Record 17072026 · 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
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 Falkland Islands, commonly referred to as the Falklands, is an archipelago in the South Atlantic Ocean on the Patagonian Shelf. The principal islands are about 300 mi (500 km) east of South America's southern Patagonian coast and 752 mi (1,210 km) from Cap
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
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.
The Falklands War was a ten-week war in the South Atlantic in 1982 between Argentina and the United Kingdom over possession of the Falkland Islands and its dependencies of South Georgia and the South Sandwich Islands. The conflict began on 2 April 1982 when Ar
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
The FIFA World Cup is an international association football competition contested by the senior men's national teams of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The championship has been awarded every fou
Jude Victor William Bellingham is an English professional footballer who plays as a midfielder for La Liga club Real Madrid and the England national team. Regarded as one of the best players in the world, he is known for his athleticism and ball-winning abilit
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
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
Sir Nigel John Dermot "Sam" Neill was a New Zealand actor. Known as a leading man in film and television, he received nominations for three Primetime Emmy Awards and two Golden Globe Awards. He was appointed an Officer of the Order of the British Empire in the
Thomas Tuchel is a German professional football manager and former player who is the manager of the England national team.
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
Argentina, officially the Argentine Republic, is a country in the far south of South America. It covers an area of 2,780,085 km2 (1,073,397 mi2), making it the second-largest country in South America after Brazil, the fourth-largest country in the Americas, an
Halroy Candis Williams was an American actor, best known for his recurring roles as Officer Smith ("Smitty") on Sanford and Son (1972–1976), Harley Foster on The Waltons (1973–1980), and as family patriarch Lester Jenkins on the NBC sitcom 227 which originally
Argentina national football team
The Argentina national football team, nicknamed la Albiceleste, represents Argentina in men's international football and is administered by the Argentine Football Association, the governing body of football in Argentina. It has been a member of FIFA since 1912
Google LLC is an American multinational technology corporation focused on information technology, online advertising, search engine technology, email, cloud computing, software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (
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
Diego Armando Maradona was an Argentine professional football player and manager. Widely regarded as one of the greatest players in history, he was one of the two joint winners of the FIFA Player of the Century award, alongside Pelé.
Lionel Sebastián Scaloni is an Argentine professional football manager and former player who is the head coach of the Argentina national team. He is also an Italian citizen. Under his leadership, Argentina won the 2022 FIFA World Cup. Scaloni is regarded as on
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
Enzo Jeremías Fernández is an Argentine professional footballer who plays as a midfielder for Premier League club Chelsea and the Argentina national team.
Erling Braut Haaland is a Norwegian professional footballer who plays as a striker for Premier League club Manchester City and the Norway national team. Regarded as one of the best players in the world and the greatest Norwegian player of all time, he is known
Valentín "Colo" Barco is an Argentine professional footballer who plays as a midfielder for Premier League club Chelsea and the Argentina national team.
The final match of the 2026 FIFA World Cup—the 23rd edition of the FIFA competition for men's national soccer teams—was played at MetLife Stadium in East Rutherford, New Jersey, part of the New York metropolitan area, on July 19, 2026. It was contested by Spai
The 2028 UEFA European Football Championship, commonly referred to as UEFA Euro 2028 or simply Euro 2028, will be the 18th UEFA European Championship, the quadrennial international football championship for European teams. It will be co-hosted by England, Scot
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
Harry Edward Kane is an English professional footballer who plays as a striker for Bundesliga club Bayern Munich and captains the England national team. He is regarded as one of the best players in the world, one of the best strikers of his generation, and one
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Cross-Cluster Weighted Forests
Building trustworthy machine learning algorithms for biological applications requires adapting to data heterogeneity from different sources, batches, distributions, or studies. We propose the 'Cross-Cluster Weighted Forest' (CCWF), an ensembling approach that explicitly leverages heterogeneity in the feature distribution to produce more accurate and more generalizable predictors than the standard Random Fores
Recent advances in large language models (LLMs) have made natural language interfaces (NLIs) widely accessible for data exploration, yet analysts who have a broad analytical objective still face the challenge of decomposing it into effective step-by-step queries, especially over unfamiliar, multi-table relational databases. Rather than generating high-level analytical agendas, we investigate how to augment an NLI wit
Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration
Regularization is a cornerstone of modern reinforcement learning. Regularized policy iteration (RPI) provides a fundamental scheme for solving regularized Markov decision processes (RMDPs), and the widely used soft actor-critic algorithm arises as a special case when the regularizer is Shannon entropy. Despite its empirical success, the theoretical underpinnings of RPI remain unclear. In this paper, we address this g
Online polarization has attracted the attention of researchers for many years. Its effects on society are a cause for concern, and the design of personalized depolarization strategies appears to be a key solution. Such strategies should rely on a fine and accurate measurement, and a clear understanding of polarization behaviors. However, the literature still lacks ways to characterize them finely. We propose GRAIL, t
Seeing Through Uncertainty: Free-Energy-Inspired Real-Time Adaptation for Robust Visual Navigation
Navigation in the natural world is a feat of adaptive inference, where biological organisms maintain goal-directed behaviour despite noisy and incomplete sensory streams. Central to this ability is the Free Energy Principle (FEP), which posits that perception is a generative process where the brain minimises Variational Free Energy (VFE) to maintain accurate internal models of the world. While Deep Neural Networks (D
A short review on the maximum clique problem algorithms with classical, AI, and quantum methods
This manuscript provides a comprehensive review of the Maximum Clique Problem, a computational problem that involves finding subsets of vertices in a graph that are all pairwise adjacent to each other. As such, this review is a continuation of the series of previous reviews from 1994, 1999 and 2014. The manuscript covers in a simple way classical algorithms and includes a review of recent developments in graph neural
Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network
The cellular network of magnetic Induction (MI) communication holds promise in long-distance underground environments. In the traditional MI communication, there is no fast-fading channel since the MI channel is treated as a quasi-static channel. However, for the vehicle (mobile) MI (VMI) communication, the unpredictable antenna vibration brings the remarkable fast-fading. As such fast-fading cannot be modeled by the
QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL
Fine-tuning large language models (LLMs) for specific domain tasks has achieved great success in Text-to-SQL tasks. However, these fine-tuned models often face challenges with multi-turn Text-to-SQL tasks caused by ambiguous or unanswerable questions. It is desired to enhance LLMs to handle multiple types of questions in multi-turn Text-to-SQL tasks. To address this, we propose a novel data augmentation method, calle
Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis
Deep neural networks have demonstrated remarkable effectiveness across a wide range of tasks such as semantic segmentation. Nevertheless, these networks are vulnerable to adversarial attacks that add imperceptible perturbations to the input image, leading to false predictions. This vulnerability is particularly dangerous in safety-critical applications like automated driving. While adversarial examples and defense st
Machine Learning (ML) has become central to Autonomous Vehicles (AVs), supporting perception, prediction, planning, control, and decision-making in dynamic environments. However, achieving full autonomy in cluttered and complex scenarios, such as intricate intersections, diverse scenes, varied trajectories, and complex missions, remains challenging; moreover, data labeling is still a major bottleneck. These limitatio
Empirical evidence of Large Language Model's influence on human spoken communication
From the printing press to social media, innovations in communication technology have repeatedly reshaped how ideas spread through human culture. Chatbots powered by generative artificial intelligence constitute a new medium, encoding cultural patterns in their neural representations and disseminating them in conversations with hundreds of millions of people. Whether these patterns transmit into human language, and u
When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
The deployment of large language models (LLMs) like ChatGPT and Gemini has shown their powerful natural language generation capabilities. However, these models can inadvertently learn and retain sensitive information and harmful content during training, raising significant ethical and legal concerns. To address these issues, machine unlearning has been introduced as a potential solution. While existing unlearning met
PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction
Chinese ASR correction is challenging because errors are often \emph{phonetic} (many characters share similar Pinyin) while the correction model must also obey a \emph{length constraint} under noisy N-best hypotheses. Existing approaches either exploit Pinyin only at the prompt/feature level without integrating it into model representations or rely on generative decoding that can drift in length. We propose \textbf{P
Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces
We study distributionally robust optimization (DRO) for robust inference when the worst-case distribution is continuous, leading to significant computational challenges due to the infinite-dimensional nature of the optimization problem. Unlike traditional discrete DRO approaches, which often suffer from scalability issues, limited generalization, and costly worst-case inference, our framework exploits Brenier's t
Federated Deep Subspace Clustering
This paper introduces FDSC, a private-protected subspace clustering (SC) approach with federated learning (FC) schema. In each client, there is a deep subspace clustering network accounting for grouping the isolated data, composed of a encode network, a self-expressive layer, and a decode network. FDSC is achieved by uploading the encode network to communicate with other clients in the server. Besides, FDSC is also e
The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models
The recent rise in the popularity of large language models has spurred the development of extensive code datasets needed to train them. This has left limited code available for collection and use in the downstream investigation of specific behaviors, or evaluation of large language models without suffering from data contamination. To address this problem, we release The Heap, a large multilingual dataset covering 57
GenTL: A General Transfer Learning Model for Building Thermal Dynamics
Transfer Learning (TL) is an emerging field in modeling building thermal dynamics. This method reduces the data required for a data-driven model of a target building by leveraging knowledge from a source building. Consequently, it enables the creation of data-efficient models that can be used for advanced control and fault detection & diagnosis. A major limitation of the TL approach is its inconsistent performanc
We study a class of stochastic nonsmooth optimization problems in which an outer variable minimizes the expectation of a pointwise maximum. This minimization--expectation--maximization (minEmax) problem arises in Wasserstein distributionally robust optimization and adversarially robust training, and it cannot in general be reformulated as a finite-dimensional minimax problem when the underlying distribution is not em
Simulating Automotive Radar with Lidar and Camera Inputs
Low-cost millimeter automotive radar has received more and more attention due to its ability to handle adverse weather and lighting conditions in autonomous driving. However, the lack of quality datasets hinders research and development. We report a new method that is able to simulate 4D millimeter wave radar signals including pitch, yaw, range, and Doppler velocity along with radar signal strength (RSS) using camera
We introduce the Switching Non-Stationary Markov Decision Process (SNS-MDP) framework, in which the environment transitions among a finite set of MDPs governed by a latent Markov chain while the agent observes only the external state. We show that the long-term effect of this switching is equivalent to stationary dynamics parameterized by the stationary distribution of the hidden Markov chain. For fixed policies, we
Scaling Evaluation-time Compute with Reasoning Models as Evaluators
As language model (LM) outputs get more and more natural, it is becoming more difficult than ever to evaluate their quality. Simultaneously, increasing LMs' "thinking" time through scaling test-time compute has proven an effective technique to solve challenging problems in domains such as math and code. This raises a natural question: can an LM's evaluation capability also be improved by spending more
Limits of Discrete Energy of Families of Increasing Sets
The Hausdorff dimension of a set can be detected using the Riesz energy. Here, we consider situations where a sequence of points, $\{x_n\}$, ``fills in'' a set $E \subset \mathbb{R}^d$ in an appropriate sense and investigate the degree to which the discrete analog to the Riesz energy of these sets can be used to bound the Hausdorff dimension of $E$. We also discuss applications to data science and Erdős/Falco
Deep Reinforcement Learning Based Navigation with Macro Actions and Topological Maps
This paper addresses the challenge of navigation in large, visually complex environments with sparse rewards. We propose a method that uses object-oriented macro actions grounded in a topological map, allowing a simple Deep Q-Network (DQN) to learn effective navigation policies. The agent builds a map by detecting objects from RGBD input and selecting discrete macro actions that correspond to navigating to these obje
Primal-dual algorithm for contextual stochastic combinatorial optimization
This paper introduces a novel approach to contextual stochastic optimization, integrating operations research and machine learning to address decision-making under uncertainty. Traditional methods often fail to leverage contextual information, which underscores the necessity for new algorithms. In this study, we utilize neural networks with combinatorial optimization layers to encode policies. Our goal is to minimize
Conditioning Residuals for Diffusion Models via Representation Feedback
Diffusion models now serve as a common foundation for multimedia generation, and useful intermediate representations emerge during their generative training. Standard architectures, however, propagate these representations through the main feature stream, without explicitly reintroducing their encoded semantics to later denoising layers. Meanwhile, such backbones already provide a conditioning pathway for global modu
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