Record 29122025 · captured 2026-08-25
The world looked up Brigitte Bardot. 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.
Brigitte Anne-Marie Bardot, often referred to by her initials B.B., was a French actress, singer, model, and animal rights activist. She became one of the best-known symbols of the sexual revolution and gained international fame for portraying characters assoc
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
Avatar: Fire and Ash is a 2025 American epic science fiction film directed by James Cameron from a screenplay he co-wrote with Rick Jaffa and Amanda Silver. Produced by Lightstorm Entertainment, it is the third installment in the Avatar film series and the seq
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
Marty Supreme is a 2025 American sports comedy-drama film directed by Josh Safdie, who co-wrote it with Ronald Bronstein. Set in the 1950s, it stars Timothée Chalamet as table tennis player Marty Mauser and follows his quest to become world champion. Gwyneth P
Somaliland, officially the Republic of Somaliland, is a partially recognised state in the Horn of Africa. It is located on the southern coast of the Gulf of Aden and bordered by Djibouti to the northwest, Ethiopia to the south and west, and Somalia to the east
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.
List of highest-grossing Indian films
This ranking lists the highest-grossing Indian films produced by Indian cinema, based on conservative global box office estimates as reported by organisations classified as green by Wikipedia. The figures are not adjusted for inflation. However, there is no of
2026 PDC World Darts Championship
The 2026 PDC World Darts Championship was a professional darts tournament that took place from 11 December 2025 to 3 January 2026 at Alexandra Palace in London, England. The 33rd World Darts Championship organised by the Professional Darts Corporation (PDC), i
The 2025 Worlds End was a professional wrestling pay-per-view (PPV) event produced by All Elite Wrestling (AEW). It was the third annual Worlds End and took place on December 27, 2025, at the Now Arena in the Chicago suburb of Hoffman Estates, Illinois. It hos
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
One Battle After Another is a 2025 American action thriller film written, directed, and produced by Paul Thomas Anderson. Inspired by the 1990 novel Vineland by Thomas Pynchon, the film's ensemble cast includes Leonardo DiCaprio, Sean Penn, Benicio del Toro, R
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
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
Malik Antonio Willis is an American professional football quarterback for the Miami Dolphins of the National Football League (NFL). He played college football for the Auburn Tigers and Liberty Flames, winning the 2020 Dudley Award with the latter. Willis was s
Jacques Charrier was a French actor, film producer, painter and ceramist.
Roger Vadim Plemiannikov was a French screenwriter, film director, and producer, as well as an author, artist, and occasional actor. His best-known works are visually lavish films with erotic qualities, such as And God Created Woman (1956), Blood and Roses (19
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
The Housemaid is a 2025 American erotic psychological thriller film directed by Paul Feig and written by Rebecca Sonnenshine. It is based on the 2022 novel by Freida McFadden, and stars Sydney Sweeney and Amanda Seyfried. In the film, Millie Calloway, a young
Derrick Lamar Henry Jr. is an American professional football running back for the Baltimore Ravens of the National Football League (NFL). Nicknamed "King Henry", he is known for his imposing style of play and larger build than the average running back. Noted f
List of Stranger Things episodes
Stranger Things is an American science fiction, horror, mystery, and drama television series created by the Duffer Brothers for the streaming service Netflix. The brothers, as well as Karl Gajdusek in the first season only, are the program's showrunners. They
Marie-Jeanne Bardot, known professionally as Mijanou Bardot, is a French writer, businesswoman, and retired actress. She is the younger sister of Brigitte Bardot.
Martin Reisman was an American table tennis player and author. He won the U.S. Men's Singles Championship in 1958 and 1960 and the U.S. Hardbat Championship in 1997. He advocated the traditional hardbat style of table tennis.
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
The 2025 Africa Cup of Nations was the 35th edition of the Africa Cup of Nations, a biennial football tournament contested by the men's national teams of the Confederation of African Football (CAF). It was hosted by Morocco — the second time the country had ho
Avatar is a 2009 epic science fiction film written and directed by James Cameron. It features an ensemble cast including Sam Worthington, Zoe Saldaña, Stephen Lang, Michelle Rodriguez, and Sigourney Weaver. It is the first installment in the Avatar film series
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
A Primer on the Signature Method in Machine Learning
We provide an introduction to the signature method, focusing on its theoretical properties and machine learning applications. Our presentation is divided into two parts. In the first part, we present the definition and fundamental properties of the signature of a path. The signature is a sequence of numbers associated with a path that captures many of its important analytic and geometric properties. As a sequence of
Learning pairwise Markov network structures using correlation neighborhoods
Markov networks are widely studied and used throughout multivariate statistics and computer science. In particular, the problem of learning the structure of Markov networks from data without invoking chordality assumptions in order to retain expressiveness of the model class has been given a considerable attention in the recent literature, where numerous constraint-based or score-based methods have been introduced. H
Co-Teaching for Unsupervised Domain Adaptation and Expansion
Unsupervised Domain Adaptation (UDA) essentially trades a model's performance on a source domain for improving its performance on a target domain. To overcome this, Unsupervised Domain Expansion (UDE) has been introduced, which adapts the model to the target domain while preserving its performance in the source domain. In both UDA and UDE, a model tailored to a given domain is assumed to well handle samples from
Pre-training Vision Transformers with Formula-driven Supervised Learning
In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k and can approach that of the JFT-300M dataset without the use of real images, human supervision, or self-supervision during the pre-training of vision transformers (ViTs). For example, ViT-Base pre-trained on ImageNet-21k and JFT-300M showed 83.0 and 84.1% top-1 accuracy when fi
Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models
Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-ta
Populist rhetoric employed on online media is characterized as deeply impassioned and often imbued with strong emotions. The aim of this paper is to empirically investigate the differences in affective nonverbal communication of political leaders. We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries, analyze their facial expressions of emotion and
Creative Agents: Empowering Agents with Imagination for Creative Tasks
We study building embodied agents for open-ended creative tasks. While existing methods build instruction-following agents that can perform diverse open-ended tasks, none of them demonstrates creativity -- the ability to give novel and diverse solutions implicit in the language instructions. This limitation comes from their inability to convert abstract language instructions into concrete goals and perform long-horiz
SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustments
Split Federated Learning (SFL) is a distributed machine learning framework which strategically divides the learning process between a server and clients and collaboratively trains a shared model by aggregating local models updated based on data from distributed clients. However, data heterogeneity and partial client participation result in label distribution skew, which severely degrades the learning performance. To
MAD: Multi-Alignment MEG-to-Text Decoding
Deciphering language from brain activity is a crucial task in brain-computer interface (BCI) research. Non-invasive cerebral signaling techniques including electroencephalography (EEG) and magnetoencephalography (MEG) are becoming increasingly popular due to their safety and practicality, avoiding invasive electrode implantation. However, current works under-investigated three points: 1) a predominant focus on EEG wi
Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond
Self-supervised learning (SSL), which aims to learn meaningful prior representations from unlabeled data, has been proven effective for skeleton-based action understanding. Different from the image domain, skeleton data possesses sparser spatial structures and diverse representation forms, with the absence of background clues and the additional temporal dimension, presenting new challenges for spatial-temporal motion
Recent advances in Hierarchical Multi-label Classification (HMC), particularly neurosymbolic-based approaches, have demonstrated improved consistency and accuracy by enforcing constraints on a neural model during training. However, such work assumes the existence of such constraints a-priori. In this paper, we relax this strong assumption and present an approach based on Error Detection Rules (EDR) that allow for lea
Revisiting Bi-Encoder Neural Search: An Encoding--Searching Separation Perspective
This paper reviews, analyzes, and proposes a new perspective on the bi-encoder architecture for neural search. While the bi-encoder architecture is widely used due to its simplicity and scalability at test time, it has some notable issues such as low performance on seen datasets and weak zero-shot performance on new datasets. In this paper, we analyze these issues and summarize two main critiques: the encoding inform
Making Robust Generalizers Less Rigid with Loss Concentration
While the traditional formulation of machine learning tasks is in terms of performance on average, in practice we are often interested in how well a trained model performs on rare or difficult data points at test time. To achieve more robust and balanced generalization, methods applying sharpness-aware minimization to a subset of worst-case examples have proven successful for image classification tasks, but only usin
Machine learning in computational pathology (CPath) often aggregates patch-level predictions from multi-gigapixel Whole Slide Images (WSIs) to generate WSI-level prediction scores for crucial tasks such as survival prediction and drug effect prediction. However, current methods do not explicitly characterize distributional differences between patch sets within WSIs. We introduce HistoKernel, a novel Maximum Mean Disc
GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussion
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse NLP tasks. Extensive research has explored how to enhance the logical reasoning abilities such as Chain-of-Thought, Chain-of-Thought with Self-Consistency, Tree-Of-Thoughts, and multi-agent debates. In the context of multi-agent debates, significant performance improvements can be achieved with an increasing number
Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rank
State-of-the-art Extreme Multi-Label Text Classification models rely on multi-label attention to focus on key tokens in input text, but learning good attention weights is challenging. We introduce PLANT - Pretrained and Leveraged Attention - a plug-and-play strategy for initializing attention. PLANT works by planting label-specific attention using a pretrained Learning-to-Rank model guided by mutual information gain.
Online Convex Optimization with Memory and Limited Predictions
This paper addresses an online convex optimization problem where the cost function at each step depends on a history of past decisions (i.e., memory), and the decision maker has access to limited predictions of future cost values within a finite window. The goal is to design an algorithm that minimizes the dynamic regret against the optimal sequence of decisions in hindsight. To this end, we propose a novel predictiv
An Exploration of Higher Education Course Evaluation by Large Language Models
Course evaluation plays a critical role in ensuring instructional quality and guiding curriculum development in higher education. However, traditional evaluation methods, such as student surveys, classroom observations, and expert reviews, are often constrained by subjectivity, high labor costs, and limited scalability. With recent advancements in large language models (LLMs), new opportunities have emerged for gener
New advances in universal approximation with neural networks of minimal width
We prove several universal approximation results at minimal or near-minimal width for approximation of $L^p(\mathbb{R}^{d_x}, \mathbb{R}^{d_y})$ and $C^0(\mathbb{R}^{d_x}, \mathbb{R}^{d_y})$ on compact sets. Our approach uses a unified coding scheme that yields explicit constructions relying only on standard analytic tools. We show that feedforward neural networks with two leaky ReLU activations $σ_α$, $σ_{-α}$ achie
Impact of Data Snooping on Deep Learning Models for Locating Vulnerabilities in Lifted Code
This study examines the impact of data snooping on neural networks used to detect vulnerabilities in lifted code, and builds on previous research that used word2vec and unidirectional and bidirectional transformer-based embeddings. The research specifically focuses on how model performance is affected when embedding models are trained with datasets, which include samples used for neural network training and validatio
CEKER: A Generalizable LLM Framework for Literature Analysis with a Case Study in Unikernel Security
Literature reviews are a critical component of formulating and justifying new research, but are a manual and often time-consuming process. This research introduces a novel, generalizable approach to literature analysis called CEKER which uses a three-step process to streamline the collection of literature, the extraction of key insights, and the summarized analysis of key trends and gaps. Leveraging Large Language Mo
The expansion of long-context Large Language Models (LLMs) creates significant memory system challenges. While Processing-in-Memory (PIM) is a promising accelerator, we identify that it suffers from critical inefficiencies when scaled to long contexts: severe channel underutilization, performance-limiting I/O bottlenecks, and massive memory waste from static KV cache management. In this work, we propose PIMphony, a P
Bimanual dexterous manipulation remains significant challenges in robotics due to the high DoFs of each hand and their coordination. Existing single-hand manipulation techniques often leverage human demonstrations to guide RL methods but fail to generalize to complex bimanual tasks involving multiple sub-skills. In this paper, we introduce VTAO-BiManip, a novel framework that combines visual-tactile-action pretrainin
Indoor localization in challenging non-line-of-sight (NLOS) environments often leads to poor accuracy with traditional approaches. Deep learning (DL) has been applied to tackle these challenges; however, many DL approaches overlook computational complexity, especially for floating-point operations (FLOPs), making them unsuitable for resource-limited devices. Transformer-based models have achieved remarkable success i
Computerized Assessment of Motor Imitation for Distinguishing Autism in Video (CAMI-2DNet)
Motor imitation impairments are commonly reported in individuals with autism spectrum conditions (ASCs), suggesting that motor imitation could be used as a phenotype for addressing autism heterogeneity. Traditional methods for assessing motor imitation are subjective, labor-intensive, and require extensive human training. Modern Computerized Assessment of Motor Imitation (CAMI) methods, such as CAMI-3D for motion cap
Notable events recorded on this day and month across all years.