Record 26052026 · captured 2026-08-25
The world looked up Memorial Day. 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.
Memorial Day is a federal holiday in the United States for mourning the U.S. military personnel who died while serving in the U.S. Armed Forces. It is observed on the last Monday of May.
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
Euphoria is an American psychological drama television series created and written by Sam Levinson for HBO. Based on the Israeli miniseries of the same name created by Ron Leshem, the series stars Zendaya as drug-addicted teenager Rue Bennett, who also serves a
Kyle Thomas Busch, nicknamed "Rowdy", was an American professional stock car racing driver and racing team owner who competed from 2001 until his death in 2026. Throughout his career, Busch raced under several car numbers, though he was most prominently identi
The 2026 Double or Nothing was a professional wrestling pay-per-view (PPV) event produced by All Elite Wrestling (AEW). It was the eighth annual Double or Nothing and took place during Memorial Day weekend on May 24, 2026, at Louis Armstrong Stadium in Queens,
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
The third and final season of the American psychological drama television series Euphoria, inspired by Ron Leshem's miniseries of the same name, premiered on HBO on April 12, 2026. Series creator Sam Levinson serves as showrunner for the season. The season cen
The Boroughs is an American science fiction television series created by Jeffrey Addiss and Will Matthews and executive produced by The Duffer Brothers.
The Alexander horned sphere is a pathological embedding of the 2-sphere into 3-dimensional Euclidean space. The topological object was discovered by J. W. Alexander.
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
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
Victor Wembanyama, nicknamed "Wemby" and "the Alien", is a French professional basketball player for the San Antonio Spurs of the National Basketball Association (NBA). He was selected first overall by the Spurs in the 2023 NBA draft and is considered one of t
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
Diocese of Monterey in California
The Diocese of Monterey in California is a diocese of the Catholic Church in the Central Coast region of California. It comprises Monterey, San Benito, San Luis Obispo and Santa Cruz counties. The mother church is the Cathedral of San Carlos Borromeo in Monter
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
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
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
Lady Godiva of Coventry is a 1955 American Technicolor historical drama film, directed by Arthur Lubin. It starred Maureen O'Hara in the title role. Alec Harford, the English actor who portrayed Tom the Tailor, died eight months before the film's release.
The Enhanced Games (TEG) is a multi-sport event. Founded by Australian businessman Aron D'Souza, it allows athletes to use performance-enhancing substances without being subject to drug tests; organizers claim these substances must be FDA-approved and used und
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
Neale Francis Daniher was an Australian rules footballer who played with the Essendon Football Club in the Victorian Football League (VFL) / Australian Football League (AFL). He was later the coach of the Melbourne Football Club between 1998 and 2007 and also
Drishyam 3 is a 2026 Indian Malayalam-language crime thriller film written and directed by Jeethu Joseph. Produced by Antony Perumbavoor for Aashirvad Cinemas, it is a sequel to Drishyam 2 (2021) and the third installment in the Drishyam film series. The film
Josep "Pep" Guardiola Sala is a Spanish football manager and former player from Catalonia. He is the global ambassador of the City Football Group and was most recently the manager of Premier League club Manchester City. Widely regarded as one of the greatest f
Mitchell Chase Johnson is an American professional basketball coach and former player who is the head coach for the San Antonio Spurs of the National Basketball Association (NBA). He replaced Gregg Popovich, who stepped down after 29 seasons. He previously ser
Danielle Fabiola "Inde" Navarrette is an American actress and former online streamer. She began her acting career as a teenager with roles in short films, before landing roles in the Netflix drama series 13 Reasons Why (2020) and The CW's superhero drama serie
The Abraham Accords are a set of agreements that established diplomatic normalization between Israel and several Arab states, beginning with the United Arab Emirates and Bahrain. Announced in August and September 2020 and signed in Washington, D.C. on Septembe
Ella Bright is an American-British actress and singer. She began acting as a child. For her performance in the CBBC adaptation of Malory Towers (2020–2025), she received Children's BAFTA and Emmy Award nominations. She has since starred in the Prime Video seri
On May 21, 2026, a chemical leak occurred at a GKN Aerospace manufacturing facility in Garden Grove in Orange County, California, about 35 miles (56 km) southeast of Los Angeles. First responders with the Orange County Fire Authority (OCFA) determined that a c
In materials science, a disappearing polymorph is a form of a crystal structure that is suddenly unable to be produced, instead transforming into a different crystal structure with the same chemical composition during nucleation. Sometimes the resulting transf
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
An Offline Risk-aware Policy Selection Method for Bayesian Markov Decision Processes
In Offline Model Learning for Planning and in Offline Reinforcement Learning, the limited data set hinders the estimate of the Value function of the relative Markov Decision Process (MDP). Consequently, the performance of the obtained policy in the real world is bounded and possibly risky, especially when the deployment of a wrong policy can lead to catastrophic consequences. For this reason, several pathways are bei
This commentary tests a methodology proposed by Munk et al. (2022) for using failed predictions in machine learning as a method to identify ambiguous and rich cases for qualitative analysis. Using a dataset describing actions performed by fictional characters interacting with machine vision technologies in 500 artworks, movies, novels and videogames, I trained a simple machine learning algorithm (using the kNN algori
Forgettable Federated Linear Learning with Certified Data Unlearning
Federated Learning (FL) enables collaborative model training across distributed clients while preserving user privacy. Recently, Federated Unlearning (FU) has emerged to address the "right to be forgotten" and to remove the influence of poisoned or target clients without retraining the entire FL system. However, many FU methods require communication with retained or target clients, introduce additional securi
Virchow: A Million-Slide Digital Pathology Foundation Model
The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnosis and treatment of cancer. Such applications will depend on models' abilities to capture the diverse patterns observed in pathology images. To address this challenge, we present Virchow, a foundation model for computational pathology.
Recent work has proposed Wasserstein k-means (Wk-means) clustering as a powerful method to classify regimes in time series data, and one-dimensional asset returns in particular. In this paper, we begin by studying in detail the behaviour of the Wasserstein k-means clustering algorithm applied to synthetic one-dimensional time series data. We extend the previous work by studying, in detail, the dynamics of the cluster
Compositional Semantics for Open Vocabulary Spatio-semantic Representations
Vision-language models (VLMs) transform environment percepts into vision-language semantics interpretable by LLMs. However, completing complex tasks often requires reasoning about information beyond what is currently perceived. We propose latent compositional semantic embeddings z* as a principled learning-based knowledge representation for queryable spatio-semantic memories. We mathematically prove that z* can alway
We consider the scenario of supervised learning in Deep Learning (DL) networks, and exploit the arbitrariness of choice in the Riemannian metric relative to which the gradient descent flow can be defined (a general fact of differential geometry). In the standard approach to DL, the gradient flow on the space of parameters (weights and biases) is defined with respect to the Euclidean metric. Here instead, we choose th
Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization, has demonstrated remarkable performance advancements. By fusing both approaches, ERL has emerged as a promising research direction. This survey offers a comprehensive overview of the diverse research branches in ERL. Specifically, we systematically summarize recent advancements i
Trained quantum neural networks are Gaussian processes
We study quantum neural networks made by parametric one-qubit gates and fixed two-qubit gates in the limit of infinite width, where the generated function is the expectation value of the sum of single-qubit observables over all the qubits. First, we prove that the probability distribution of the function generated by the untrained network with randomly initialized parameters converges in distribution to a Gaussian pr
Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining
Graphs with abundant attributes are essential in modeling interconnected entities and enhancing predictions across various real-world applications. Traditional Graph Neural Networks (GNNs) often require re-training for different graph tasks and datasets. Although the emergence of Large Language Models (LLMs) has introduced new paradigms in natural language processing, their potential for generic graph mining, trainin
The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks
Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of usages. Then, these labels are represented in a graph on which Word Sense Induction (WSI) is applied to derive sense clusters. Finally, LSCD labels are derived by comparing sense clusters over time. T
Recommender systems play a crucial role in internet economies by connecting users with relevant products. However, designing effective recommender systems faces the key challenges: the exploration-exploitation tradeoff in securing incentive to explore new products against user's self-interested preferences. While prior work addresses Bayesian Incentive Compatibility (BIC) in fixed-design linear bandits (Sellke &a
The Wisdom of a Crowd of Brains: A Universal Brain Encoder
Image-to-fMRI encoding is important for both neuroscience research and practical applications. However, such "Brain-Encoders" have been typically trained per-subject and per fMRI-dataset, thus restricted to very limited training data. In this paper we propose a Universal Brain-Encoder, which can be trained jointly on data from many different subjects/datasets/machines. What makes this possible is our new voxe
Semantic segmentation, as a crucial component of complex visual interpretation, plays a fundamental role in autonomous vehicle vision systems. Recent studies have significantly improved the accuracy of semantic segmentation by exploiting complementary information and developing multimodal methods. Despite the gains in accuracy, multimodal semantic segmentation methods suffer from high computational complexity and low
Retrieved In-Context Principles from Previous Mistakes
In-context learning (ICL) has been instrumental in adapting Large Language Models (LLMs) to downstream tasks using correct input-output examples. Recent advances have attempted to improve model performance through principles derived from mistakes, yet these approaches suffer from lack of customization and inadequate error coverage. To address these limitations, we propose Retrieved In-Context Principles (RICP), a nov
SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation
In medical image segmentation, specialized computer vision techniques, notably transformers grounded in attention mechanisms and residual networks employing skip connections, have been instrumental in advancing performance. Nonetheless, previous models often falter when segmenting small, irregularly shaped tumors. To this end, we introduce SMAFormer, an efficient, Transformer-based architecture that fuses multiple at
A Greedy Hierarchical Approach to Whole-Network Filter-Pruning in CNNs
Deep convolutional neural networks (CNNs) have achieved impressive performance in many computer vision tasks. However, their large model sizes require heavy computational resources, making pruning redundant filters from existing pre-trained CNNs an essential task in developing efficient models for resource-constrained devices. Whole-network filter pruning algorithms prune varying fractions of filters from each layer,
The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot
Large Language Models (LLMs) are reshaping knowledge work, yet their impact on voluntary, self-guided open innovation forums (contributors choose tasks without managerial direction) may differ fundamentally from effects observed in organizational settings. We study this question in open-source software development, where individuals' contributions collectively drive innovation at a community level. Unlike product
Investigating the Effect of Network Pruning on Performance and Interpretability
Deep Neural Networks (DNNs) are often over-parameterized for their tasks and can be compressed quite drastically by removing weights, a process called pruning. We investigate the impact of different pruning techniques on the classification performance and interpretability of GoogLeNet. We systematically apply unstructured and structured pruning, as well as connection sparsity (pruning of input weights) methods to the
The increasing availability of sensitive textual data has created an urgent need for robust de-identification methods that enable compliant data sharing while preserving downstream utility. This paper presents DeID-Clinic, a multi-layered framework for automated pseudonymization and re-identification risk assessment of clinical free-text data. Our approach integrates domain-adapted transformer models, including BioBE
Querying structural and functional niches on spatial transcriptomics data
Cells in multicellular organisms coordinate to form structural and functional niches. With spatial transcriptomics (ST) enabling gene expression profiling in spatial contexts, it has been revealed that spatial niches serve as cohesive and recurrent units in physiological and pathological processes. These observations suggest universal tissue organization principles encoded by conserved niche patterns, and call for a
MambaBEV: An EV-based 3D detection model with Mamba2
Accurate 3D object detection in autonomous driving relies on Bird's Eye View (BEV) perception and effective temporal fusion. However, existing fusion strategies based on convolutional layers or deformable self-attention struggle to model global context in BEV space, leading to reduced accuracy for large objects.To address this limitation, we propose MambaBEV, a novel BEV-based 3D object detection model that lever
Uncovering Autoregressive LLM Knowledge of Thematic Fit in Event Representation
The thematic fit estimation task measures semantic arguments' compatibility with a given semantic role for a given predicate. We investigate if autoregressive LLMs have consistent, expressible knowledge of event arguments' thematic fit by experimenting with various prompt designs, manipulating input context, reasoning, and output forms. We set a new state-of-the-art on thematic fit benchmarks, but show that c
The Meme Is the Message: Generative Memesis and AI Visuals in the 2024 USA Presidential Elections
Visual content on social media has become increasingly influential in shaping political discourse and civic engagement, but it also limits participation due to the increased cost of multimedia production. In tandem, the growth of generative AI provides novel ways for citizens to participate in politics by lowering these costs. Drawing on a dataset of 239,526 Instagram images, we analyze the effects of synthetic image
We propose a scalable preconditioned primal-dual hybrid gradient algorithm for solving partial differential equations (PDEs). We multiply the PDE with a dual test function to obtain an inf-sup problem whose loss functional involves lower-order differential operators. The Primal-Dual Hybrid Gradient (PDHG) algorithm is then leveraged for this saddle point problem. By introducing suitable precondition operators to the
Notable events recorded on this day and month across all years.