Record 23072026 · 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
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.
.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 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
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
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
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
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
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
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
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
Mark Henry Rowswell, CM, known by his Chinese stage name Dashan, is a Canadian performer and television personality who gained prominence in China. He became known in the late 1980s through regular appearances on China Central Television (CCTV), particularly i
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
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
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
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
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 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
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
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
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").
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
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.
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
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
Pan Am Flight 526A, a Douglas DC-4, took off from San Juan–Isla Grande Airport, Puerto Rico, at 12:11 PM AST on April 11, 1952, on a flight to Idlewild International Airport, New York City, with 64 passengers and 5 crew members on board. Due to inadequate main
Courtney Alexis Stodden is an American media personality, model, and singer. After competing in beauty pageants in her home state of Washington and releasing original music, then 16-year-old Stodden came to international attention after being wed to 51-year-ol
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Auto-adaptive Resonance Equalization using Dilated Residual Networks
In music and audio production, attenuation of spectral resonances is an important step towards a technically correct result. In this paper we present a two-component system to automate the task of resonance equalization. The first component is a dynamic equalizer that automatically detects resonances and offers to attenuate them by a user-specified factor. The second component is a deep neural network that predicts t
Deep Learning in Mining Biological Data
Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Broadly categorized in three types (i.e., sequences, images, and signals), these data are huge in amount and complex in nature. Mining such an enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine le
Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation
This article investigates the origin of numerical issues in maximum likelihood parameter estimation for Gaussian process (GP) interpolation and investigates simple but effective strategies for improving commonly used open-source software implementations. This work targets a basic problem but a host of studies, particularly in the literature of Bayesian optimization, rely on off-the-shelf GP implementations. For the c
Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals
Many canonical models in causal inference and structural econometrics have recursive identification formulas. In causal inference, recursion arises when identification requires both pre- and post-treatment covariates. For example, short-term surrogate outcomes are measured after the treatment, and serve as necessary covariates when identifying long-term effects. Post-treatment covariates are also required for identif
Is Smaller Always Faster? Tradeoffs in Compressing Self-Supervised Speech Transformers
Transformer-based self-supervised models have achieved remarkable success in speech processing, but their large size and high inference cost present significant challenges for real-world deployment. While numerous compression techniques have been proposed, inconsistent evaluation metrics make it difficult to compare their practical effectiveness. In this work, we conduct a comprehensive study of four common compressi
Kernel Ridge Regression Inference
We provide uniform confidence bands for kernel ridge regression (KRR), a widely used nonparametric regression estimator for nonstandard data such as preferences, sequences, and graphs. Despite the prevalence of these data--e.g., student preferences in school matching mechanisms--the inferential theory of KRR is not fully known. We construct valid and sharp confidence sets that shrink at nearly the minimax rate, allow
Rational kernel-based interpolation for complex-valued frequency response functions
This work is concerned with the kernel-based approximation of a complex-valued function from data, where the frequency response function of a partial differential equation in the frequency domain is of particular interest. In this setting, kernel methods are employed more and more frequently, however, standard kernels do not perform well. Moreover, the role and mathematical implications of the underlying pair of kern
A convergence result of a continuous model of deep learning via a Łojasiewicz--Simon inequality
We study an idealized training process for deep neural networks in a continuous-depth, mean-field model in which each layer is parameterized by a probability measure on a Euclidean parameter space. The training dynamics are formulated as a Wasserstein-type gradient flow of an objective with a fixed $L^2$-regularization. Under suitable analyticity and growth assumptions, together with a coercivity assumption and suffi
Advancing bioinformatics with large language models: components, applications and perspectives
Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solvi
A Survey on Semantic Modeling for Building Energy Management
Building Energy Management (BEM) is central to reducing energy use and CO2 emissions in the building sector. Although IoT technologies now provide extensive operational data, heterogeneous data models, device descriptions, and contextual representations continue to limit semantic interoperability, limiting the development of generalisable, autonomous, context-aware BEM applications. Ontologies address this challenge
Local search for valued constraint satisfaction parameterized by treedepth
Sometimes local search algorithms cannot efficiently find even local peaks. To understand why, I look at the structure of ascents in fitness landscapes from valued constraint satisfaction problems (VCSPs) parameterized by the treedepth of their constraint graphs. There are existing constructions of VCSPs with logarithm treedepth that represent fitness landscapes where all ascents are exponential from some initial ass
Any-Time Regret-Guaranteed Algorithm for Control of Linear Quadratic Systems
We propose a computationally efficient algorithm that achieves anytime regret of order $\mathcal{O}(\sqrt{t})$, with explicit dependence on the system dimensions and on the solution of the Discrete Algebraic Riccati Equation (DARE). Our approach builds on the SDP-based framework of \cite{cohen2019learning}, using an appropriately tuned regularization and a sufficiently accurate initial estimate to construct confidenc
LaSEr-Edit: Localized Span-level Error Editing with Energy-based Localization
As large language models (LLMs) are widely adopted in real-world applications, it has become critical to ensure LLMs satisfy safety constraints, such as non-toxicity and logical consistency, as well as task- and situation-specific constraints. Controlling the output through instructions is a simple and tempting approach; however, it remains brittle, is opaque in how it influences model behavior, and thus cannot relia
Footprints of Data in a Classifier: Understanding the Privacy Risks and Solution Strategies
The widespread deployment of Artificial Intelligence (AI) across government and private industries brings both advancements and heightened privacy and security concerns. Article 17 of the General Data Protection Regulation (GDPR) mandates the Right to Erasure, requiring data to be permanently removed from a system to prevent potential compromise. While existing research primarily focuses on erasing sensitive data att
Differentially Private Neural Network Training Under the Hidden State Assumption
Current differentially private learning paradigms face a severe utility bottleneck: DP-SGD degrades performance through noise accumulation over training steps, while aggregation-based approaches such as PATE suffer from data inefficiency due to disjoint data partitioning. We propose \textbf{Differentially Private Decoupled Training (DP-DT)}, a framework that decouples representation learning from privacy enforcement.
A Confidence Interval for the $\ell_2$ Expected Calibration Error
Recent advances in machine learning have significantly improved prediction accuracy in various applications. However, ensuring the calibration of probabilistic predictions remains a significant challenge. Despite efforts to enhance model calibration, the rigorous statistical evaluation of model calibration remains less explored. In this work, we develop confidence intervals the $\ell_2$ Expected Calibration Error (EC
Distributed Optimization via Energy Conservation Laws in Dilated Coordinates
Continuous-time models can reveal accelerated structures in distributed optimization, but their rates need not survive direct discretization. We introduce a second-order primal--dual flow for smooth convex distributed optimization and construct an exactly conserved energy that yields an $\mathcal O(t^{-2})$ rate for both the aggregate objective gap and the squared consensus error. We then prove a horizon-wise $Ω(k^{-
The Hive Mind is a Single Reinforcement Learning Agent
Decision-making is an essential attribute of any intelligent agent or group. Natural systems are known to converge to effective strategies through at least two distinct mechanisms: collective decision-making via imitation of others, and trial-and-error by a single agent. This paper establishes an equivalence between these two paradigms. We show that the emergent distributed cognition (sometimes referred to as the \te
Continuous monitoring and real-time control of high-dimensional distributed systems are often crucial in applications to ensure a desired physical behavior, without degrading stability and system performances. Traditional feedback control design that relies on full-order models, such as high-dimensional state-space representations or partial differential equations, fails to meet these requirements due to the delay in
Tokenization is a necessary component within the current architecture of many language mod-els, including the transformer-based large language models (LLMs) of Generative AI, yet its impact on the model's cognition is often overlooked. We argue that LLMs demonstrate that the Distributional Hypothesis (DH) is sufficient for reasonably human-like language performance (particularly with respect to inferential lexica
Reduced Order Modeling with Shallow Recurrent Decoder Networks
Reduced Order Modeling is of paramount importance for efficiently inferring high-dimensional spatio-temporal fields in parametric contexts, enabling computationally tractable parametric analyses, uncertainty quantification and control. However, conventional dimensionality reduction techniques are typically limited to known and constant parameters, inefficient for nonlinear and chaotic dynamics, and uninformed to the
OpenNotes gives patients access to their EHR notes, but dense medical jargon limits comprehension. We evaluate closed-source and open-source LLMs for extracting and prioritizing the jargon terms most relevant to individual patients, using 90 expert-annotated EHR notes. We test combinations of general vs. structured prompts, zero-shot vs. few-shot prompting, fine-tuning, and GPT-4o-based data augmentation, the last pa
SemICP: Semantic Non-Rigid Point Cloud Registration with Elastic Energy Regularization
Purpose: Accurate point cloud registration is essential in computer-aided interventions (CAI) to align multi-modal medical images for intraoperative guidance. Classical methods, such as Iterative Closest Point (ICP), remain attractive for their explainability and minimal training requirements, but typically ignore anatomical semantics and biomechanical properties during regularization. Methods: We present Semantic IC
SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding
We present SEED (Semantic Evaluation for Visual Brain Decoding), a novel metric for evaluating the semantic decoding performance of visual brain decoding models. It integrates three complementary metrics, each capturing a different aspect of semantic similarity between images inspired by neuroscientific findings. Using carefully crowd-sourced human evaluation data, we demonstrate that SEED achieves the highest alignm
Theory-to-Practice Gap for Neural Networks and Neural Operators
This work studies the sampling complexity of learning with ReLU neural networks and neural operators. For mappings belonging to relevant approximation spaces, we derive upper bounds on the best-possible convergence rate of any learning algorithm, with respect to the number of samples. In the finite-dimensional case, these bounds imply a gap between the parametric and sampling complexities of learning, known as the \e
Notable events recorded on this day and month across all years.