Record 26112025 · captured 2026-08-25
The world looked up Dharmendra. 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.
Dharmendra was an Indian actor, producer and politician, primarily known for his work in Hindi films. He is regarded as one of the greatest and most commercially successful actors in the history of Indian cinema. Known as the "He-man", he was popular for his h
Sawyer Storm Sweeten was an American child actor. He was best known for his role as Geoffrey Barone on the sitcom Everybody Loves Raymond.
Google Chrome is a cross-platform web browser developed by Google. It was launched in September 2008 for Microsoft Windows and was built with free software components from Apple WebKit and Mozilla Firefox. Versions for Linux, macOS, iOS, iPadOS, and Android we
Sir Richard Charles Nicholas Branson is an English business magnate who co-founded the Virgin Group in 1970, and, as of 2016, controlled five companies.
Wicked: For Good is a 2025 musical fantasy film directed by Jon M. Chu and written by Winnie Holzman and Dana Fox. The sequel to Wicked (2024), it adapts the second act of the 2003 stage musical by Stephen Schwartz and Holzman, which was loosely based on Grego
This is a list of lists of deaths of significant people, organized by year. New deaths articles are added to their respective month and then linked below.
Lucy Lambert Hale was the daughter of U.S. Senator John Parker Hale of New Hampshire, and was a noted Washington, D.C., society belle. She attracted many admirers including Oliver Wendell Holmes Jr., Robert Todd Lincoln; and stage actor and presidential assass
Hema Malini Dharmendra Deol is an Indian actress, director, producer, and politician who is currently serving as a member of the Lok Sabha from the Bharatiya Janata Party (BJP), representing Mathura constituency since 2014. She was a member of the Rajya Sabha
Bruce Lee was a Hong Kong and American martial artist, actor, and filmmaker. He was the founder of Jeet Kune Do, a hybrid martial arts philosophy, which was formed from his experiences in unarmed fighting and self-defense—as well as eclectic, Zen Buddhist, and
Mark Edward Kelly is an American politician and a retired astronaut and naval officer. He is the senior United States senator from Arizona, a seat he has held since 2020. He is a member of the Democratic Party.
Madylin Sweeten is an American actress, best known for her portrayal of Ally Barone on the CBS family sitcom Everybody Loves Raymond (1996–2005).
The Tulsa race massacre was a two-day-long terrorist massacre perpetrated by white supremacists that took place in the Greenwood District of Tulsa, Oklahoma, United States, between May 31 and June 1, 1921. Mobs of white residents, some of whom had been armed a
James Ezekiel Chambers, known professionally as Jimmy Cliff, was a Jamaican ska, rocksteady, reggae and soul musician. He was considered to be one of Jamaica's most celebrated musicians and was credited with helping to popularise reggae music internationally.
6-7 was an Internet meme, slang term, and gesture that became popular in 2025 on TikTok and Instagram Reels. It has no fixed meaning.
Peter Richard Boyle was an American actor. He is known for his work as a character actor on film and television and received several awards including a Primetime Emmy Award and a Screen Actors Guild Award.
Lev Parnas is a Soviet-born American businessman and former associate of Rudy Giuliani. Parnas, Giuliani, Igor Fruman, John Solomon, Yuriy Lutsenko, Dmytro Firtash and his allies, Victoria Toensing and Joe diGenova, were involved in creating the false Biden–Uk
Cynthia Erivo is a British actress and singer. Known for her work on both stage and screen, she is the recipient of several accolades and one of few individuals nominated for an Emmy, a Grammy, an Oscar, and a Tony Award (EGOT), winning all but the Oscar. Eriv
Pluribus is an American post-apocalyptic science fiction television series created by Vince Gilligan for Apple TV. Set and filmed primarily in Albuquerque, New Mexico, the series follows novelist Carol Sturka, who finds herself isolated after an alien virus tr
It: Welcome to Derry is an American supernatural horror television series based on Stephen King's 1986 novel It. Serving as a prequel to the films It (2017) and It Chapter Two (2019), the series was developed by Andy Muschietti, Barbara Muschietti and Jason Fu
Udo Kierspe, known professionally as Udo Kier, was a German actor. Known primarily as a character actor who often portrayed eccentric and deviant figures, he appeared in more than 200 films in both leading and supporting roles throughout Europe and the America
The 2026 ICC Men's T20 World Cup was the tenth edition of the ICC Men's T20 World Cup, co-hosted by Board of Control for Cricket in India and Sri Lanka Cricket from 7 February to 8 March 2026. Sri Lanka had previously hosted the competition in 2012 and India i
The Family Man (Indian TV series)
The Family Man is an Indian Hindi-language spy thriller streaming television series created by Raj & DK for Amazon Prime Video. It features Manoj Bajpayee as Srikant Tiwari, a middle-class man secretly working as an intelligence officer for the Threat Analysis
Puyi was the last emperor of China, having reigned as the Xuantong Emperor of the Qing dynasty and later as the Kangde Emperor of Manchukuo, a Japanese puppet state during World War II. After the war, he was held as a war criminal in the Soviet Union and China
Stranger Things is an American television series created by the Duffer Brothers for Netflix. Produced by Monkey Massacre Productions and 21 Laps Entertainment, the first season was released on Netflix on July 15, 2016. The second and third seasons followed in
On May 31, 2014, twelve-year-olds Anissa Weier and Morgan Geyser lured their friend Payton Leutner into a wooded area of Davids Park in Waukesha, Wisconsin, where they attempted to murder and sacrifice her to the Slender Man, a fictional supernatural being ori
Wicked, is a 2024 American musical fantasy film directed by Jon M. Chu and written by Winnie Holzman and Dana Fox. It adapts the first act of the 2003 stage musical by Stephen Schwartz and Holzman, which was loosely based on Gregory Maguire's 1995 novel, a re-
The fifth and final season of the American science fiction horror drama television series Stranger Things, marketed as Stranger Things 5, was released on the streaming service Netflix in two volumes and the finale. The first set of four episodes was released o
Patricia Helen Heaton is an American actress. Heaton achieved her career breakthrough and global fame with her portrayal of Debra Barone in the CBS sitcom Everybody Loves Raymond (1996–2005). She began her career appearing in a recurring role in the ABC drama
Ariana Grande-Butera is an American singer, songwriter, and actress. Known for her four-octave vocal range, which extends into the whistle register, she is an influential figure in popular music. Publications such as Rolling Stone and Billboard have deemed Gra
Raymond Albert Romano is an American stand-up comedian and actor. He is best known for his role as Raymond "Ray" Barone on the CBS sitcom Everybody Loves Raymond (1996–2005), for which he won three Primetime Emmy Awards. He is also known for being the voice of
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter. While this is readily applicable to data such as images, which can be represented as regular grids in the Euclidean space, extending the convolution operator to work on graphs proves more challenging, due to their irregular structure. In this paper, we prop
Natural Image Stitching Using Depth Maps
Natural image stitching aims to create a single, natural-looking mosaic from overlapped images that capture the same 3D scene from different viewing positions. Challenges inevitably arise when the scene is non-planar and captured by handheld cameras since parallax is non-negligible in such cases. In this paper, we propose a novel image stitching method using depth maps, which generates accurate alignment mosaics agai
Adversarial Bandits against Arbitrary Strategies
We study the adversarial bandit problem against arbitrary strategies, where the difficulty is captured by an unknown parameter $S$, which is the number of switches in the best arm in hindsight. To handle this problem, we adopt the master-base framework using the online mirror descent method (OMD). We first provide a master-base algorithm with simple OMD, achieving $\tilde{O}(S^{1/2}K^{1/3}T^{2/3})$, in which $T^{2/3}
We present a fast, differentially private algorithm for high-dimensional covariance-aware mean estimation with nearly optimal sample complexity. Only exponential-time estimators were previously known to achieve this guarantee. Given $n$ samples from a (sub-)Gaussian distribution with unknown mean $μ$ and covariance $Σ$, our $(\varepsilon,δ)$-differentially private estimator produces $\tildeμ$ such that $\|μ- \tildeμ\
Influence of the Geometry of the world model on Curiosity Based Exploration
In human spatial awareness, 3-D projective geometry structures information integration and action planning through perspective taking within an internal representation space. The way different perspectives are related and transform a world model defines a specific perception and imagination scheme. In mathematics, such collection of transformations corresponds to a 'group', whose 'actions' characteriz
Zoo Guide to Network Embedding
Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted lots of interest in the past few decades, and has been efficiently applied to fundamental problems in network inference, such as link pred
A Double Machine Learning Approach to Combining Experimental and Observational Data
Experimental and observational studies often lack validity due to untestable assumptions. We propose a double machine learning approach to combine experimental and observational studies, allowing practitioners to test for assumption violations and estimate treatment effects consistently. Our framework proposes a falsification test for external validity and ignorability under milder assumptions. We provide consistent
MGAS: Multi-Granularity Architecture Search for Trade-Off Between Model Effectiveness and Efficiency
Neural architecture search (NAS) has gained significant traction in automating the design of neural networks. To reduce search time, differentiable architecture search (DAS) reframes the traditional paradigm of discrete candidate sampling and evaluation into a differentiable optimization over a super-net, followed by discretization. However, most existing DAS methods primarily focus on optimizing the coarse-grained o
Video Understanding with Large Language Models: A Survey
With the burgeoning growth of online video platforms and the escalating volume of video content, the demand for proficient video understanding tools has intensified markedly. Given the remarkable capabilities of large language models (LLMs) in language and multimodal tasks, this survey provides a detailed overview of recent advancements in video understanding that harness the power of LLMs (Vid-LLMs). The emergent ca
In this paper, we introduce Segmentation-Driven Deformation Multi-View Stereo (SD-MVS), a method that can effectively tackle challenges in 3D reconstruction of textureless areas. We are the first to adopt the Segment Anything Model (SAM) to distinguish semantic instances in scenes and further leverage these constraints for pixelwise patch deformation on both matching cost and propagation. Concurrently, we propose a u
Although deep learning-based personalized recommendation systems provide qualified recommendations, they strain data center resources. The main bottleneck is the embedding layer, which is highly memory-intensive due to its sparse, irregular access patterns to embeddings. Recent near-memory processing (NMP) and processing-in-memory (PIM) architectures have addressed these issues by exploiting parallelism within memory
Zero-Shot Video Translation via Token Warping
With the revolution of generative AI, video-related tasks have been widely studied. However, current state-of-the-art video models still lag behind image models in visual quality and user control over generated content. In this paper, we introduce TokenWarping, a novel framework for temporally coherent video translation. Existing diffusion-based video editing approaches rely solely on key and value patches in self-at
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems
While intrusion detection systems (IDSs) benefit from the diversity and generalization of IoT data features, the data diversity (e.g., the heterogeneity and high dimensions of data) also makes it difficult to train effective machine learning models in IoT IDSs. This also leads to potentially redundant/noisy features that may decrease the accuracy of the detection engine in IDSs. This paper first introduces a novel ne
Categorical Flow Matching on Statistical Manifolds
We introduce Statistical Flow Matching (SFM), a novel and mathematically rigorous flow-matching framework on the manifold of parameterized probability measures inspired by the results from information geometry. We demonstrate the effectiveness of our method on the discrete generation problem by instantiating SFM on the manifold of categorical distributions whose geometric properties remain unexplored in previous disc
AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways
An Artificial Intelligence (AI) agent is a software entity that autonomously performs tasks or makes decisions based on pre-defined objectives and data inputs. AI agents, capable of perceiving user inputs, reasoning and planning tasks, and executing actions, have seen remarkable advancements in algorithm development and task performance. However, the security challenges they pose remain under-explored and unresolved.
Demystifying Higher-Order Graph Neural Networks
Higher-order graph neural networks (HOGNNs) and the related architectures from Topological Deep Learning are an important class of GNN models that harness polyadic relations between vertices beyond plain edges. They have been used to eliminate issues such as over-smoothing or over-squashing, to significantly enhance the accuracy of GNN predictions, to improve the expressiveness of GNN architectures, and for numerous
Sliding-window attention offers a hardware-efficient solution to the memory and throughput challenges of Large Language Models (LLMs) in long-context scenarios. Existing methods typically employ a single window length across all attention heads and input sizes. However, this uniform approach fails to capture the heterogeneous attention patterns inherent in LLMs, ignoring their distinct accuracy-latency trade-offs. To
GMT: Effective Global Framework for Multi-Camera Multi-Target Tracking
Multi-Camera Multi-Target (MCMT) tracking aims to locate and associate the same targets across multiple camera views. Existing methods typically adopt a two-stage framework, involving single-camera tracking followed by inter-camera tracking. However, in this paradigm, multi-view information is used only to recover missed matches in the first stage, providing a limited contribution to overall tracking. To address this
A Diffusion Model for Simulation Ready Coronary Anatomy with Morpho-skeletal Control
Virtual interventions enable the physics-based simulation of device deployment within coronary arteries. This framework allows for counterfactual reasoning by deploying the same device in different arterial anatomies. However, current methods to create such counterfactual arteries face a trade-off between controllability and realism. In this study, we investigate how Latent Diffusion Models (LDMs) can custom synthesi
MovieDreamer: Hierarchical Generation for Coherent Long Visual Sequence
Recent advancements in video generation have primarily leveraged diffusion models for short-duration content. However, these approaches often fall short in modeling complex narratives and maintaining character consistency over extended periods, which is essential for long-form video production like movies. We propose MovieDreamer, a novel hierarchical framework that integrates the strengths of autoregressive models w
MSP-MVS: Multi-Granularity Segmentation Prior Guided Multi-View Stereo
Recently, patch deformation-based methods have demonstrated significant strength in multi-view stereo by adaptively expanding the reception field of patches to help reconstruct textureless areas. However, such methods mainly concentrate on searching for pixels without matching ambiguity (i.e., reliable pixels) when constructing deformed patches, while neglecting the deformation instability caused by unexpected edge-s
E$^{3}$NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images
Neural Radiance Fields (NeRF) achieves impressive novel view rendering performance by learning implicit 3D representation from sparse view images. However, it is difficult to reconstruct a sharp NeRF from blurry input that often occurs in the wild. To solve this problem, we propose a novel Efficient Event-Enhanced NeRF (E$^{3}$NeRF), reconstructing sharp NeRF by utilizing both blurry images and corresponding event st
OpenScan: A Benchmark for Generalized Open-Vocabulary 3D Scene Understanding
Open-vocabulary 3D scene understanding (OV-3D) aims to localize and classify novel objects beyond the closed set of object classes. However, existing approaches and benchmarks primarily focus on the open vocabulary problem within the context of object classes, which is insufficient in providing a holistic evaluation to what extent a model understands the 3D scene. In this paper, we introduce a more challenging task c
Quantifying Behavioral Dissimilarity Between Mathematical Expressions
Quantifying the similarity between mathematical expressions is a fundamental problem in computational mathematics, symbolic reasoning, and scientific discovery. While behavioral notions of similarity have previously been explored in the context of software and program analysis, existing measures for mathematical expressions rely primarily on syntactic form, assessing similarity through symbolic structure rather than
Vision Language Models Can Parse Floor Plan Maps
Vision language models (VLMs) can simultaneously reason about images and texts to tackle many tasks, from visual question answering to image captioning. This paper focuses on map parsing, a novel task that is unexplored within the VLM context and particularly useful to mobile robots. Map parsing requires understanding not only the labels but also the geometric configurations of a map, i.e., what areas are like and ho
Notable events recorded on this day and month across all years.