Record 04122025 · 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.
Complete record JSON
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
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
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
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.
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
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
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
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,
Elden Jerome Campbell was an American professional basketball player who was a power forward and center in the National Basketball Association (NBA) from 1990 to 2005, primarily with the Los Angeles Lakers. He played college basketball for the Clemson Tigers,
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.
Virat Kohli is an Indian international cricketer and former all-format captain of the Indian national cricket team. He is a right-handed batter and an occasional right-arm medium-pace bowler. Considered one of the greatest batters in limited-overs cricket, he
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,
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
List of international cricket centuries by Virat Kohli
Virat Kohli is an Indian cricketer and a former captain of the Indian national cricket team. A right-handed top-order batsman, he has made 85 centuries in international cricket—30 in Test cricket, 54 in One Day Internationals (ODIs) and 1 in Twenty20 Internati
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
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
Christopher Emmanuel Paul Sr., nicknamed "CP3" and "the Point God", is an American former professional basketball player. Regarded as one of the greatest point guards of all time, he won the NBA Rookie of the Year Award, an NBA All-Star Game Most Valuable Play
Paul Franklin Dano is an American actor and filmmaker. His work includes both independent film and blockbusters, and his accolades include nominations for a British Academy Film Award, a Golden Globe Award and two Primetime Emmy Awards.
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
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
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.
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
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
Tennessee's 7th congressional district
The 7th congressional district of Tennessee is a congressional district located in parts of Middle and West Tennessee. It has been represented by Republican Matt Van Epps since December 4, 2025. The 7th district has significant urban, suburban, and rural areas
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
Enrique Roberto "Henry" Cuellar is an American politician and attorney serving as the U.S. representative for Texas's 28th congressional district since 2005. He is a member of the Democratic Party, and his district spans from the Rio Grande toward the suburbs
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.
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
Arvid Anand Olof Lindblad is a British and Swedish racing driver who competes in Formula One for Racing Bulls under a British flag.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Rethinking the Learning Paradigm for Facial Expression Recognition
Due to the subjective crowdsourcing annotations and the inherent inter-class similarity of facial expressions, the real-world Facial Expression Recognition (FER) datasets usually exhibit ambiguous annotation. To simplify the learning paradigm, most previous methods convert ambiguous annotation results into precise one-hot annotations and train FER models in an end-to-end supervised manner. In this paper, we rethink t
Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks
Federated Learning (FL) is a collaborative machine learning (ML) framework that combines on-device training and server-based aggregation to train a common ML model among distributed agents. In this work, we propose an asynchronous FL design with periodic aggregation to tackle the straggler issue in FL systems. Considering limited wireless communication resources, we investigate the effect of different scheduling poli
The generalized Hierarchical Gaussian Filter
Hierarchical Bayesian models of perception and learning feature prominently in contemporary cognitive neuroscience where, for example, they inform computational concepts of mental disorders. This includes predictive coding and hierarchical Gaussian filtering (HGF), which differ in the nature of hierarchical representations. In this work, we present a new class of artificial neural networks that unifies computational
Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients
We present an unbiased method for Bayesian posterior means based on kinetic Langevin dynamics that combines advanced splitting methods with enhanced gradient approximations. Our approach avoids Metropolis correction by coupling Markov chains at different discretization levels in a multilevel Monte Carlo approach. Theoretical analysis demonstrates that our proposed estimator is unbiased, attains finite variance, and s
A Group Fairness Lens for Large Language Models
The need to assess LLMs for bias and fairness is critical, with current evaluations often being narrow, missing a broad categorical view. In this paper, we propose evaluating the bias and fairness of LLMs from a group fairness lens using a novel hierarchical schema characterizing diverse social groups. Specifically, we construct a dataset, GFAIR, encapsulating target-attribute combinations across multiple dimensions.
Tree Ensembles for Contextual Bandits
We propose a new framework for contextual multi-armed bandits based on tree ensembles. Our framework adapts two widely used bandit methods, Upper Confidence Bound and Thompson Sampling, for both standard and combinatorial settings. As part of this framework, we propose a novel method of estimating the uncertainty in tree ensemble predictions. We further demonstrate the effectiveness of our framework via several exper
Accelerating data-driven algorithm selection for combinatorial partitioning problems
Data-driven algorithm selection is a powerful approach for choosing effective heuristics for computational problems. It operates by evaluating a set of candidate algorithms on a collection of representative training instances and selecting the one with the best empirical performance. However, running each algorithm on every training instance is computationally expensive, making scalability a central challenge. In pra
Marginalize, Rather than Impute: Probabilistic Wind Power Forecasting with Incomplete Data
Machine learning methods are widely and successfully used for probabilistic wind power forecasting, yet the pervasive issue of missing values (e.g., due to sensor faults or communication outages) has received limited attention. The prevailing practice is impute-then-predict, but conditioning on point imputations biases parameter estimates and fails to propagate uncertainty from missing features. Our approach treats m
Variational Inference of Parameters in Opinion Dynamics Models
Despite the frequent use of agent-based models (ABMs) for studying social phenomena, parameter estimation remains a challenge, often relying on costly simulation-based heuristics. This work uses variational inference to estimate the parameters of an opinion dynamics ABM, by transforming the estimation problem into an optimization task that can be solved directly. Our proposal relies on probabilistic generative ABMs (
LLM-based agents for automating the enhancement of user story quality: An early report
In agile software development, maintaining high-quality user stories is crucial, but also challenging. This study explores the use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams. We developed a reference model for an Autonomous LLM-based Agent System and implemented it at the company. The quality of user stories in the study and the effectiveness of thes
Are you a robot? Detecting Autonomous Vehicles from Behavior Analysis
The tremendous hype around autonomous driving is eagerly calling for emerging and novel technologies to support advanced mobility use cases. As car manufactures keep developing SAE level 3+ systems to improve the safety and comfort of passengers, traffic authorities need to establish new procedures to manage the transition from human-driven to fully-autonomous vehicles while providing a feedback-loop mechanism to fin
MAP-Former: Multi-Agent-Pair Gaussian Joint Prediction
There is a gap in risk assessment of trajectories between the trajectory information coming from a traffic motion prediction module and what is actually needed. Closing this gap necessitates advancements in prediction beyond current practices. Existing prediction models yield joint predictions of agents' future trajectories with uncertainty weights or marginal Gaussian probability density functions (PDFs) for sin
On the Volatility of Shapley-Based Contribution Metrics in Federated Learning
Federated learning (FL) is a collaborative and privacy-preserving Machine Learning paradigm, allowing the development of robust models without the need to centralize sensitive data. A critical challenge in FL lies in fairly and accurately allocating contributions from diverse participants. Inaccurate allocation can undermine trust, lead to unfair compensation, and thus participants may lack the incentive to join or a
Margin-aware Preference Optimization for Aligning Diffusion Models without Reference
Modern preference alignment methods, such as DPO, rely on divergence regularization to a reference model for training stability-but this creates a fundamental problem we call "reference mismatch." In this paper, we investigate the negative impacts of reference mismatch in aligning text-to-image (T2I) diffusion models, showing that larger reference mismatch hinders effective adaptation given the same amount of
Rethinking Data Input for Point Cloud Upsampling
Point cloud upsampling is crucial for tasks like 3D reconstruction. While existing methods rely on patch-based inputs, and there is no research discussing the differences and principles between point cloud model full input and patch based input. Ergo, we propose a novel approach using whole model inputs i.e. Average Segment input. Our experiments on PU1K and ABC datasets reveal that patch-based inputs consistently ou
SLO-aware GPU Frequency Scaling for Energy Efficient LLM Inference Serving
As Large Language Models (LLMs) gain traction, their reliance on power-hungry GPUs places ever-increasing energy demands, raising environmental and monetary concerns. Inference dominates LLM workloads, presenting a critical challenge for providers: minimizing energy costs under Service-Level Objectives (SLOs) that ensure optimal user experience. In this paper, we present \textit{throttLL'eM}, a framework that red
Casper: Prompt Sanitization for Protecting User Privacy in Web-Based Large Language Models
Web-based Large Language Model (LLM) services have been widely adopted and have become an integral part of our Internet experience. Third-party plugins enhance the functionalities of LLM by enabling access to real-world data and services. However, the privacy consequences associated with these services and their third-party plugins are not well understood. Sensitive prompt data are stored, processed, and shared by cl
Large Language Model-Based Agents for Software Engineering: A Survey
The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs with the capabilities of perceiving and utilizing external resources and tools. To date, LLM-based agents have been applied and shown remarkable effectiveness in Software Engineering
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
Recently advancements in large multimodal models have led to significant strides in image comprehension capabilities. Despite these advancements, there is a lack of the robust benchmark specifically for assessing the Image-to-Web conversion proficiency of these large models. Primarily, it is essential to ensure the integrity of the web elements generated. These elements comprise visible and invisible categories. Prev
How to Train Long-Context Language Models (Effectively)
We study continued training and supervised fine-tuning (SFT) of a language model (LM) to make effective use of long-context information. We first establish a reliable evaluation protocol to guide model development -- instead of perplexity or simple needle-in-a-haystack (NIAH) tests, we use a broad set of long-context downstream tasks, and we evaluate models after SFT as this better reveals long-context abilities. Sup
The Duality of Generative AI and Reinforcement Learning in Robotics: A Review
Recently, generative AI and reinforcement learning (RL) have been redefining what is possible for AI agents that take information flows as input and produce intelligent behavior. As a result, we are seeing similar advancements in embodied AI and robotics for control policy generation. Our review paper examines the integration of generative AI models with RL to advance robotics. Our primary focus is on the duality bet
DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenes
Urban scene generation has been developing rapidly recently. However, existing methods primarily focus on generating static and single-frame scenes, overlooking the inherently dynamic nature of real-world driving environments. In this work, we introduce DynamicCity, a novel 4D occupancy generation framework capable of generating large-scale, high-quality dynamic 4D scenes with semantics. DynamicCity mainly consists o
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing
Remote sensing has evolved from simple image acquisition to complex systems capable of integrating and processing visual and textual data. This review examines the development and application of multi-modal language models (MLLMs) in remote sensing, focusing on their ability to interpret and describe satellite imagery using natural language. We cover the technical underpinnings of MLLMs, including dual-encoder archit
StableV2V: Stablizing Shape Consistency in Video-to-Video Editing
Recent advancements of generative AI have significantly promoted content creation and editing, where prevailing studies further extend this exciting progress to video editing. In doing so, these studies mainly transfer the inherent motion patterns from the source videos to the edited ones, where results with inferior consistency to user prompts are often observed, due to the lack of particular alignments between the
Concentration of Cumulative Reward in Markov Decision Processes
In this paper, we investigate the concentration properties of cumulative reward in Markov Decision Processes (MDPs), focusing on both asymptotic and non-asymptotic settings. We introduce a unified approach to characterize reward concentration in MDPs, covering both infinite-horizon settings (i.e., average and discounted reward frameworks) and finite-horizon setting. Our asymptotic results include the law of large num
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