Record 11122025 · captured 2026-08-25
The world looked up Dhurandhar. 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.
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 e-Government in Saudi Arabia was established as per Royal Decree No. 7/B/33181 dated 7 September 2003. The e-Government was created by the Ministry of Communications and Information Technology. In 2005, the Ministry of Communications and Information Techno
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
Madeleine Sophie Wickham, known by her pen name Sophie Kinsella, was an English author. She is especially known for her best-selling Shopaholic series of novels. By the time of her death, her books had sold over 50 million copies in more than 60 countries and
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
Philip Michael Rivers is an American former professional football quarterback who played in the National Football League (NFL) for 18 seasons, primarily with the Chargers franchise. He played college football for the NC State Wolfpack, winning ACC Player of th
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
Andrew Roane Dick is an American actor and comedian. Dick was born in Charleston, South Carolina, and joined The Second City and studied improvisational theater. Dick has had a long career as a stand-up comedian; he has appeared throughout the United States, h
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.
Sherrone Banfield Moore is an American former college football head coach and player. He most recently served as the head football coach for the University of Michigan. Moore served as Michigan's acting head coach in four games during the national championship
Sean John Combs, also known professionally as Diddy, is an American former rapper, record producer, record executive, and actor. He is credited with the discovery and development of musical artists such as the Notorious B.I.G., Mary J. Blige, and Usher, among
Akshaye Vinod Khanna is an Indian actor who predominantly works in Hindi films. Known for his acting versatility and strong portrayals, he has appeared in over 40 films. Khanna is often regarded as one of the finest actors in Hindi cinema. He is a recipient of
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
Casandra Elizabeth Ventura is an American singer, dancer, actress, and model. Born in New London, Connecticut, she began her musical career in 2004 after meeting producer Ryan Leslie, who signed her to his record label, NextSelection Lifestyle Group. She was t
Major Mohit Sharma was an Indian Army Officer who was posthumously awarded the Ashoka Chakra, India's highest peace-time military decoration. Sharma was from the elite 1st Para SF.
Sara Arjun is an Indian actress who primarily appears in Tamil and Hindi films. The daughter of actor Raj Arjun, she appeared in several television commercials, including advertisements for Clinic Plus, and a short Hindi film before the age of six. She gained
The Soiled Dove Plea was a speech delivered by the attorney Temple Lea Houston on behalf of a prostitute, Minnie Stacey, in 1899 in Woodward, Oklahoma. The accused prostitute was scheduled to be tried for prostitution one morning, and the judge discovered she
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
Luigi Nicholas Mangione is an American former software engineer who pleaded guilty to the killing of Brian Thompson, the CEO of UnitedHealthcare, in December 2024.
The 2025 Miami mayoral election had its initial round held on November 4, 2025, with a runoff election then held on December 9, 2025. The election saw Eileen Higgins elected mayor of Miami, becoming the first Democrat to be elected to this position since 1997
Eileen Higgins is an American politician and former diplomat serving as the 44th mayor of Miami since 2025. A member of the Democratic Party, she served as a member of the Miami-Dade County Commission from 2018 to 2025.
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
Aslam Khan, better known as Chaudhary Aslam, was a Pakistani police officer in the Sindh Police. He was known for his involvement in several encounter killings of criminals and militants. On 9 January 2014, he was killed in a suicide car bombing carried out by
Jeffrey Garcia was an American stand-up comedian and actor. In animation, he voiced Sheen Estevez in Jimmy Neutron: Boy Genius as well as its two Nickelodeon spin-off television show series—The Adventures of Jimmy Neutron, Boy Genius and Planet Sheen—along wit
XXX may refer to:
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
Frank Matthews (drug trafficker)
Frank Larry Matthews, also known as Black Caesar, Mark IV and Pee Wee, was an American drug trafficker and crime boss who sold heroin and cocaine throughout the eastern United States from 1965 to 1972. He operated in 21 states and supplied drug dealers through
Ilhan Abdullahi Omar is an American politician serving as the U.S. representative for Minnesota's 5th congressional district since 2019. The district includes all of Minneapolis and some of its first-ring suburbs. From 2017 to 2019, Omar served in the Minnesot
Limonene is a slightly yellow-green liquid aliphatic hydrocarbon classified as a cyclic monoterpene, and is the major component in the fragrance and essential oil of citrus fruit peels, taking its name from Italian limone ("lemon").
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
A fundamental challenge in autonomous vehicles is adjusting the steering angle at different road conditions. Recent state-of-the-art solutions addressing this challenge include deep learning techniques as they provide end-to-end solution to predict steering angles directly from the raw input images with higher accuracy. Most of these works ignore the temporal dependencies between the image frames. In this paper, we t
Two Causal Principles for Improving Visual Dialog
This paper unravels the design tricks adopted by us, the champion team MReaL-BDAI, for Visual Dialog Challenge 2019: two causal principles for improving Visual Dialog (VisDial). By "improving", we mean that they can promote almost every existing VisDial model to the state-of-the-art performance on the leader-board. Such a major improvement is only due to our careful inspection on the causality behind the mode
Automatic question-answering (QA) systems have boomed during last few years, and commonly used techniques can be roughly categorized into Information Retrieval (IR)-based and generation-based. A key solution to the IR based models is to retrieve the most similar knowledge entries of a given query from a QA knowledge base, and then rerank those knowledge entries with semantic matching models. In this paper, we aim to
Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses
How related are the representations learned by neural language models, translation models, and language tagging tasks? We answer this question by adapting an encoder-decoder transfer learning method from computer vision to investigate the structure among 100 different feature spaces extracted from hidden representations of various networks trained on language tasks. This method reveals a low-dimensional structure whe
With the adoption of autonomous vehicles on our roads, we will witness a mixed-autonomy environment where autonomous and human-driven vehicles must learn to co-exist by sharing the same road infrastructure. To attain socially-desirable behaviors, autonomous vehicles must be instructed to consider the utility of other vehicles around them in their decision-making process. Particularly, we study the maneuver planning p
Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in the real world where they will be surrounded by human-driven vehicles (HVs) is extremely challenging. Prior works have shown the possibilities of creating inter-agent cooperation between a group of AVs that follow a social utility. Such altruistic AVs can form alliances and affect the behavior of HVs to achieve socially desirable
Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving
Autonomous vehicle (AV) navigation in the presence of Human-driven vehicles (HVs) is challenging, as HVs continuously update their policies in response to AVs. In order to navigate safely in the presence of complex AV-HV social interactions, the AVs must learn to predict these changes. Humans are capable of navigating such challenging social interaction settings because of their intrinsic knowledge about other agents
It is expected that autonomous vehicles(AVs) and heterogeneous human-driven vehicles(HVs) will coexist on the same road. The safety and reliability of AVs will depend on their social awareness and their ability to engage in complex social interactions in a socially accepted manner. However, AVs are still inefficient in terms of cooperating with HVs and struggle to understand and adapt to human behavior, which is part
Low back pain (LBP) and sciatica may require surgical therapy when they are symptomatic of severe pain. However, there is no effective measures to evaluate the surgical outcomes in advance. This work combined elements of Eastern medicine and machine learning, and developed a preoperative assessment tool to predict the prognosis of lumbar spinal surgery in LBP and sciatica patients. Standard operative assessments, tra
Large language models (LLMs) produce seemingly meaningful outputs, yet they are trained on text alone without direct interaction with the world. This leads to a modern variant of the classical symbol grounding problem in AI: can LLMs' internal states and outputs be about extra-linguistic reality, independently of the meaning human interpreters project onto them? We argue that they can. We first distinguish refere
Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning
Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can often be substantial heterogeneity (e.g., class imbalance) across the local data distributions observed by each of these clients. Under such non-iid label distributions across clients, FL suffers from the &
Information-Theoretic Active Correlation Clustering
Correlation clustering is a flexible framework for partitioning data based solely on pairwise similarity or dissimilarity information, without requiring the number of clusters as input. However, in many practical scenarios, these pairwise similarities are not available a priori and must be obtained through costly measurements or human feedback. This motivates the use of active learning to query only the most informat
Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation
Topic relevance between query and document is a very important part of social search, which can evaluate the degree of matching between document and user's requirement. In most social search scenarios such as Dianping, modeling search relevance always faces two challenges. One is that many documents in social search are very long and have much redundant information. The other is that the training data for search
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some cases privacy concerns, among others. This paper presents an overview of the 2nd edition of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized
Hard Work Does Not Always Pay Off: Poisoning Attacks on Neural Architecture Search
We study the robustness of data-centric methods to find neural network architectures, known as neural architecture search (NAS), against data poisoning. To audit this robustness, we design a poisoning framework that enables the systematic evaluation of the ability of NAS to produce architectures under data corruption. Our framework examines four off-the-shelf NAS algorithms, representing different approaches to archi
Breast cancer (BC) stands as one of the most common malignancies affecting women worldwide, necessitating advancements in diagnostic methodologies for better clinical outcomes. This article provides a comprehensive exploration of the application of Explainable Artificial Intelligence (XAI) techniques in the detection and diagnosis of breast cancer. As Artificial Intelligence (AI) technologies continue to permeate the
Multi-Scale Direction-Aware Network for Infrared Small Target Detection
Infrared small target detection faces the problem that it is difficult to effectively separate the background and the target. Existing deep learning-based methods focus on edge and shape features, but ignore the richer structural differences and detailed information embedded in high-frequency components from different directions, thereby failing to fully exploit the value of high-frequency directional features in tar
A survey on the impacts of recommender systems on users, items, and human-AI ecosystems
Recommendation systems and assistants (in short, recommenders) influence through online platforms most actions of our daily lives, suggesting items or providing solutions based on users' preferences or requests. This survey systematically reviews, categories, and discusses the impact of recommenders in four human-AI ecosystems -- social media, online retail, urban mapping and generative AI ecosystems. Its scope i
The pervasive expectations about ideal body types in Western society can lead to body image concerns, dissatisfaction, and in extreme cases, eating disorders and other psychopathologies related to body image. While previous research has focused on online pro-anorexia communities glorifying the "thin ideal," less attention has been given to the broader spectrum of body image concerns or how emerging disorders
Entropy-Informed Weighting Channel Normalizing Flow for Deep Generative Models
Normalizing Flows (NFs) are widely used in deep generative models for their exact likelihood estimation and efficient sampling. However, they require substantial memory since the latent space matches the input dimension. Multi-scale architectures address this by progressively reducing latent dimensions while preserving reversibility. Existing multi-scale architectures use simple, static channel-wise splitting, limiti
Generative AI Augmented Induction-based Formal Verification
Generative Artificial Intelligence (GenAI) has demonstrated its capabilities in the present world that reduce human effort significantly. It utilizes deep learning techniques to create original and realistic content in terms of text, images, code, music, and video. Researchers have also shown the capabilities of modern Large Language Models (LLMs) used by GenAI models that can be used to aid hardware development. For
Towards Robust Infrared Small Target Detection: A Feature-Enhanced and Sensitivity-Tunable Framework
Recently, single-frame infrared small target (SIRST) detection technology has attracted widespread attention. Different from most existing deep learning-based methods that focus on improving network architectures, we propose a feature-enhanced and sensitivity-tunable (FEST) framework, which is compatible with existing SIRST detection networks and further enhances their detection performance. The FEST framework improv
Prompt engineering is critical for effective interaction with large language models (LLMs) such as ChatGPT. However, efforts to teach this skill to students have been limited. This study designed and implemented a prompt engineering intervention, examining its influence on undergraduate students' AI self-efficacy, AI knowledge, and proficiency in creating effective prompts. The intervention involved 27 students w
LLM-Barber: Block-Aware Rebuilder for Sparsity Mask in One-Shot for Large Language Models
Large language models (LLMs) have seen substantial growth, necessitating efficient model pruning techniques. Existing post-training pruning methods primarily measure weight importance in converged dense models, often overlooking changes in weight significance during the pruning process, leading to performance degradation. To address this issue, we present LLM-Barber (Block-Aware Rebuilder for Sparsity Mask in One-Sho
Point Neuron Learning: A New Physics-Informed Neural Network Architecture
Machine learning and neural networks have advanced numerous research domains, but challenges such as large training data requirements and inconsistent model performance hinder their application in certain scientific problems. To overcome these challenges, researchers have investigated integrating physics principles into machine learning models, mainly through: (i) physics-guided loss functions, generally termed as ph
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