Record 25032026 · 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
Massimiliana Landini Aleotti is an Italian billionaire businesswoman. She is the co-owner and inheritor of the pharmaceutical company Menarini based in Florence, Tuscany, and one of the world's ten richest women.
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
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
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
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
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
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
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
Markwayne Mullin is an American politician and businessman who has served since 2026 as the ninth United States secretary of homeland security. A member of the Republican Party, Mullin served from 2023 to 2026 as the junior United States senator from Oklahoma
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
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
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
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
Żyrardów County is a unit of territorial administration and local government (powiat) in Masovian Voivodeship, east-central Poland. It came into being on 1 January 1999, as a result of the Polish local government reforms passed in 1998. Its administrative seat
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
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.
Janko Polić Kamov was a Croatian novelist, playwright, writer, and poet. Although his oeuvre is small due to his short life, he is considered a significant writer in Croatian literature. Emblematic of the contemporary anger and displeasure over the hypocrisy a
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.
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
Mijares Mexican Restaurant is Pasadena, California's oldest Mexican food restaurant. Located at 145 Palmetto Dr., it was founded in 1920.
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
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
William Henry Cosby Jr. is an American former comedian, actor, and media personality. Often deemed a trailblazer for African Americans in the entertainment industry, Cosby was well known in the United States for his stand-up comedy routines and television care
Hillel Slovak was an Israeli-American musician, best known as an early guitarist of the Los Angeles rock band the Red Hot Chili Peppers, with whom he recorded two albums. His guitar work was rooted in funk and hard rock, and he often experimented with other ge
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Summarizing the performances of a background subtraction algorithm measured on several videos
There exist many background subtraction algorithms to detect motion in videos. To help comparing them, datasets with ground-truth data such as CDNET or LASIESTA have been proposed. These datasets organize videos in categories that represent typical challenges for background subtraction. The evaluation procedure promoted by their authors consists in measuring performance indicators for each video separately and to ave
On the Road with 16 Neurons: Mental Imagery with Bio-inspired Deep Neural Networks
This paper proposes a strategy for visual prediction in the context of autonomous driving. Humans, when not distracted or drunk, are still the best drivers you can currently find. For this reason we take inspiration from two theoretical ideas about the human mind and its neural organization. The first idea concerns how the brain uses a hierarchical structure of neuron ensembles to extract abstract concepts from visua
The 2020s Political Economy of Machine Translation
This paper explores the hypothesis that the diversity of human languages, right now a barrier to interoperability in communication and trade, will become significantly less of a barrier as machine translation technologies are deployed over the next several years.But this new boundary-breaking technology does not reduce all boundaries equally, and it creates new challenges for the distribution of ideas and thus for in
Inference of Multiscale Gaussian Graphical Model
Gaussian Graphical Models (GGMs) are widely used in high-dimensional data analysis to synthesize the interaction between variables. In many applications, such as genomics or image analysis, graphical models rely on sparsity and clustering to reduce dimensionality and improve performances. This paper explores a slightly different paradigm where clustering is not knowledge-driven but performed simultaneously with the g
Mixture Domain Adaptation to Improve Semantic Segmentation in Real-World Surveillance
Various tasks encountered in real-world surveillance can be addressed by determining posteriors (e.g. by Bayesian inference or machine learning), based on which critical decisions must be taken. However, the surveillance domain (acquisition device, operating conditions, etc.) is often unknown, which prevents any possibility of scene-specific optimization. In this paper, we define a probabilistic framework and present
An Accurate and Interpretable Framework for Trustworthy Process Monitoring
Trustworthy process monitoring seeks to build an accurate and interpretable monitoring framework, which is critical for ensuring the safety of energy conversion plant (ECP) that operates under extreme working conditions such as high pressure and temperature. Contemporary self-attentive models, however, fall short in this domain for two main reasons. First, they rely on step-wise correlations that fail to involve phys
HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations
Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis. Traditional methods for identifying "hit" molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug to its
Mapping the Challenges of HCI: An Application and Evaluation of ChatGPT for Mining Insights at Scale
Large language models (LLMs) are increasingly used for analytical tasks, yet their effectiveness in real-world applications remains underexamined, partly due to the opacity of proprietary models. We evaluate ChatGPT (GPT-3.5 and GPT-4) on the practical task of extracting research challenges from a large scholarly corpus in Human-Computer Interaction (HCI). Using a two-step approach, we first apply GPT-3.5 to extract
All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks
Variational algorithms require architectures that naturally constrain the optimization space to run efficiently. Geometric quantum machine learning achieves this goal by encoding group structure into parameterized quantum circuits to include the symmetries of a problem as an inductive bias. However, constructing such circuits is challenging as a concrete guiding principle has yet to emerge. In this paper, we propose
Offline to Online Learning for Real-Time Bandwidth Estimation
Real-time video applications require accurate bandwidth estimation (BWE) to maintain user experience across varying network conditions. However, increasing network heterogeneity challenges general-purpose BWE algorithms, necessitating solutions that adapt to end-user environments. While widely adopted, heuristic-based methods are difficult to individualize without extensive domain expertise. Conversely, online reinfo
Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction
In computed tomography (CT), the forward model consists of a linear Radon transform followed by an exponential nonlinearity based on the attenuation of light according to the Beer-Lambert Law. Conventional reconstruction often involves inverting this nonlinearity and then solving a linear inverse problem. However, this nonlinear measurement preprocessing is poorly conditioned in the vicinity of high-density materials
Sparse Learning and Class Probability Estimation with Weighted Support Vector Machines
Classification and probability estimation are fundamental tasks with broad applications across modern machine learning and data science, spanning fields such as biology, medicine, engineering, and computer science. Recent development of weighted Support Vector Machines (wSVMs) has demonstrated considerable promise in robustly and accurately predicting class probabilities and performing classification across a variety
Self-attention transformers have demonstrated accuracy for image classification with smaller data sets. However, a limitation is that tests to-date are based upon single class image detection with known representation of image populations. For instances where the input image classes may be greater than one and test sets that lack full information on representation of image populations, accuracy calculations must adap
Tennis is so popular that coaches and players are curious about factors other than skill, such as momentum. This article will try to define and quantify momentum, providing a basis for real-time analysis of tennis matches. Based on the tennis Grand Slam men's singles match data in recent years, we built two models, one is to build a model based on data-driven, and the other is to build a model based on empirical
Arena: Efficiently Training Large Models via Dynamic Scheduling and Adaptive Parallelism Co-Design
Efficiently training large-scale models (LMs) in GPU clusters involves two separate avenues: inter-job dynamic scheduling and intra-job adaptive parallelism (AP). However, existing dynamic schedulers struggle with large-model scheduling due to the mismatch between static parallelism (SP)-aware scheduling and AP-based execution, leading to cluster inefficiencies such as degraded throughput and prolonged job queuing. T
Reliable OOD Virtual Screening with Extrapolatory Pseudo-Label Matching
Machine learning (ML) models are increasingly deployed for virtual screening in drug discovery, where the goal is to identify novel, chemically diverse scaffolds while minimizing experimental costs. This creates a fundamental challenge: the most valuable discoveries lie in out-of-distribution (OOD) regions beyond the training data, yet ML models often degrade under distribution shift. Standard novelty-rejection strat
Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency
We consider achieving equivariance in machine learning systems via frame averaging. Current frame averaging methods involve a costly sum over large frames or rely on sampling-based approaches that only yield approximate equivariance. Here, we propose Minimal Frame Averaging (MFA), a mathematical framework for constructing provably minimal frames that are exactly equivariant. The general foundations of MFA also allow
Addressing Large Action Spaces in 3D Floorplanning via Spatial Generalization
Many recent machine learning approaches to floorplanning represent placement decisions using discrete canvas coordinates, which creates scalability bottlenecks as the action space grows. In this work, we study the effect of learning a continuous action representation for 3D floorplanning. By reasoning in a continuous placement space and discretizing only at inference time, our method decouples the output structure fr
Clusterpath Gaussian Graphical Modeling
Graphical models serve as effective tools for visualizing conditional dependencies between variables. However, as the number of variables grows, interpretation becomes increasingly difficult, and estimation uncertainty increases due to the large number of parameters relative to the number of observations. To address these challenges, we introduce the Clusterpath estimator of the Gaussian Graphical Model (CGGM) that e
High frame-rate (HFR) videos of action recognition improve fine-grained expression while reducing the spatio-temporal relation and motion information density. Thus, large amounts of video samples are continuously required for traditional data-driven training. However, samples are not always sufficient in real-world scenarios, promoting few-shot action recognition (FSAR) research. We observe that most recent FSAR work
Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features
Temporal difference (TD) learning with linear function approximation (linear TD) is a classic and powerful prediction algorithm in reinforcement learning. While it is well-understood that linear TD converges almost surely to a unique point, this convergence traditionally requires the assumption that the features used by the approximator are linearly independent. However, this linear independence assumption does not h
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data
In federated learning, the heterogeneity of client data has a great impact on the performance of model training. Many heterogeneity issues in this process are raised by non-independently and identically distributed (non-IID) data. To address the issue of label distribution skew, we propose a hybrid federated learning framework called HFLDD, which integrates dataset distillation to generate approximately independent a
SwiftQueue: Optimizing Low-Latency Applications with Swift Packet Queuing
Low Latency, Low Loss, and Scalable Throughput (L4S), as an emerging router-queue management technique, has seen steady deployment in the industry. An L4S-enabled router assigns each packet to the queue based on the packet header marking. Currently, L4S employs per-flow queue selection, i.e. all packets of a flow are marked the same way and thus use the same queues, even though each packet is marked separately. Howev
Language models such as GPT and Llama have shown remarkable ability on diverse natural language tasks, yet their performance on complex table tasks (e.g., NL-to-Code and data cleaning) remains suboptimal. Improving performance typically requires task-specific fine-tuning, which depends on expensive human labeling and is prone to overfitting. In this work, we propose Table-LLM-Specialist, a self-trained fine-tuning pa
Artificial intelligence for partial differential equations in computational mechanics: A review
In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Science: Artificial intelligence for science), which has attracted widespread attention. In AI for Science, using artificial intelligence algorithms to solve partial differential equations (AI for PDEs: Artificial intelligence for partial differ
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