It was form
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
Project Hail Mary is a 2026 American science fiction film produced and directed by Phil Lord and Christopher Miller and written by Drew Goddard, based on the 2021 novel of the same name by Andy Weir. It stars Ryan Gosling, who also produced the film, as Ryland
An anti-lock braking system (ABS) is a safety anti-skid braking system used on aircraft and on land vehicles, such as cars, motorcycles, trucks, and buses. ABS operates by preventing the wheels from locking up during braking, thereby maintaining tractive conta
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
Kristi Lynn Arnold Noem is an American politician serving as the United States special envoy for the Shield of the Americas since 2026. From 2025 to 2026, she served as the eighth United States secretary of homeland security. A member of the Republican Party,
Good Friday, also known as Holy Friday, Great Friday, Great and Holy Friday, or Friday of the Passion of the Lord, is a solemn Christian holy day commemorating the crucifixion of Jesus and his death at Calvary (Golgotha), part of the Passion of Jesus. It is ob
The Drama is a 2026 American dark romantic comedy film written and directed by Kristoffer Borgli. It stars Zendaya and Robert Pattinson as a happily engaged couple whose relationship is tested by an unexpected revelation the week before their wedding.
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
Victor Jerome Glover Jr. is a United States Navy captain, test pilot, and NASA astronaut. A former F/A‑18 pilot and graduate of the United States Air Force Test Pilot School, in 2020, he piloted the first operational flight of SpaceX's Crew Dragon to the Inter
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 Orion Multi-Purpose Crew Vehicle (MPCV) is a partially reusable crewed spacecraft used by NASA for the Artemis lunar exploration program. It consists of a crew module (CM), a space capsule built by Lockheed Martin, and is paired with a European Service Mod
Malachi Daniel Barton is an American actor. Known for his roles in Disney Channel productions, he first gained prominence starring in the television series Stuck in the Middle (2016–2018) as Beast Diaz. He also starred as Colby Madden in The Villains of Valley
Lamina-associated polypeptide 2 (LAP2), isoforms beta/gamma is a protein that in humans is encoded by the TMPO gene. LAP2 is an inner nuclear membrane (INM) protein.
Corrosion is a natural process that converts a refined metal into a more chemically stable oxide. It is the gradual deterioration of materials by chemical or electrochemical reaction with their environment. Corrosion engineering is the field dedicated to contr
Something Very Bad Is Going to Happen
Something Very Bad Is Going to Happen is an American horror television miniseries created by Haley Z. Boston for Netflix. Boston serves as the series showrunner and is also an executive producer along with the Duffer Brothers. Camila Morrone and Adam DiMarco s
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Risk-Aware Linear Bandits: Theory and Applications in Smart Order Routing
Motivated by practical considerations in machine learning for financial decision-making, such as risk aversion and large action space, we consider risk-aware bandits optimization with applications in smart order routing (SOR). Specifically, based on preliminary observations of linear price impacts made from the NASDAQ ITCH dataset, we initiate the study of risk-aware linear bandits. In this setting, we aim at minimiz
In distributed optimization, a large number of machines alternate between local computations and communication with a coordinating server. Communication, which can be slow and costly, is the main bottleneck in this setting. To reduce this burden and therefore accelerate distributed gradient descent, two strategies are popular: 1) communicate less frequently; that is, perform several iterations of local computations b
Estimating Head Motion from MR-Images
Head motion is an omnipresent confounder of magnetic resonance image (MRI) analyses as it systematically affects morphometric measurements, even when visual quality control is performed. In order to estimate subtle head motion, that remains undetected by experts, we introduce a deep learning method to predict in-scanner head motion directly from T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion reco
Robust Multi-agent Communication via Multi-view Message Certification
Many multi-agent scenarios require message sharing among agents to promote coordination, hastening the robustness of multi-agent communication when policies are deployed in a message perturbation environment. Major relevant works tackle this issue under specific assumptions, like a limited number of message channels would sustain perturbations, limiting the efficiency in complex scenarios. In this paper, we take a fu
The use of attention-based deep learning models in stochastic filtering, e.g. transformers and deep Kalman filters, has recently come into focus; however, the potential for these models to solve stochastic filtering problems remains largely unknown. The paper provides an affirmative answer to this open problem in the theoretical foundations of machine learning by showing that a class of continuous-time transformer mo
Information theory for dimensionality reduction in dynamical systems
The dynamics of many-body systems can often be captured in terms of only a few relevant variables. Mathematical and numerical approaches exist to identify these variables by exploiting a separation of time scales between slow relevant and fast irrelevant variables, but such a separation of scales is not always obvious or even available. In this work, we introduce an information-theoretic framework for dimensionality
Tackling Non-IIDness in HAPS-Aided Federated Learning
High-altitude platform stations (HAPS) enable large-scale federated learning (FL) in non-terrestrial networks (NTN) by providing wide-area coverage and predominantly line-of-sight (LoS) connectivity to many ground users. However, practical deployments face heterogeneous and non-independently and identically distributed (non-IID) client data, which degrades accuracy and slows convergence. We propose a weighted attribu
Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing
Remote sensing images present unique challenges to image analysis due to the extensive geographic coverage, hardware limitations, and misaligned multi-scale images. This paper revisits the classical multi-scale representation learning problem but under the general framework of self-supervised learning for remote sensing image understanding. We present Cross-Scale MAE, a self-supervised model built upon the Masked Aut
Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI
Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rational under model decisions. This paper presents the first industrial application of ExIFFI, a recent approach for
Inspired by the 'Bias Considerations in Bilingual Natural Language Processing' report by Statistics Canada, this study delves into potential biases in multilingual sentiment analysis between English and French. Given a 50-50 dataset of French and English, we aim to determine if there exists a language bias and explore how the incorporation of more diverse datasets in the future might affect the equity of mult
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability
Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks. Despite the recent surge in graph pooling approaches, there is a lack of standardized experimental settings and fair benchmarks to evaluate their performance. To address this issue, we have constructed a comprehensive benchmark that includes 17 graph pooling methods and 28 different grap
J-CHAT: Japanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue Language Modeling
Spoken dialogue is essential for human-AI interactions, providing expressive capabilities beyond text. Developing effective spoken dialogue systems (SDSs) requires large-scale, high-quality, and diverse spoken dialogue corpora. However, existing datasets are often limited in size, spontaneity, or linguistic coherence. To address these limitations, we introduce J-CHAT, a 76,000-hour open-source Japanese spoken dialogu
Interpretable Classification via a Rule Network with Selective Logical Operators
We introduce the Rule Network with Selective Logical Operators (RNS), a novel neural architecture that employs \textbf{selective logical operators} to adaptively choose between AND and OR operations at each neuron during training. Unlike existing approaches that rely on fixed architectural designs with predetermined logical operations, our selective logical operators treat weight parameters as hard selectors, enablin
Autonomous driving systems require a comprehensive understanding of the environment, achieved by extracting visual features essential for perception, planning, and control. However, models trained solely on single-task objectives or generic datasets often lack the contextual information needed for robust performance in complex driving scenarios. In this work, we propose a unified encoder trained on multiple computer
In-context Learning in Presence of Spurious Correlations
Large language models exhibit a remarkable capacity for in-context learning, where they learn to solve tasks given a few examples. Recent work has shown that transformers can be trained to perform simple regression tasks in-context. This work explores the possibility of training an in-context learner for classification tasks involving spurious features. We find that the conventional approach of training in-context le
Sparse Mixture-of-Experts (MoE) models can outperform dense large language models at similar computation by activating only a small set of experts per token. However, stacking many expert modules introduces substantial parameter memory, which makes MoE models difficult to deploy in memory-constrained environments such as single-GPU devices. Offloading alleviates this issue by storing inactive experts in CPU memory an
Detecting Reference Errors in Scientific Literature with Large Language Models
Reference errors, such as citation and quotation errors, are common in scientific papers. Such errors can result in the propagation of inaccurate information, but are difficult and time-consuming to detect, posing a significant challenge to scientific publishing. To support automatic detection of reference errors, this work evaluated the ability of large language models in OpenAI's GPT family to detect quotation
Robot Collapse: Supply Chain Backdoor Attacks Against VLM-based Robotic Manipulation
Robotic manipulation policies are increasingly empowered by \textit{large language models} (LLMs) and \textit{vision-language models} (VLMs), leveraging their understanding and perception capabilities. Recently, inference-time attacks against robotic manipulation have been extensively studied, yet backdoor attacks targeting model supply chain security in robotic policies remain largely unexplored. To fill this gap, w
Prognostics for Autonomous Deep-Space Habitat Health Management under Multiple Unknown Failure Modes
Deep-space habitats (DSHs) are safety-critical systems that must operate autonomously for long periods, often beyond the reach of ground-based maintenance or expert intervention. Monitoring system health and anticipating failures are therefore essential. Prognostics based on remaining useful life (RUL) prediction support this goal by estimating how long a subsystem can operate before failure. Critical DSH subsystems,
InTraGen: Trajectory-controlled Video Generation for Object Interactions
Advances in video generation have significantly improved the realism and quality of created scenes. This has fueled interest in developing intuitive tools that let users leverage video generation as world simulators. Text-to-video (T2V) generation is one such approach, enabling video creation from text descriptions only. Yet, due to the inherent ambiguity in texts and the limited temporal information offered by text
Learning to Translate Noise for Robust Image Denoising
Deep learning-based image denoising techniques often struggle with poor generalization performance to out-of-distribution real-world noise. To tackle this challenge, we propose a novel noise translation framework that performs denoising on an image with translated noise rather than directly denoising an original noisy image. Specifically, our approach translates complex, unknown real-world noise into Gaussian noise,
Modeling Multi-Objective Tradeoffs with Monotonic Utility Functions
Countless science and engineering applications in multi-objective optimization (MOO) necessitate that decision-makers (DMs) select a Pareto-optimal (PO) solution which aligns with their preferences. Evaluating individual solutions is often expensive, and the high-dimensional trade-off space makes exhaustive exploration of the full Pareto frontier (PF) infeasible. We introduce a novel, principled two-step process for
Automatic selection of the best neural architecture for time series forecasting
Time series forecasting plays a pivotal role in a wide range of applications, including weather prediction, healthcare, structural health monitoring, predictive maintenance, energy systems, and financial markets. While models such as LSTM, GRU, Transformers, and State-Space Models (SSMs) have become standard tools in this domain, selecting the optimal architecture remains a challenge. Performance comparisons often de
Fragility-aware Classification for Understanding Risk and Improving Generalization
Classification models play a central role in data-driven decision-making applications such as medical diagnosis, recommendation systems, and risk assessment. Traditional performance metrics, such as accuracy and AUC, focus on overall error rates but fail to account for the confidence of incorrect predictions, i.e., the risk of confident misjudgments. This limitation is particularly consequential in safety-critical an
Human Misperception of Generative-AI Alignment: A Laboratory Experiment
We conduct an incentivized laboratory experiment to study people's perception of generative artificial intelligence (GenAI) alignment in the context of economic decision-making. Using a panel of economic problems spanning the domains of risk, time preference, social preference, and strategic interactions, we ask human subjects to make choices for themselves and to predict the choices made by GenAI on behalf of a
Notable events recorded on this day and month across all years.