Record 29052026 · captured 2026-08-25
The world looked up Claude Lemieux. 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.
Claude Percy Lemieux was a Canadian professional ice hockey player. He played as a right winger for 21 seasons in the National Hockey League (NHL) with six teams between 1983 and 2009. Lemieux won four Stanley Cup championships during his career, including two
Obsession is a 2025 American supernatural horror film written, directed, and edited by Curry Barker. The film follows Bear, a music store employee who buys a supernatural toy that grants his wish for his friend Nikki to fall in love with him, which makes her b
Spider-Noir is an American superhero series developed by Oren Uziel for MGM+ and Prime Video. Based on Marvel Comics featuring the character Spider-Man Noir, the series follows an aging private investigator and superhero in 1930s New York City who grapples wit
Backrooms is a 2026 American science fiction psychological horror film directed and co-scored by Kane Parsons, and written by Will Soodik. It is based on Parsons's web series which was inspired by the "Backrooms" creepypasta. In the film, Clark, a furniture st
The following notable deaths occurred in 2026. Names are reported under the date of death, in alphabetical order. A typical entry reports information in the following sequence:Name, age, country of citizenship at birth, subsequent nationality, what subject was
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
James Dell Talarico is an American politician and educator who has served since 2018 as a member of the Texas House of Representatives. He is the Democratic nominee in the 2026 U.S. Senate election in Texas.
Anthony Michael Gordon is an English professional footballer who plays as a left winger for La Liga club Barcelona and the England national team.
Disclosure Day is a 2026 American science fiction thriller film directed and produced by Steven Spielberg from a screenplay by David Koepp, based on a story by Spielberg. The film stars an ensemble cast, including Emily Blunt, Josh O'Connor, Colin Firth, Eve H
Vaibhav Sooryavanshi, also spelled Vaibhav Suryavanshi, is an Indian cricketer. He is a left-handed top order batter and an occasional slow left-arm orthodox bowler. He made his international debut for the Indian cricket team in the T20I series against England
Harambe was a western lowland gorilla who lived at the Cincinnati Zoo. On May 28, 2016, a three-year-old boy visiting the zoo climbed under a fence into an outdoor gorilla enclosure where he was grabbed and violently dragged and thrown by Harambe. Fearing for
Spencer William Pratt is an American reality television personality. In 2007, he began dating Heidi Montag, a primary cast member of the reality television series The Hills and came to prominence after being cast in the series. A feud between them and Montag's
Neatsville is an unincorporated community in Adair County, in the U.S. state of Kentucky. It is located at the junction of Kentucky Route 206 and Kentucky Route 76. Its elevation is 705 feet (215 m). For unknown reasons, the town's name was spelled as Neetsvil
007 First Light is a 2026 action-adventure video game developed and published by IO Interactive. Based on the James Bond franchise, it tells an original narrative inspired by the novels and short stories by Ian Fleming, and the film series starring the charact
Pulaski County High School (Kentucky)
Pulaski County High School (PCHS) is a public high school located in Pulaski County, Kentucky, United States. It is operated by Pulaski County Schools. It serves the communities of Eubank, Shopville, Woodstock, and parts of the City of Somerset, Kentucky. It i
Murder of Dominic Russo and Davion Flanagan
The murder of Dominic Russo and Davion Flanagan occurred during the early morning hours of July 31, 2022, when Mackenzie Shirilla intentionally crashed her vehicle into a brick wall in Strongsville, Ohio, United States, killing two passengers: her boyfriend, D
Eid al-Adha is the second of the two main festivals in Islam, alongside Eid al-Fitr. It falls on the 10th of Dhu'l-Hijja, the twelfth and final month of the Islamic calendar. Celebrations and observances are generally carried forward to the three following day
.xyz is a top-level domain name that was proposed in ICANN's new generic top-level domain (gTLD) Program for consisting of the last three letters of the Latin-script alphabet. XYZ.com and CentralNic are the registries for the domain, which was created by entre
Michael Joseph Jackson was an American singer, songwriter, dancer, and philanthropist. Dubbed the "King of Pop", he is widely regarded as one of the most culturally significant figures of the 20th century. His musical achievements broke American racial barrier
Off Campus is an American romantic drama television series created by Louisa Levy for Amazon Prime Video. It is based on the Off-Campus book series by Elle Kennedy. The series premiered on May 13, 2026 and received positive reviews. In February 2026, ahead of
The Boroughs is an American science fiction television series created by Jeffrey Addiss and Will Matthews and executive produced by The Duffer Brothers.
2026 French Open – Men's singles
Alexander Zverev defeated Flavio Cobolli in the final, 6–1, 4–6, 6–4, 6–7(5–7), 6–1, to win the men's singles tennis title at the 2026 French Open. It was his first major title, following three previous runner-up outcomes. Zverev was the first German man to wi
Widow's Bay is an American comedy horror television series created by Katie Dippold for Apple TV, and starring Matthew Rhys, Kate O'Flynn, Kevin Carroll, Dale Dickey, Kingston Rumi Southwick, and Stephen Root. The series is set in the fictional New England isl
Doddalahalli Kempegowda Shivakumar, commonly known as D. K. Shi or DKS, is an Indian politician and businessman who is serving as the 18th Chief Minister of Karnataka since 3 June 2026. A Leader of the Indian National Congress, he formerly served as the Deputy
Oliver Glasner is an Austrian professional football manager and former player who is currently head coach of Premier League club Nottingham Forest.
Karuppu (transl. Black) is a 2026 Indian Tamil-language fantasy action drama film directed by RJ Balaji from a screenplay he co-wrote with Ashwin Ravichandran, Rahul Raj, T. S. Gopi Krishnan and Karan Aravind Kumar. Produced by Dream Warrior Pictures, the film
Star Wars: The Mandalorian and Grogu is a 2026 American science fiction film directed by Jon Favreau, who co-wrote the film with Dave Filoni and Noah Kloor. Produced by Lucasfilm and Fairview Entertainment, and distributed by Walt Disney Studios Motion Picture
Michael is a 2026 biographical film directed by Antoine Fuqua and written by John Logan. It follows the early life of the American singer Michael Jackson, from his time with the Jackson 5 in the 1960s to the Bad World Tour in the late 1980s. Jackson is portray
Siddaramaiah, also referred to by his nickname Siddu, is an Indian politician, lawyer, former lecturer and statesman who served as the 16th Chief Minister of Karnataka from 2013 to 2018 and 2023 to 2026. He had completed his first term being only the second pe
The Saddle Ridge Hoard is the name given to a hoard of 1,427 gold coins unearthed in Northern California in 2013. The face value of the coins totaled $27,980, but was assessed to be worth $10 million. The hoard contained $27,460 in twenty-dollar coins, $500 in
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes
This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation, and adapting to arbitrary unseen shapes in real world with minimal sim-to-real cost. The core idea is to decouple the generalization of the sensory-motor policy to the design of a fast-adaptable perception module and a simulated generic policy module. The framework consists of a segmentation network (SN),
Bayesian Reasoning for Physics Informed Neural Networks
We introduce an evidence-driven Bayesian formulation of physics-informed neural networks that enables automatic optimization of loss weights between PDE residuals, boundary conditions, and observational data. Unlike existing Bayesian PINN approaches based on sampling or variational inference, the proposed method uses a Laplace approximation to compute model evidence analytically, enabling efficient hyperparameter tun
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation
Machine learning has been successfully applied to improve the efficiency of Mixed-Integer Linear Programming (MILP) solvers. However, the learning-based solvers often suffer from severe performance degradation on unseen MILP instances -- especially on large-scale instances from a perturbed environment -- due to the limited diversity of training distributions. To tackle this problem, we propose a novel approach, which
Matrix Completion with Hypergraphs:Sharp Thresholds and Efficient Algorithms
This paper considers the problem of completing a rating matrix based on sub-sampled matrix entries as well as observed social graphs and hypergraphs. We show that there exists a \emph{sharp threshold} on the sample probability for the task of exactly completing the rating matrix -- the task is achievable when the sample probability is above the threshold, and is impossible otherwise -- demonstrating a phase transitio
An Empirical Study of the Influence of Adversarial Fine-Tuning on Compressed Neural Networks
As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversarial attacks has become increasingly critical. DL models have been found to be susceptible to adversarial attacks by introducing small, targeted perturbations to disrupt the input data. Adversarial training has been presented as a mitigation strategy that can result in mor
Gaga: Group Any Gaussians via 3D-aware Memory Bank
We introduce Gaga, a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot class-agnostic segmentation models. Contrasted to prior 3D scene segmentation approaches that rely on video object tracking or contrastive learning methods, Gaga utilizes spatial information and effectively associates object masks across diverse camera poses through a novel 3D-
Cross-Language Evolution of Divergent Collective Memory Around the Arab Spring
The Arab Spring was a historic set of protests beginning in 2011 that toppled governments and led to major conflicts. Collective memories of events like these can vary significantly across social contexts in response to political, cultural, and linguistic factors. While Wikipedia plays an important role in documenting both historic and current events, little attention has been given to how Wikipedia articles, created
CompilerDream: Learning a Compiler World Model for General Code Optimization
Effective code optimization in compilers is crucial for computer and software engineering. The success of these optimizations primarily depends on the selection and ordering of the optimization passes applied to the code. While most compilers rely on a fixed sequence of optimization passes, current methods to find the optimal sequence either employ impractically slow search algorithms or learning methods that struggl
A Survey on Recent Advances in Conversational Data Generation
Recent advancements in conversational systems have significantly enhanced human-machine interactions across various domains. However, training these systems is challenging due to the scarcity of specialized dialogue data. Traditionally, conversational datasets were created through crowdsourcing, but this method has proven costly, limited in scale, and labor-intensive. As a solution, the development of synthetic dialo
Early Detection of Misinformation for Infodemic Management: A Domain Adaptation Approach
An infodemic refers to an enormous amount of true information and misinformation disseminated during a disease outbreak. Detecting misinformation at the early stage of an infodemic is key to reduce its harm to public health. An early stage infodemic is characterized by a large volume of unlabeled information concerning a disease. As a result, conventional misinformation detection methods are not suitable for this mis
Certified Causal Defense with Generalizable Robustness
While machine learning models have proven effective across various scenarios, it is widely acknowledged that many models are vulnerable to adversarial attacks. Recently, there have emerged numerous efforts in adversarial defense. Among them, certified defense is well known for its theoretical guarantees against arbitrary adversarial perturbations on input within a certain range (e.g., $l_2$ ball). However, most exist
Extending Explainable Ensemble Trees (E2Tree) to regression contexts
Ensemble methods such as random forests have transformed the landscape of supervised learning, offering highly accurate prediction through the aggregation of multiple weak learners. However, despite their effectiveness, these methods often lack transparency, impeding users' comprehension of how RF models arrive at their predictions. Explainable ensemble trees (E2Tree) is a novel methodology for explaining random
Crafting Desirable Climate Trajectories with RL Explored Socio-Environmental Simulations
Climate change poses an existential threat, necessitating effective climate policies to enact impactful change. Decisions in this domain are incredibly complex, involving conflicting entities and evidence. In the last decades, policymakers increasingly use simulations and computational methods to guide some of their decisions. Integrated Assessment Models (IAMs) are one of such methods, which combine social, economic
Are LLMs Socially Adaptive? Contrasting Belief Evolution in Large Language Models and Humans
As large language models (LLMs) increasingly engage in complex social interactions, ensuring that their behaviors align with human ethical principles and intentions, known as value alignment, has become a critical scientific challenge. Existing benchmarks often rely on static assessments and fail to capture the longitudinal dynamics of decision-making or the latent cognitive processes driving agent behavior. In this
Jailbreaking and Mitigation of Vulnerabilities in Large Language Models
Large Language Models (LLMs) have transformed artificial intelligence by advancing natural language understanding and generation, enabling applications across fields beyond healthcare, software engineering, and conversational systems. Despite these advancements in the past few years, LLMs have shown considerable vulnerabilities, particularly to prompt injection and jailbreaking attacks. This review analyzes the state
Noise-Aware Differentially Private Variational Inference
Differential privacy (DP) provides robust privacy guarantees for statistical inference, but this can lead to unreliable results and biases in downstream applications. While several noise-aware approaches have been proposed which integrate DP perturbation into the inference, they are limited to specific types of simple probabilistic models. In this work, we propose a novel method for noise-aware approximate Bayesian i
Dataset-Driven Channel Masks in Transformers for Multivariate Time Series
Recent advancements in foundation models have been successfully extended to the time series (TS) domain, facilitated by the emergence of large-scale TS datasets. However, previous efforts have primarily Capturing channel dependency (CD) is essential for modeling multivariate time series (TS), and attention-based methods have been widely employed for this purpose. Nonetheless, these methods primarily focus on modifyin
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
Time series anomaly detection (TSAD) underpins real-time monitoring in cloud services and web systems, allowing rapid identification of anomalies to prevent costly failures. Most TSAD methods driven by forecasting models tend to overfit by emphasizing minor fluctuations. Our analysis reveals that effective TSAD should focus on modeling "normal" behavior through smooth local patterns. To achieve this, we refor
Neural Networks and (Virtual) Extended Formulations
Neural networks with piecewise linear activation functions, such as rectified linear units (ReLU) or maxout, are among the most fundamental models in modern machine learning. We make a step towards proving lower bounds on the size of such neural networks by linking their representative capabilities to the notion of the extension complexity $\mathrm{xc}(P)$ of a polytope $P$. This is a well-studied quantity in combina
Large vision-language models (LVLMs) have achieved impressive results in various vision-language tasks. However, despite showing promising performance, LVLMs suffer from hallucinations caused by language bias, leading to diminished focus on images and ineffective visual comprehension. We identify two primary reasons for this bias: 1. Different scales of training data between the pretraining stage of LLM and multimoda
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
Conventional federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the federated label-noise (F-LN) problem. Worse still, the F-LN problem is exacerbated by the heterogeneity of FL, whereas clients experience different label-noise types, ratios, and data distribution. In this study, we first observe an intriguing phenomenon that the global model of F
Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems
Personalized learning represents a promising educational strategy within intelligent educational systems, aiming to enhance learners' practice efficiency. However, the discrepancy between offline metrics and online performance significantly impedes their progress. To address this challenge, we introduce Agent4Edu, a novel personalized learning simulator leveraging recent advancements in human intelligence through
Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists
Generative AI's novel capacities raise questions about the future role of human expertise: does AI level the playing field between professional artists and laypeople, or does expertise enhance AI use? Do the cognitive skills experts make use of in analyzing and drawing visual art also transfer to using these new tools? This pre-registered study conducts experimental comparisons between 50 professional artists and
A Quotient Homology Theory of Representation in Neural Networks
Previous research has proven that the set of maps implemented by neural networks with a ReLU activation function is identical to the set of piecewise linear continuous maps. Furthermore, such networks induce a hyperplane arrangement splitting the input domain of the network into convex polyhedra $G_J$ over which a network $Φ$ operates in an affine manner. In this work, we leverage these properties to define an equiva
CriticalKV: Optimizing KV Cache Eviction from an Output Perturbation Perspective
Large language models have revolutionized natural language processing but face significant challenges of high storage and runtime costs, due to the transformer architecture's reliance on self-attention, particularly the large KV cache for long-sequence inference. Recent efforts to reduce KV cache size by pruning less critical entries based on attention weights remain empirical and lack formal grounding. This pape
Notable events recorded on this day and month across all years.