Record 22072026 · 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
Kaylee Beth Hottle was a deaf American actress who starred in the monster film Godzilla vs. Kong (2021) and its sequel, Godzilla x Kong: The New Empire (2024). For the latter, she was nominated for the Saturn Award for Best Performance by a Younger Actor.
Lamine Yamal Nasraoui Ebana, commonly known as Lamine Yamal, is a Spanish professional footballer who plays as a right winger for the La Liga club Barcelona and the Spain national team. He is widely regarded as one of the best players in the world.
Andrew Murray Burnham is a British politician who has served as Prime Minister of the United Kingdom and Leader of the Labour Party since July 2026. He has been Member of Parliament (MP) for Makerfield in Greater Manchester since June 2026, and was Mayor of Gr
Joseph Kevin Keegan was an English football player and manager who played as an attacking midfielder or forward. Nicknamed "King Kev" or "Mighty Mouse", Keegan was recognised for his dribbling ability, finishing and presence in the air, as much as he was for h
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
Shakira Isabel Mebarak Ripoll, known mononymously as Shakira, is a Colombian singer-songwriter, dancer, and record producer. Referred to as the "Queen of Latin Music", she has had a significant impact on the musical landscape of Latin America and has been cred
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
Avengers: Doomsday is an upcoming American superhero film based on the Marvel Comics superhero team the Avengers. Produced by Marvel Studios and distributed by Walt Disney Studios Motion Pictures, it is intended to be the sequel to Avengers: Endgame (2019) and
Lionel Andrés "Leo" Messi is an Argentine professional footballer who plays as a forward for and captains both Major League Soccer (MLS) club Inter Miami and the Argentina national team. Widely regarded as one of the greatest players in history, Messi has set
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
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
Gerard Piqué Bernabeu is a Spanish former professional footballer who played as a centre-back. He is considered to be one of the greatest defenders of his generation and is one of the most decorated players with 37 trophies. In 2022, he founded the Kings Leagu
Sonam Wangchuk is an Indian engineer, educator, and activist. He is the founding director of the Students' Educational and Cultural Movement of Ladakh (SECMOL), which was founded in 1988 by a group of students who struggled with the public education system. He
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
Marie-France van Heel is a marketing executive. She is married to Andy Burnham, the prime minister of the United Kingdom and the leader of the Labour Party.
Rodrigo Hernández Cascante, known simply as Rodri or Rodrigo, is a Spanish professional footballer who plays as a defensive midfielder for La Liga club Barcelona and captains the Spain national team. Widely regarded as one of the best midfielders in the world,
Leandro Daniel Paredes is an Argentine professional footballer who plays as a defensive midfielder for Argentine Primera División club Boca Juniors and the Argentina national team. He previously played for Roma, Zenit Saint Petersburg and Paris Saint-Germain,
Ferran Torres García is a Spanish professional footballer who plays as a forward or winger for Ligue 1 club Paris Saint-Germain and the Spain national team.
The 2030 FIFA World Cup is scheduled to be the 24th FIFA World Cup, the quadrennial international football tournament that is contested by the men's national teams of the member associations of FIFA. The tournament is planned to be jointly hosted by Morocco, P
In mathematics, the Jacobian conjecture is a conjecture concerning polynomials in several variables that states that if a polynomial function from an -dimensional space to itself has a Jacobian determinant that is a non-zero constant, then the function has a p
Marc Cucurella Saseta is a Spanish professional footballer who plays as a left-back or left wing-back for La Liga club Real Madrid and the Spain national team. He is considered to be one of the best left-backs in the world.
The FIFA World Cup is an international association football competition among the senior men's national teams of the members of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The tournament has been held every
Other Mommy is an upcoming American supernatural psychological horror film directed by Rob Savage and written by Nathan Elston. The film is based on the 2024 novel Incidents Around the House by Josh Malerman. It stars Jessica Chastain as a mother who must do e
The Spain national football team have represented Spain in men's international football competition since 1920, and are governed by the Royal Spanish Football Federation. They are the reigning World and European champions, having won the most recent FIFA World
Emory Andrew Tate III is an American and British social media personality and former professional kickboxer who built a webcam pornography business before gaining notoriety for promoting various highly controversial positions in the manosphere. His commentary
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
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
Cristiano Ronaldo dos Santos Aveiro is a Portuguese professional footballer who plays as a forward for and captains the Saudi Pro League club Al-Nassr and the Portugal national team. Nicknamed CR7, he is widely regarded as one of the greatest players in histor
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
An Empirical Study of Person Re-Identification with Attributes
Person re-identification aims to identify a person from an image collection, given one image of that person as the query. There is, however, a plethora of real-life scenarios where we may not have a priori library of query images and therefore must rely on information from other modalities. In this paper, an attribute-based approach is proposed where the person of interest (POI) is described by a set of visual attrib
Interactive Natural Language-based Person Search
In this work, we consider the problem of searching people in an unconstrained environment, with natural language descriptions. Specifically, we study how to systematically design an algorithm to effectively acquire descriptions from humans. An algorithm is proposed by adapting models, used for visual and language understanding, to search a person of interest (POI) in a principled way, achieving promising results with
Internet-of-Things Architectures for Secure Cyber-Physical Spaces: the VISOR Experience Report
Internet of things (IoT) technologies are becoming a more and more widespread part of civilian life in common urban spaces, which are rapidly turning into cyber-physical spaces. Simultaneously, the fear of terrorism and crime in such public spaces is ever-increasing. Due to the resulting increased demand for security, video-based IoT surveillance systems have become an important area for research. Considering the lar
Learning to Assess Danger from Movies for Cooperative Escape Planning in Hazardous Environments
There has been a plethora of work towards improving robot perception and navigation, yet their application in hazardous environments, like during a fire or an earthquake, is still at a nascent stage. We hypothesize two key challenges here: first, it is difficult to replicate such scenarios in the real world, which is necessary for training and testing purposes. Second, current systems are not fully able to take advan
Deep reinforcement learning for quantum multiparameter estimation
Estimation of physical quantities is at the core of most scientific research and the use of quantum devices promises to enhance its performances. In real scenarios, it is fundamental to consider that the resources are limited and Bayesian adaptive estimation represents a powerful approach to efficiently allocate, during the estimation process, all the available resources. However, this framework relies on the precise
When Are Scoring Rules Proper? Bridging Theory and Practice in Survival Model Evaluation
Proper scoring rules encourage probabilistic predictions that match the true underlying distribution and are central to model evaluation, with increasing relevance in automated workflows such as AutoML. In survival analysis, however, their behavior under censoring is not fully understood. We study commonly used squared and logarithmic scoring rules for right-censored survival data under independent censoring, introdu
Bayesian inference of composition-dependent phase diagrams
Phase diagrams serve as a highly informative tool for materials design, encapsulating information about the phases that a material can manifest under specific conditions. In this work, we develop a method in which Bayesian inference is employed to combine thermodynamic data from molecular dynamics (MD), melting point simulations, and phonon calculations, process these data, and yield a temperature-concentration phase
Box-supervised polyp segmentation attracts increasing attention for its cost-effective potential. Existing solutions often rely on learning-free methods or pretrained models to laboriously generate pseudo masks, triggering Dice constraint subsequently. In this paper, we found that a model guided by the simplest box-filled masks can accurately predict polyp locations/sizes, but suffers from shape collapsing. In respon
EAR-Net: Pursuing End-to-End Absolute Rotations from Multi-View Images
Absolute rotation estimation is an important topic in 3D computer vision. Existing works in literature generally employ a multi-stage (at least two-stage) estimation strategy where multiple independent operations (feature matching, two-view rotation estimation, and rotation averaging) are implemented sequentially. However, such a multi-stage strategy inevitably leads to the accumulation of the errors caused by each i
Saving the legacy of Hero Ibash: Evaluating Four Language Models for Aminoacian
This study assesses four cutting-edge language models in the underexplored Aminoacian language. Through evaluation, it scrutinizes their adaptability, effectiveness, and limitations in text generation, semantic coherence, and contextual understanding. Uncovering insights into these models' performance in a low-resourced language, this research pioneers pathways to bridge linguistic gaps. By offering benchmarks an
High-throughput imaging is often constrained by a trade-off between acquisition speed and image quality. Fast imaging modalities, such as wide-field fluorescence microscopy, enable large-scale data acquisition but suffer from reduced contrast and resolution, whereas high-resolution techniques, like confocal or super-resolution techniques, provide superior image quality at the cost of reduced throughput and increased
Denoising Monte Carlo Renders with Diffusion Models
Physically-based renderings contain Monte Carlo noise, with variance that increases as the number of rays per pixel decreases. This noise, while zero-mean for good modern renderers, can have heavy tails (most notably, for scenes containing specular or refractive objects). Learned methods for restoring low fidelity renders are highly developed, because suppressing render noise means one can save compute and use fast r
With an evolutionary approach, the basis of morality can be explained as adaptations to problems of cooperation. With 'evolution' taken in a broad sense, AIs that satisfy the conditions for evolution to apply will be subject to the same cooperative evolutionary pressure as biological entities. Here the adaptiveness of increased cooperation as material safety and wealth increase is discussed -- for humans, for
Survival of the Cheapest: Cost-Aware Hardware Adaptation for Adversarial Robustness
Deploying adversarially robust machine learning systems requires continuous trade-offs between robustness, cost, and latency. We present an autonomic decision-support framework providing a quantitative foundation for adaptive hardware selection and hyper-parameter tuning in cloud-native deep learning. The framework applies accelerated failure time (AFT) models to quantify the effect of hardware choice, batch size, ep
Linear convergence of proximal descent schemes on the Wasserstein space
We investigate proximal descent methods, inspired by the minimizing movement scheme introduced by Jordan, Kinderlehrer and Otto, for optimizing entropy-regularized functionals on the Wasserstein space. We establish linear convergence under flat convexity assumptions, thereby relaxing the common reliance on geodesic convexity. Our analysis circumvents the need for discrete-time adaptations of the Evolution Variational
Soft-TransFormers for Continual Learning
Inspired by the Well-initialized Lottery Ticket Hypothesis (WLTH), we introduce Soft-TransFormers (Soft-TF), a continual learning framework that adapts a frozen pre-trained Transformer through task-specific soft subnetworks: real-valued multiplicative masks over the query, key, value, and output projections of selected self-attention layers. The masks are initialized at one, so optimization starts exactly at the pre-
MACAW: A Causal Generative Model for Medical Imaging
Although deep learning techniques show promising results for many neuroimaging tasks in research settings, they have not yet found widespread use in clinical scenarios. One of the reasons for this problem is that many machine learning models only identify correlations between the input images and the outputs of interest, which can lead to many practical problems, such as encoding of uninformative biases and reduced e
Generalized Least Squares Kernelized Tensor Factorization
Recovering incomplete multidimensional tensor-structured data is a fundamental task in many real-world applications. Smoothness-constrained low-rank tensor factorization effectively captures global and long-range correlations, but often struggles to characterize short-scale, high-frequency, or locally varying structures. We propose GLSKF, a complementary Generalized Least Squares Kernelized Tensor Factorization frame
XCOMPS: A Multilingual Benchmark of Conceptual Minimal Pairs
We introduce XCOMPS in this work, a multilingual conceptual minimal pair dataset covering 17 languages. Using this dataset, we evaluate LLMs' multilingual conceptual understanding through metalinguistic prompting, direct probability measurement, and neurolinguistic probing. By comparing base, instruction-tuned, and knowledge-distilled models, we find that: 1) LLMs exhibit weaker conceptual understanding for low-r
The accurate quantum chemical calculation of excited states is a challenging task, often requiring computationally demanding methods. When entire ground and excited potential energy surfaces (PESs) are desired, for instance to predict the interaction of light excitation and structural changes, one is often forced to use cheaper computational methods at the cost of reduced accuracy. Here we introduce a method for the
A Self-Supervised Framework for Space Object Behaviour Characterisation
Foundation Models, which leverage large neural networks pre-trained on unlabelled data before fine-tuning for specific tasks, are increasingly being applied to specialised domains. Recent examples include ClimaX for climate and Clay for satellite Earth observation, but a Foundation Model for Space Object Behavioural Analysis has not yet been developed. As orbital populations grow, automated methods for characterising
Human-AI Governance (HAIG): A Trust-Utility Approach
This paper introduces the Human-AI Governance (HAIG) framework, contributing to the AI Governance (AIG) field by foregrounding the relational dynamics between human and AI actors rather than treating AI systems as objects of governance alone. Current categorical frameworks (e.g., human-in-the-loop models) inadequately capture how AI systems evolve from tools to partners, particularly as foundation models demonstrate
Topology-Driven Clustering: Enhancing Performance with Betti Number Filtration
Clustering aims at partitioning data points into groups of similar objects without knowing about the class labels. However, clustering datasets with complex geometric structures, such as nonconvex shapes, multiple scales, or intertwined manifolds, remains challenging for traditional algorithms that primarily rely on Euclidean or kernel-based similarity measures. Topological Data Analysis (TDA), particularly persisten
Interpretable fault diagnosis (FD) plays a critical role in industrial manufacturing, as it improves human-machine understanding and operational efficiency. However, harsh operating environments often introduce strong background interference or noise, which weakens the discriminative capability and interpretability of existing FD methods. To address this issue, this paper proposes FE-MCFormer, a time-frequency fusion
This study investigates why physics-informed machine learning (PIML) can fail in macroscopic traffic flow modeling. We define failure as cases where a PIML model underperforms both purely data-driven and purely physics-based baselines by a given threshold. Unlike in other fields, physics residuals themselves do not hinder optimization in this setting. Instead, effective updates require both data and physics gradients
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