Record 07042026 · 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
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
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
Michael Malone is an American basketball coach who is the head coach of the North Carolina Tar Heels men's basketball team. He previously coached in the National Basketball Association (NBA), where he was the head coach of the Sacramento Kings from 2013 to 201
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.
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
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
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
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
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
Dōtaku are richly decorated Japanese bells cast in bronze. They were used for about 400 years, between the second century BCE and the second century CE, and were used almost exclusively as decorations during rituals. They were richly decorated with patterns re
Donald John Trump is an American politician, media personality, and businessman who is the 47th president of the United States. A member of the Republican Party, he served as the 45th president from 2017 to 2021.
Scosthrop is a civil parish in North Yorkshire, England. The population as taken at the 2011 Census was less than 100. Details are included in the civil parish of Kirkby Malham. In 2015, North Yorkshire County Council estimated the settlement to have approxima
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
La-related protein 6 also known as Acheron or La ribonucleoprotein domain family member 6 (LARP6), is a protein that in humans is encoded by the LARP6 gene.
Apollo 13 was the seventh crewed mission in the Apollo space program and would have been the third Moon landing. The craft was launched from Kennedy Space Center on April 11, 1970, but the landing was aborted after an oxygen tank in the service module (SM) exp
On May 11, 2022, Anna Moriah "Mo" Wilson, a 25‑year‑old professional cyclist, was fatally shot at a friend's residence in Austin, Texas. Investigators identified Kaitlin Marie Armstrong as the suspect after surveillance footage placed her near the scene and ev
Lauren Marie Betts is an American professional basketball player for the Washington Mystics of the Women's National Basketball Association (WNBA). She played for Grandview High School in Aurora, Colorado, where she was ranked as the number one recruit in her c
Project Hail Mary is a 2021 hard science fiction novel by American writer Andy Weir. It centers on science teacher and former biologist Ryland Grace, who wakes up aboard a spacecraft, afflicted with amnesia.
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
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
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
The Pitt is an American medical drama television series created by R. Scott Gemmill and executive produced by John Wells and Noah Wyle. It is Gemmill, Wells, and Wyle's second collaboration; they previously worked together on ER (1994–2009). It stars Wyle, Tra
Cori Rashel Close is an American basketball coach who is the head coach for the UCLA Bruins women's team. She played college basketball as a guard for the UC Santa Barbara Gauchos from 1989 to 1993, serving as a team captain during her final two seasons and he
Anna's Archive is an open source search engine for shadow libraries that was launched by the pseudonymous Anna shortly after law enforcement efforts to shut down Z-Library in 2022. The site aggregates records from Z-Library, Sci-Hub, and Library Genesis (LibGe
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
Blue Film is a 2025 American drama film written and directed by Elliot Tuttle. It stars Kieron Moore as a sex worker and Reed Birney as a former middle school teacher — and self-admitted pederast — who reconnect.
Peter Brian Hegseth is an American government official, veteran, and former television personality who has served as the 29th United States secretary of defense since 2025.
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Local Variation as a Statistical Hypothesis Test
The goal of image oversegmentation is to divide an image into several pieces, each of which should ideally be part of an object. One of the simplest and yet most effective oversegmentation algorithms is known as local variation (LV) (Felzenszwalb and Huttenlocher 2004). In this work, we study this algorithm and show that algorithms similar to LV can be devised by applying different statistical models and decisions, t
Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning
We present a reinforcement learning (RL) framework to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that can be modeled as a Markov Decision Process (MDP). Specifically, we learn a policy that maximizes the probability of satisfying the LTL formula without learning the transition probabilities. We introduce a novel rewarding and path-dependent
Mitigating Value Hallucination in Dyna Planning via Multistep Predecessor Models
Dyna-style reinforcement learning (RL) agents improve sample efficiency over model-free RL agents by updating the value function with simulated experience generated by an environment model. However, it is often difficult to learn accurate models of environment dynamics, and even small errors may result in failure of Dyna agents. In this paper, we highlight that one potential cause of that failure is bootstrapping off
Learning Optimal Strategies for Temporal Tasks in Stochastic Games
Synthesis from linear temporal logic (LTL) specifications provides assured controllers for systems operating in stochastic and potentially adversarial environments. Automatic synthesis tools, however, require a model of the environment to construct controllers. In this work, we introduce a model-free reinforcement learning (RL) approach to derive controllers from given LTL specifications even when the environment is
Model-Free Learning of Safe yet Effective Controllers
We study the problem of learning safe control policies that are also effective; i.e., maximizing the probability of satisfying a linear temporal logic (LTL) specification of a task, and the discounted reward capturing the (classic) control performance. We consider unknown environments modeled as Markov decision processes. We propose a model-free reinforcement learning algorithm that learns a policy that first maximiz
3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective
This research aims to study a self-supervised 3D clothing reconstruction method, which recovers the geometry shape and texture of human clothing from a single image. Compared with existing methods, we observe that three primary challenges remain: (1) 3D ground-truth meshes of clothing are usually inaccessible due to annotation difficulties and time costs; (2) Conventional template-based methods are limited to modelin
SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation
Recently, over-height vehicle strike frequently occurs, causing great economic cost and serious safety problems. Hence, an alert system which can accurately discover any possible height limiting devices in advance is necessary to be employed in modern large or medium sized cars, such as touring cars. Detecting and estimating the height limiting devices act as the key point of a successful height limit alert system. T
Uncertainty in Real-Time Semantic Segmentation on Embedded Systems
Application for semantic segmentation models in areas such as autonomous vehicles and human computer interaction require real-time predictive capabilities. The challenges of addressing real-time application is amplified by the need to operate on resource constrained hardware. Whilst development of real-time methods for these platforms has increased, these models are unable to sufficiently reason about uncertainty pre
Graph State-Space Models and Latent Relational Inference
State-space models effectively model multivariate time series by updating over time a representation of the system state from which predictions are made. The state representation is usually a vector without any explicit structure. Relational inductive biases, e.g., associated with dependencies among input signals and state representations, are not explicitly exploited during processing, leaving unattended opportuniti
Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents' strategies, and exploiting this opponent model by playing a best response. However, existing approaches typically require domain-specific heurstics to come up with such a model, and algorithms for approximating best responses are hard to scale in large, imperfect information domains. In this work, we intr
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior. Traditional MCMC approaches avoid these assumptions at the cost of increased computation due to its incompatibility to subsampling of the likelihood. New Piecewise Deterministic Markov Process (PDMP) samplers permit subsampling, though intr
Neural Exploitation and Exploration of Contextual Bandits
In this paper, we study utilizing neural networks for the exploitation and exploration of contextual multi-armed bandits. Contextual multi-armed bandits have been studied for decades with various applications. To solve the exploitation-exploration trade-off in bandits, there are three main techniques: epsilon-greedy, Thompson Sampling (TS), and Upper Confidence Bound (UCB). In recent literature, a series of neural ba
Polarimetric Imaging for Perception
Autonomous driving and advanced driver-assistance systems rely on a set of sensors and algorithms to perform the appropriate actions and provide alerts as a function of the driving scene. Typically, the sensors include color cameras, radar, lidar and ultrasonic sensors. Strikingly however, although light polarization is a fundamental property of light, it is seldom harnessed for perception tasks. In this work we anal
Importance Sparsification for Sinkhorn Algorithm
Sinkhorn algorithm has been used pervasively to approximate the solution to optimal transport (OT) and unbalanced optimal transport (UOT) problems. However, its practical application is limited due to the high computational complexity. To alleviate the computational burden, we propose a novel importance sparsification method, called Spar-Sink, to efficiently approximate entropy-regularized OT and UOT solutions. Speci
This paper considers the problem of understanding the behavior of a general class of accelerated gradient methods on smooth nonconvex functions. Motivated by some recent works that have proposed effective algorithms, based on Polyak's heavy ball method and the Nesterov accelerated gradient method, to achieve convergence to a local minimum of nonconvex functions, this work proposes a broad class of Nesterov-type a
We consider the problem of learning a sparse graph underlying an undirected Gaussian graphical model, a key problem in statistical machine learning. Given $n$ samples from a multivariate Gaussian distribution with $p$ variables, the goal is to estimate the $p \times p$ inverse covariance matrix (aka precision matrix), assuming it is sparse (i.e., has a few nonzero entries). We propose GraphL0BnB, a new estimator base
Studying Lobby Influence in the European Parliament
We present a method based on natural language processing (NLP), for studying the influence of interest groups (lobbies) in the law-making process in the European Parliament (EP). We collect and analyze novel datasets of lobbies' position papers and speeches made by members of the EP (MEPs). By comparing these texts on the basis of semantic similarity and entailment, we are able to discover interpretable links bet
Physics-Informed Neural Networks for Accelerating Power System State Estimation
State estimation is the cornerstone of the power system control center since it provides the operating condition of the system in consecutive time intervals. This work investigates the application of physics-informed neural networks (PINNs) for accelerating power systems state estimation in monitoring the operation of power systems. Traditional state estimation techniques often rely on iterative algorithms that can b
A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis
Human immunodeficiency virus (HIV) is a major public health concern in the United States (U.S.), with about 1.2 million people living with it and about 35,000 newly infected each year. There are considerable geographical disparities in HIV burden and care access across the U.S. The 'Ending the HIV Epidemic (EHE)' initiative by the U.S. Department of Health and Human Services aims to reduce new infections by 9
Beyond principlism: Practical strategies for ethical AI use in research practices
The rapid adoption of generative artificial intelligence (AI) in scientific research, particularly large language models (LLMs), has outpaced the development of ethical guidelines, leading to a "Triple-Too" problem: too many high-level ethical initiatives, too abstract principles lacking contextual and practical relevance, and too much focus on restrictions and risks over benefits and utilities. Existing appr
Federated Transfer Learning with Differential Privacy
Federated learning has emerged as a powerful framework for analysing distributed data, yet two challenges remain pivotal: heterogeneity across sites and privacy of local data. In this paper, we address both challenges within a federated transfer learning framework, aiming to enhance learning on a target data set by leveraging information from multiple heterogeneous source data sets while adhering to privacy constrain
Floralens: a Deep Learning Model for the Portuguese Native Flora
Machine-learning techniques, especially deep convolutional neural networks, are pivotal for image-based identification of biological species in many Citizen Science platforms. In this paper, we describe the construction of a dataset for the Portuguese native flora based on publicly available research-grade datasets, and the derivation of a high-accuracy model from it using off-the-shelf deep convolutional neural netw
Ray-driven Spectral CT Reconstruction Based on Neural Base-Material Fields
In spectral CT reconstruction, the basis materials decomposition involves solving a large-scale nonlinear system of integral equations, which is highly ill-posed mathematically. This paper proposes a model that parameterizes the attenuation coefficients of the object using a neural field representation, thereby avoiding the complex calculations of pixel-driven projection coefficient matrices during the discretization
Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection
With the wide application of knowledge distillation between an ImageNet pre-trained teacher model and a learnable student model, unsupervised anomaly detection has witnessed a significant achievement in the past few years. The success of this framework mainly relies on how to keep the feature discrepancy between the teacher and student model, in which it has two underlying sub-assumptions: (1) The teacher model can r
Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since the subgroup structure is typically unknown, it is more challenging to identify and evaluate subgroup effects than population effects. We propose a new solution to this problem: \emph{Causal k-Means Clustering}, which leverages the k-means c
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