Record 27072026 · 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
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
.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
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
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
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
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
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
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
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
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
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
Dharmendra Pradhan is an Indian politician who served as the 9th Minister of Education from 2021 until his resignation in 2026. He previously served as Minister of Petroleum and Natural Gas from 2014 to 2021, Minister of Steel from 2019 to 2021, and Minister o
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
Shawn Adam Levy is a Canadian and American filmmaker and actor. He is the founder of 21 Laps Entertainment. His work has spanned numerous genres, and his films as a director have grossed a collective $3.5 billion worldwide.
Errol Spence Jr. is an American professional boxer who competed from 2012 to 2026. He is a former unified champion in the welterweightdivision, having held the World Boxing Association (WBA), World Boxing Council (WBC), and International Boxing Federation (IBF
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
Avatar Aang: The Last Airbender
Avatar Aang: The Last Airbender is a 2026 American animated fantasy action-adventure film directed by Lauren Montgomery from a screenplay by Tim Hedrick and Christopher Yost, based on a story by Bryan Konietzko, Michael Dante DiMartino, Hedrick, and Kenneth Li
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
List of Marvel Cinematic Universe films
The Marvel Cinematic Universe (MCU) centers on American superhero films produced by Marvel Studios, based on characters that appear in publications by Marvel Comics. The MCU is the shared universe in which all of the films are set. Marvel Studios has released
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
Ryan Thomas Gosling is a Canadian actor. Known for his work in both independent films and major studio features, his films as a leading actor have grossed over US$4 billion worldwide. He has received various accolades including a Golden Globe Award, in additio
Yan Diomande is an Ivorian professional footballer who plays as a winger for La Liga club Real Madrid and the Ivory Coast national team.
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
In Greek and Roman mythology, Odysseus, also known by the Latin variant Ulysses, is a legendary Greek king of Ithaca and the hero of Homer's epic poem, the Odyssey. Odysseus also plays a key role in Homer's Iliad and other works in that same epic cycle.
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
The Long Walk is a 2025 American dystopian survival thriller film directed by Francis Lawrence and written by JT Mollner. It is based on the 1979 novel by Stephen King. The film stars Cooper Hoffman, David Jonsson, Garrett Wareing, Tut Nyuot, Joshua Odjick, Ch
Anthony Oluwafemi Olaseni "AJ" Joshua is a British professional boxer. He held the unified heavyweight championship twice between 2017 and 2021. He also held the International Boxing Organization (IBO) title during his reigns as champion. At regional level, he
2026 Formula One World Championship
The 2026 FIA Formula One World Championship is a motor racing championship for Formula One cars and the 77th running of the Formula One World Championship. It is recognised by the Fédération Internationale de l'Automobile (FIA), the governing body of internati
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Participatory Budgeting with Project Groups
We study a generalization of the standard approval-based model of participatory budgeting (PB), in which voters are providing approval ballots over a set of predefined projects and---in addition to a global budget limit, there are several groupings of the projects, each group with its own budget limit. We study the computational complexity of identifying project bundles that maximize voter satisfaction while respecti
Gradient flows on graphons: existence, convergence, continuity equations
Wasserstein gradient flows on probability measures have found a host of applications in various optimization problems. They typically arise as the continuum limit of exchangeable particle systems evolving by some mean-field interaction involving a gradient-type potential. However, in many problems, such as in multi-layer neural networks, the so-called particles are edge weights on large graphs whose nodes are exchang
Stochastic optimization on matrices and a graphon McKean-Vlasov limit
We consider stochastic gradient descents on the space of large symmetric matrices of suitable functions that are invariant under permuting the rows and columns using the same permutation. We establish deterministic limits of these random curves as the dimensions of the matrices go to infinity while the entries remain bounded. Under a ``small noise'' assumption the limit is shown to be the gradient flow of fun
MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting
Accurate forecasting of renewable generation is crucial to facilitate the integration of Renewable Energy Sources into the power system. Focusing on photovoltaic (PV) units, forecasting methods can be divided into two main categories: physics-based and data-based strategies, with Artificial Intelligence (AI)-based models providing state-of-the-art performance. However, while these AI-based models can capture complex
A data-driven inverse optimization problem (DDIOP) is the problem of estimating the objective-function parameters (weights) that explain observed optimal-solution data, and it arises in many applications, including mixed integer linear programming (MILP). In inverse optimization for MILPs, the prediction error of the features is discontinuous with respect to the weights, so applying gradient-based optimization direct
Highly Versatile FPGA-Implemented Cyber Coherent Ising Machine
In recent years, quantum Ising machines have drawn a lot of attention, but due to physical implementation constraints, it has been difficult to achieve dense coupling, such as full coupling with sufficient spins to handle practical large-scale applications. Consequently, classically computable equations have been derived from quantum master equations for these quantum Ising machines. Parallel implementations of these
Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
Conventional uncertainty-aware temporal difference (TD) learning often models TD errors as zero-mean Gaussian. This assumption can miss the heavy-tailed and heteroscedastic residuals induced by bootstrapping and exploration. We introduce a state-conditioned shape head based on the Generalized Gaussian Distribution (GGD) and use a numerically modified GGD loss as an online surrogate for nonstationary TD residuals. We
Numerically solving a large parametric nonlinear dynamical system is challenging due to its high complexity and the high computational costs. In recent years, machine-learning-aided surrogates are being actively researched. However, many methods fail in accurately generalizing in the entire time interval $[0, T]$, when the training data is available only in a training time interval $[0, T_0]$, with $T_0<T$. To imp
This proceedings contains abstracts and position papers for the work to be presented at the fourth Logic and Practice of Programming (LPOP) Workshop. The workshop is to be held in Dallas, Texas, USA, and as a hybrid event, on October 13, 2024, in conjunction with the 40th International Conference on Logic Programming (ICLP). The focus of this workshop is integrating reasoning systems for trustworthy AI, especially in
Efficiently Training Time-to-First-Spike Spiking Neural Networks from Scratch
Spiking Neural Networks (SNNs), with their event-driven and biologically inspired mechanisms, are well-suited for energy-efficient neuromorphic hardware. Neural coding, which is critical to SNNs, determines how information is represented via spikes. While Time-to-First-Spike (TTFS) coding uses a single spike per neuron to offer extreme sparsity and energy efficiency, it often suffers from unstable training and low ac
Forensics Adapter: Unleashing CLIP for Generalizable Face Forgery Detection
We describe Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector. Although CLIP is highly versatile, adapting it for face forgery detection is non-trivial as forgery-related knowledge is entangled with a wide range of unrelated knowledge. Existing methods treat CLIP merely as a feature extractor, lacking task-specific adaptation, which limits their
Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face Detection
Face-swapping DeepFakes have become an escalating societal concern, attracting increasing attention in recent years. To counter this, we investigate a new proactive defense framework to prevent individuals from being victimized in DeepFake videos. The core idea of this framework is to contaminate the inputs of DeepFake models by disrupting face detectors, based on the observation that face detectors are commonly used
Ask for More Than Bayes Optimal: A Theory of Indecisions for Selective Hypothesis Testing
Selective classification is a powerful tool for automated decision-making in high-risk scenarios, allowing classifiers to act only when confident and abstain when uncertainty is high. Given a target accuracy, our goal is to minimize the number of indecisions, which are observations that we do not automate. For difficult problems, the target accuracy may be unattainable without abstaining from making a decision. By us
Analyzing the Ethical Logic of Eight Large Language Models
This study examines the expressed ethical logic of eight prominent large language models from OpenAI, Meta, Perplexity, Anthropic, Google, Mistral, DeepSeek, and xAI. Each model answered direct questions about its ethical principles and responded to five classic moral dilemmas. Responses were analyzed using the consequentialist/deontological distinction, Moral Foundations Theory, and Kohlbergs stages of moral develop
Online Pricing and Allocation with Demand Learning and Fulfillment Cost
We study online learning for a seller that jointly chooses per-period inventory positions and a uniform price, then fulfills realized demand through a downstream allocation. The main difficulty is not only demand learning: the price shifts demand and reshapes the transportation LP, making the population objective globally non-convex and non-smooth. To solve this problem, we propose OCSAA, an algorithm that exploits d
We consider a teacher-student model of supervised learning with a fully-trained two-layer neural network whose width $k$ and input dimension $d$ are large and proportional. We provide an effective theory for approximating the Bayes-optimal generalisation error of the network for any activation function in the regime of sample size $n$ scaling quadratically with the input dimension, i.e., around the interpolation thre
MIGT: Memory Instance Gated Transformer Framework for Financial Portfolio Management
Deep reinforcement learning (DRL) has been applied in financial portfolio management to improve returns in changing market conditions. However, unlike most fields where DRL is widely used, the stock market is more volatile and dynamic as it is affected by several factors such as global events and investor sentiment. Therefore, it remains a challenge to construct a DRL-based portfolio management framework with strong
Portfolio management remains a crucial challenge in finance, with traditional methods often falling short in complex and volatile market environments. While deep reinforcement approaches have shown promise, they still face limitations in dynamic risk management, exploitation of temporal markets, and incorporation of complex trading strategies such as short-selling. These limitations can lead to suboptimal portfolio p
Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learning
Critical learning periods (CLPs) in federated learning (FL) refer to early stages during which low-quality contributions (e.g., sparse training data availability) can permanently impair the performance of the global model. However, existing incentive mechanisms typically assume temporal homogeneity, treating all training rounds as equally important, thereby failing to prioritize and attract high-quality contributions
Action Flow Matching for Continual Robot Learning
Continual learning in robotics seeks systems that can constantly adapt to changing environments and tasks, mirroring human adaptability. A key challenge is refining dynamics models, essential for planning and control, while addressing issues such as safe adaptation, catastrophic forgetting, outlier management, data efficiency, and balancing exploration with exploitation -- all within task and onboard resource constra
We explore the promising performance of a transformer model in predicting outputs of parametric dynamical systems with external time-varying input signals. The outputs of such systems vary not only with physical parameters but also with external time-varying input signals. Accurately catching the dynamics of such systems is challenging. We have adapted and extended an existing transformer model, called temporal fusio
From Mind to Machine: The Rise of Manus AI as a Fully Autonomous Digital Agent
Manus AI is a general-purpose AI agent introduced in early 2025, marking a significant advancement in autonomous artificial intelligence. Developed by the Chinese startup Monica.im, Manus is designed to bridge the gap between "mind" and "hand" - combining the reasoning and planning capabilities of large language models with the ability to execute complex, end-to-end tasks that produce tangible outcome
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework
As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety. In this paper we introduce PCS-UQ, a framework based on the Predictability, Computability, and Stability (PCS) principles for veridical data science. Starting with a candidate set of models or algorithms, PCS-UQ integrates a rigorous prediction-check to screen out unsuitable models in the set and
When Ethics and Payoffs Diverge: LLM Agents in Morally Charged Social Dilemmas
Recent advances in LLMs have enabled their use in complex agentic roles, involving decision-making with humans or other agents, making ethical alignment a critical concern. While prior work has examined LLMs' moral judgment and strategic behavior separately, there is limited understanding of how they act when moral imperatives directly conflict with profit incentives. We introduce \msimfull (\msim) to evaluate ho
Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models
Speaker-dependent modelling can substantially improve performance in speech-based health monitoring applications. While mixed-effect models are commonly used for such speaker adaptation, they require computationally expensive retraining for each new observation, making them impractical in a production environment. We reformulate this task as a meta-learning problem and explore three approaches of increasing complexit
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