Record 25112025 · 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
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
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 Golden Party Badge was an award authorised by Adolf Hitler in a decree in October 1933. It was a special award given to all Nazi Party members who had, as of 9 November 1933, registered numbers from 1 to 100,000 and had unbroken party membership. The recip
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.
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
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
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
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.
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
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
Tatiana Celia Kennedy Schlossberg was an American environmental journalist and author. She worked as a science and climate reporter for The New York Times and wrote for several other publications, including The Atlantic, The Washington Post, Vanity Fair, and B
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
1989 Tiananmen Square protests and massacre
Protests led by students and workers, known in China as the June Fourth Incident, were held in Tiananmen Square in Beijing, China, from 15 April to 4 June 1989. After weeks of unsuccessful attempts between the demonstrators and the Chinese government to find a
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-
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
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
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
Caroline Bouvier Kennedy is an American author, diplomat, and attorney. She served as the United States ambassador to Japan from 2013 to 2017 and ambassador to Australia from 2022 to 2024. Most of Kennedy's professional life has been in literature, law, politi
Eberechi Oluchi "Ebere" Eze is an English professional footballer who plays as an attacking midfielder for Premier League club Arsenal and the England national team.
Ajay Singh Deol, professionally known as Sunny Deol, is an Indian actor, filmmaker and former politician who works in Hindi films. One of the most commercially successful actors in the history of Indian cinema, he has worked in more than 100 films in a career
Esha Deol is an Indian actress who works in Hindi films. The daughter of actors Dharmendra and Hema Malini, Deol made her acting debut in the romantic thriller Koi Mere Dil Se Poochhe (2002), which won her the Filmfare Award for Best Female Debut.
The Beast in Me is an American psychological crime thriller television miniseries for Netflix, starring Claire Danes and Matthew Rhys. Created by Gabe Rotter, the series follows an author (Danes) who begins writing a book about her new next-door neighbor (Rhys
Charles Edward Kelly is an American football coach who is the offensive coordinator for Northwestern. He came to prominence as a college football head coach for the University of Oregon from 2009 to 2012, leading them to the 2011 BCS National Championship Game
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.
James Abram Garfield was the 20th president of the United States, serving from March 1881 until his death in September that year after being shot in July. A preacher, lawyer, and Civil War general, Garfield served nine terms in the United States House of Repre
John Matthew Stafford is an American professional football quarterback for the Los Angeles Rams of the National Football League (NFL). He played college football for the Georgia Bulldogs, receiving first-team All-American honors in 2008, and was selected first
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
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.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
The Shape of Sight: A Homological Framework for Unifying Visual Perception
Visual perception, the brain's construction of a stable world from sensory data, faces several long-standing, fundamental challenges. While often studied separately, these problems have resisted a single, unifying computational framework. In this perspective, we propose a homological framework for visual perception. We argue that the brain's latent representations are governed by their topological parity. Thi
Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder
We present a coupled Variational Auto-Encoder (VAE) method that improves the accuracy and robustness of the probabilistic inferences on represented data. The new method models the dependency between input feature vectors (images) and weighs the outliers with a higher penalty by generalizing the original loss function to the coupled entropy function, using the principles of nonlinear statistical coupling. We evaluate
K-FACE: A Large-Scale KIST Face Database in Consideration with Unconstrained Environments
In this paper, we introduce a new large-scale face database from KIST, denoted as K-FACE, and describe a novel capturing device specifically designed to obtain the data. The K-FACE database contains more than 1 million high-quality images of 1,000 subjects selected by considering the ratio of gender and age groups. It includes a variety of attributes, including 27 poses, 35 lighting conditions, three expressions, and
Advancing Autonomous Driving: DepthSense with Radar and Spatial Attention
Depth perception is crucial for spatial understanding and has traditionally been achieved through stereoscopic imaging. However, the precision of depth estimation using stereoscopic methods depends on the accurate calibration of binocular vision sensors. Monocular cameras, while more accessible, often suffer from reduced accuracy, especially under challenging imaging conditions. Optical sensors, too, face limitations
Description of Corner Cases in Automated Driving: Goals and Challenges
Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC). Since many modules of automated driving systems are based on machine learning (ML), CC are an essential part of the data for their development. However, there is only a limited amount of CC data in large-scale data collections, which makes them challenging in the context of
Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models
In the foreseeable future, autonomous vehicles will require human assistance in situations they can not resolve on their own. In such scenarios, remote assistance from a human can provide the required input for the vehicle to continue its operation. Typical sensors used in autonomous vehicles include camera and lidar sensors. Due to the massive volume of sensor data that must be sent in real-time, highly efficient da
Multiview point cloud registration with anisotropic and space-varying localization noise
In this paper, we address the problem of registering multiple point clouds corrupted with high anisotropic localization noise. Our approach follows the widely used framework of Gaussian mixture model (GMM) reconstruction with an expectation-maximization (EM) algorithm. Existing methods are based on an implicit assumption of space-invariant isotropic Gaussian noise. However, this assumption is violated in practice in
Learning to Admit Optimally in an $M/M/k/k+N$ Queueing System with Unknown Service Rate
Motivated by applications of the Erlang-B blocking model and the extended $M/M/k/k+N$ model that allows for some queueing, beyond communication networks to sizing and pricing in production, messaging, and app-based parking systems, we study admission control for such systems with unknown service rate. In our model, a dispatcher either admits every arrival into the system (when there is room) or blocks it. Every serve
Passengers (drivers) of level 3-5 autonomous personal mobility vehicles (APMV) and cars can perform non-driving tasks, such as reading books and smartphones, while driving. It has been pointed out that such activities may increase motion sickness. Many studies have been conducted to build countermeasures, of which various computational motion sickness models have been developed. Many of these are based on subjective
Unifying Summary Statistic Selection for Approximate Bayesian Computation
Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference. We characterize three different classes of summaries and demonstrate their importance for correctly analyzing dimensionality reduction algorithms. We demonstrate that minimizing the expected posterior entropy (EPE) under the prior predictive distribution of the model provides a unifying principle
FL-Defender: Combating Targeted Attacks in Federated Learning
Federated learning (FL) enables learning a global machine learning model from local data distributed among a set of participating workers. This makes it possible i) to train more accurate models due to learning from rich joint training data, and ii) to improve privacy by not sharing the workers' local private data with others. However, the distributed nature of FL makes it vulnerable to targeted poisoning attacks
Towards Healing the Blindness of Score Matching
Score-based divergences have been widely used in machine learning and statistics applications. Despite their empirical success, a blindness problem has been observed when using these for multi-modal distributions. In this work, we discuss the blindness problem and propose a new family of divergences that can mitigate the blindness problem. We illustrate our proposed divergence in the context of density estimation and
Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flow
Optimal Power Flow (OPF) is a very traditional research area within the power systems field that seeks for the optimal operation point of electric power plants, and which needs to be solved every few minutes in real-world scenarios. However, due to the nonconvexities that arise in power generation systems, there is not yet a fast, robust solution technique for the full Alternating Current Optimal Power Flow (ACOPF).
L-HYDRA: Multi-Head Physics-Informed Neural Networks
We introduce multi-head neural networks (MH-NNs) to physics-informed machine learning, which is a type of neural networks (NNs) with all nonlinear hidden layers as the body and multiple linear output layers as multi-head. Hence, we construct multi-head physics-informed neural networks (MH-PINNs) as a potent tool for multi-task learning (MTL), generative modeling, and few-shot learning for diverse problems in scientif
High-dimensional multi-view clustering methods
Multi-view clustering has been widely used in recent years in comparison to single-view clustering, for clear reasons, as it offers more insights into the data, which has brought with it some challenges, such as how to combine these views or features. Most of recent work in this field focuses mainly on tensor representation instead of treating the data as simple matrices. This permits to deal with the high-order corr
Citywide Air Pollution Forecasting tries to precisely predict the air quality multiple hours ahead for the entire city. This topic is challenged since air pollution varies in a spatiotemporal manner and depends on many complicated factors. Our previous research has solved the problem by considering the whole city as an image and leveraged a Convolutional Long Short-Term Memory (ConvLSTM) model to learn the spatiotemp
VeML: An End-to-End Machine Learning Lifecycle for Large-scale and High-dimensional Data
An end-to-end machine learning (ML) lifecycle consists of many iterative processes, from data preparation and ML model design to model training and then deploying the trained model for inference. When building an end-to-end lifecycle for an ML problem, many ML pipelines must be designed and executed that produce a huge number of lifecycle versions. Therefore, this paper introduces VeML, a Version management system de
Fairness in Streaming Submodular Maximization over a Matroid Constraint
Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset. If datapoints have sensitive attributes such as gender or race, it becomes important to enforce fairness to avoid bias and discrimination. This has spurred significant interest in developing fair machine learning algorithms. Recently, such algorithms have been developed for monotone submo
When Does Bottom-up Beat Top-down in Hierarchical Community Detection?
Hierarchical clustering of networks consists in finding a tree of communities, such that lower levels of the hierarchy reveal finer-grained community structures. There are two main classes of algorithms tackling this problem. Divisive (top-down) algorithms recursively partition the nodes into two communities, until a stopping rule indicates that no further split is needed. In contrast, agglomerative (bottom-up) algor
Convergence and concentration properties of constant step-size SGD through Markov chains
We consider the optimization of a smooth and strongly convex objective using constant step-size stochastic gradient descent (SGD) and study its properties through the prism of Markov chains. We show that, for unbiased gradient estimates with mildly controlled variance, the iteration converges to an invariant distribution in total variation distance. We also establish this convergence in Wasserstein-2 distance under a
PanoDiffusion: 360-degree Panorama Outpainting via Diffusion
Generating complete 360-degree panoramas from narrow field of view images is ongoing research as omnidirectional RGB data is not readily available. Existing GAN-based approaches face some barriers to achieving higher quality output, and have poor generalization performance over different mask types. In this paper, we present our 360-degree indoor RGB-D panorama outpainting model using latent diffusion models (LDM), c
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields
High spatial resolution wind data are essential for a wide range of applications in climate, oceanographic and meteorological studies. Large-scale spatial interpolation or downscaling of bivariate wind fields having velocity in two dimensions is a challenging task because wind data tend to be non-Gaussian with high spatial variability and heterogeneity. In spatial statistics, cokriging is commonly used for predicting
Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced research and applications in computer vision, fueling modern recognition systems and generative models. We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective. However, CLIP only provides very limited information about its data and how it has been collected, leading
Ultrasound is a vital diagnostic technique in health screening, with the advantages of non-invasive, cost-effective, and radiation free, and therefore is widely applied in the diagnosis of nodules. However, it relies heavily on the expertise and clinical experience of the sonographer. In ultrasound images, a single nodule might present heterogeneous appearances in different cross-sectional views which makes it hard t
PINNsFailureRegion Localization and Refinement through White-box AdversarialAttack
Physics-informed neural networks (PINNs) have shown great promise in solving partial differential equations (PDEs). However, vanilla PINNs often face challenges when solving complex PDEs, especially those involving multi-scale behaviors or solutions with sharp or oscillatory characteristics. To precisely and adaptively locate the critical regions that fail in the solving process we propose a sampling strategy grounde
Notable events recorded on this day and month across all years.