Record 15122025 · 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
On 14 December 2025, an antisemitic and Islamic State (IS)-inspired terrorist attack occurred at the Archer Park area of Bondi Beach in Sydney, New South Wales, Australia, during a celebration of the Jewish holiday of Hanukkah attended by around 1,000 people.
Fernando Gabriel Mendoza V is an American professional football quarterback for the Las Vegas Raiders of the National Football League (NFL). Mendoza played college football for the California Golden Bears for three seasons before transferring to the Indiana Ho
Wake Up Dead Man is a 2025 American mystery film written and directed by Rian Johnson. It is the third film in the Knives Out series. The film stars Daniel Craig, who reprises his role as master detective Benoit Blanc as he investigates the death of a Catholic
Peter Greene was an American actor. A character actor, he was generally known for portraying villains, corrupt police officers, and criminals. He began his acting career in 1990, landing small roles in television and film with his film debut being Laws of Grav
John Felix Anthony Cena is an American actor, retired professional wrestler, and former rapper. He is signed to WWE as a brand ambassador. Cena wrestled for WWE for 24 years, becoming a record-setting 17-time world champion, before transitioning fully into his
Joshua Mathias O'Connor is an English actor. From 2016 to 2019, he had a major role portraying Larry Durrell in ITV's The Durrells. He had his breakthrough playing the lead role of a gay sheep farmer in Francis Lee's romantic drama God's Own Country (2017), fo
Richard Wayne Van Dyke is an American actor, comedian, singer, dancer and writer. His work spans screen and stage, and his awards include six Emmy Awards, a Grammy Award, and a Tony Award. He was inducted into the Hollywood Walk of Fame in 1993, and then the T
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
Brown University is a private Ivy League research university in the College Hill neighborhood of Providence, Rhode Island, United States. The university is the seventh-oldest institution of higher education in the United States, founded in 1764 as the College
Saturday Night's Main Event XLII
Saturday Night's Main Event XLII, also promoted as Saturday Night's Main Event: John Cena's Final Match, was a professional wrestling television special produced by WWE. It took place on December 13, 2025, from the Capital One Arena in Washington, D.C., and wa
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
The Port Arthur massacre was a mass shooting that occurred on 28 April 1996 at Port Arthur, a tourist town in the Australian state of Tasmania. The perpetrator, Martin Bryant, murdered 35 people and wounded 23 others, in the deadliest massacre in modern Austra
Hanukkah is a Jewish holiday that commemorates the Maccabean Revolt against the Seleucid Empire in the 2nd century BCE, when the Maccabees successfully recovered Jerusalem and the Second Temple.
2025 Brown University shooting
On December 13, 2025, a mass shooting occurred at Brown University in Providence, Rhode Island, United States, during the second day of final examination week for the fall semester. The shooter, Cláudio Manuel Neves Valente, entered the Barus and Holley Buildi
List of mass shootings in Australia
This article is a list of mass shootings in Australia. Mass shootings are firearm-related violence with at least four casualties. Excluded are shootings associated with acts of war, such as the 1944 Cowra breakout, which saw over 200 soldiers killed. Also excl
Bondi Beach is a beach and the surrounding suburb in Sydney, New South Wales, Australia. Bondi Beach is located 7 kilometres east of the Sydney central business district, in the local government area of Waverley Council, in the Eastern Suburbs. In the 2021 Aus
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
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
List of topics named after Leonhard Euler
In mathematics and physics, many topics are named in honor of Swiss mathematician Leonhard Euler (1707–1783), who made many important discoveries and innovations. Many of these items named after Euler include their own unique function, equation, formula, ident
Nitin Nabin is an Indian politician, political organiser, and activist who has been serving as the 16th national president of the Bharatiya Janata Party (BJP) since January 2026 and an MP in the upper chamber of the Indian Parliament, the Rajya Sabha since Apr
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
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
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.
Diego Pavia is an American professional football quarterback. He played college football for the New Mexico Military Broncos, New Mexico State Aggies, and Vanderbilt Commodores before signing with the Baltimore Ravens as an undrafted free agent in 2026 and was
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
Saturday Night's Main Event is a series of American professional wrestling television specials produced by WWE. The series originally broadcast from 1985 to 1992, by NBC until 1991 then briefly by Fox. The specials were briefly revived on NBC from 2006 to 2008
Heated Rivalry is a Canadian sports romance television series created, written, and directed by Jacob Tierney for Crave. Based on the Game Changers book series by Rachel Reid, the show takes its title from the 2019 second installment. It stars Hudson Williams
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Personalized Federated Learning with Exact Stochastic Gradient Descent
We propose a Stochastic Gradient Descent (SGD)-type algorithm for Personalized Federated Learning which can be particularly attractive for mobile energy-limited regimes due to its low per-client computational cost. The model to be trained includes a set of common weights for all clients, and a set of personalized weights that are specific to each client. At each optimization round, randomly selected clients perform m
3DSGrasp: 3D Shape-Completion for Robotic Grasp
Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD t
GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism
Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their superior performance in various graph analytical tasks. Mini-batch training is commonly used to train GNNs on large graphs, and data parallelism is the standard approach to scale mini-batch training across multiple GPUs. Data parallel approaches contain redundant work as subgraphs sampled by differe
Joint Learning of Wording and Formatting for Singable Melody-to-Lyric Generation
Despite progress in melody-to-lyric generation, a substantial singability gap remains between machine-generated lyrics and those written by human lyricists. In this work, we aim to narrow this gap by jointly learning both wording and formatting for melody-to-lyric generation. After general-domain pretraining, our model acquires length awareness through an self-supervised stage trained on a large text-only lyric corpu
AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform
This study investigates how artificial intelligence (AI) influences various online labor markets (OLMs) over time. Employing the Difference-in-Differences method, we discovered two distinct scenarios following ChatGPT's launch: displacement effects featuring reduced work volume and earnings, exemplified by translation & localization OLM; productivity effects featuring increased work volume and earnings, exemp
DoDo-Code: an Efficient Levenshtein Distance Embedding-based Code for 4-ary IDS Channel
With the emergence of new storage and communication methods, the insertion, deletion, and substitution (IDS) channel has attracted considerable attention. However, many topics on the IDS channel and the associated Levenshtein distance remain open, making the invention of a novel IDS-correcting code a hard task. Furthermore, current studies on single-IDS-correcting code misalign with the requirements of applications w
In this paper, we introduce a new embedding model called M3-Embedding, which is distinguished for its versatility in \textit{Multi-Linguality}, \textit{Multi-Functionality}, and \textit{Multi-Granularity}. It provides a uniform support for the semantic retrieval of more than 100 working languages. It can simultaneously accomplish the three common retrieval functionalities: dense retrieval, multi-vector retrieval, and
Efficient Action Counting with Dynamic Queries
Temporal repetition counting aims to quantify the repeated action cycles within a video. The majority of existing methods rely on the similarity correlation matrix to characterize the repetitiveness of actions, but their scalability is hindered due to the quadratic computational complexity. In this work, we introduce a novel approach that employs an action query representation to localize repeated action cycles with
As Machine Learning grows in popularity across various fields, equity has become a key focus for the AI community. However, fairness-oriented approaches are still underexplored in smart mobility. Addressing this gap, our study investigates the balance between performance optimization and algorithmic fairness in shared micromobility services providing a novel framework based on Reinforcement Learning. Exploiting Q-lea
The Expressive Capacity of State Space Models: A Formal Language Perspective
Recently, recurrent models based on linear state space models (SSMs) have shown promising performance in language modeling (LM), competititve with transformers. However, there is little understanding of the in-principle abilities of such models, which could provide useful guidance to the search for better LM architectures. We present a comprehensive theoretical study of the capacity of such SSMs as it compares to tha
Large Language Models (LLMs) struggle with reliably generating highly structured outputs, such as program code, mathematical formulas, or well-formed markup. Constrained decoding approaches mitigate this problem by greedily restricting what tokens an LLM can output at each step to guarantee that the output matches a given constraint. Specifically, in grammar-constrained decoding (GCD), the LLM's output must follo
M2NO: An Efficient Multi-Resolution Operator Framework for Dynamic Multi-Scale PDE Solvers
Solving high-dimensional partial differential equations (PDEs) efficiently requires handling multi-scale features across varying resolutions. To address this challenge, we present the Multiwavelet-based Multigrid Neural Operator (M2NO), a deep learning framework that integrates a multigrid structure with predefined multiwavelet spaces. M2NO leverages multi-resolution analysis to selectively transfer low-frequency err
From Next-Token to Mathematics: The Learning Dynamics of Mathematical Reasoning in Language Models
Large Language Models (LLMs) solely trained on next-token prediction learn to solve a wide range of problems involving mathematical reasoning. But how does this ability evolve during training? We show the first analysis of how mathematical reasoning abilities of several open-weight LLMs develop during pre-training and post-training. To this end, we construct MathCAMPS, a synthetic dataset of novel mathematical reason
Rolling in the deep of cognitive and AI biases
Nowadays, we delegate many of our decisions to Artificial Intelligence (AI) that acts either in solo or as a human companion in decisions made to support several sensitive domains, like healthcare, financial services and law enforcement. AI systems, even carefully designed to be fair, are heavily criticized for delivering misjudged and discriminated outcomes against individuals and groups. Numerous work on AI algorit
Visual-Friendly Concept Protection via Selective Adversarial Perturbations
Personalized concept generation by tuning diffusion models with a few images raises potential legal and ethical concerns regarding privacy and intellectual property rights. Researchers attempt to prevent malicious personalization using adversarial perturbations. However, previous efforts have mainly focused on the effectiveness of protection while neglecting the visibility of perturbations. They utilize global advers
Mathematical modeling in systems toxicology enables a comprehensive understanding of the effects of pharmaceutical substances on cardiac health. However, the complexity of these models limits their widespread application in early drug discovery. In this paper, we introduce a novel approach to solving parameterized models of cardiac action potentials by combining meta-learning techniques with Systems Biology-Informed
Multimodal Learning for Scalable Representation of High-Dimensional Medical Data
Integrating artificial intelligence (AI) with healthcare data is rapidly transforming medical diagnostics and driving progress toward precision medicine. However, effectively leveraging multimodal data, particularly digital pathology whole slide images (WSIs) and genomic sequencing, remains a significant challenge due to the intrinsic heterogeneity of these modalities and the need for scalable and interpretable frame
Noise-Robust and Resource-Efficient ADMM-based Federated Learning
Federated learning (FL) leverages client-server communications to train global models on decentralized data. However, communication noise or errors can impair model accuracy. To address this problem, we propose a novel FL algorithm that enhances robustness against communication noise while also reducing communication load. We derive the proposed algorithm through solving the weighted least-squares (WLS) regression pr
MiSS: Revisiting the Trade-off in LoRA with an Efficient Shard-Sharing Structure
Low-Rank Adaptation (LoRA) is a widely adopted technique for parameter-efficient fine-tuning, but its slow convergence has spurred the development of numerous variants. Nevertheless, existing methods often fail to improve performance, memory footprint, and computational efficiency simultaneously. To address this challenge, we revisit the causes of LoRA's slow convergence. Building on these insights, we propose Ma
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation
Synthetic tabular data generation has gained significant attention for its potential in data augmentation and privacy-preserving data sharing. While recent methods like diffusion and auto-regressive models (i.e., transformer) have advanced the field, generative adversarial networks (GANs) remain highly competitive due to their training efficiency and strong data generation capabilities. In this paper, we introduce Ta
Assumption-Lean Post-Integrated Inference with Surrogate Control Outcomes
Data integration methods aim to extract low-dimensional embeddings from high-dimensional outcomes to remove unwanted variations, such as batch effects and unmeasured covariates, across heterogeneous datasets. However, multiple hypothesis testing after integration can be biased due to data-dependent processes. We introduce a robust post-integrated inference method that accounts for latent heterogeneity by utilizing co
Foundation models (FMs) such as large language models (LLMs) have significantly impacted many fields, including software engineering (SE). The interaction between SE and FMs has led to the integration of FMs into SE practices (FM4SE) and the application of SE methodologies to FMs (SE4FM). While several literature surveys exist on academic contributions to these trends, we are the first to provide a practitioner's
Large Continual Instruction Assistant
Continual Instruction Tuning (CIT) is adopted to continually instruct Large Models to follow human intent data by data. It is observed that existing gradient update would heavily destroy the performance on previous datasets during CIT process. Instead, Exponential Moving Average (EMA), owns the ability to trace previous parameters, which can aid in decreasing forgetting. Nonetheless, its stable balance weight fails t
WARPD: World model Assisted Reactive Policy Diffusion
With the increasing availability of open-source robotic data, imitation learning has become a promising approach for both manipulation and locomotion. Diffusion models are now widely used to train large, generalized policies that predict controls or trajectories, leveraging their ability to model multimodal action distributions. However, this generality comes at the cost of larger model sizes and slower inference, an
Denoising Diffusion Models for Anomaly Localization in Medical Images
This review explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their application to image reconstruction and their conditioning using guidance mechanisms, we provide an overview of available datasets and evaluation metrics suitable for their application to anomaly localization in medical images. In this context
Notable events recorded on this day and month across all years.