Record 24032026 · 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
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
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
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
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
Robert Swan Mueller III was an American lawyer who served as the sixth director of the Federal Bureau of Investigation (FBI) from 2001 to 2013.
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
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
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.
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
Richard Andrew Pitino is an American basketball coach who is the head men's basketball coach at St. John's University. He has been the head coach of several teams in NCAA Division I and in the NBA, including Boston University (1978–1983), Providence College (1
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
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
Peaky Blinders: The Immortal Man
Peaky Blinders: The Immortal Man is a 2026 British crime drama film directed by Tom Harper and written by Steven Knight. It is a continuation of the British television series Peaky Blinders (2013–2022), and stars Cillian Murphy alongside an ensemble cast inclu
OnlyFans is an Internet content paid subscription service based in London, England. The service is widely known for its popularity with pornographers, but it also hosts other content creators, including athletes, musicians, and comedians.
Air Canada Express Flight 8646
Air Canada Express Flight 8646 was a regularly scheduled international passenger flight from Montréal–Trudeau International Airport in Quebec, Canada, to LaGuardia Airport in New York, United States. The flight was operated by Jazz Aviation, an airline operati
Valerie Ritchie Perrine was an American actress. She was best known for her portrayal of Honey Bruce in the film Lenny (1974). For the role, she won the BAFTA Award for Most Promising Newcomer to Leading Film Roles and the Best Actress Award at the Cannes Film
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
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
Nicholas Brendon Schultz was an American actor, artist, and writer. He was best known for playing Xander Harris in the television series Buffy the Vampire Slayer (1997–2003) and Kevin Lynch in Criminal Minds (2007–2014).
David Jude Heyworth Law is an English actor. He began his career in British theatre before landing small roles in various television productions and feature films. Law gained international recognition for his role in Anthony Minghella's The Talented Mr. Ripley
Barry Keoghan is an Irish actor. His accolades include a BAFTA Award, along with nominations for an Academy Award and two Golden Globe Awards. In 2020, he was listed at number 27 on The Irish Times list of Ireland's greatest film actors.
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.
Ilyas Kashmiri was a Pakistani Special Forces Operator turned Islamist jihadist militant leader. He was a major insurgent in Jammu and Kashmir, and had fought against Indian troops there during several engagements. He was killed on 3 June 2011 in US drone atta
Roanoke Township is located in Woodford County, Illinois at T27N, R1W. As of the 2010 census, its population was 2,558 and it contained 1,044 housing units. Roanoke Township includes within its boundaries the village of Roanoke, Illinois.
Giorgio Ficara is an Italian essayist and writer. He is Full Professor of Italian Literature at the University of Turin.
Uzair Jan Baloch is a Pakistani gangster and former crime lord. He was also the head of the outlawed Peoples' Aman Committee based in Lyari, Karachi, Sindh, Pakistan.
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
Sardar Abdul Rehman Baloch, known by the alias Rehman Dakait, was a Pakistani gangster based in Karachi's Lyari neighbourhood who formed the Peoples' Aman Committee which was affiliated with the Pakistan People's Party. The Government of Sindh had set a reward
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Relative Error Embeddings for the Gaussian Kernel Distance
A reproducing kernel can define an embedding of a data point into an infinite dimensional reproducing kernel Hilbert space (RKHS). The norm in this space describes a distance, which we call the kernel distance. The random Fourier features (of Rahimi and Recht) describe an oblivious approximate mapping into finite dimensional Euclidean space that behaves similar to the RKHS. We show in this paper that for the Gaussian
Persistent Homology as Stopping-Criterion for Voronoi Interpolation
In this study the Voronoi interpolation is used to interpolate a set of points drawn from a topological space with higher homology groups on its filtration. The technique is based on Voronoi tessellation, which induces a natural dual map to the Delaunay triangulation. Advantage is taken from this fact calculating the persistent homology on it after each iteration to capture the changing topology of the data. The boun
Estimate of the Neural Network Dimension using Algebraic Topology and Lie Theory
In this paper we present an approach to determine the smallest possible number of neurons in a layer of a neural network in such a way that the topology of the input space can be learned sufficiently well. We introduce a general procedure based on persistent homology to investigate topological invariants of the manifold on which we suspect the data set. We specify the required dimensions precisely, assuming that ther
Homological Time Series Analysis of Sensor Signals from Power Plants
In this paper, we use topological data analysis techniques to construct a suitable neural network classifier for the task of learning sensor signals of entire power plants according to their reference designation system. We use representations of persistence diagrams to derive necessary preprocessing steps and visualize the large amounts of data. We derive deep architectures with one-dimensional convolutional layers
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent
We aim to make stochastic gradient descent (SGD) adaptive to (i) the noise $σ^2$ in the stochastic gradients and (ii) problem-dependent constants. When minimizing smooth, strongly-convex functions with condition number $κ$, we prove that $T$ iterations of SGD with exponentially decreasing step-sizes and knowledge of the smoothness can achieve an $\tilde{O} \left(\exp \left( \frac{-T}κ \right) + \frac{σ^2}{T} \right)$
In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the ac-curacy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for personalized epileptic EEG detection (i.e., training of detection model for a specific person), includin
No-Regret Bayesian Recommendation to Homogeneous Users
We introduce and study the online Bayesian recommendation problem for a recommender system platform. The platform has the privilege to privately observe a utility-relevant \emph{state} of a product at each round and uses this information to make online recommendations to a stream of myopic users. This paradigm is common in a wide range of scenarios in the current Internet economy. The platform commits to an online re
Noise-contrastive Online Change Point Detection
We suggest a novel procedure for online change point detection. Our approach expands an idea of maximizing a discrepancy measure between points from pre-change and post-change distributions. This leads to flexible algorithms suitable for both parametric and nonparametric scenarios. We prove non-asymptotic bounds on the average running length of the procedure and its expected detection delay. The efficiency of the alg
Deep Reinforcement Learning (DRL) has made considerable advances in simulated and physical robot control tasks, especially when problems admit a fully observed Markov Decision Process (MDP) formulation. When observations only partially capture the underlying state, the problem becomes a Partially Observable MDP (POMDP), and performance rankings between algorithms can change. We empirically compare Proximal Policy Opt
Policy Optimization over General State and Action Spaces
Reinforcement learning (RL) problems over general state and action spaces are notoriously challenging. In contrast to the tableau setting, one can not enumerate all the states and then iteratively update the policies for each state. This prevents the application of many well-studied RL methods especially those with provable convergence guarantees. In this paper, we first present a substantial generalization of the re
Hybrid Quantum Generative Adversarial Networks for Molecular Simulation and Drug Discovery
In molecular research, the modelling and analysis of molecules through simulation is an important part that has a direct influence on medical development, material science and drug discovery. The processing power required to design protein chains with hundreds of peptides is huge. Classical computing techniques, including state-of-the-art machine learning models being deployed on classical computing machines, have pr
Better Generative Replay for Continual Federated Learning
Federated learning is a technique that enables a centralized server to learn from distributed clients via communications without accessing the client local data. However, existing federated learning works mainly focus on a single task scenario with static data. In this paper, we introduce the problem of continual federated learning, where clients incrementally learn new tasks and history data cannot be stored due to
Geometric Imbalance in Semi-Supervised Node Classification
Class imbalance in graph data presents a significant challenge for effective node classification, particularly in semi-supervised scenarios. In this work, we formally introduce the concept of geometric imbalance, which captures how message passing on class-imbalanced graphs leads to geometric ambiguity among minority-class nodes in the riemannian manifold embedding space. We provide a rigorous theoretical analysis of
Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems
Recent advancements in Graph Neural Networks (GNN) have facilitated their widespread adoption in various applications, including recommendation systems. GNNs have proven to be effective in addressing the challenges posed by recommendation systems by efficiently modeling graphs in which nodes represent users or items and edges denote preference relationships. However, current GNN techniques represent nodes by means of
On Consistency of Signature Using Lasso
Signatures are iterated path integrals of continuous and discrete-time processes, and their universal nonlinearity linearizes the problem of feature selection in time series data analysis. This paper studies the consistency of signature using Lasso regression, both theoretically and numerically. We establish conditions under which the Lasso regression is consistent both asymptotically and in finite sample. Furthermor
Phenotype-preserving metric design for high-content image reconstruction by generative inpainting
In the past decades, automated high-content microscopy demonstrated its ability to deliver large quantities of image-based data powering the versatility of phenotypic drug screening and systems biology applications. However, as the sizes of image-based datasets grew, it became infeasible for humans to control, avoid and overcome the presence of imaging and sample preparation artefacts in the images. While novel techn
Exploring ChatGPT's Empathic Abilities
Empathy is often understood as the ability to share and understand another individual's state of mind or emotion. With the increasing use of chatbots in various domains, e.g., children seeking help with homework, individuals looking for medical advice, and people using the chatbot as a daily source of everyday companionship, the importance of empathy in human-computer interaction has become more apparent. Therefo
MALA is a popular gradient-based Markov chain Monte Carlo method to access the Gibbs-posterior distribution. Stochastic MALA (sMALA) scales to large data sets, but changes the target distribution from the Gibbs-posterior to a surrogate posterior which only exploits a reduced sample size. We introduce a corrected stochastic MALA (csMALA) with a simple correction term for which distance between the resulting surrogate
Conditional Variational Diffusion Models
Inverse problems aim to determine parameters from observations, a crucial task in engineering and science. Lately, generative models, especially diffusion models, have gained popularity in this area for their ability to produce realistic solutions and their good mathematical properties. Despite their success, an important drawback of diffusion models is their sensitivity to the choice of variance schedule, which cont
Polarimetric Light Transport Analysis for Specular Inter-reflection
Polarization is well known for its ability to decompose diffuse and specular reflections. However, the existing decomposition methods only focus on direct reflection and overlook multiple reflections, especially specular inter-reflection. In this paper, we propose a novel decomposition method for handling specular inter-reflection of metal objects by using a unique polarimetric feature: the rotation direction of line
Lower Bainite (LB) and Tempered Martensite (TM) are two common microstructures in modern high-strength steels. LB and TM can render similar mechanical properties for steels, yet LB is often considered superior to TM in resistance to hydrogen embrittlement. Such performance difference has conventionally been attributed to their distinction in certain microstructural features, particularly carbides. The present study d
WiFi-GEN: High-Resolution Indoor Imaging from WiFi Signals Using Generative AI
Indoor imaging is a critical task for robotics and internet-ofthings. WiFi as an omnipresent signal is a promising candidate for carrying out passive imaging and synchronizing the up-to-date information to all connected devices. This is the first research work to consider WiFi indoor imaging as a multi-modal image generation task that converts the measured WiFi power into a high-resolution indoor image. Our proposedW
High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization
Uncertainty quantification for estimation through stochastic optimization solutions in an online setting has gained popularity recently. This paper introduces a novel inference method focused on constructing confidence intervals with efficient computation and fast convergence to the nominal level. Specifically, we propose to use a small number of independent multi-runs to acquire distribution information and construc
Understanding Neural Network Systems for Image Analysis using Vector Spaces and Inverse Maps
There is strong interest in developing mathematical methods that can be used to understand complex neural networks used in image analysis. In this paper, we introduce techniques from Linear Algebra to model neural network layers as maps between signal spaces. First, we demonstrate how signal spaces can be used to visualize weight spaces and convolutional layer kernels. We also demonstrate how residual vector spaces c
Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection
Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains using only data from a single source domain during training. This is a practical yet challenging task as it requires the model to address domain shift without incorporating target domain data into training. In this paper, we propose a novel phrase grounding-based style transfer (PGST) approach f
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