Record 28072026 · 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.
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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
William Edgar Oddie was an English comedian, conservationist, television presenter and writer. He was a member of the British comedy trio The Goodies.
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
.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
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
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
David Graeme Garden is a Scottish comedian, actor, author, artist and television presenter. He is best known as a member of The Goodies and a regular panellist on I'm Sorry I Haven't a Clue. He is the only surviving member of The Goodies following the deaths o
Redemption was a 2026 professional wrestling pay-per-view (PPV) event produced by the American promotion All Elite Wrestling (AEW). It took place on July 26, 2026, at the Bell Centre in Montreal, Quebec, Canada, marking AEW's first PPV to be held in Montreal.
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
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
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
The third season of the American fantasy drama television series House of the Dragon premiered on HBO on June 21, 2026, in the United States and concluded on August 9, 2026. It consists of eight episodes, each of approximately one hour. The season covers the e
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
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
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
David Jonsson is a British actor. He began his career on the West End, winning a Black British Theatre Award for his performance in the play and breathe... (2021). He is known for his performance in the BBC Two and HBO television series Industry (2020–2022), a
Yan Diomande is an Ivorian professional footballer who plays as a winger for La Liga club Real Madrid and the Ivory Coast national team.
Jackson Koivun is an American professional golfer who plays on the PGA Tour. He attended Auburn University, where as a freshman he had one of the best seasons in all of collegiate golf history. In 2024, he won the SEC Championship, was runner-up at the NCAA Di
Timothy Julian Brooke-Taylor was an English actor and comedian. He was best known as a member of The Goodies.
Pralhad Venkatesh Joshi is an Indian politician who is currently serving as the 13th Minister of Consumer Affairs, Food and Public Distribution and 10th Minister of New and Renewable Energy since 2024. In July 2026, Joshi assumed office of the 10th Minister of
House of the Dragon is an American fantasy drama television series created by George R. R. Martin and Ryan Condal for HBO. A prequel to Game of Thrones (2011–2019), it is the second television series in Martin's A Song of Ice and Fire franchise. Based on parts
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
The Goodies were a trio of British comedians: Tim Brooke-Taylor (1940–2020), Graeme Garden and Bill Oddie (1941–2026). The trio created, wrote for and performed in their eponymous television comedy show from 1970 until 1982, combining sketches and situation co
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
Carly Elisabeth Simon is an American musician, singer, songwriter, and author. She rose to fame in the 1970s with a string of hit records; her 13 top 40 U.S. hits include "That's the Way I've Always Heard It Should Be" (No. 10), "Anticipation" (No. 13), "The R
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
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
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
Eric M. Bieniemy Jr. is an American professional football coach and former running back who is the offensive coordinator for the Kansas City Chiefs of the National Football League (NFL). He played college football for the Colorado Buffaloes and is their all-ti
The Cockroach Janta Party, also known as the Cockroach movement, is an Indian youth-based satirical political movement founded on 16 May 2026 by Abhijeet Dipke, a political communications strategist and activist. The CJP harnessed widespread political and econ
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
VIP: Finding Important People in Images
People preserve memories of events such as birthdays, weddings, or vacations by capturing photos, often depicting groups of people. Invariably, some individuals in the image are more important than others given the context of the event. This paper analyzes the concept of the importance of individuals in group photographs. We address two specific questions -- Given an image, who are the most important individuals in i
Deep Learning Estimation of Absorbed Dose for Nuclear Medicine Diagnostics
In radionuclide therapy with $^{177}\mathrm{Lu}$, the absorbed-dose distribution can be approximated by convolving the time-integrated activity distribution with a dose voxel kernel for a single tissue type. This approximation is fast but inaccurate: it treats the body as homogeneous and therefore ignores the tissue heterogeneity that governs where energy is deposited. The heterogeneity can be recovered by combining
Like a Baby: Visually Situated Neural Language Acquisition
We examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2% decrease in perplexity, even when no visual context is available at test. Fine-tuning the embeddings of a pre-trained state-of-the-art bidirectional language model (BERT) in the language modeling
High-dimensional state and action spaces combined with sparse reward structures in reinforcement learning (RL) environments typically require advanced control architectures. Hierarchical Reinforcement Learning (HRL) demonstrates superior performance compared to atomic RL approaches in these challenging scenarios. HRL can manage the complexity of commands to achieve task objectives through its hierarchical structure.
On a linear fused Gromov-Wasserstein distance for graph structured data
We present a framework for embedding graph structured data into a vector space, taking into account node features and topology of a graph into the optimal transport (OT) problem. Then we propose a novel distance between two graphs, named linearFGW, defined as the Euclidean distance between their embeddings. The advantages of the proposed distance are twofold: 1) it can take into account node feature and structure of
Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines
Online travel agencies (OTA's) advertise their website offers on meta-search bidding engines. The problem of predicting the number of clicks a hotel would receive for a given bid amount is an important step in the management of an OTA's advertisement campaign on a meta-search engine, because bid times number of clicks defines the cost to be generated. Various regressors are ensembled in this work to improve c
Transfer learning for conflict and duplicate detection in software requirement pairs
Consistent and holistic expression of software requirements is important for the success of software projects. In this study, we aim to enhance the efficiency of the software development processes by automatically identifying conflicting and duplicate software requirement specifications. We formulate the conflict and duplicate detection problem as a requirement pair classification task. We design a novel transformers
The emergence of clusters in self-attention dynamics
Viewing Transformers as interacting particle systems, we describe the geometry of learned representations when the weights are not time dependent. We show that particles, representing tokens, tend to cluster toward particular limiting objects as time tends to infinity. Cluster locations are determined by the initial tokens, confirming context-awareness of representations learned by Transformers. Using techniques from
A Semi-supervised Physics-Aware Triple-Stream Underwater Image Enhancement Network
Underwater images normally suffer from degradation due to the transmission medium of water bodies. Both traditional prior-based approaches and deep learning-based methods have been used to address this problem. However, the inflexible assumption of the former often impairs their effectiveness in handling diverse underwater scenes, while the generalization of the latter to unseen images is usually weakened by insuffic
This paper presents an artificial intelligence tool designed to assist students with dyslexia, ADHD, and short attention spans in processing text-based information more efficiently. The proposed solution addresses both cognitive and visual reading barriers by pairing a cloud-hosted large language model with adaptive typographic formatting. At its core, the tool streams a request to Google's Gemini API to generate
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization
The ProxSkip algorithm for distributed optimization is gaining increasing attention due to its effectiveness in reducing communication. However, existing analyses of ProxSkip are limited to the strongly convex setting and fail to achieve linear speedup with respect to the number of nodes. Key questions regarding its behavior in the non-convex setting and the achievability of linear speedup remain open. In this paper,
Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia
The construction industry in Australia is characterized by its intricate supply chains and vulnerability to myriad risks. As such, effective supply chain risk management (SCRM) becomes imperative. This paper employs different transformer models, and train for Named Entity Recognition (NER) in the context of Australian construction SCRM. Utilizing NER, transformer models identify and classify specific risk-associated
EyePreserve: Identity-Preserving Iris Synthesis
Synthesis of same-identity biometric iris images, both for existing and non-existing identities, while preserving the identity across a wide range of pupil sizes, is complex due to the intricate iris muscle constriction mechanism, requiring a precise model of iris non-linear texture deformations to be embedded into the synthesis pipeline. This paper presents EyePreserve, a novel, fully data-driven non-linear texture
Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss
Constructing the architecture of a neural network is a challenging pursuit for the machine learning community, and the dilemma of whether to go deeper or wider remains a persistent question. This paper explores a comparison between deeper neural networks (DeNNs) with a flexible number of layers and wider neural networks (WeNNs) with limited hidden layers, focusing on their optimal generalization error in Sobolev loss
Motivation: Retrosynthesis planning poses a formidable challenge in the organic chemical industry. Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in AI for science. Various deep learning-based methods have been proposed for this task in recent years, incorporating diverse levels of additional chemical knowledge depen
Hypergraph $p$-Laplacian regularization on point clouds for data interpolation
As a generalization of graphs, hypergraphs are widely used to model higher-order relations in data. This paper explores the benefit of the hypergraph structure for the interpolation of point cloud data that contain no explicit structural information. We define the $\varepsilon_n$-ball hypergraph and the $k_n$-nearest neighbor hypergraph on a point cloud and study the $p$-Laplacian regularization on the hypergraphs. W
Learning the Distribution Map in Reverse Causal Performative Prediction
In numerous predictive scenarios, the predictive model affects the sampling distribution; for example, job applicants often meticulously craft their resumes to navigate through a screening systems. Such shifts in distribution are particularly prevalent in the realm of social computing, yet, the strategies to learn these shifts from data remain remarkably limited. Inspired by a microeconomic model that adeptly charact
Synthetic Data Generation for 3D Myocardium Deformation Analysis
Accurate analysis of 3D myocardium deformation using high-resolution computerized tomography (CT) datasets with ground truth (GT) annotations is crucial for advancing cardiovascular imaging research. However, the scarcity of such datasets poses a significant challenge for developing robust myocardium deformation analysis models. To address this, we propose a novel approach to synthetic data generation for enriching c
CLMASP: Coupling Large Language Models with Answer Set Programming for Robotic Task Planning
Large Language Models (LLMs) possess extensive foundational knowledge and moderate reasoning abilities, making them suitable for general task planning in open-world scenarios. However, it is challenging to ground a LLM-generated plan to be executable for the specified robot with certain restrictions. This paper introduces CLMASP, an approach that couples LLMs with Answer Set Programming (ASP) to overcome the limitati
Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures
Due to the intricate structure of vascular trees, minor segmentation errors can significantly alter connectivity patterns and increase variability in extracted morphological properties. Global metrics such as the Dice coefficient, precision, and recall often overlook inaccuracies in specific regions of a sample. To address this, we define a Local Vessel Salience (LVS) index to quantify the difficulty of identifying s
Procedural Content Generation via Generative Artificial Intelligence
The attempt to utilize machine learning in PCG has been made in the past. In this survey paper, we investigate how generative artificial intelligence (AI), which saw a significant increase in interest in the mid-2010s, is being used for PCG. We review applications of generative AI for the creation of various types of content, including terrains, items, and even storylines. While generative AI is effective for PCG, on
Quantum Hamiltonian Embedding of Images for Data Reuploading Classifiers
When applying quantum computing to machine learning tasks, one of the first considerations is the design of the quantum machine learning model itself. Conventionally, the design of quantum machine learning algorithms relies on the ``quantisation" of classical learning algorithms, such as using quantum linear algebra to implement important subroutines of classical algorithms, if not the entire algorithm, seeking t
Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost
Today's large language models (LLMs) can solve challenging question-answering tasks, and prompt engineering techniques, such as chain-of-thought (CoT), have gained attention for enhancing the explanation and correctness of outputs. However, many models and techniques tend to produce excessively verbose and lengthy answers, leading to issues with both conciseness and generation time. To address this, this paper an
CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases
Large Language Models (LLMs) excel in stand-alone code tasks like HumanEval and MBPP, but struggle with handling entire code repositories. This challenge has prompted research on enhancing LLM-codebase interaction at a repository scale. Current solutions rely on similarity-based retrieval or manual tools and APIs, each with notable drawbacks. Similarity-based retrieval often has low recall in complex tasks, while man
Coarse Graining with Neural Operators for Simulating Chaotic Systems
Accurately predicting the long-term behavior of chaotic systems is crucial for various applications such as climate modeling. However, achieving such predictions typically requires iterative computations over a dense spatiotemporal grid to account for the unstable nature of chaotic systems, which is expensive and impractical in many real-world situations. An alternative approach to such a full-resolved simulation is
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