Record 26032026 · 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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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
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
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
Huw Edwards is a Welsh former news presenter. He was the lead presenter of BBC News at Ten, the late evening news programme of BBC Television, from 2003 to 2023. He resigned from the BBC in 2024, during a police investigation into indecent images of children o
General elections were held in Denmark on 24 March 2026. All 179 seats in the Folketing were up for election, including 175 in Denmark proper, 2 in Greenland, and 2 in the Faroe Islands. It was the first election during Frederik X's reign, who became king in 2
Carlos Ray "Chuck" Norris was an American martial artist, actor, screenwriter, and author. He held black belts in karate, taekwondo, Tang Soo Do, Brazilian jiu-jitsu, and judo. After serving in the United States Air Force, he won numerous martial arts champion
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
Alan Michael Ritchson is an American actor. He made his acting debut as Aquaman/Arthur Curry on The CW superhero series Smallville (2005–10), where he appeared as a guest star between the fifth and tenth seasons. He subsequently had a starring role in the Spik
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.
Aditya Dhar is an Indian filmmaker who works in Hindi cinema. Having previously worked as a lyricist, Dhar made his directorial debut with the 2019 war film Uri: The Surgical Strike, a commercially successful venture which earned him the National Film Award fo
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
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
Miley Ray Cyrus is an American singer, songwriter, and actress. An influential figure in popular music, Cyrus is known for her evolving artistry and image reinventions. She was an established child actress before developing a successful entertainment career as
Daredevil: Born Again season 2
The second season of the American television series Daredevil: Born Again is based on Marvel Comics featuring the character Daredevil. It sees blind vigilante Matt Murdock / Daredevil gathering allies to resist Wilson Fisk, who is the mayor of New York City, a
Mohamed Salah Hamed Mahrous Ghaly is an Egyptian professional footballer who plays as a right winger for Süper Lig club Trabzonspor and captains the Egypt national team. He is widely regarded as one of the best players of his generation and one of the greatest
Melanie Jane Brisbane-Schilling was an Australian television personality, psychologist and relationship coach, best known for appearing as one of the dating experts on the Nine Network reality series Married at First Sight from 2016 until her death in 2026.
The third season of The Walking Dead, an American post-apocalyptic horror television series on AMC, premiered on October 14, 2012, and concluded on March 31, 2013, consisting of 16 episodes. Developed for television by Frank Darabont, the series is based on th
Cox's timepiece is a clock developed in the 1760s by James Cox. It was developed in collaboration with John Joseph Merlin. Cox claimed that his design was a true perpetual motion machine, but as the device is powered from changes in atmospheric pressure via a
Harry Potter is an upcoming fantasy television series developed for HBO, based on the series of eponymous fantasy books by British author J. K. Rowling. Produced by HBO, Warner Bros. Television, Brontë Film & TV, and Heyday Films, it stars Dominic McLaughlin a
Dame Sarah Elisabeth Mullally is an Anglican bishop and England's former chief nursing officer who has served as the 106th archbishop of Canterbury since 28 January 2026. As archbishop, she is the senior bishop of the Church of England and the ceremonial head
Leonid Saveliyovych Radvinsky was an American billionaire businessman and the majority owner of OnlyFans. Born in the Ukrainian SSR, Radvinsky was the founder of the cam site MyFreeCams, and the majority owner of OnlyFans, a content subscription service websit
The Visit (2015 American film)
The Visit is a 2015 American found footage horror film written, co-produced and directed by M. Night Shyamalan and starring Olivia DeJonge, Ed Oxenbould, Deanna Dunagan, Peter McRobbie, and Kathryn Hahn. The film centers around two young siblings, teenage girl
Acacia ixiophylla, commonly known as umbrella wattle or sticky leaved wattle, is a species of flowering plant in the family Fabaceae and is endemic to eastern Australia. It is a spreading shrub with leathery, narrowly oblong to elliptic phyllodes, spherical he
Kayleigh Rose Amstutz, known professionally as Chappell Roan, is an American singer and songwriter. She is known for her camp and drag queen–influenced style.
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
The 82nd Airborne Division is a United States Army airborne infantry division that specializes in parachute assault, based in Fort Bragg, North Carolina. Currently, the division is commanded by Major General Brandon Tegtmeier.
The Madison is a contemporary Western television series created by Taylor Sheridan for Paramount+. The series follows the Clyburn family, originally from New York City, who relocate to the Madison River valley of southwest Montana for emotional recovery follow
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Human strategic decision making in parametrized games
Many real-world games contain parameters which can affect payoffs, action spaces, and information states. For fixed values of the parameters, the game can be solved using standard algorithms. However, in many settings agents must act without knowing the values of the parameters that will be encountered in advance. Often the decisions must be made by a human under time and resource constraints, and it is unrealistic t
Entire Space Counterfactual Learning for Reliable Content Recommendations
Post-click conversion rate (CVR) estimation is a fundamental task in developing effective recommender systems, yet it faces challenges from data sparsity and sample selection bias. To handle both challenges, the entire space multitask models are employed to decompose the user behavior track into a sequence of exposure $\rightarrow$ click $\rightarrow$ conversion, constructing surrogate learning tasks for CVR estimati
A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective
Enterprise financial risk analysis aims at predicting the future financial risk of enterprises. Due to its wide and significant application, enterprise financial risk analysis has always been the core research topic in the fields of Finance and Management. Based on advanced computer science and artificial intelligence technologies, enterprise risk analysis research is experiencing rapid developments and making signif
WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation
Transformer has been very successful in various computer vision tasks and understanding the working mechanism of transformer is important. As touchstones, weakly-supervised semantic segmentation (WSSS) and class activation map (CAM) are useful tasks for analyzing vision transformers (ViT). Based on the plain ViT pre-trained with ImageNet classification, we find that multi-layer, multi-head self-attention maps can pro
MedAugment: Universal Automatic Data Augmentation Plug-in for Medical Image Analysis
Data augmentation (DA) has been widely leveraged in computer vision to alleviate data shortage, while its application in medical imaging faces multiple challenges. The prevalent DA approaches in medical image analysis encompass conventional DA, synthetic DA, and automatic DA. However, these approaches may result in experience-driven design and intensive computation costs. Here, we propose a suitable yet general autom
A Generalizable Deep Learning System for Cardiac MRI
Cardiac MRI allows for a comprehensive assessment of myocardial structure, function and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capable of representing the breadth of human cardiovascular disease and health. Our deep-learning model is trained via self-supervised contrastive learning, in which visual concepts in cine-sequence cardiac MRI scans are learned from the raw tex
Perturbative adaptive importance sampling for Bayesian LOO cross-validation
Importance sampling (IS) is an efficient stand-in for model refitting in performing (LOO) cross-validation (CV) on a Bayesian model. IS inverts the Bayesian update for a single observation by reweighting posterior samples. The so-called importance weights have high variance -- we resolve this issue through adaptation by transformation. We observe that removing a single observation perturbs the posterior by $\mathcal{
Learning To Guide Human Decision Makers With Vision-Language Models
There is growing interest in AI systems that support human decision-making in high-stakes domains (e.g., medical diagnosis) to improve decision quality and reduce cognitive load. Mainstream approaches pair human experts with a machine-learning model, offloading low-risk decisions to the model so that experts can focus on cases that require their judgment. This separation of responsibilities setup, however, is inadequ
Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation
Matrix factorization (MF) is a widely used collaborative filtering (CF) algorithm for recommendation systems (RSs), due to its high prediction accuracy, great flexibility and high efficiency in big data processing. However, with the dramatically increased number of users/items in current RSs, the computational complexity for training a MF model largely increases. Many existing works have accelerated MF, by either put
Adverse weather conditions, low-light environments, and bumpy road surfaces pose significant challenges to SLAM in robotic navigation and autonomous driving. Existing datasets in this field predominantly rely on single sensors or combinations of LiDAR, cameras, and IMUs. However, 4D millimeter-wave radar demonstrates robustness in adverse weather, infrared cameras excel in capturing details under low-light conditions
Fast Computation of Superquantile-Constrained Optimization Through Implicit Scenario Reduction
Superquantiles have recently gained significant interest as a risk-aware metric for addressing fairness and distribution shifts in statistical learning and decision making problems. This paper introduces a fast, scalable and robust second-order computational framework to solve large-scale optimization problems with superquantile-based constraints. Unlike empirical risk minimization, superquantile-based optimization r
Hamiltonian Mechanics of Feature Learning: Bottleneck Structure in Leaky ResNets
We study Leaky ResNets, which interpolate between ResNets and Fully-Connected nets depending on an 'effective depth' hyper-parameter $\tilde{L}$. In the infinite depth limit, we study 'representation geodesics' $A_{p}$: continuous paths in representation space (similar to NeuralODEs) from input $p=0$ to output $p=1$ that minimize the parameter norm of the network. We give a Lagrangian and Hamiltonian
Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework
Coded computing has emerged as a promising framework for tackling significant challenges in large-scale distributed computing, including the presence of slow, faulty, or compromised servers. In this approach, each worker node processes a combination of the data, rather than the raw data itself. The final result then is decoded from the collective outputs of the worker nodes. However, there is a significant gap betwee
Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training
Recurrent neural networks (RNNs) are a widely used tool for sequential data analysis; however, they are still often seen as black boxes. Visualizing the internal dynamics of RNNs is a critical step toward understanding their functional principles and developing better architectures and optimization strategies. Prior studies typically emphasize network representations only after training, overlooking how those represe
Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimation
Heterogeneous treatment effect (HTE) estimation from observational data poses significant challenges due to treatment selection bias. Existing methods address this bias by minimizing distribution discrepancies between treatment groups in latent space, focusing on global alignment. However, the fruitful aspect of local proximity, where similar units exhibit similar outcomes, is often overlooked. In this study, we prop
Translating technical terms into lexically similar, low-resource Indian languages remains a challenge due to limited parallel data and the complexity of linguistic structures. We propose a novel use-case of Sanskrit-based segments for linguistically informed translation of such terms, leveraging subword-level similarity and morphological alignment across related languages. Our approach uses character-level segmentati
In many real-world applications, modeling both the internal structure of sets and their temporal relationships is essential for capturing complex underlying patterns. Sequential multiple-instance learning aims to address this challenge by learning permutation-invariant representations of sets distributed across discrete timesteps. However, existing methods either focus on learning set representations at a static leve
An efficient wavelet-based physics-informed neural network for multiscale problems
Physics-informed neural networks (PINNs) are a class of deep learning models that utilize physics in the form of differential equations to address complex problems, including those with limited data availability. However, solving differential equations with rapid oscillations, steep gradients, or singular behavior remains challenging for PINNs. To address this, we propose an efficient wavelet-based physics-informed n
Dynamic Neural Potential Field: Online Trajectory Optimization in the Presence of Moving Obstacles
Generalist robot policies must operate safely and reliably in everyday human environments such as homes, offices, and warehouses, where people and objects move unpredictably. We present Dynamic Neural Potential Field (NPField-GPT), a learning-enhanced model predictive control (MPC) framework that couples classical optimization with a Transformer-based predictor of footprint-aware repulsive potentials. Given an occupa
Morph: A Motion-free Physics Optimization Framework for Human Motion Generation
Human motion generation has been widely studied due to its crucial role in areas such as digital humans and humanoid robot control. However, many current motion generation approaches disregard physics constraints, frequently resulting in physically implausible motions with pronounced artifacts such as floating and foot sliding. Meanwhile, training an effective motion physics optimizer with noisy motion data remains l
Phrase-Instance Alignment for Generalized Referring Segmentation
Generalized Referring expressions can describe one object, several related objects, or none at all. Existing generalized referring segmentation (GRES) models treat all cases alike, predicting a single binary mask and ignoring how linguistic phrases correspond to distinct visual instances. To this end, we reformulate GRES as an instance-level reasoning problem, where the model first predicts multiple instance-aware ob
Learning-based Sketches for Frequency Estimation in Data Streams without Ground Truth
Estimating the frequency of items on the high-volume, fast data stream has been extensively studied in many areas, such as database and network measurement. Traditional sketches provide only coarse estimates under strict memory constraints. Although some learning-augmented methods have emerged recently, they typically rely on offline training with real frequencies or/and labels, which are often unavailable. Moreover,
Promoting Cooperation in the Public Goods Game using Artificial Intelligent Agents
The tragedy of the commons illustrates a fundamental social dilemma where individual rational actions lead to collectively undesired outcomes, threatening the sustainability of shared resources. Strategies to escape this dilemma, however, are in short supply. In this study, we explore how artificial intelligence (AI) agents can be leveraged to enhance cooperation in public goods games, moving beyond traditional regul
General Coded Computing in a Probabilistic Straggler Regime
Coded computing has demonstrated promising results in addressing straggler resiliency in distributed computing systems. However, most coded computing schemes are designed for exact computation, requiring the number of responding servers to exceed a certain recovery threshold. Additionally, these schemes are tailored for highly structured functions. Recently, new coded computing schemes for general computing functions
Symmetry-Guided Memory Augmentation for Efficient Locomotion Learning
Training reinforcement learning (RL) policies for legged locomotion often requires extensive environment interactions, which are costly and time-consuming. We propose Symmetry-Guided Memory Augmentation (SGMA), a framework that improves training efficiency by combining structured experience augmentation with memory-based context inference. Our method leverages robot and task symmetries to generate additional, physica
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