Record 23032026 · 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
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.
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
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
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).
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
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
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
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
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
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
Jorginho (footballer, born December 1991)
Jorge Luiz Frello Filho, known as Jorginho, is a professional footballer who plays as a defensive midfielder for Campeonato Brasileiro Série A club Flamengo. Born in Brazil, he represented the Italy national team.
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
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
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
Diego Garcia is the largest island of the Chagos Archipelago. It has been used as a joint UK–U.S. military base since the 1970s, following the expulsion of the Chagossians by the UK government. The Chagos Islands are a British overseas territory, though a trea
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
Nico O'Reilly is an English professional footballer who plays as a left-back or midfielder for Premier League club Manchester City and the England national team.
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.
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
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
Olivier Rioux is a Canadian college basketball player for the UC Irvine Anteaters of the Big West Conference. He previously played for the Florida Gators. Guinness World Records declared him as the tallest teenager in the world in 2021 when he measured 7 ft 5
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.
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.
An ant mill is an observed phenomenon in which a group of army ants, separated from the main foraging party, lose the pheromone track and begin to follow one another, forming a continuously rotating circle. This circle is commonly known as a “death spiral” bec
The English Football League Cup, often referred to as the League Cup and officially known as the Carabao Cup for sponsorship reasons, is an annual knockout competition in men's domestic football in England.
One Battle After Another is a 2025 American action thriller film written, directed, and produced by Paul Thomas Anderson. Inspired by the 1990 novel Vineland by Thomas Pynchon, the film's ensemble cast includes Leonardo DiCaprio, Sean Penn, Benicio del Toro, R
Sinners is a 2025 American horror film produced, written, and directed by Ryan Coogler. Set in 1932 in the Mississippi Delta, it stars Michael B. Jordan in dual roles as criminal twin brothers who return to their hometown in the Jim Crow South, where they are
Peaky Blinders is a British historical crime drama television series created by Steven Knight. Set in Birmingham, it follows the exploits of the Peaky Blinders crime gang in the direct aftermath of the First World War. The fictional gang is loosely based on an
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Exact MAP inference in general higher-order graphical models using linear programming
This paper is concerned with the problem of exact MAP inference in general higher-order graphical models by means of a traditional linear programming relaxation approach. In fact, the proof that we have developed in this paper is a rather simple algebraic proof being made straightforward, above all, by the introduction of two novel algebraic tools. Indeed, on the one hand, we introduce the notion of delta-distributio
Predicting Hidden Links and Missing Nodes in Scale-Free Networks with Artificial Neural Networks
There are many networks in real life which exist as form of Scale-free networks such as World Wide Web, protein-protein interaction network, semantic networks, airline networks, interbank payment networks, etc. If we want to analyze these networks, it is really necessary to understand the properties of scale-free networks. By using the properties of scale free networks, we can identify any type of anomalies in those
Deep Face Restoration: A Survey
Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images, which is a domain-specific image restoration problem in the low-level computer vision area. The early face restoration methods mainly use statistical priors and degradation models, which are difficult to meet the requirements of real-world applications in practice. In recent years, face restoration has witnessed great pr
torchgfn: A PyTorch GFlowNet library
The growing popularity of generative flow networks (GFlowNets or GFNs) from a range of researchers with diverse backgrounds and areas of expertise necessitates a library that facilitates the testing of new features (e.g., training losses and training policies) against standard benchmark implementations, or on a set of common environments. We present torchgfn, a PyTorch library that aims to address this need. Its core
EgoSpot:Egocentric Multimodal Control for Hands-Free Mobile Manipulation
We propose a novel hands-free control framework for the Boston Dynamics Spot robot using the Microsoft HoloLens 2 mixed-reality headset. Enabling accessible robot control is critical for allowing individuals with physical disabilities to benefit from robotic assistance in daily activities, teleoperation, and remote interaction tasks. However, most existing robot control interfaces rely on manual input devices such as
Information-theoretic generalization bounds for learning from quantum data
Learning tasks play an increasingly prominent role in quantum information and computation. They range from fundamental problems such as state discrimination and metrology over the framework of quantum probably approximately correct (PAC) learning, to the recently proposed shadow variants of state tomography. However, the many directions of quantum learning theory have so far evolved separately. We propose a general m
Hyper-STTN: Hypergraph Augmented Spatial-Temporal Transformer Network for Trajectory Prediction
Predicting crowd intentions and trajectories is critical for a range of real-world applications, involving social robotics and autonomous driving. Accurately modeling such behavior remains challenging due to the complexity of pairwise spatial-temporal interactions and the heterogeneous influence of groupwise dynamics. To address these challenges, we propose Hyper-STTN, a Hypergraph-based Spatial-Temporal Transformer
SRGS: Super-Resolution 3D Gaussian Splatting
Low-resolution (LR) multi-view capture limits the fidelity of 3D Gaussian Splatting (3DGS). 3DGS super-resolution (SR) is therefore important, yet challenging because it must recover missing high-frequency details while enforcing cross-view geometric consistency. We revisit SRGS, a simple baseline that couples plug-in 2D SR priors with geometry-aware cross-view regularization, and observe that most subsequent advance
In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies
We present a new random walk for uniformly sampling high-dimensional convex bodies. It achieves state-of-the-art runtime complexity with stronger guarantees on the output than previously known, namely in Rényi divergence (which implies TV, $\mathcal{W}_2$, KL, $χ^2$). The proof departs from known approaches for polytime algorithms for the problem -- we utilize a stochastic diffusion perspective to show contraction to
Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens
Large language models (LLMs) have shown promising efficacy across various tasks, becoming powerful tools in numerous aspects of human life. However, Transformer-based LLMs suffer a performance degradation when modeling long-term contexts due to they discard some information to reduce computational overhead. In this work, we propose a simple yet effective method to enable LLMs to take a deep breath, encouraging them t
Causal Learning in Biomedical Applications: Krebs Cycle as a Benchmark
Learning causal relationships from time series data is an important but challenging problem. Existing synthetic datasets often contain hidden artifacts that can be exploited by causal discovery methods, reducing their usefulness for benchmarking. We present a new benchmark dataset based on simulations of the Krebs cycle, a key biochemical pathway. The data are generated using a particle-based simulator that models mo
Generalized Continuous-Time Models for Nesterov's Accelerated Gradient Methods
Recent research has indicated a substantial rise in interest in understanding Nesterov's accelerated gradient methods via their continuous-time models. However, most existing studies focus on specific classes of Nesterov's methods, which hinders the attainment of an in-depth understanding and a unified perspective. To address this deficit, we present generalized continuous-time models that cover a broad range
A new paradigm for global sensitivity analysis
It is well-known that Sobol indices, which count among the most popular sensitivity indices, are based on the Sobol decomposition. Here we challenge this construction by redefining Sobol indices without the Sobol decomposition. In fact, we show that Sobol indices are a particular instance of a more general concept which we call sensitivity measures. A sensitivity measure of a system taking inputs and returning output
Learning Representations for Independence Testing
Many tools exist to detect dependence between random variables, a core question across a wide range of machine learning, statistical, and scientific endeavors. Although several statistical tests guarantee eventual detection of any dependence with enough samples, standard tests may require an exorbitant amount of samples for detecting subtle dependencies between high-dimensional random variables with complex distribut
In this paper we propose an end-to-end algorithm for indirect data-driven control for bilinear systems with stability guarantees. We consider the case where the collected i.i.d. data is affected by probabilistic noise with possibly unbounded support and leverage tools from statistical learning theory to derive finite sample identification error bounds. To this end, we solve the bilinear identification problem by solv
Simulation-based Inference with the Python Package sbijax
Neural simulation-based inference (SBI) describes an emerging family of methods for Bayesian inference with intractable likelihood functions that use neural networks as surrogate models. Here we introduce sbijax, a Python package that implements a wide variety of state-of-the-art methods in neural simulation-based inference using a user-friendly programming interface. sbijax offers high-level functionality to quickly
A Survey of AI-Generated Video Evaluation
The growing capabilities of AI in generating video content have brought forward significant challenges in effectively evaluating these videos. Unlike static images or text, video content involves complex spatial and temporal dynamics which may require a more comprehensive and systematic evaluation of its contents in aspects like video presentation quality, semantic information delivery, alignment with human intention
AtGCN: A Graph Convolutional Network For Ataxic Gait Detection
Video-based gait analysis can be defined as the task of diagnosing pathologies, such as ataxia, using videos of patients walking in front of a camera. This paper presents a graph convolution network called AtGCN for detecting ataxic gait and identifying its severity using 2D videos. The problem is especially challenging as the deviation of an ataxic gait from a healthy gait is very subtle. The datasets for ataxic gai
LISAA: A Framework for Large Language Model Information Security Awareness Assessment
The popularity of large language models (LLMs) continues to grow, and LLM-based assistants have become ubiquitous. Information security awareness (ISA) is an important yet underexplored area of LLM safety. ISA encompasses LLMs' security knowledge, which has been explored in the past, as well as their attitudes and behaviors, which are crucial to LLMs' ability to understand implicit security context and reject
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem
The quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm (VQA) ideal for noisy intermediate-scale quantum (NISQ) processors, and is highly successful in solving combinatorial optimization problems (COPs). It has been observed that the optimal parameters obtained from one instance of a COP can be transferred to another instance, resulting in generally good solutions for the latter. In t
Online Clustering of Data Sequences with Bandit Information
We study the problem of online clustering of data sequences in the multi-armed bandit (MAB) framework under the fixed-confidence setting. There are $M$ arms, each providing i.i.d. samples from a parametric distribution whose parameters are unknown. The $M$ arms form $K$ clusters based on the distance between the true parameters. In the MAB setting, one arm can be sampled at each time. The objective is to estimate the
Communication-Efficient Stochastic Distributed Learning
We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direction Method of Multipliers (ADMM) to address the challenges of high communication costs, and large datasets. Our design tackles these challenges i) by enabling the agents to perform multiple local training steps between each round of commun
GoDe: Gaussians on Demand for Progressive Level of Detail and Scalable Compression
Recent progress in compressing explicit radiance field representations, particularly 3D Gaussian Splatting, has substantially reduced memory consumption while improving real-time rendering performance. However, existing approaches remain inherently single-rate: each compression level requires a separately optimized model, yielding a set of fixed operating points rather than a truly scalable representation. This limit
On the Theory of Bias Tuning in Event Cameras
This paper lays the foundation of a theory for bias tuning in neuromorphic cameras, a novel sensing technology also known as "event cameras". We begin by formulating the high-level effect of the sensitivity biases on the camera's event rate in mathematical terms. We then show that, as a corollary of the Poincare-Miranda theorem, the commonly used tuning principles of rate budgeting and polarity balancing
Flow-based Conformal Prediction for Multi-dimensional Time Series
Time series prediction underpins a broad range of downstream tasks across many scientific domains. Recent advances and increasing adoption of black-box machine learning models for time series prediction highlight the critical need for uncertainty quantification. While conformal prediction has gained attention as a reliable uncertainty quantification method, conformal prediction for time series faces two key challenge
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