Record 09042026 · captured 2026-08-25
The world looked up Aubrey Plaza. 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.
Aubrey Christina Plaza is an American actress, comedian, writer, and producer. She began performing improv and sketch comedy at the Upright Citizens Brigade Theatre. After graduating from New York University Tisch School of the Arts, Plaza gained wide recognit
Christopher Jacob Abbott is an American actor known for his work in film, television, and theater. In 2011, he made his first film appearance in Martha Marcy May Marlene and his Broadway debut in the revival of the play The House of Blue Leaves. He then gained
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
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
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
Jeffrey Lance Baena was an American screenwriter and film director. His most successful films were 2004's I Heart Huckabees and 2020's Horse Girl, though his projects to receive the most contemporaneous critical acclaim were the 2016 and 2017 films Joshy and T
The fifth and final season of the American satirical superhero television series The Boys, the first series in the franchise based on the comic book series of the same name created by Garth Ennis and Darick Robertson, was developed for television by Eric Kripk
Jasveen Sangha is a British-American convicted felon and drug dealer known as the Ketamine Queen. She gained international attention following her indictment and subsequent guilty plea in connection with the overdose death of actor Matthew Perry. Prosecutors a
The Gilgo Beach serial killings were a series of murders on Long Island, New York, between 1993 and 2010. The case gained national attention in late 2010 and 2011, when police searching for a missing woman, Shannan Gilbert, discovered the remains of ten victim
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
Dianna Marie Russini is an American former sports journalist who worked as a National Football League (NFL) reporter and insider.
The Strait of Hormuz is a waterway between the Persian Gulf and the Gulf of Oman. On the north coast lies Iran, and on the south coast lies the Musandam Peninsula under the Musandam Governorate of Oman, with a portion of the southwest of the peninsula under th
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.
Benjamin Roberts-Smith is an Australian former soldier in the Special Air Service Regiment (SASR). He is one of Australia's most highly decorated soldiers, having received the Medal for Gallantry (2006), the Victoria Cross for Australia (2011)—the highest awar
Duchess Charlotte Georgine of Mecklenburg-Strelitz
Duchess Charlotte Georgine of Mecklenburg-Strelitz was a member of the House of Mecklenburg-Strelitz and a Duchess of Mecklenburg-Strelitz by birth and a Duchess of Saxe-Hildburghausen through her marriage to Frederick, Duke of Saxe-Hildburghausen.
Aldo Ferrabino was an Italian historian, philosopher, librarian, writer, and poet. A graduate of the University of Turin, he taught ancient history at the University of Padua and the Sapienza University of Rome, later becoming rector at the University of Padua
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
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
Galactic Warriors is a 1985 fighting video game developed and published by Konami for arcades. It was released in Japan in November 1985. It is Konami's second fighting game released after their 1985 arcade-hit Yie Ar Kung-Fu. It was ported to Microsoft's Game
The Boys is an American satirical superhero streaming television series developed by Eric Kripke for Amazon Prime Video. Based on the comic book series of the same name by Garth Ennis and Darick Robertson, it follows the eponymous team of vigilantes as they co
Mircea Lucescu was a Romanian professional football player and manager.
17776 is a serialized speculative fiction multimedia hypertext narrative by Jon Bois, published online through SB Nation. Set in the distant future in which all humans have become immortal and infertile, the series follows three sapient space probes that watch
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.
Michael George Vrabel is an American professional football coach and former linebacker who is the head coach for the New England Patriots of the National Football League (NFL). Vrabel previously played in the NFL for 14 seasons, most notably with the Patriots.
Adam Back is a British cryptographer and cypherpunk. He is the CEO of Blockstream, which he co-founded in 2014. He invented Hashcash, which is used in the bitcoin mining process. An investigation by The New York Times suggested that he may be Satoshi Nakamoto,
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
Matthew Adam Garber was a British child actor, most notable as Michael Banks in the 1964 film Mary Poppins. His other screen credits include The Three Lives of Thomasina (1963) and The Gnome-Mobile (1967), appearing alongside actress Karen Dotrice in all three
"Trump Always Chickens Out" (TACO) is a pejorative description of the perceived tendency of United States president Donald Trump to make threats, only to later delay or renege on them as a way to increase time for negotiations, allow markets to rebound, and av
List of Masters Tournament champions
The Masters Tournament is a golf competition that was established in 1934, with Horton Smith winning the inaugural tournament. The Masters is the first of four major championships to be played each year, with the final round of the Masters always being schedul
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
A Robust 3D Registration Method via Simultaneous Inlier Identification and Model Estimation
Robust 3D registration is a fundamental problem in computer vision and robotics, where the goal is to estimate the geometric transformation between two sets of measurements in the presence of noise, mismatches, and extreme outlier contamination. Existing robust registration methods are mainly built on either maximum consensus (MC) estimators, which first identify inliers and then estimate the transformation, or M-est
Sobolev Norm Learning Rates for Conditional Mean Embeddings
We develop novel learning rates for conditional mean embeddings by applying the theory of interpolation for reproducing kernel Hilbert spaces (RKHS). We derive explicit, adaptive convergence rates for the sample estimator under the misspecifed setting, where the target operator is not Hilbert-Schmidt or bounded with respect to the input/output RKHSs. We demonstrate that in certain parameter regimes, we can achieve un
Decentralized Online Learning for Random Inverse Problems Over Graphs
We propose a decentralized online learning algorithm for distributed random inverse problems over network graphs with online measurements, and unifies the distributed parameter estimation in Hilbert spaces and the least mean square problem in reproducing kernel Hilbert spaces (RKHS-LMS). We transform the convergence of the algorithm into the asymptotic stability of a class of inhomogeneous random difference equations
DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models
We demonstrate that pre-trained text-to-image diffusion models, despite being trained on raster images, possess a remarkable capacity to guide vector sketch synthesis. In this paper, we introduce DiffSketcher, a novel algorithm for generating vectorized free-hand sketches directly from natural language prompts. Our method optimizes a set of Bézier curves via an extended Score Distillation Sampling (SDS) loss, success
We present a framework for smooth optimization of explicitly regularized objectives for (structured) sparsity. These non-smooth and possibly non-convex problems typically rely on solvers tailored to specific models and regularizers. In contrast, our method enables fully differentiable and approximation-free optimization and is thus compatible with the ubiquitous gradient descent paradigm in deep learning. The propose
ConfusionPrompt: Practical Private Inference for Online Large Language Models
State-of-the-art large language models (LLMs) are typically deployed as online services, requiring users to transmit detailed prompts to cloud servers. This raises significant privacy concerns. In response, we introduce ConfusionPrompt, a novel framework for private LLM inference that protects user privacy by: (i) decomposing the original prompt into smaller sub-prompts, and (ii) generating pseudo-prompts alongside t
Don't Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget
We study how to best spend a budget of noisy labels to compare the accuracy of two binary classifiers. It's common practice to collect and aggregate multiple noisy labels for a given data point into a less noisy label via a majority vote. We prove a theorem that runs counter to conventional wisdom. If the goal is to identify the better of two classifiers, we show it's best to spend the budget on collecting a
Inspired by the concept of active learning, we propose active inference$\unicode{x2013}$a methodology for statistical inference with machine-learning-assisted data collection. Assuming a budget on the number of labels that can be collected, the methodology uses a machine learning model to identify which data points would be most beneficial to label, thus effectively utilizing the budget. It operates on a simple yet p
Though numerous solvers have been proposed for the MaxSAT problem, and the benchmark environment such as MaxSAT Evaluations provides a platform for the comparison of the state-of-the-art solvers, existing assessments were usually evaluated based on the quality, e.g., fitness, of the best-found solutions obtained within a given running time budget. However, concerning solely the final obtained solutions regarding spec
Resistance Distance and Linearized Optimal Transport on Graphs
We study the linearization of a discrete transportation distance between probability distributions on finite weighted graphs originally due to Maas (``Gradient flows of the entropy for finite {M}arkov chains,'' J. Funct. Anal. 261(8), 2011) which demonstrates various connections to the underlying combinatorial structure of the graph. For a connected graph and a reference density $μ$ on its vertices, our main
Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation
This paper introduces a novel approach to enhance the performance of pre-trained neural networks in medical image segmentation using gradient-based Neural Architecture Search (NAS) methods. We present the concept of Implantable Adaptive Cell (IAC), small modules identified through Partially-Connected DARTS based approach, designed to be injected into the skip connections of an existing and already trained U-shaped mo
Thompson Sampling for Infinite-Horizon Discounted Decision Processes
This paper develops a viable notion of learning for sampling-based algorithms that applies in broader settings than previously considered. More specifically, we model a discounted infinite-horizon MDPs with Borel state and action spaces, whose rewards and transitions depend on an unknown parameter. To analyze adaptive learning algorithms based on sampling we introduce a general canonical probability space in this set
AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models
In this paper, we introduce analytic federated learning (AFL), a new training paradigm that brings analytical (i.e., closed-form) solutions to the federated learning (FL) with pre-trained models. Our AFL draws inspiration from analytic learning -- a gradient-free technique that trains neural networks with analytical solutions in one epoch. In the local client training stage, the AFL facilitates a one-epoch training,
Differentially Private Best-Arm Identification
Best Arm Identification (BAI) problems are progressively used for data-sensitive applications, such as designing adaptive clinical trials, tuning hyper-parameters, and conducting user studies. Motivated by the data privacy concerns invoked by these applications, we study the problem of BAI with fixed confidence in both the local and central models, i.e. $ε$-local and $ε$-global Differential Privacy (DP). First, to qu
An effective integration of rich feature representations with robust classification mechanisms remains a key challenge in visual understanding tasks. This study introduces two novel deep learning models, SleepNet and DreamNet, which are designed to improve representation utilization through feature enrichment and reconstruction strategies. SleepNet integrates supervised learning with representations obtained from pre
UAVDB: Point-Guided Masks for UAV Detection and Segmentation
Accurate detection of Unmanned Aerial Vehicles (UAVs) is critical for surveillance, security, and airspace monitoring. However, existing datasets remain limited in scale, resolution, and the ability to capture objects across extreme size variations. To address these challenges, we present UAVDB, a benchmark dataset for UAV detection and segmentation, constructed via a point-guided weak supervision pipeline. We introd
Matrix Profile for Anomaly Detection on Multidimensional Time Series
The Matrix Profile (MP), a versatile tool for time series data mining, has been shown effective in time series anomaly detection (TSAD). This paper delves into the problem of anomaly detection in multidimensional time series, a common occurrence in real-world applications. For instance, in a manufacturing factory, multiple sensors installed across the site collect time-varying data for analysis. The Matrix Profile, n
Explainable AI needs formalization
The field of "explainable artificial intelligence" (XAI) seemingly addresses the desire that decisions of machine learning systems should be human-understandable. However, in its current state, XAI itself needs scrutiny. Popular methods cannot reliably answer relevant questions about ML models, their training data, or test inputs, because they systematically attribute importance to input features that are ind
In this paper, we build a reinforcement learning framework to study how children compose numbers using base-ten blocks. Studying numerical cognition in toddlers offers a powerful window into the learning process itself, because numbers sit at the intersection of language, logic, perception, and culture. Specifically, we utilize state of the art (SOTA) reinforcement learning algorithms and neural network architectures
Analyzing Multimodal Interaction Strategies for LLM-Assisted Manipulation of 3D Scenes
As more applications of large language models (LLMs) for 3D content for immersive environments emerge, it is crucial to study user behaviour to identify interaction patterns and potential barriers to guide the future design of immersive content creation and editing systems which involve LLMs. In an empirical user study with 12 participants, we combine quantitative usage data with post-experience questionnaire feedbac
Despite significant advancements, segmentation based on deep neural networks in medical and surgical imaging faces several challenges, two of which we aim to address in this work. First, acquiring complete pixel-level segmentation labels for medical images is time-consuming and requires domain expertise. Second, typical segmentation pipelines cannot detect out-of-distribution (OOD) pixels, leaving them prone to spuri
From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps
Mobile apps are essential in daily life but frequently employ deceptive patterns, such as visual emphasis or linguistic nudging, to manipulate user behavior. Existing research largely relies on manual detection, which is time-consuming and cannot keep pace with rapidly evolving apps. Although recent work has explored automated approaches, these methods are limited to intra-page patterns, depend on manual app explorat
MSG Score: Automated Video Verification for Reliable Multi-Scene Generation
While text-to-video diffusion models have advanced significantly, creating coherent long-form content remains unreliable due to stochastic sampling artifacts. This necessitates generating multiple candidates, yet verifying them creates a severe bottleneck; manual review is unscalable, and existing automated metrics lack the adaptability and speed required for runtime monitoring. Another critical issue is the trade-of
Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal
We study the kernel instrumental variable (KIV) algorithm, a kernel-based two-stage least-squares method for nonparametric instrumental variable regression. We provide a convergence analysis covering both identified and non-identified regimes: when the structural function is not identified, we show that the KIV estimator converges to the minimum-norm IV solution in the reproducing kernel Hilbert space associated with
MozzaVID: Mozzarella Volumetric Image Dataset
Influenced by the complexity of volumetric imaging, there is a shortage of established datasets useful for benchmarking volumetric deep-learning models. As a consequence, new and existing models are not easily comparable, limiting the development of architectures optimized specifically for volumetric data. To counteract this trend, we introduce MozzaVID -- a large, clean, and versatile volumetric classification datas
Notable events recorded on this day and month across all years.