Record 08122025 · captured 2026-08-25
The world looked up Lando Norris. 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.
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.
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
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
Bigg Boss (Hindi TV series) season 19
Bigg Boss 19, also known as Bigg Boss: Iss Baar Chalegi Gharwalon Ki Sarkaar, was the nineteenth season of the Indian Hindi-language reality television series Bigg Boss. It premiered on 24 August 2025 on Colors TV and streamed digitally on JioHotstar. Salman K
Curtis John Cignetti is an American college football coach who is the head football coach at Indiana University Bloomington. He previously served as the head coach at Indiana University of Pennsylvania (IUP) from 2011 to 2016, Elon University from 2017 to 2018
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
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
UFC 323: Dvalishvili vs. Yan 2 was a mixed martial arts event produced by the Ultimate Fighting Championship that took place on December 6, 2025, at the T-Mobile Arena in Paradise, Nevada, part of the Las Vegas Valley, United States.
2025 Formula One World Championship
The 2025 FIA Formula One World Championship was a motor racing championship for Formula One cars and the 76th running of the Formula One World Championship. It was recognised by the Fédération Internationale de l'Automobile (FIA), the governing body of interna
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
Petr Evgenyevich Yan is a Russian professional mixed martial artist. He currently competes in the Bantamweight division of the Ultimate Fighting Championship (UFC), where he is the current and two-time UFC Bantamweight Champion. He is also a former interim Ban
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
List of Formula One World Drivers' Champions
The World Drivers' Championship is presented by the Fédération Internationale de l'Automobile (FIA), motorsport's world governing body, to the most successful driver over the course of the season of Formula One races, through a points system based on individua
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
Thomas Read Wilson is an English television presenter, author, actor and singer. He is best known as the client coordinator on the E4 reality television series; Celebs Go Dating. In 2021, he was the runner-up on Celebrity Best Home Cook. In 2025, he was the ru
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
Five Nights at Freddy's 2 (film)
Five Nights at Freddy's 2 is a 2025 American supernatural horror film based on the video game series Five Nights at Freddy's created by Scott Cawthon and the sequel to the 2023 film adaptation. The film was directed by Emma Tammi and written by Cawthon. Josh H
Max Emilian Verstappen is a Dutch and Belgian racing driver who competes under the Dutch flag in Formula One for Red Bull Racing. Verstappen has won four Formula One World Drivers' Championship titles, which he won consecutively from 2021 to 2024 with Red Bull
Margarida Matias "Magui" Ferreira Corceiro is a Portuguese actress and fashion model.
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
Justin Pierre James Trudeau is a Canadian politician who served as the 23rd prime minister of Canada from 2015 to 2025. He led the Liberal Party from 2013 until his resignation in 2025 and was the member of Parliament (MP) for Papineau from 2008 until 2025.
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
Joshua Van Bawi Thawng is a Burmese and American professional mixed martial artist currently competing in the Flyweight division of the Ultimate Fighting Championship (UFC), where he is the current UFC Flyweight Champion. Van is the first fighter from Myanmar
Merab Dvalishvili is a Georgian and American professional mixed martial artist and sambo practitioner. He currently competes in the Bantamweight division of the Ultimate Fighting Championship (UFC), where he is the former UFC Bantamweight Champion. Dvalishvili
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,
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 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
Juan Carlos I was King of Spain from 1975 until his abdication in 2014. Although chosen by Francisco Franco as his successor, Juan Carlos helped dismantle the Francoist system and oversee the Spanish transition to democracy.
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.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Statistical Guarantees for Approximate Stationary Points of Shallow Neural Networks
Since statistical guarantees for neural networks are usually restricted to global optima of intricate objective functions, it is unclear whether these theories explain the performances of actual outputs of neural network pipelines. The goal of this paper is, therefore, to bring statistical theory closer to practice. We develop statistical guarantees for shallow linear neural networks that coincide up to logarithmic f
The blockchain technology provides integrity and reliability of the information, thus offering a suitable solution to guarantee trustability in a multi-stakeholder scenario that involves actors defining business agreements. The Ride2Rail project investigated the use of the blockchain to record as smart contracts the agreements between different stakeholders defined in a multimodal transportation domain. Modelling an
Towards Data-efficient Customer Intent Recognition with Prompt-based Learning Paradigm
Recognizing customer intent accurately with language models based on customer-agent conversational data is essential in today's digital customer service marketplace, but it is often hindered by the lack of sufficient labeled data. In this paper, we introduce the prompt-based learning paradigm that significantly reduces the dependency on extensive datasets. Utilizing prompted training combined with answer mapping
ProtoP-OD: Explainable Object Detection with Prototypical Parts
Interpretation and visualization of the behavior of detection transformers tends to highlight the locations in the image that the model attends to, but it provides limited insight into the \emph{semantics} that the model is focusing on. This paper introduces an extension to detection transformers that constructs prototypical local features and uses them in object detection. These custom features, which we call protot
Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving
Efficient data utilization is crucial for advancing 3D scene understanding in autonomous driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully supervised methods. Addressing this, our study extends into semi-supervised learning for LiDAR semantic segmentation, leveraging the intrinsic spatial priors of driving scenes and multi-sensor complements to augment the efficacy of unlabeled da
EnterpriseEM: Fine-tuned Embeddings for Enterprise Semantic Search
Enterprises grapple with the significant challenge of managing proprietary unstructured data, hindering efficient information retrieval. This has led to the emergence of AI-driven information retrieval solutions, designed to adeptly extract relevant insights to address employee inquiries. These solutions often leverage pre-trained embedding models and generative models as foundational components. While pre-trained em
iMotion-LLM: Instruction-Conditioned Trajectory Generation
We introduce iMotion-LLM, a large language model (LLM) integrated with trajectory prediction modules for interactive motion generation. Unlike conventional approaches, it generates feasible, safety-aligned trajectories based on textual instructions, enabling adaptable and context-aware driving behavior. It combines an encoder-decoder multimodal trajectory prediction model with a pre-trained LLM fine-tuned using LoRA,
Second Maximum of a Gaussian Random Field and Exact (t-)Spacing test
In this article, we introduce the novel concept of the second maximum of a Gaussian random field on a Riemannian submanifold. This second maximum serves as a powerful tool for characterizing the distribution of the maximum. By utilizing an ad-hoc Kac Rice formula, we derive the explicit form of the maximum's distribution, conditioned on the second maximum and some regressed component of the Riemannian Hessian. Th
A Scene-aware Models Adaptation Scheme for Cross-scene Online Inference on Mobile Devices
Emerging Artificial Intelligence of Things (AIoT) applications desire online prediction using deep neural network (DNN) models on mobile devices. However, due to the movement of devices, unfamiliar test samples constantly appear, significantly affecting the prediction accuracy of a pre-trained DNN. In addition, unstable network connection calls for local model inference. In this paper, we propose a light-weight schem
PLANesT-3D: A new annotated dataset for segmentation of 3D plant point clouds
Creation of new annotated public datasets is crucial in helping advances in 3D computer vision and machine learning meet their full potential for automatic interpretation of 3D plant models. Despite the proliferation of deep neural network architectures for segmentation and phenotyping of 3D plant models in the last decade, the amount of data, and diversity in terms of species and data acquisition modalities are far
GLDiTalker: Speech-Driven 3D Facial Animation with Graph Latent Diffusion Transformer
Speech-driven talking head generation is a critical yet challenging task with applications in augmented reality and virtual human modeling. While recent approaches using autoregressive and diffusion-based models have achieved notable progress, they often suffer from modality inconsistencies, particularly misalignment between audio and mesh, leading to reduced motion diversity and lip-sync accuracy. To address this, w
A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?
Translating users' natural language queries (NL) into SQL queries (i.e., Text-to-SQL, a.k.a. NL2SQL) can significantly reduce barriers to accessing relational databases and support various commercial applications. The performance of Text-to-SQL has been greatly enhanced with the emergence of Large Language Models (LLMs). In this survey, we provide a comprehensive review of Text-to-SQL techniques powered by LLMs,
In whole slide images (WSIs) analysis, attention-based multi-instance learning (MIL) models are susceptible to spurious correlations and degrade under domain shift. These methods may assign high attention weights to non-tumor regions, such as staining biases or artifacts, leading to unreliable tumor region localization. In this paper, we revisit max-pooling-based MIL methods from a causal perspective. Under mild assu
Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matching
Long-horizon events forecasting is a crucial task across various domains, including retail, finance, healthcare, and social networks. Traditional models for event sequences often extend to forecasting on a horizon using an autoregressive (recursive) multi-step strategy, which has limited effectiveness due to typical convergence to constant or repetitive outputs. To address this limitation, we introduce DEF, a novel a
Building high-quality datasets for specialized tasks is a time-consuming and resource-intensive process that often requires specialized domain knowledge. We propose Corpus Retrieval and Augmentation for Fine-Tuning (CRAFT), a method for generating synthetic datasets, given a small number of user-written few-shots that demonstrate the task to be performed. Given these examples, CRAFT uses large-scale public web-crawle
As a key stage of Recommender Systems (RSs), Multi-Task Fusion (MTF) is responsible for merging multiple scores output by Multi-Task Learning (MTL) into a single score, finally determining the recommendation results. Recently, Reinforcement Learning (RL) has been applied to MTF to maximize long-term user satisfaction within a recommendation session. However, due to limitations in modeling paradigm, all existing RL al
Point-PNG: Conditional Pseudo-Negatives Generation for Point Cloud Pre-Training
We propose Point-PNG, a novel self-supervised learning framework that generates conditional pseudo-negatives in the latent space to learn point cloud representations that are both discriminative and transformation-sensitive. Conventional self-supervised learning methods focus on achieving invariance, discarding transformation-specific information. Recent approaches incorporate transformation sensitivity by explicitly
Neural Eulerian Scene Flow Fields
We reframe scene flow as the task of estimating a continuous space-time ODE that describes motion for an entire observation sequence, represented with a neural prior. Our method, EulerFlow, optimizes this neural prior estimate against several multi-observation reconstruction objectives, enabling high quality scene flow estimation via pure self-supervision on real-world data. EulerFlow works out-of-the-box without tun
Semantic Communication and Control Co-Design for Multi-Objective Distinct Dynamics
This letter introduces a machine-learning approach to learning the semantic dynamics of correlated systems with different control rules and dynamics. By leveraging the Koopman operator in an autoencoder (AE) framework, the system's state evolution is linearized in the latent space using a dynamic semantic Koopman (DSK) model, capturing the baseline semantic dynamics. Signal temporal logic (STL) is incorporated th
MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications
Diffusion models have achieved significant success in both natural image and medical image domains, encompassing a wide range of applications. Previous investigations in medical images have often been constrained to specific anatomical regions, particular applications, and limited datasets, resulting in isolated diffusion models. This paper introduces a diffusion-based foundation model to address a diverse range of m
Rises in the number of animal abuse cases are reported around the world. While chatbots have been effective in influencing their users' perceptions and behaviors, little if any research has hitherto explored the design of chatbots that embody animal identities for the purpose of eliciting empathy toward animals. We therefore conducted a mixed-methods experiment to investigate how specific design cues in such chat
SPARTAN: A Sparse Transformer World Model Attending to What Matters
Capturing the interactions between entities in a structured way plays a central role in world models that flexibly adapt to changes in the environment. Recent works motivate the benefits of models that explicitly represent the structure of interactions and formulate the problem as discovering local causal structures. In this work, we demonstrate that reliably capturing these relationships in complex settings remains
Edge-Only Universal Adversarial Attacks in Distributed Learning
Distributed learning frameworks, which partition neural network models across multiple computing nodes, enhance efficiency in collaborative edge-cloud systems, but may also introduce new vulnerabilities to evasion attacks, often in the form of adversarial perturbations. In this work, we present a new threat model that explores the feasibility of generating universal adversarial perturbations (UAPs) when the attacker
Training Multi-Layer Binary Neural Networks With Local Binary Error Signals
Binary Neural Networks (BNNs) significantly reduce computational complexity and memory usage in machine and deep learning by representing weights and activations with just one bit. However, most existing training algorithms for BNNs rely on quantization-aware floating-point Stochastic Gradient Descent (SGD), limiting the full exploitation of binary operations to the inference phase only. In this work, we propose, for
Human Evaluation of Procedural Knowledge Graph Extraction from Text with Large Language Models
Procedural Knowledge is the know-how expressed in the form of sequences of steps needed to perform some tasks. Procedures are usually described by means of natural language texts, such as recipes or maintenance manuals, possibly spread across different documents and systems, and their interpretation and subsequent execution is often left to the reader. Representing such procedures in a Knowledge Graph (KG) can be the
Notable events recorded on this day and month across all years.