Record 31032026 · captured 2026-08-25
The world looked up Dhurandhar: The Revenge. 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.
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
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
Scott Robert Mills is an English radio DJ, television presenter and occasional actor. He is best known for presenting the Scott Mills show on BBC Radio 1 from 2004 to 2022 and then, on BBC Radio 2, hosting the station's flagship breakfast show from January 202
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
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
Vanessa Kay is an American model. She is the ex-wife of Donald Trump Jr. They were married from 2005 to 2018, and had five children.
Daniel S. Hurley is an American men's college basketball coach who is the head coach of the UConn Huskies. In 2023 and 2024, Hurley led UConn to back-to-back NCAA Division I national championships, and led the Huskies to another title game appearance in 2026.
Braylon Anthony Mullins is an American college basketball player for the UConn Huskies of the Big East Conference.
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
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.
Avram David "Avi" Lewis is a Canadian politician and filmmaker who has served as leader of the New Democratic Party (NDP) since 2026.
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
Dhurandhar is a 2025 Indian Hindi-language spy action thriller film written and directed by Aditya Dhar. It is produced by Aditya Dhar, Lokesh Dhar and Jyoti Deshpande under Jio Studios and B62 Studios. The film features an ensemble cast consisting of Ranveer
Rahul Arunoday Banerjee, better known as Rahul Banerjee, was an Indian actor and writer. He primarily worked in Bengali films and television series. He made his first stage appearance at the age of three in Raj Darshan, a production staged by his father Biswan
Mary Beth Hurt was an American actress of stage and screen. She was a three-time Tony Award-nominated actress, as well as a BAFTA Award and Independent Spirit Award nominee. Hurt was also the recipient of an Obie Award and a Clarence Derwent Award.
Sara Arjun is an Indian actress who primarily appears in Tamil and Hindi films. The daughter of actor Raj Arjun, she appeared in several television commercials, including advertisements for Clinic Plus, and a short Hindi film before the age of six. She gained
Vaibhav Sooryavanshi, also spelled Vaibhav Suryavanshi, is an Indian cricketer. He is a left-handed top order batter and an occasional slow left-arm orthodox bowler. He made his international debut for the Indian cricket team in the T20I series against England
Roberto De Zerbi is an Italian professional football head coach and former player who is the head coach of Premier League club Tottenham Hotspur.
John Fitzgerald Kennedy Jr., also referred to as JFK Jr., was an American businessman, attorney, magazine publisher, and journalist. He was the son of the 35th U.S. president John F. Kennedy, and First Lady Jacqueline Kennedy.
Magistrates' Court, Christchurch
The former Magistrates' Court is a heritage-listed court building in Christchurch, New Zealand. It was built in 1880 and was used as a court building until 2017.
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
"It's Christmas Day" is a Christmas song by South Korean singer-songwriter Roy Kim. It was released as a digital single on 19 December 2014, and distributed through CJ E&M Music.
Paradise is an American post-apocalyptic political thriller television series created by Dan Fogelman and starring Sterling K. Brown, Julianne Nicholson, and James Marsden. It was released on Hulu in the United States on January 26, 2025. The series has receiv
BlaZeon is a horizontally scrolling shoot 'em up arcade game released by Atlus in 1992 and was ported to the Super Nintendo Entertainment System in the same year. The game's most distinguishable feature is that players come equipped with a device that allows t
Carolyn Jeanne Bessette-Kennedy was an American fashion publicist. Raised in Greenwich, Connecticut, she graduated from Boston University and joined Calvin Klein, where she rose from a sales position in Boston to publicity and show-production roles in New York
Gary Lynn Woodland is an American professional golfer who plays on the PGA Tour. He has won one major championship, the 2019 U.S. Open.
Alexander Duong was an American comedian and television actor. He was best known for playing the recurring role of criminal and gang leader Sonny Le in the American police procedural television series Blue Bloods.
Kharg Island, also spelled Khark Island and often referred to as the "Forbidden Island", is a continental island of Iran in the Persian Gulf. The island is 25 kilometres (16 mi) off the coast of Iran and 660 kilometres (410 mi) northwest of the Strait of Hormu
On the morning of 26 August 2025, police officers Neal Thompson and Vadim De Waart-Hottart were killed in a shooting at a property near the regional Victorian town of Porepunkah, Australia, while attempting to execute a warrant. A third officer was injured. Th
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
When AI systems make errors in high-stakes domains like medical diagnosis or autonomous vehicles, a single algorithmic flaw across varying operational contexts can generate highly heterogeneous losses that challenge traditional insurance assumptions. Algorithmic insurance constitutes a novel form of financial coverage for AI-induced damages, representing an emerging market that addresses algorithm-driven liability. H
Learning general conditional independence structures via the neighbourhood lattice
We study the problem of learning multivariate dependencies in nonparametric and high-dimensional settings. This includes but is not limited to graphical models. Our approach effectively combines several features that are missing from previous work on this problem: We show how the entire dependence structure can be learned nonparametrically while simultaneously evading the curse of dimensionality and relaxing common a
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets
The statistical supervised learning framework assumes an input-output set with a joint probability distribution that is reliably represented by the training dataset. The learner is then required to output a prediction rule learned from the training dataset's input-output pairs. In this work, we provide meaningful insights into the asymptotic equipartition property (AEP) \citep{Shannon:1948} in the context of mach
Motivated by the increasingly popular Score-based Generative Modeling (SGM), we study the Inexact Langevin Dynamics (ILD) and Inexact Langevin Algorithm (ILA) where a score function estimate is used in place of the exact score. We establish {\em stable} biased convergence guarantees in terms of the Kullback-Leibler (KL) divergence. To achieve these guarantees, we impose two key assumptions: 1) the target distribution
Continual Graph Learning: A Survey
Continual Graph Learning (CGL) enables models to incrementally learn from streaming graph-structured data without forgetting previously acquired knowledge. Experience replay is a common solution that reuses a subset of past samples during training. However, it may lead to information loss and privacy risks. Generative replay addresses these concerns by synthesizing informative subgraphs for rehearsal. Existing genera
Correcting Auto-Differentiation in Neural-ODE Training
Does the use of auto-differentiation yield reasonable updates for deep neural networks (DNNs)? Specifically, when DNNs are designed to adhere to neural ODE architectures, can we trust the gradients provided by auto-differentiation? Through mathematical analysis and numerical evidence, we demonstrate that when neural networks employ high-order methods, such as Linear Multistep Methods (LMM) or Explicit Runge-Kutta Met
Machine Learning (ML) has become pervasive, and its deployment in Network Intrusion Detection Systems (NIDS) is inevitable due to its automated nature and high accuracy compared to traditional models in processing and classifying large volumes of data. However, ML has been found to have several flaws, most importantly, adversarial attacks, which aim to trick ML models into producing faulty predictions. While most adv
Background: Abdominal wall defects, such as incisional hernias, are a common source of pain and discomfort and often require repeated surgical interventions. Traditional mesh repair techniques typically rely on fixed overlap based on defect size, without considering important biomechanical factors like muscle activity, internal pressure, and tissue elasticity. This study aims to introduce a biomechanical approach to
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
In this work, we propose a novel prior learning method for advancing generalization and uncertainty estimation in deep neural networks. The key idea is to exploit scalable and structured posteriors of neural networks as informative priors with generalization guarantees. Our learned priors provide expressive probabilistic representations at large scale, like Bayesian counterparts of pre-trained models on ImageNet, and
We consider the infinite-horizon, average-reward restless bandit problem in discrete time. We propose a new class of policies that are designed to drive a progressively larger subset of arms toward the optimal distribution. We show that our policies are asymptotically optimal with an $O(1/\sqrt{N})$ optimality gap for an $N$-armed problem, assuming only a unichain and aperiodicity assumption. Our approach departs fro
Learning the Model While Learning Q: Finite-Time Sample Complexity of Online SyncMBQ
Reinforcement learning has witnessed significant advancements, particularly with the emergence of model-based approaches. Among these, $Q$-learning has proven to be a powerful algorithm in model-free settings. However, the extension of $Q$-learning to a model-based framework remains relatively unexplored. In this paper, we investigate the sample complexity of $Q$-learning when integrated with a model-based approach.
Estimation of Energy-dissipation Lower-bounds for Neuromorphic Learning-in-memory
Neuromorphic or neurally-inspired optimizers rely on local but parallel parameter updates to solve problems that range from quadratic programming to Ising machines. An ideal realization of such an optimizer not only uses a compute-in-memory (CIM) paradigm to address the so-called memory-wall (i.e. energy dissipated due to repeated memory read access), but also uses a learning-in-memory (LIM) paradigm to address the e
AltChart: Enhancing VLM-based Chart Summarization Through Multi-Pretext Tasks
Chart summarization is a crucial task for blind and visually impaired individuals as it is their primary means of accessing and interpreting graphical data. Crafting high-quality descriptions is challenging because it requires precise communication of essential details within the chart without vision perception. Many chart analysis methods, however, produce brief, unstructured responses that may contain significant h
Monitoring Simulated Physical Weakness Using Detailed Behavioral Features and Personalized Modeling
Aging and chronic conditions affect older adults' daily lives, making the early detection of developing health issues crucial. Weakness, which is common across many conditions, can subtly alter physical movements and daily activities. However, these behavioral changes can be difficult to detect because they are gradual and often masked by natural day-to-day variability. To isolate the behavioral phenotype of weak
iiANET: Inception Inspired Attention Hybrid Network for efficient Long-Range Dependency
The recent emergence of hybrid models has introduced a transformative approach to computer vision, gradually moving beyond conventional convolutional neural networks and vision transformers. However, efficiently combining these two approaches to better capture long-range dependencies in complex images remains a challenge. In this paper, we present iiANET (Inception Inspired Attention Network), an efficient hybrid vis
LSM-GNN: Large-scale Storage-based Multi-GPU GNN Training by Optimizing Data Transfer Scheme
Graph Neural Networks (GNNs) are widely used today in recommendation systems, fraud detection, and node/link classification tasks. Real world GNNs continue to scale in size and require a large memory footprint for storing graphs and embeddings that often exceed the memory capacities of the target GPUs used for training. To address limited memory capacities, traditional GNN training approaches use graph partitioning a
Few-shot multi-class anomaly detection is crucial in real industrial settings, where only a few normal samples are available while numerous object types must be inspected. This setting is challenging as defect patterns vary widely across categories while normal samples remain scarce. Existing vision-language model-based approaches typically depend on class-specific anomaly descriptions or auxiliary modules, limiting
CBF-LLM: Safe Control for LLM Alignment
This paper proposes a control-based framework for aligning large language models (LLMs) by leveraging a control barrier function (CBF) to ensure user-desirable text generation. The presented framework applies the safety filter, designed based on the CBF, to the output generation of the baseline LLM, i.e., the sequence of the token, with the aim of intervening in the generated text. The overall text-generation system
Continual Robot Skill and Task Learning via Dialogue
Interactive robot learning is a challenging problem as the robot is present with human users who expect the robot to learn novel skills to solve novel tasks perpetually with sample efficiency. In this work we present a framework for robots to continually learn tasks and visuo-motor skills and query for novel skills via dialog interactions with human users. Our robot agent maintains a skill library, and uses an existi
HYDRA: Hybrid Data Multiplexing and Run-time Layer Configurable DNN Accelerator
Deep neural networks (DNNs) offer plenty of challenges in executing efficient computation at edge nodes, primarily due to the huge hardware resource demands. The article proposes HYDRA, hybrid data multiplexing, and runtime layer configurable DNN accelerators to overcome the drawbacks. The work proposes a layer-multiplexed approach, which further reuses a single activation function within the execution of a single la
The Northeast Materials Database for Magnetic Materials
The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the North
Match Stereo Videos via Bidirectional Alignment
Video stereo matching is the task of estimating consistent disparity maps from rectified stereo videos. There is considerable scope for improvement in both datasets and methods within this area. Recent learning-based methods often focus on optimizing performance for independent stereo pairs, leading to temporal inconsistencies in videos. Existing video methods typically employ sliding window operation over time dimen
Recent Advances of Multimodal Continual Learning: A Comprehensive Survey
Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting. As models have evolved from small to large pre-trained architectures, and from supporting unimodal to multimodal data, multimodal continual learning (MMCL) methods have recently emerged. The primary complexity of MMCL is that it extends beyond a simp
Multi-Agent Actor-Critics in Autonomous Cyber Defense
The need for autonomous and adaptive defense mechanisms has become paramount in the rapidly evolving landscape of cyber threats. Multi-Agent Deep Reinforcement Learning (MADRL) presents a promising approach to enhancing the efficacy and resilience of autonomous cyber operations. This paper explores the application of Multi-Agent Actor-Critic algorithms which provides a general form in Multi-Agent learning to cyber de
Road safety remains a critical challenge worldwide, with approximately 1.35 million fatalities annually attributed to traffic accidents, often due to human errors. As we advance towards higher levels of vehicle automation, challenges still exist, as driving with automation can cognitively over-demand drivers if they engage in non-driving-related tasks (NDRTs), or lead to drowsiness if driving was the sole task. This
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