Record 03122025 · captured 2026-08-25
The world looked up Google Chrome. 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.
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
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
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
Lane Monte Kiffin is an American football coach who is the head coach of the LSU Tigers. He served as the head coach of the Oakland Raiders from 2007 to 2008, the University of Tennessee in 2009, USC from 2010 to 2013, Florida Atlantic from 2017 to 2019, and O
Drake Lee Maye is an American professional football quarterback for the New England Patriots of the National Football League (NFL). He played college football for the North Carolina Tar Heels, winning the Shaun Alexander Award and ACC Football Player of the Ye
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
Raj Nidimoru and Krishna Dasarakothapalli, collectively credited as Raj & DK, are an Indian filmmaker duo known for their work as writers, directors, and producers in Hindi cinema. They are noted for creating, directing, and producing the Hindi-language thrill
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
Samantha Ruth Prabhu is an Indian actress who works predominantly in Tamil and Telugu films. One of South India's highest-paid actresses, Samantha is the recipient of several accolades, including four Filmfare Awards South, two Nandi Awards and a Tamil Nadu St
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,
Michael Saul Dell is an American billionaire businessman and investor. He is the founder, chairman, and CEO of Dell Technologies, one of the world's largest technology infrastructure companies.
Tere Ishk Mein is a 2025 Indian Hindi-language romantic drama film directed by Aanand L. Rai from a screenplay written by Himanshu Sharma and Neeraj Yadav. Billed as a spiritual sequel to Raanjhanaa (2013), the film stars Dhanush and Kriti Sanon. It follows Sh
Frank Mitchell Bradley is a United States Navy admiral who has commanded the United States Special Operations Command since October 3, 2025.
Sienna Rose Diana Miller is a British actress. She began her career as a model, appearing in the pages of Italian Vogue and for the 2003 Pirelli Calendar. Her acting breakthrough came in the 2004 films Layer Cake and Alfie. She portrayed socialite Edie Sedgwic
Arvid Anand Olof Lindblad is a British and Swedish racing driver who competes in Formula One for Racing Bulls under a British flag.
Robin Arnold Smith was a South African-born English cricketer. He was a part of the English squad which finished as runners-up at the 1992 Cricket World Cup.
Aftyn Alyssa Behn is an American politician who has represented the 51st district of the Tennessee House of Representatives since 2023. Before being elected to office, Behn worked in social services and community advocacy, including serving as a healthcare org
Jaxson Chase Dart is an American professional football quarterback for the New York Giants of the National Football League (NFL). Dart played college football for the USC Trojans and Ole Miss Rebels and was selected by the Giants in the first round of the 2025
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
Victoria was Queen of the United Kingdom of Great Britain and Ireland from 20 June 1837 until her death in 1901. Her reign of 63 years and 216 days, which was longer than those of any of her predecessors, constituted the Victorian era, a period of industrial,
William Stein is an American college football coach and former player who is currently the head football coach at the University of Kentucky. Stein played college football for the Louisville Cardinals as a quarterback from 2008 to 2012. He has held various ass
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
Michael George Vrabel is an American professional football coach and former linebacker who is the head coach for the New England Patriots of the National Football League (NFL). Vrabel previously played in the NFL for 14 seasons, most notably with the Patriots.
Noah Cameron Schnapp is an American actor. He made his acting debut in 2015 with his portrayal of Charlie Brown in the animated film The Peanuts Movie and his supporting role in Steven Spielberg's Bridge of Spies. Schnapp gained international recognition for h
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
Paul Albert Anka is a Canadian and American singer, songwriter, and actor. His songs include "Diana", "You Are My Destiny", "Lonely Boy", "Put Your Head on My Shoulder", "(You're) Having My Baby" and "My Way".
2025 Tennessee's 7th congressional district special election
The 2025 Tennessee's 7th congressional district special election was held on December 2, 2025, to fill the vacant seat in Tennessee's 7th congressional district. The deadline for entering the special election was on October 7, 2025. Republican Matt Van Epps de
2025 Honduran general election
General elections were held in Honduras on 30 November 2025. Voters elected the President, all 128 members of the National Congress, and 20 representatives to the Central American Parliament (PARLACEN). The National Electoral Council (CNE) declared National Pa
Peter Brian Hegseth is an American government official, veteran, and former television personality who has served as the 29th United States secretary of defense since 2025.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
SkipGram word embedding models with negative sampling, or SGN in short, is an elegant family of word embedding models. In this paper, we formulate a framework for word embedding, referred to as Word-Context Classification (WCC), that generalizes SGN to a wide family of models. The framework, which uses some ``noise examples'', is justified through theoretical analysis. The impact of noise distribution on the
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm
Direction of arrival (DoA) estimation of multiple signals is pivotal in sensor array signal processing. A popular multi-signal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance super-resolution DoA recovery while being highly applicable in practice. MUSIC is a model-based algorithm, relying on an accurate mathematical description of the relationship between
Learning Robust Convolutional Neural Networks with Relevant Feature Focusing via Explanations
Existing image recognition techniques based on convolutional neural networks (CNNs) basically assume that the training and test datasets are sampled from i.i.d distributions. However, this assumption is easily broken in the real world because of the distribution shift that occurs when the co-occurrence relations between objects and backgrounds in input images change. Under this type of distribution shift, CNNs learn
Gaussian and Non-Gaussian Universality of Data Augmentation
We provide universality results that quantify how data augmentation affects the variance and limiting distribution of estimates through simple surrogates, and analyze several specific models in detail. The results confirm some observations made in machine learning practice, but also lead to unexpected findings: Data augmentation may increase rather than decrease the uncertainty of estimates, such as the empirical pre
Real-world image recognition systems often face corrupted input images, which cause distribution shifts and degrade the performance of models. These systems often use a single prediction model in a central server and process images sent from various environments, such as cameras distributed in cities or cars. Such single models face images corrupted in heterogeneous ways in test time. Thus, they require to instantly
Permutation-Aware Action Segmentation via Unsupervised Frame-to-Segment Alignment
This paper presents an unsupervised transformer-based framework for temporal activity segmentation which leverages not only frame-level cues but also segment-level cues. This is in contrast with previous methods which often rely on frame-level information only. Our approach begins with a frame-level prediction module which estimates framewise action classes via a transformer encoder. The frame-level prediction module
Global universal approximation of functional input maps on weighted spaces
We introduce so-called functional input neural networks defined on a possibly infinite dimensional weighted space with values also in a possibly infinite dimensional output space. To this end, we use an additive family to map the input weighted space to the hidden layer, on which a non-linear scalar activation function is applied to each neuron, and finally return the output via some linear readouts. Relying on Stone
Predicting Human Perceptions of Robot Performance During Navigation Tasks
Understanding human perceptions of robot performance is crucial for designing socially intelligent robots that can adapt to human expectations. Current approaches often rely on surveys, which can disrupt ongoing human-robot interactions. As an alternative, we explore predicting people's perceptions of robot performance using non-verbal behavioral cues and machine learning techniques. We contribute the SEAN TOGETH
Large Language Models for Robotics: A Survey
The human ability to learn, generalize, and control complex manipulation tasks through multi-modality feedback suggests a unique capability, which we refer to as dexterity intelligence. Understanding and assessing this intelligence is a complex task. Amidst the swift progress and extensive proliferation of large language models (LLMs), their applications in the field of robotics have garnered increasing attention. LL
Computational Copyright: Towards A Royalty Model for Music Generative AI
The rapid rise of generative AI has intensified copyright and economic tensions in creative industries, particularly in music. Current approaches addressing this challenge often focus on preventing infringement or establishing one-time licensing, which fail to provide the sustainable, recurring economic incentives necessary to maintain creative ecosystems. To address this gap, we propose Generative Content ID, a fram
PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs
Neural Networks can be effectively compressed through pruning, significantly reducing storage and compute demands while maintaining predictive performance. Simple yet effective methods like magnitude pruning remove less important parameters and typically require a costly retraining procedure to restore performance. However, with the rise of LLMs, full retraining has become infeasible due to memory and compute constra
Diffusion model for relational inference
Dynamical behaviors of complex interacting systems, including brain activities, financial price movements, and physical collective phenomena, are associated with underlying interactions between the system's components. The issue of uncovering interaction relations in such systems using observable dynamics is called relational inference. In this study, we propose a Diffusion model for Relational Inference (DiffRI)
Unifying Linear-Time Attention via Latent Probabilistic Modelling
Transformers have achieved state-of-the-art results across a range of domains, but their quadratic attention mechanism poses significant challenges for long-sequence modelling. Recent efforts to design linear-time attention mechanisms have yielded more scalable alternatives, yet often at the cost of performance, particularly on discrete data such as language. In this work, we revisit linear attention through the lens
ContourDiff: Unpaired Medical Image Translation with Structural Consistency
Accurately translating medical images between different modalities, such as Computed Tomography (CT) to Magnetic Resonance Imaging (MRI), has numerous downstream clinical and machine learning applications. While several methods have been proposed to achieve this, they often prioritize perceptual quality with respect to output domain features over preserving anatomical fidelity. However, maintaining anatomy during tra
Test-time Similarity Modification for Person Re-identification toward Temporal Distribution Shift
Person re-identification (re-id), which aims to retrieve images of the same person in a given image from a database, is one of the most practical image recognition applications. In the real world, however, the environments that the images are taken from change over time. This causes a distribution shift between training and testing and degrades the performance of re-id. To maintain re-id performance, models should co
Brain-aligning of semantic vectors improves neural decoding of visual stimuli
The development of algorithms to accurately decode neural information has long been a research focus in the field of neuroscience. Brain decoding typically involves training machine learning models to map neural data onto a preestablished vector representation of stimulus features. These vectors are usually derived from image- and/or text-based feature spaces. Nonetheless, the intrinsic characteristics of these vecto
3D medical vision-language (VL) pretraining has shown potential in radiology by leveraging large-scale multimodal datasets with CT-report pairs. However, existing methods primarily rely on a global VL alignment directly adapted from 2D scenarios. The entire 3D image is transformed into one global embedding, resulting in a loss of sparse but critical semantics essential for accurately aligning with the corresponding d
Anomalous Change Point Detection Using Probabilistic Predictive Coding
Change point detection (CPD) and anomaly detection (AD) are essential techniques in various fields to identify abrupt changes or abnormal data instances. However, existing methods are often constrained to univariate data, face scalability challenges with large datasets due to computational demands, and experience reduced performance with high-dimensional or intricate data, as well as hidden anomalies. Furthermore, th
XXLTraffic: Expanding and Extremely Long Traffic forecasting beyond test adaptation
Traffic forecasting is crucial for smart cities and intelligent transportation initiatives, where deep learning has made significant progress in modeling complex spatio-temporal patterns in recent years. However, current public datasets have limitations in reflecting the distribution shift nature of real-world scenarios, characterized by continuously evolving infrastructures, varying temporal distributions, and long
Aligning Diffusion Models with Noise-Conditioned Perception
Recent advancements in human preference optimization, initially developed for Language Models (LMs), have shown promise for text-to-image Diffusion Models, enhancing prompt alignment, visual appeal, and user preference. Unlike LMs, Diffusion Models typically optimize in pixel or VAE space, which does not align well with human perception, leading to slower and less efficient training during the preference alignment st
Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach
A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about the potential of new technologies to offer a personal tutor for every learner and a teaching assistant for every teacher. The full extent of this dream, however, has not yet materialised. We argue that this is primarily due to the difficult
A process algebraic framework for multi-agent dynamic epistemic systems
This paper combines the classical model of labeled transition systems with the epistemic model for reasoning about knowledge. The result is a unifying framework for modeling and analyzing multi-agent, knowledge-based, dynamic systems. On the modeling side, we propose a process algebraic, agent-oriented specification language that makes such a framework easy to use for practical purposes. On the verification side, we
Pre-trained Language Models Improve the Few-shot Prompt Ability of Decision Transformer
Decision Transformer (DT) has emerged as a promising class of algorithms in offline reinforcement learning (RL) tasks, leveraging pre-collected datasets and Transformer's capability to model long sequences. Recent works have demonstrated that using parts of trajectories from training tasks as prompts in DT enhances its performance on unseen tasks, giving rise to Prompt-DT methods. However, collecting data from sp
Mutually-Aware Feature Learning for Few-Shot Object Counting
Few-shot object counting has garnered significant attention for its practicality as it aims to count target objects in a query image based on given exemplars without additional training. However, the prevailing extract-and-match approach has a shortcoming: query and exemplar features lack interaction during feature extraction since they are extracted independently and later correlated based on similarity. This can le
Towards a vision foundation model for comprehensive assessment of Cardiac MRI
Cardiac magnetic resonance imaging (CMR), considered the gold standard for noninvasive cardiac assessment, is a diverse and complex modality requiring a wide variety of image processing tasks for comprehensive assessment of cardiac morphology and function. Advances in deep learning have enabled the development of state-of-the-art (SoTA) models for these tasks. However, model training is challenging due to data and la
Notable events recorded on this day and month across all years.