Record 30032026 · 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
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
Avram David "Avi" Lewis is a Canadian politician and filmmaker who has served as leader of the New Democratic Party (NDP) since 2026.
Israel Mobolaji Temitayo Odunayo Oluwafemi Owolabi Adesanya is a Nigerian-New Zealander professional mixed martial artist, former kickboxer, and boxer. As a mixed martial artist, he currently competes in the Middleweight division of the Ultimate Fighting Champ
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
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.
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
Palm Sunday is the Christian moveable feast that falls on the Sunday before Easter. The feast commemorates Christ's triumphal entry into Jerusalem, an event mentioned in each of the four canonical Gospels. Its name originates from the palm branches waved by th
UFC Fight Night: Adesanya vs. Pyfer
UFC Fight Night: Adesanya vs. Pyfer was a mixed martial arts event produced by the Ultimate Fighting Championship that took place on March 28, 2026, at Climate Pledge Arena in Seattle, Washington, United States.
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 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.
The Boeing E-3 Sentry is an American airborne early warning and control (AEW&C) aircraft developed by Boeing. E-3s are commonly known as AWACS. Derived from the Boeing 707 airliner, it provides all-weather surveillance, command, control, and communications, an
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.
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
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.
Joseph Oliver Pyfer is an American professional mixed martial artist who competes in the Middleweight division of the Ultimate Fighting Championship (UFC). As of June 27, 2026, he is #4 in the Meta UFC middleweight rankings.
Carlos Austin Boozer Jr. is an American former professional basketball player. A two-time NBA All-Star, he played for the Cleveland Cavaliers, Utah Jazz, Chicago Bulls, and Los Angeles Lakers, and then spent his last season playing overseas with the Guangdong
Send Help is a 2026 American survival horror film directed and co-produced by Sam Raimi and written by Damian Shannon and Mark Swift. The film stars Rachel McAdams and Dylan O'Brien as an employee and her boss, respectively, who become stranded on a desert isl
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
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
James Stewart Tolkan was an American character actor. He was best known for portraying the strict high-school vice principal Mr. Strickland in Back to the Future (1985) and Back to the Future Part II (1989), and the character's ancestor, Marshal James Strickla
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.
Eldrick Tont "Tiger" Woods is an American professional golfer. He is widely regarded as one of the greatest golfers of all time and as one of the most famous athletes in modern history. Woods is tied for first in PGA Tour wins, ranks second in men's major cham
Moses Itauma is a British professional boxer. He has held the Commonwealth heavyweight title since 2025.
Braylon Anthony Mullins is an American college basketball player for the UConn Huskies of the Big East Conference.
Dawood Ibrahim Kaskar is an Indian gangster, mob boss, drug lord and narcoterrorist. He is the leader of the organised crime syndicate D-Company, which he founded in Mumbai in the 1970s. Dawood is wanted on multiple charges of murder, extortion, targeted killi
2026 Formula One World Championship
The 2026 FIA Formula One World Championship is a motor racing championship for Formula One cars and the 77th running of the Formula One World Championship. It is recognised by the Fédération Internationale de l'Automobile (FIA), the governing body of internati
Pilt Carin Ersdotter (1814–1885), was a Swedish milkmaid from Djura in Dalarna who became famous for her beauty. She sold milk on the street of Stockholm in 1833-1834, and attracted so much attention that she became a mascot to be displayed in the salons of th
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Weakly Supervised Learning for Facial Affective Behavior Analysis : A Review
Recent advances in deep learning (DL) and computational capacity have enabled facial affective behavior analysis (FABA) to progress from static images captured in controlled settings to fine-grained analysis of facial expressions in real-world video data. However, training accurate DL models for FABA typically requires large-scale, expert-annotated datasets, which are costly to obtain and inherently noisy due to the
Does Audio Deepfake Detection Generalize?
Current text-to-speech algorithms produce realistic fakes of human voices, making deepfake detection a much-needed area of research. While researchers have presented various techniques for detecting audio spoofs, it is often unclear exactly why these architectures are successful: Preprocessing steps, hyperparameter settings, and the degree of fine-tuning are not consistent across related work. Which factors contribut
Scale-Adaptive Balancing of Exploration and Exploitation in Classical Planning
Balancing exploration and exploitation has been an important problem in both game tree search and automated planning. However, while the problem has been extensively analyzed within the Multi-Armed Bandit (MAB) literature, the planning community has had limited success when attempting to apply those results. We show that a more detailed theoretical understanding of MAB literature helps improve existing planning algor
Distance Functions and Normalization Under Stream Scenarios
Data normalization is an essential task when modeling a classification system. When dealing with data streams, data normalization becomes especially challenging since we may not know in advance the properties of the features, such as their minimum/maximum values, and these properties may change over time. We compare the accuracies generated by eight well-known distance functions in data streams without normalization,
Evaluating Neural Language Models as Cognitive Models of Language Acquisition
The success of neural language models (LMs) on many technological tasks has brought about their potential relevance as scientific theories of language despite some clear differences between LM training and child language acquisition. In this paper we argue that some of the most prominent benchmarks for evaluating the syntactic capacities of LMs may not be sufficiently rigorous. In particular, we show that the templat
CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with Trajectory Optimization
Trajectory Optimization (TO) and Reinforcement Learning (RL) are powerful and complementary tools to solve optimal control problems. On the one hand, TO can efficiently compute locally-optimal solutions, but it tends to get stuck in local minima if the problem is not convex. On the other hand, RL is typically less sensitive to non-convexity, but it requires a much higher computational effort. Recently, we have propos
Putting Context in Context: the Impact of Discussion Structure on Text Classification
Current text classification approaches usually focus on the content to be classified. Contextual aspects (both linguistic and extra-linguistic) are usually neglected, even in tasks based on online discussions. Still in many cases the multi-party and multi-turn nature of the context from which these elements are selected can be fruitfully exploited. In this work, we propose a series of experiments on a large dataset f
Error Estimation for Physics-informed Neural Networks Approximating Semilinear Wave Equations
This paper provides rigorous error bounds for physics-informed neural networks approximating the semilinear wave equation. We provide bounds for the generalization and training error in terms of the width of the network's layers and the number of training points for a tanh neural network with two hidden layers. Our main result is a bound of the total error in the $H^1([0,T];L^2(Ω))$-norm in terms of the training
U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models
Diffusion models have demonstrated remarkable performance in text-to-image synthesis, producing realistic and high resolution images that faithfully adhere to the corresponding text-prompts. Despite their great success, they still fall behind in sketch-to-image synthesis tasks, where in addition to text-prompts, the spatial layout of the generated images has to closely follow the outlines of certain reference sketche
The Inefficiency of Genetic Programming for Symbolic Regression
We analyse the search behaviour of genetic programming for symbolic regression in practically relevant but limited settings, allowing exhaustive enumeration of all solutions. This enables us to quantify the success probability of finding the best possible expressions, and to compare the search efficiency of genetic programming to random search in the space of semantically unique expressions. This analysis is made pos
Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly
Video anomaly understanding (VAU) aims to automatically comprehend unusual occurrences in videos, thereby enabling various applications such as traffic surveillance and industrial manufacturing. While existing VAU benchmarks primarily concentrate on anomaly detection and localization, our focus is on more practicality, prompting us to raise the following crucial questions: "what anomaly occurred?", "why d
AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian Detection
Multispectral pedestrian detection has been shown to be effective in improving performance within complex illumination scenarios. However, prevalent double-stream networks in multispectral detection employ two separate feature extraction branches for multi-modal data, leading to nearly double the inference time compared to single-stream networks utilizing only one feature extraction branch. This increased inference t
Extreme Value Monte Carlo Tree Search for Classical Planning
Despite being successful in board games and reinforcement learning (RL), Monte Carlo Tree Search (MCTS) combined with Multi Armed Bandits (MABs) has seen limited success in domain-independent classical planning until recently. Previous work (Wissow and Asai 2024) showed that UCB1, designed for bounded rewards, does not perform well as applied to cost-to-go estimates in classical planning, which are unbounded in $\R$,
Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients
We propose a novel deep symbolic regression approach to enhance the robustness and interpretability of data-driven mathematical expression discovery. Our work is aligned with the popular DSR framework which focuses on learning a data-specific expression generator, without relying on pretrained models or additional search or planning procedures. Despite the success of existing DSR methods, they are built on recurrent
QPT V2: Masked Image Modeling Advances Visual Scoring
Quality assessment and aesthetics assessment aim to evaluate the perceived quality and aesthetics of visual content. Current learning-based methods suffer greatly from the scarcity of labeled data and usually perform sub-optimally in terms of generalization. Although masked image modeling (MIM) has achieved noteworthy advancements across various high-level tasks (e.g., classification, detection etc.). In this work, w
Self-supervised learning has emerged as a powerful paradigm for pretraining foundation models using large-scale data. Existing pretraining approaches predominantly rely on masked reconstruction or next-token prediction strategies, demonstrating strong performance across various downstream tasks, including geoscience applications. However, these approaches do not fully capture the knowledge of causal interplay between
Equivariant neural networks and piecewise linear representation theory
Equivariant neural networks are neural networks with symmetry. Motivated by the theory of group representations, we decompose the layers of an equivariant neural network into simple representations. The nonlinear activation functions lead to interesting nonlinear equivariant maps between simple representations. For example, the rectified linear unit (ReLU) gives rise to piecewise linear maps. We show that these consi
CGRA4ML: A Hardware/Software Framework to Implement Neural Networks for Scientific Edge Computing
The scientific community increasingly relies on machine learning (ML) for near-sensor processing, leveraging its strengths in tasks such as pattern recognition, anomaly detection, and real-time decision-making. These deployments demand accelerators that combine extremely high performance with programmability, ease of integration, and straightforward verification. We present cgra4ml, an open-source, modular framework
Assessing the performance of systems to classify Multi-Party Conversations (MPC) is challenging due to the interconnection between linguistic and structural characteristics of conversations. Conventional evaluation methods often overlook variances in model behavior across different levels of structural complexity on interaction graphs. In this work, we propose a methodological pipeline to investigate model performanc
Diversity of Thought Elicits Stronger Reasoning Capabilities in Multi-Agent Debate Frameworks
Large language models (LLMs) excel in natural language generation but often confidently produce incorrect responses, especially in tasks like mathematical reasoning. Chain-of-thought prompting, self-verification, and multi-agent debate are among the strategies proposed to improve the reasoning and factual accuracy of LLMs. Building on Du et al.'s multi-agent debate framework, we find that multi-agent debate helps
ReMe: Scaffolding Personalized Cognitive Training via Controllable LLM-Mediated Conversations
Global aging calls for scalable and engaging cognitive interventions. Computerized cognitive training (CCT) is a promising non-pharmacological approach, yet many unsupervised programs rely on rigid, hand-authored puzzles that are difficult to personalize and can hinder adherence. Large language models (LLMs) offer more natural interaction, but their open-ended generation complicates the controlled task structure requ
Efficient Energy-Optimal Path Planning for Electric Vehicles Considering Vehicle Dynamics
The rapid adoption of electric vehicles (EVs) in modern transport systems has made energy-aware routing a critical task in their successful integration, especially within large-scale transport networks. In cases where an EV's remaining energy is limited and charging locations are not easily accessible, some destinations may only be reachable through an energy-optimal path: a route that consumes less energy than a
Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial Editing
Interactive computer vision (CV) plays a crucial role in various real-world applications, whose performance is highly dependent on communication networks. Nonetheless, the data-oriented characteristics of conventional communications often do not align with the special needs of interactive CV tasks. To alleviate this issue, the recently emerged semantic communications only transmit task-related semantic information an
Nonmyopic Global Optimisation via Approximate Dynamic Programming
Global optimisation to optimise expensive-to-evaluate black-box functions without gradient information. Bayesian optimisation, one of the most well-known techniques, typically employs Gaussian processes as surrogate models, leveraging their probabilistic nature to balance exploration and exploitation. However, these processes become computationally prohibitive in high-dimensional spaces. Recent alternatives, based on
Building Foundations for Natural Language Processing of Historical Turkish: Resources and Models
This paper introduces foundational resources and models for natural language processing (NLP) of historical Turkish, a domain that has remained underexplored in computational linguistics. We present the first named entity recognition (NER) dataset, HisTR, and the first Universal Dependencies treebank, OTA-BOUN, for a historical form of the Turkish language along with transformer-based models trained using these datas
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