Record 29072026 · captured 2026-08-25
The world looked up The Odyssey (2026 film). 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.
The Odyssey is a 2026 epic action fantasy film written and directed by Christopher Nolan, who produced it with his wife Emma Thomas. An adaptation of Homer's ancient Greek epic poem the Odyssey, it stars an ensemble cast including Matt Damon, Tom Holland, Anne
Spider-Man: Brand New Day is a 2026 American superhero film based on the Marvel Comics character Spider-Man. Produced by Columbia Pictures, Marvel Studios, and Pascal Pictures, and distributed by Sony Pictures Releasing, it is the 38th film in the Marvel Cinem
Lindsey Olin Graham was an American politician, attorney, and military officer who represented South Carolina in the United States Senate from 2003 until his death in 2026. A member of the Republican Party, he represented South Carolina's 3rd congressional dis
The Odyssey is one of two major epics of ancient Greek literature attributed to Homer. It is one of the oldest surviving works of literature and remains popular with modern audiences. Like the Iliad, the Odyssey is divided into 24 books. It follows the heroic
The 2026 Commonwealth Games, officially known as the XXIII Commonwealth Games and commonly known as Glasgow 2026, was a multi-sport event held from 23 July to 2 August 2026 in Glasgow, the largest city in Scotland, for members of the Commonwealth of Nations. T
Jeffrey Preston Bezos is an American businessman, and the founder, executive chairman, and former president and CEO of Amazon, the world's largest e-commerce and cloud computing company. According to the Bloomberg Billionaires Index and Forbes, he was the worl
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
.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
Kaitlan Collins is an American journalist and news anchor at CNN. She is the former co-anchor of CNN This Morning. She has hosted The Source at 9 p.m. since July 10, 2023. She also served as the network's chief White House correspondent from January 2021 until
On 29 March 2024, a malicious backdoor was discovered in the compression software XZ Utils. The backdoor gives an attacker who possesses a specific private key the ability to remotely execute code on an affected system through OpenSSH, a set of networking util
India at the 2026 Commonwealth Games
India competed at the 2026 Commonwealth Games, held in Glasgow, Scotland, from 23 July to 2 August 2026. It was the country's 19th appearance at the Commonwealth Games, after making its debut at the 1934 Commonwealth Games. The Indian contingent consisted of 1
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
Sir Christopher Edward Nolan is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have earned over $7.7 billion worldwide, making him the thi
Zendaya Maree Stoermer Coleman, known mononymously as Zendaya, is an American actress and singer-songwriter. Known for her work in television and blockbusters, her films as a leading actress have grossed over $9.8 billion worldwide. Her accolades include two P
Jana Nayagan is a 2026 Indian Tamil-language political action drama film directed by H. Vinoth and produced by Venkat K. Narayana under KVN Productions. The film stars C. Joseph Vijay, Bobby Deol, Pooja Hegde, and Mamitha Baiju in the lead role alongside Nassa
Thomas Stanley Holland is a British actor. His accolades include a BAFTA Award as well as two Critics' Choice Awards nominations. Holland's films as a leading actor have grossed over $14.9 billion worldwide, making him the Fourth highest-grossing actor of all
William Edgar Oddie was an English comedian, conservationist, television presenter and writer. He was a member of the British comedy trio The Goodies.
Zinedine Yazid Zidane, popularly known as Zizou, is a French professional football manager and former player who played as an attacking midfielder. He is the head coach of the France national team. Widely regarded as one of the greatest players of all time, Zi
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
Limonene is a slightly yellow-green liquid aliphatic hydrocarbon classified as a cyclic monoterpene, and is the major component in the fragrance and essential oil of citrus fruit peels, taking its name from Italian limone ("lemon").
The 2026 SummerSlam, also promoted as SummerSlam: Minnesota, was a professional wrestling pay-per-view (PPV) and livestreaming event produced by WWE. It was the 39th annual SummerSlam and took place as a two-night event on Saturday, August 1, and Sunday, Augus
Masters of the Universe (2026 film)
Masters of the Universe is a 2026 American sword-and-sorcery film based on the media franchise by Mattel. It is the second live-action film adaptation, the 1987 film was the first. It was directed by Travis Knight and written by Chris Butler, Aaron Nee, Adam N
Madison Elle Beer is an American singer-songwriter and television personality. Beer first gained media attention after Justin Bieber posted a link to her cover of "At Last" in 2012, when she was 13 years old. In 2018, she released her debut extended play (EP),
The Trojan War was a legendary conflict in Greek mythology that took place around the thirteenth or early twelfth century BC. The war was waged by the Achaeans (Greeks) against the city of Troy after Paris of Troy took Helen from her husband Menelaus, king of
Benjamin Alan Carroll is an Australian politician and lawyer who has served as the 50th premier of Victoria and the leader of the Victorian Labor Party since July 2026. He has been the member of parliament (MP) for the district of Niddrie since 2012.
Darline Lettie Graham Nordone is an American politician and public administrator serving as the junior United States senator from South Carolina, a seat she has held since July 14, 2026. A member of the Republican Party, she was appointed to fill the vacancy c
The red-lipped batfish or Galápagos batfish is a fish of unusual morphology found around the Galápagos Islands and off Ecuador at depths of 3 to 76 m. Red-lipped batfish are closely related to rosy-lipped batfish, which are found near Cocos Island off the Paci
In Greek mythology, Agamemnon was a king of Mycenae who commanded the Achaeans during the Trojan War. He was the son of King Atreus and Queen Aerope, the brother of Menelaus, the husband of Clytemnestra, and the father of Iphigenia, Iphianassa, Electra, Laodik
Roberto Mancini is an Italian football manager and former player who is the head coach of the Italy national team.
David Anthony Burke, known professionally as D4vd, is an American singer-songwriter. Born in Queens, New York City, and raised in Houston, Texas, Burke began composing music in 2021 for his Fortnite gameplay montages. He first achieved commercial success with
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
A2D2: Audi Autonomous Driving Dataset
Research in machine learning, mobile robotics, and autonomous driving is accelerated by the availability of high quality annotated data. To this end, we release the Audi Autonomous Driving Dataset (A2D2). Our dataset consists of simultaneously recorded images and 3D point clouds, together with 3D bounding boxes, semantic segmentation, instance segmentation, and data extracted from the automotive bus. Our sensor suite
Methods to integrate multinormals and compute classification measures
Univariate and multivariate normal probability distributions are widely used when modeling decisions under uncertainty. Computing the performance of such models requires integrating these distributions over specific domains, which can vary widely across models. Besides some special cases, there exist no general analytical expressions, standard numerical methods or software for these integrals. Here we present mathema
Learning From How Humans Correct
In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning
Keypoint-Guided Optimal Transport: Models, Algorithms, and Applications
Existing Optimal Transport (OT) methods mainly derive the optimal transport plan/matching under the criterion of transport cost/distance minimization, which may cause incorrect matching in some cases. In real applications, annotating a few matched keypoints across domains is reasonable or even efortless in annotation burden. It is valuable to investigate how to leverage the annotated keypoints to guide the correct ma
Developing artificial intelligence (AI) algorithms for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other stakeholders together. By understanding AI as 'sociotechnical' where the social and the technical nature of the work and the models are inseparable, we explore the AI development workflow and how stakeholders navigate the challenges and tensions of sharing and g
Functional Autoencoder for Smoothing and Representation Learning
A common pipeline in functional data analysis is to first convert the discretely observed data to smooth functions, and then represent the functions by a finite-dimensional vector of coefficients summarizing the information. Existing methods for data smoothing and dimensional reduction mainly focus on learning the linear mappings from the data space to the representation space, however, learning only the linear repre
Mondrian: On-Device High-Performance Video Analytics with Compressive Packed Inference
In this paper, we present Mondrian, an edge system that enables high-performance object detection on high-resolution video streams. Many lightweight models and system optimization techniques have been proposed for resource-constrained devices, but they do not fully utilize the potential of the accelerators over dynamic, high-resolution videos. To enable such capability, we devise a novel Compressive Packed Inference
SLAM for Indoor Mapping of Wide Area Construction Environments
Simultaneous localization and mapping (SLAM), i.e., the reconstruction of the environment represented by a (3D) map and the concurrent pose estimation, has made astonishing progress. Meanwhile, large scale applications aiming at the data collection in complex environments like factory halls or construction sites are becoming feasible. However, in contrast to small scale scenarios with building interiors separated to
Scale adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification
The Intrinsic Dimension (ID) is a key concept in unsupervised learning and feature selection, as it is a lower bound to the number of variables which are necessary to describe a system. However, in almost any real-world dataset the ID depends on the scale at which the data are analysed. Quite typically at a small scale, the ID is very large, as the data are affected by measurement errors. At large scale, the ID can a
FFNet: MetaMixer-based Efficient Convolutional Mixer Design
Transformer, composed of self-attention and Feed-Forward Network, has revolutionized the landscape of network design across various vision tasks. While self-attention is extensively explored as a key factor in performance, FFN has received little attention. FFN is a versatile operator seamlessly integrated into nearly all AI models to effectively harness rich representations. Recent works also show that FFN functions
Vision Transformers (ViTs) have gained significant attention, but their high computing cost limits the practical applications. While post-training quantization (PTQ) reduces model size and speeds up inference, it often degrades performance, especially in low-bit settings. We identify two key reasons for the performance degradation: 1) existing quantization methods fail to align with the power-law distribution of post
Towards understanding evolution of science through language model series
We introduce AnnualBERT, a series of language models designed specifically to capture the temporal evolution of scientific text. Deviating from the prevailing paradigms of subword tokenizations and "one model to rule them all", AnnualBERT adopts whole words as tokens and is composed of a base RoBERTa model pretrained from scratch on the full-text of 1.7 million arXiv papers published until 2008 and a collecti
COCO-OLAC: A Benchmark for Occluded Panoptic Segmentation and Image Understanding
To help address the occlusion problem in panoptic segmentation and image understanding, this paper proposes a new large-scale dataset named COCO-OLAC (COCO Occlusion Labels for All Computer Vision Tasks), which is derived from the COCO dataset by manually labelling images into three perceived occlusion levels. Using COCO-OLAC, we systematically assess and quantify the impact of occlusion on panoptic segmentation on s
Transformers have become the leading choice in natural language processing over other deep learning architectures. This trend has also permeated the field of time series analysis, especially for long-horizon forecasting, showcasing promising results both in performance and running time. In this paper, we introduce Local Attention Mechanism (LAM), an efficient attention mechanism tailored for time series analysis. Thi
Generating robust and reactive manipulation strategies that can adapt to changing context information is a challenging task in robotics. Over the years, Learning from Demonstration (LfD) has emerged as an intuitive and effective solution for generating reactive policies, particularly by following dynamical-system(DS)-based approaches. However, most state-of-the-art DS-based approaches focus on addressing the robustne
Diffusion Model-based Parameter Estimation in Dynamic Power Systems
Parameter estimation, which represents a classical inverse problem, is often ill-posed as different parameter combinations can yield identical outputs. This non-uniqueness presents a critical barrier to accurate and unique identification. Here we introduce a parameter estimation framework to address such limits: the Joint Conditional Diffusion Model-based Inverse Problem Solver. By leveraging the stochasticity of dif
Brain age gap estimation (BrainAGE) is a promising imaging-derived biomarker of neurobiological aging and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI (T1w), overlooking functional vascular changes that may precede tissue damage and cognitive decline. Artificial intelligence-generated cerebral blood volume (AICBV) maps, synthesized from non-contrast MRI, offer an alternative t
Towards a Statistical Understanding of Neural Networks: Beyond the Neural Tangent Kernel Theories
A primary advantage of neural networks lies in their feature learning characteristics, which is challenging to theoretically analyze due to the complexity of their training dynamics. We examine feature learning and its potential benefits for generalization from a statistical perspective. After reviewing the neural tangent kernel (NTK) theory and recent results in kernel regression, which address the generalization is
TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer
Transformers have shown impressive results in tabular data generation. However, they lack domain-specific inductive biases which are critical for preserving the intrinsic characteristics of tabular data. They also suffer from poor scalability and efficiency due to quadratic computational complexity. In this paper, we propose TabTreeFormer, a hybrid transformer architecture that integrates inductive biases of tree-bas
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge
This paper investigates the novel application of Large Language Models (LLMs) with vision capabilities to analyze satellite imagery for village-level poverty prediction. Although LLMs were originally designed for natural language understanding, their adaptability to perform multimodal tasks, including geospatial analysis, has opened new frontiers in data-driven research. By leveraging advancements in vision-enabled L
COMPOL: A Unified Neural Operator Framework for Scalable Multi-Physics Simulations
Multiphysics simulations play an essential role in accurately modeling complex interactions across diverse scientific and engineering domains Although neural operators especially the Fourier Neural Operator FNO have significantly improved computational efficiency they often fail to effectively capture intricate correlations inherent in coupled physical processes To address this limitation we introduce COMPOL a novel
Faces of Fairness: Examining Bias in Facial Expression Recognition Datasets and Models
Automated Facial Expression Recognition (FER), involves two critical aspects: data and model design. Both significantly influence bias and fairness in FER tasks. However, issues related to bias and fairness in FER datasets and models remain underexplored. This study investigates bias and fairness in FER datasets and models. The bias of four common in-the-wild FER datasets, including AffectNet, ExpW, Fer2013, and RAF-
Eye Tracking Based Cognitive Evaluation of Automatic Readability Assessment Methods
Automatic methods for scoring text readability have been studied for over a century, and are widely used in research and in user-facing applications in many domains. Thus far, the development and evaluation of such methods have primarily relied on two types of offline human behavioral data, performance on reading comprehension tests and ratings of text readability levels. In this work, we instead focus on a fundament
A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning
Machine learning is increasingly deployed through outsourced and cloud-based pipelines, which improve accessibility but also raise concerns about computational integrity, data privacy, and model confidentiality. Zero-knowledge proofs (ZKPs) provide a compelling foundation for verifiable machine learning because they allow one party to certify that a training, testing, or inference result was produced by the claimed c
Notable events recorded on this day and month across all years.