Record 11082026 · captured 2026-08-25
The world looked up Roblox. 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.
Roblox is an online game platform and game creation system developed by Roblox Corporation that allows users to program and play games created by themselves or other users. It was developed by David Baszucki and Erik Cassel in 2004, and released to the public
Spider-Man: Brand New Day is a 2026 American superhero film based on the Marvel Comics character Spider-Man. Produced by Columbia Pictures, Marvel Studios, and Pascal Pictures, and distributed by Sony Pictures Releasing, it is the 38th film in the Marvel Cinem
The Odyssey is a 2026 epic action fantasy film written and directed by Christopher Nolan, who produced it with his wife Emma Thomas. An adaptation of Homer's ancient Greek epic poem the Odyssey, it stars an ensemble cast including Matt Damon, Tom Holland, Anne
The Last House is a 2026 American science fiction horror film written by Matthew Robinson, and directed by Louis Leterrier. It stars Greta Lee and Wagner Moura. The film follows a family that finds themselves inexplicably sealed in their home, with the whole w
The third season of the American fantasy drama television series House of the Dragon premiered on HBO on June 21, 2026, in the United States and concluded on August 9, 2026. It consists of eight episodes, each of approximately one hour. The season covers the e
House of the Dragon is an American fantasy drama television series created by George R. R. Martin and Ryan Condal for HBO. A prequel to Game of Thrones (2011–2019), it is the second television series in Martin's A Song of Ice and Fire franchise. Based on parts
Ben Jones (American actor and politician)
Benjamin Lewis Jones was an American actor, writer, politician, and businessman best known for his role as Cooter Davenport in The Dukes of Hazzard. Jones also served for four years in the United States House of Representatives from January 3, 1989, to January
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
Abdulrahman Mohamed El-Sayed, commonly known as Abdul El-Sayed, is an American politician and epidemiologist who is the Democratic nominee in the 2026 United States Senate election in Michigan. A progressive member of the Democratic Party, El-Sayed was a candi
Daniel Joseph Kinahan is an Irish suspected crime boss and former boxing promoter. He has been named by the High Court of Ireland as a senior figure in organised crime on a global scale.
Zendaya Maree Stoermer Coleman, known mononymously as Zendaya, is an American actress and singer-songwriter. Known for her work in television and blockbusters, her films as a leading actress have grossed over $9.8 billion worldwide. Her accolades include two P
DC is a 2026 Indian Tamil-language romantic action film directed by Arun Matheswaran and produced by Kalanithi Maran's Sun Pictures. The film stars Lokesh Kanagaraj, Wamiqa Gabbi and Sanjana Krishnamoorthy. It follows Das, an outlaw and Chandra, a brutalised 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
.xyz is a top-level domain name that was proposed in ICANN's new generic top-level domain (gTLD) Program for consisting of the last three letters of the Latin-script alphabet. XYZ.com and CentralNic are the registries for the domain, which was created by entre
Thomas Stanley Holland is a British actor. His accolades include a BAFTA Award as well as two Critics' Choice Awards nominations. Holland's films as a leading actor have grossed over $14.9 billion worldwide, making him the Fourth highest-grossing actor of all
Faisal bin Abdulaziz Al Saud was King of Saudi Arabia from 1964 until his assassination in 1975. Before his ascension, he served as Crown Prince of Saudi Arabia from 1953 to 1964, and he was briefly regent to his half-brother King Saud in 1964. He was prime mi
A stenomask is a hand-held microphone built into a padded, soundproof enclosure that fits over the speaker's mouth or nose. Some lightweight versions may be fitted with an elastic neck strap to hold them in place while freeing the user's hands for other tasks.
Donald Arvid Nelson was an American professional basketball player and head coach. He coached the Milwaukee Bucks, the New York Knicks, the Dallas Mavericks, and the Golden State Warriors of the National Basketball Association (NBA). After an All-American care
Anne Jacqueline Hathaway is an American actress. Her accolades include an Academy Award, a British Academy Film Award, a Golden Globe Award, and a Primetime Emmy Award. Her films have grossed over $6.8 billion worldwide.
List of highest-grossing films
Films generate income from several revenue streams, including theatrical exhibition, home video, television broadcast rights, and merchandising. However, theatrical box-office earnings are the primary metric for trade publications in assessing the success of a
Pan Am Flight 103 was a regularly scheduled Pan Am flight from Frankfurt to Detroit via stopovers in London and New York City. Shortly after 19:00 GMT on 21 December 1988, the Boeing 747 Clipper Maid of the Seas was destroyed by a bomb while flying over the Sc
Toxic: A Fairy Tale for Grown-Ups is a 2026 Indian gangster film directed by Geetu Mohandas and jointly produced by Venkat K. Narayana and Yash through KVN Productions and Monster Mind Creations LLP respectively. It stars Yash in a dual role, alongside Kiara A
2026 in film is an overview of events in the film industry scheduled to occur in 2026. Best Picture Academy Award-winners All Quiet on the Western Front and Cimarron entered the public domain this year.
Jason Atta Kwei Arday was a British academic who was a professor of sociology of education at the University of Cambridge from 2023 to 2026. Arday received international attention and resigned amid accusations of plagiarism, false claims in his research, and f
List of Marvel Cinematic Universe films
The Marvel Cinematic Universe (MCU) centers on American superhero films produced by Marvel Studios, based on characters that appear in publications by Marvel Comics. The MCU is the shared universe in which all of the films are set. Marvel Studios has released
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
Sterling Point is an American drama television series created by Megan Park and starring Ella Rubin, Jacob Whiteduck-Lavoie, Amélie Hoeferle, Daniel Quinn-Toye, Bo Bragason, and Keen Ruffalo. The series premiered on Amazon Prime Video on August 5, 2026. In Aug
Alexandria Ocasio-Cortez, also known as AOC, is an American politician and activist who has served since 2019 as the U.S. representative for New York's 14th congressional district. She is a member of the Democratic Party and the New York City Chapter of the De
The Odyssey is one of two major epics of ancient Greek literature attributed to Homer. It is one of the oldest surviving works of literature and remains popular with modern audiences. Like the Iliad, the Odyssey is divided into 24 books. It follows the heroic
Enes Kanter Freedom is a Turkish and American human rights activist and former professional basketball player who played 11 seasons in the National Basketball Association (NBA). Born in Switzerland to parents from Turkey, he was raised in Turkey and moved to t
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Understanding Alternating Minimization for Matrix Completion
Alternating Minimization is a widely used and empirically successful heuristic for matrix completion and related low-rank optimization problems. Theoretical guarantees for Alternating Minimization have been hard to come by and are still poorly understood. This is in part because the heuristic is iterative and non-convex in nature. We give a new algorithm based on Alternating Minimization that provably recovers an unk
Self-training (ST) is a simple yet effective semi-supervised learning method. However, why and how ST improves generalization performance by using potentially erroneous pseudo-labels is still not well understood. To deepen the understanding of ST, we derive and analyze a sharp characterization of the behavior of iterative ST when training a linear classifier by minimizing the ridge-regularized convex loss on binary G
InforMask: Unsupervised Informative Masking for Language Model Pretraining
Masked language modeling is widely used for pretraining large language models for natural language understanding (NLU). However, random masking is suboptimal, allocating an equal masking rate for all tokens. In this paper, we propose InforMask, a new unsupervised masking strategy for training masked language models. InforMask exploits Pointwise Mutual Information (PMI) to select the most informative tokens to mask. W
Causal Falsification of Digital Twins
Digital twins are simulation-based models designed to predict how a real-world process will evolve in response to interventions. This modelling paradigm holds substantial promise in many applications, but rigorous procedures for assessing their accuracy are essential for safety-critical settings. We consider how to assess the accuracy of a digital twin using real-world data. We formulate this as a causal inference pr
A Convex Hull Cheapest Insertion Heuristic for the Non-Euclidean TSP
Autonomous robots frequently encounter routing problems that involve non-Euclidean cost considerations due to obstacles, traffic, or a cost function that is not simply the straight-line distance between locations to be visited. Often, the resulting Non-Euclidean Traveling Salesperson Problem (NETSP) must be solved onboard with limited computational resources, posing a significant challenge due to its NP-hard combinat
Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, and robustness. In this paper, we propose a robust approach that enables fast FLI with no degradation of accuracy. The approach is based on a SPAD TCSPC system cou
Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization
In this paper, we focus on providing convergence guarantees for stochastic subgradient methods in minimizing nonsmooth nonconvex functions. We first investigate the global stability of a general framework for stochastic subgradient methods, where the corresponding differential inclusion admits a coercive Lyapunov function. We prove that, for any sequence of sufficiently small stepsizes and approximation parameters, c
Visual recognition models are prone to learning spurious correlations induced by a biased training set where certain conditions $B$ (\eg, Indoors) are over-represented in certain classes $Y$ (\eg, Big Dogs). Synthetic data from off-the-shelf large-scale generative models offers a promising direction to mitigate this issue by augmenting underrepresented subgroups in the real dataset. However, by using a mixed distribu
We aim to present a comprehensive overview of the latest advancements in utilizing Large Language Models (LLMs) within the healthcare sector, emphasizing their transformative impact across various medical domains. LLMs have become pivotal in supporting healthcare, including physicians, healthcare providers, and patients. Our review provides insight into the applications of Large Language Models (LLMs) in healthcare,
CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts
Contrastive vision-language models, such as CLIP, have garnered considerable attention for various downstream tasks, mainly due to the remarkable ability of the learned features for generalization. However, the features they learned often blend content and style information, which somewhat limits their generalization capabilities under distribution shifts. To address this limitation, we adopt a causal generative pers
AMD:Anatomical Motion Diffusion with Interpretable Motion Decomposition and Fusion
Generating realistic human motion sequences from text descriptions is a challenging task that requires capturing the rich expressiveness of both natural language and human motion. Recent advances in diffusion models have enabled significant progress in human motion synthesis. However, existing methods struggle to handle text inputs that describe complex or long motions. In this paper, we propose the Adaptable Motion
The Internet of Medical Things transcends traditional medical boundaries, enabling a transition from reactive treatment to proactive prevention. This innovative method revolutionizes healthcare by facilitating early disease detection and tailored care, particularly in chronic disease management, where IoMT automates treatments based on real-time health data collection. Nonetheless, its benefits are countered by signi
In the fields of computer vision and natural language processing, multimodal chart question-answering, especially involving color, structure, and textless charts, poses significant challenges. Traditional methods, which typically involve either direct multimodal processing or a table-to-text conversion followed by language model analysis, have limitations in effectively handling these complex scenarios. This paper in
Characteristic Learning for Provable One Step Generation
We propose the characteristic generator, an one-step generative model that combines the sampling efficiency of generative adversarial networks (GANs) with the training stability of flow-based models. The proposed model is based on characteristics along which probability-density transport is governed by ordinary differential equations (ODEs). Specifically, we first estimate the underlying velocity field and numericall
Ethical Framework for Responsible Foundational Models in Medical Imaging
The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnosis, and treatment planning. These large-scale artificial intelligence systems, trained on extensive multimodal and multi-center datasets, demonstrate remarkable versatility across diverse medical applications. However, their integration into clinical practice presents c
Discriminative and Consistent Representation Distillation
Knowledge Distillation (KD) transfers knowledge from a large teacher to a smaller student model. While contrastive objectives have proven effective for learning structured representations in self-supervised settings, their use in distillation is hindered by two practical shortcomings: the reliance on external memory banks for negative sampling, and fixed temperature hyperparameters that limit adaptability across trai
Attention-Guided Perturbation Network for Industrial Anomaly Detection
In unsupervised image anomaly detection, reconstruction-based methods learn normal patterns for data reconstruction, but often undesirably reconstruct anomalous regions at inference, resulting in missed detections. To alleviate this, existing approaches perturb normal samples in a sample-agnostic manner by uniformly injecting noise, ignoring that foreground regions are more critical for robust reconstruction. To addr
In the context of medical records, patients often experience testimonial injustice, where the textual account undermines the validity of their experiences. Past work has demonstrated that intersectionality of demographic features is crucial to \emph{detect} such injustice. We use causal discovery to study the degree to which certain demographic features tied to marginalization (namely age, gender, and race), together
Machine Learning for Inverse Problems and Data Assimilation
The aim of this book is to demonstrate the potential for ideas in machine learning to impact on the fields of inverse problems and data assimilation. The perspective is one that is primarily aimed at researchers from inverse problems and/or data assimilation who wish to see a mathematical presentation of machine learning as it pertains to their fields. As a by-product, we include a succinct mathematical treatment of
Discrete distributions are learnable from metastable samples
Physically motivated stochastic dynamics are widely used to sample from high-dimensional distributions. However, such samplers often get trapped in metastable states, approximately sampling from a distribution that differs significantly from the desired stationary state. We rigorously show that for multivariable discrete distributions, the true stationary model can nevertheless be recovered from these metastable samp
Lying mirror using structured surfaces
We introduce an all-optical system, termed the "lying mirror", to hide input information by transforming it into misleading, ordinary-looking patterns that effectively camouflage the underlying image data and deceive the observers. This misleading transformation is achieved through passive light-matter interactions of the incident light with an optimized structured diffractive surface, enabling the optical co
Regret of exploratory policy improvement and $q$-learning
We study the convergence of $q$-learning and related algorithms introduced by Jia and Zhou (J. Mach. Learn. Res., 24 (2023), 161) for controlled diffusion processes. For exploratory policy improvement, we establish exponential convergence under growth and regularity assumptions on the model parameters. For q-learning, we derive quantitative error and regret bounds under additional assumptions on the function approxim
Motion-Aware Animatable Gaussian Avatars Deblurring
The creation of 3D human avatars from multi-view videos is a significant yet challenging task in computer vision. However, existing techniques rely on high-quality, sharp images as input, which are often impractical to obtain in real-world scenarios due to variations in human motion speed and intensity. This paper introduces a novel method for directly reconstructing sharp 3D human Gaussian avatars from blurry videos
Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration
While the advancement of large language models has spurred the development of AI agents to automate tasks, numerous use cases inherently require agents to collaborate with humans due to humans' latent preferences, domain expertise, or the need for control. To facilitate the study of human-agent collaboration, we introduce Collaborative Gym (Co-Gym), an open framework for developing and evaluating collaborative ag
A Rigorous Turing Test: a Foundation for Evaluating Artificial General Intelligence
Several studies claim that large language models have passed the Turing Test and hence can "think", yet none follow Turing's original instructions precisely. Passing the test holds significance as evidence that a machine demonstrates human-like intelligence, and as a marker for artificial-general intelligence in commercial and legal domains. We conducted Turing's three-player imitation game with an LL
Notable events recorded on this day and month across all years.