Record 10122025 · 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
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
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
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
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
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
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
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.
Dividend policy, in financial management and corporate finance, is concerned with the policies regarding dividends; more specifically paying a cash dividend in the present, as opposed to, presumably, paying an increased dividend at a later stage. Practical and
Lando Norris is a British racing driver who competes in Formula One for McLaren. Norris won the Formula One World Drivers' Championship in 2025 with McLaren, and has won 13 Grands Prix across eight seasons.
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
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
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
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.
Millie Bonnie Bongiovi, known professionally as Millie Bobby Brown, is a British actress and film producer. She gained international recognition for playing Eleven in the Netflix science fiction series Stranger Things (2016–2025), for which she received nomina
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
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
The 2026 FIFA World Cup was the 23rd FIFA World Cup, the quadrennial international men's soccer championship contested by the national teams of the member associations of FIFA. The tournament began on June 11, 2026, and concluded on July 19 with Spain winning
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
Margarida Matias "Magui" Ferreira Corceiro is a Portuguese actress and fashion model.
Zootopia 2 is a 2025 American animated buddy cop comedy film produced by Walt Disney Animation Studios, the second film in the series and a sequel to Zootopia (2016). Directed by Jared Bush and Byron Howard and written by Bush, the film stars Ginnifer Goodwin,
Raul Francisco Martínez-Malo Jr. was an American singer, songwriter, guitarist and record producer. He was both the lead singer and songwriter of country music band the Mavericks, co-writing many of their singles, as well as Rick Trevino's 2003 single "In My D
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
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
Nicholas Joseph Fuentes is an American far-right political commentator, live streamer, and influencer. He hosts the livestreamed show America First, where he has advanced white nationalism and white supremacy, Christian nationalism, the incel subculture, misog
Dorothy Gale is a character created by the American author L. Frank Baum as the protagonist in many of his Oz novels. She first appears in Baum's classic 1900 children's novel The Wonderful Wizard of Oz and reappears in most of its sequels. She is also the mai
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.
Freddie Scappaticci, later known as Frank Cowley, was a member of the Provisional Irish Republican Army (IRA) and its Internal Security Unit (ISU). He was widely identified in media reports and by former intelligence personnel as Stakeknife, a British Army For
Jasmine Felicia Crockett is an American politician serving as the U.S. representative for Texas's 30th congressional district since 2023. A member of the Democratic Party, she represented the 100th district in the Texas House of Representatives from 2021 to 20
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Human-like generalization in a machine through predicate learning
Humans readily generalize, applying prior knowledge to novel situations and stimuli. Advances in machine learning and artificial intelligence have begun to approximate and even surpass human performance, but machine systems reliably struggle to generalize information to untrained situations. We describe a neural network model that is trained to play one video game (Breakout) and demonstrates one-shot generalization t
Discovering Influential Factors in Variational Autoencoders
In the field of machine learning, it is still a critical issue to identify and supervise the learned representation without manually intervening or intuition assistance to extract useful knowledge or serve for the downstream tasks. In this work, we focus on supervising the influential factors extracted by the variational autoencoder(VAE). The VAE is proposed to learn independent low dimension representation while fac
A Theory of Relation Learning and Cross-domain Generalization
People readily generalize knowledge to novel domains and stimuli. We present a theory, instantiated in a computational model, based on the idea that cross-domain generalization in humans is a case of analogical inference over structured (i.e., symbolic) relational representations. The model is an extension of the LISA and DORA models of relational inference and learning. The resulting model learns both the content an
Generative Learning of Heterogeneous Tail Dependence
We propose a multivariate generative model to capture the complex dependence structure often encountered in business and financial data. Our model features heterogeneous and asymmetric tail dependence between all pairs of individual dimensions while also allowing heterogeneity and asymmetry in the tails of the marginals. A significant merit of our model structure is that it is not prone to error propagation in the pa
Astral Space: Convex Analysis at Infinity
Not all convex functions on $\mathbb{R}^n$ have finite minimizers; some can only be minimized by a sequence as it heads to infinity. In this work, we aim to develop a theory for understanding such minimizers at infinity. We study astral space, a compact extension of $\mathbb{R}^n$ to which such points at infinity have been added. Astral space is constructed to be as small as possible while still ensuring that all lin
The existence of spurious correlations such as image backgrounds in the training environment can make empirical risk minimization (ERM) perform badly in the test environment. To address this problem, Kirichenko et al. (2022) empirically found that the core features that are related to the outcome can still be learned well even with the presence of spurious correlations. This opens a promising strategy to first train
Counterfactuals are central in causal human reasoning and the scientific discovery process. The uplift, also called conditional average treatment effect, measures the causal effect of some action, or treatment, on the outcome of an individual. This paper discusses how it is possible to derive bounds on the probability of counterfactual statements based on uplift terms. First, we derive some original bounds on the pro
The process of identifying and characterizing B-cell epitopes, which are the portions of antigens recognized by antibodies, is important for our understanding of the immune system, and for many applications including vaccine development, therapeutics, and diagnostics. Computational epitope prediction is challenging yet rewarding as it significantly reduces the time and cost of laboratory work. Most of the existing to
Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling
The performance of Hamiltonian Monte Carlo simulations crucially depends on both the integration timestep and the number of integration steps. We present an adaptive general-purpose framework to automatically tune such parameters, based on a local loss function which promotes the fast exploration of phase-space. We show that a good correspondence between loss and autocorrelation time can be established, allowing for
RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning
Most existing Image-to-Image Translation (I2IT) methods generate images in a single run of a deep learning (DL) model. However, designing such a single-step model is always challenging, requiring a huge number of parameters and easily falling into bad global minimums and overfitting. In this work, we reformulate I2IT as a step-wise decision-making problem via deep reinforcement learning (DRL) and propose a novel fram
MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens
The effectiveness of Multimodal Large Language Models (MLLMs) demonstrates a profound capability in multimodal understanding. However, the simultaneous generation of images with coherent texts is still underdeveloped. Addressing this, we introduce a novel interleaved vision-and-language generation method, centered around the concept of ``generative vokens". These vokens serve as pivotal elements contributing to c
Diffusion Models for Wireless Communications
A comprehensive study on the applications of denoising diffusion models for wireless systems is provided. The article highlights the capabilities of diffusion models in learning complicated signal distributions, modeling wireless channels, and denoising and reconstructing distorted signals. First, fundamental working mechanism of diffusion models is introduced. Then the recent advances in applying diffusion models to
Leveraging heterogeneous spillover in maximizing contextual bandit rewards
Recommender systems relying on contextual multi-armed bandits continuously improve relevant item recommendations by taking into account the contextual information. The objective of bandit algorithms is to learn the best arm (e.g., best item to recommend) for each user and thus maximize the cumulative rewards from user engagement with the recommendations. The context that these algorithms typically consider are the us
Detecting value-expressive text posts in Russian social media
Basic values are concepts or beliefs which pertain to desirable end-states and transcend specific situations. Studying personal values in social media can illuminate how and why societal values evolve especially when the stimuli-based methods, such as surveys, are inefficient, for instance, in hard-to-reach populations. On the other hand, user-generated content is driven by the massive use of stereotyped, culturally
Sampling and estimation on manifolds using the Langevin diffusion
Error bounds are derived for sampling and estimation using a discretization of an intrinsically defined Langevin diffusion with invariant measure $\text{d}μ_ϕ\propto e^{-ϕ} \mathrm{dvol}_g $ on a compact Riemannian manifold. Two estimators of linear functionals of $μ_ϕ$ based on the discretized Markov process are considered: a time-averaging estimator based on a single trajectory and an ensemble-averaging estimator b
Improving LLM Reliability with RAG in Religious Question-Answering: MufassirQAS
Religious teachings can sometimes be complex and challenging to grasp, but chatbots can serve as effective assistants in this domain. Large Language Model (LLM) based chatbots, powered by Natural Language Processing (NLP), can connect related topics and provide well-supported responses to intricate questions, making them valuable tools for religious education. However, LLMs are prone to hallucinations as they can gen
Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark
Pre-trained vision models (PVMs) have demonstrated remarkable adaptability across a wide range of downstream vision tasks, showcasing exceptional performance. However, as these models scale to billions or even trillions of parameters, conventional full fine-tuning has become increasingly impractical due to its high computational and storage demands. To address these challenges, parameter-efficient fine-tuning (PEFT)
Event cameras are ideal for visual place recognition (VPR) in challenging environments due to their high temporal resolution and high dynamic range. However, existing methods convert sparse events into dense frame-like representations for Artificial Neural Networks (ANNs), ignoring event sparsity and incurring high computational cost. Spiking Neural Networks (SNNs) complement event data through discrete spike signals
Automated Classification of Phonetic Segments in Child Speech Using Raw Ultrasound Imaging
Speech sound disorder (SSD) is defined as a persistent impairment in speech sound production leading to reduced speech intelligibility and hindered verbal communication. Early recognition and intervention of children with SSD and timely referral to speech and language therapists (SLTs) for treatment are crucial. Automated detection of speech impairment is regarded as an efficient method for examining and screening la
BG-HGNN: Toward Efficient Learning for Complex Heterogeneous Graphs
Heterogeneous graphs, comprising diverse node and edge types connected through varied relations, are ubiquitous in real-world applications. Message-passing heterogeneous graph neural networks (HGNNs) have emerged as a powerful model class for such data. However, existing HGNNs typically allocate a separate set of learnable weights for each relation type to model relational heterogeneity. Despite their promise, these
Machine learning augmented diagnostic testing to identify sources of variability in test performance
Diagnostic tests that can detect pre-clinical or sub-clinical infection, are one of the most powerful tools in our armoury of weapons to control infectious diseases. Considerable effort has been paid to improving diagnostic testing for human, plant and animal diseases, including strategies for targeting the use of diagnostic tests towards individuals who are more likely to be infected. We use machine learning to asse
Deep generative modelling of canonical ensemble with differentiable thermal properties
It is a long-standing challenge to accurately and efficiently compute thermodynamic quantities of many-body systems at thermal equilibrium. The conventional methods, e.g., Markov chain Monte Carlo, require many steps to equilibrate. The recently developed deep learning methods can perform direct sampling, but only work at a single trained temperature point and risk biased sampling. Here, we propose a variational meth
CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale
Measuring biodiversity is crucial for understanding ecosystem health. While prior works have developed machine learning models for taxonomic classification of photographic images and DNA separately, in this work, we introduce a multimodal approach combining both, using CLIP-style contrastive learning to align images, barcode DNA, and text-based representations of taxonomic labels in a unified embedding space. This al
The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks
Artificial Intelligence (AI) is being incorporated in several optimization, scheduling, orchestration as well as in native communication network functions. This paradigm shift results in increased energy consumption, however, quantifying the end-to-end energy consumption of adding intelligence to communication systems remains an open challenge since conventional energy consumption metrics focus on either communicatio
Explosive neural networks via higher-order interactions in curved statistical manifolds
Higher-order interactions underlie complex phenomena in systems such as biological and artificial neural networks, but their study is challenging due to the scarcity of tractable models. By leveraging a generalisation of the maximum entropy principle, we introduce curved neural networks as a class of models with a limited number of parameters that are particularly well-suited for studying higher-order phenomena. Thro
Notable events recorded on this day and month across all years.