Record 04082026 · captured 2026-08-25
The world looked up Spider-Man: Brand New Day. 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.
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 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
The 2026 SummerSlam, also promoted as SummerSlam: Minnesota, was a professional wrestling pay-per-view (PPV) and livestreaming event produced by WWE. It was the 39th annual SummerSlam and took place as a two-night event on Saturday, August 1, and Sunday, Augus
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
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
.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
Bankipur Assembly constituency
Bankipur Assembly constituency is one of 243 constituencies of legislative assembly of Bihar. It is a segment of Patna Sahib Lok Sabha constituency.
Roderick Edwin Liddle was a British journalist and broadcaster. He was an editor of BBC Radio 4's Today and an associate editor of The Spectator.
Pan Am Flight 103 was a regularly scheduled Pan Am flight from Frankfurt to Detroit via stopovers in London and New York City. Shortly after 19:00 GMT on 21 December 1988, the Boeing 747 Clipper Maid of the Seas was destroyed by a bomb while flying over the Sc
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
Nirmal Purja, also known as Nims or Nimsdai, was a Nepali-British mountaineer. Before pursuing a career in mountaineering, he served in the British Army with the Brigade of Gurkhas and later in the Special Boat Service (SBS), the special forces unit of the Roy
Ariana Grande-Butera is an American singer, songwriter, and actress. Known for her four-octave vocal range, which extends into the whistle register, she is an influential figure in popular music. Publications such as Rolling Stone and Billboard have deemed Gra
Associazione Sportiva Ostiamare Lido Calcio, commonly known as Ostiamare, is an Italian association football club located in Ostia, a frazione of Rome, Lazio. It currently plays in Serie C
2026 Commonwealth Games medal table
The 2026 Commonwealth Games was a multi-sport event held in Glasgow, Scotland, from 23 July to 2 August 2026. A total of 215 medal events were contested across ten sports.
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
2026 in film is an overview of events in the film industry scheduled to occur in 2026. Best Picture Academy Award-winners All Quiet on the Western Front and Cimarron entered the public domain this year.
Spider-Man: No Way Home is a 2021 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 sequel to Spider-Man: Homecomin
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
Prashant Kishor, colloquially known as PK, is an Indian politician currently serving as the Member of Legislative Assembly representing Bankipur assembly constituency, Patna, Bihar. He is a former political strategist, political advisor, consultant, and the fo
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
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
Sadie Elizabeth Sink is an American actress. She began her career in theater as a child, playing the title role in the musical Annie (2012–2014) and young Elizabeth II in the historical play The Audience (2015) on Broadway. In 2016, she made her film debut in
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
List of highest-grossing openings for films
The following is a list of the highest-grossing opening weekends for films. The list is dominated by recent films due to inflation, steadily increasing production and marketing budgets, and modern films opening on more screens.
Jonathan Edward Bernthal is an American actor. Known for playing brash, hard-edged, ethically complex characters, he first achieved prominence for his portrayal of Shane Walsh on the AMC horror drama series The Walking Dead (2010–2012). He went on to portray F
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
In the early morning hours of November 13, 2022, four University of Idaho students—Madison Mogen, Kaylee Goncalves, Ethan Chapin, and Xana Kernodle—were fatally stabbed in an off-campus house in Moscow, Idaho. On December 30, authorities arrested 28-year-old B
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
Ceuta is an autonomous city of Spain on the North African coast. Bordered by Morocco, it lies between the Mediterranean Sea and the Atlantic Ocean. Ceuta is one of the special territories of members of the European Economic Area: it is a part of the Schengen a
Datia Assembly constituency is one of the 230 Vidhan Sabha constituencies of Madhya Pradesh state in central India. This constituency came into existence in 1951, as one of the 48 Vidhan Sabha constituencies of the erstwhile Vindhya Pradesh state.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
On Multivariate Singular Spectrum Analysis: Tensor and Matrix Variants
We introduce and analyze two extensions of Singular Spectrum Analysis (SSA) to the multivariate setting: a new variant of the well-known matrix-based method (mSSA), and a novel tensor-based approach (tSSA). For mSSA, under a spatio-temporal factor model with $N$ time series and $T$ observations per series, we establish that prediction mean-squared-error for both imputation and out-of-sample forecasting effectively sc
Decisions over Sequences: Computability and Choice
We develop a framework to study situations where decision makers face alternatives sequentially. Within this framework, we focus on endogenous stopping behavior using two broad classes of decision rules: \textit{stopping rules} and \textit{bounded stopping rules}. We establish the equivalence of these two classes and examine two of its implications. First, focusing on the procedural aspects of decision making, we def
Accelerating the convergence of second-order optimization, particularly Newton-type methods, remains a pivotal challenge in algorithmic research. In this paper, we extend previous work on the \textbf{Quadratic Gradient (QG)} and rigorously validate its applicability to general convex numerical optimization problems. We introduce a novel variant of the Quadratic Gradient that departs from the conventional fixed Hessia
DFB: A Data-Free, Low-Budget, and High-Efficacy Clean-Label Backdoor Attack
In the domain of backdoor attacks, accurate labeling of injected data is essential for evading rudimentary detection mechanisms. This imperative has catalyzed the development of clean-label attacks, which are notably more elusive as they preserve the original labels of the injected data. Current clean-label attack methodologies primarily depend on extensive knowledge of the training dataset. However, practically, suc
Differentially Private Distributed Inference for Multicenter Clinical Studies
Extracting reliable conclusions from data distributed across institutions is a core problem in healthcare: pooling patient records would improve inference, but privacy regulations and the lack of a trusted central authority frequently delay multicenter studies. We develop a framework for differentially private distributed inference in which institutions repeatedly exchange log belief-ratio statistics subject to diffe
The Elements of Differentiable Programming
Artificial intelligence has recently experienced remarkable advances, fueled by large models, vast datasets, accelerated hardware, and, last but not least, the transformative power of differentiable programming. This new programming paradigm enables end-to-end differentiation of complex computer programs (including those with control flows and data structures), making gradient-based optimization of program parameters
DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks
Fault injection attacks are a potent threat against embedded implementations of neural network models. Several attack vectors have been proposed, such as misclassification, model extraction, and trojan/backdoor planting. Most of these attacks work by flipping bits in the memory where quantized model parameters are stored. In this paper, we introduce an encoding-based protection method against bit-flip attacks on neur
OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset
We introduce OpenDebateEvidence, a comprehensive dataset for argument mining and summarization sourced from the American Competitive Debate community. This dataset includes over 3.5 million documents with rich metadata, making it one of the most extensive collections of debate evidence. OpenDebateEvidence captures the complexity of arguments in high school and college debates, providing valuable resources for trainin
Information-Theoretic Foundations for Machine Learning
The progress of machine learning over the past decade is undeniable. In retrospect, it is both remarkable and unsettling that this progress was achievable with little to no rigorous theory to guide experimentation. Despite this fact, practitioners have been able to guide their future experimentation via observations from previous large-scale empirical investigations. In this work, we propose a theoretical framework w
On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling
Statistical Shape Models (SSMs) excel at identifying population level anatomical variations, which is at the core of various clinical and biomedical applications, including morphology-based diagnostics and surgical planning. However, the effectiveness of SSMs is often constrained by the necessity for expert-driven manual segmentation, a time-intensive and expensive process that restricts their broader utility. While
Bayesian inference methods such as Markov Chain Monte Carlo (MCMC) typically require repeated computations of the likelihood function, but in some scenarios this is infeasible and alternative methods are needed. Simulation-based inference (SBI) methods address this problem by using machine learning to amortize computations. In this work, we highlight a particular synergy between the SBI method of neural likelihood es
Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models
Remote sensing (RS) image classification is central to Earth observation, but onboard deployment requires models that are accurate, efficient, and robust to sensor and transmission degradation. Following a train-on-ground, infer-onboard workflow, we evaluate 14 backbones, including CNNs, ResNets, compact Transformers trained from scratch, and pre-trained Vision Transformers, on EuroSAT and PatternNet. We assess clean
Polyatomic Complexes: A topologically-informed learning representation for atomistic systems
A representation of a molecule or material should be invariant to the symmetries of physics, unique, continuous, efficient and general. These properties, however, are hard to satisfy at once: a descriptor invariant under the full orthogonal group $O(3)$ gives a molecule and its mirror image the same value, and so cannot distinguish enantiomers whose properties differ. Pozdnyakov showed this follows from the invarianc
Using dynamic loss weighting to boost improvements in forecast stability
Rolling origin forecast instability refers to variability in forecasts for a specific period induced by updating the forecast when new data points become available. Recently, an extension to the N-BEATS model for univariate time series point forecasting was proposed to include forecast stability as an additional optimization objective, next to accuracy. It was shown that more stable forecasts can be obtained without
Optimizing Treatment Allocation in the Presence of Interference
In Influence Maximization (IM), the objective is to -- given a budget -- select the optimal set of entities in a network to target with a treatment so as to maximize the total effect. For instance, in marketing, the objective is to target the set of customers that maximizes the total response rate, resulting from both direct treatment effects on targeted customers and indirect, spillover, effects that follow from tar
Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning
Over-parameterized models are typically vulnerable to membership inference attacks, which aim to determine whether a specific sample is included in the training of a given model. Previous Weight regularizations (e.g., L1 regularization) typically impose uniform penalties on all parameters, leading to a suboptimal tradeoff between model utility and privacy. In this work, we first show that only a small fraction of par
Classifier Chain Networks for Multi-Label Classification
The classifier chain is a widely used method for analyzing multi-labeled data sets. In this study, we introduce a generalization of the classifier chain: the classifier chain network. The classifier chain network enables joint estimation of model parameters, and allows to account for the influence of earlier label predictions on subsequent classifiers in the chain. Through simulations, we evaluate the classifier chai
Radiology reports are crucial for planning treatment strategies and facilitating effective doctor-patient communication. However, the manual creation of these reports places a significant burden on radiologists. While automatic radiology report generation presents a promising solution, existing methods often rely on single-view radiographs, which constrain diagnostic accuracy. To address this challenge, we propose \t
Fairshare Data Pricing via Data Valuation for Large Language Models
Training data is the backbone of large language models (LLMs), yet today's data markets often operate under exploitative pricing -- sourcing data from marginalized groups with little pay or recognition. This paper introduces a theoretical framework for LLM data markets, modeling the strategic interactions between buyers (LLM builders) and sellers (human annotators). We begin with theoretical and empirical analysi
ApplE: A Modular Ontology of Applied Ethics and Event Context for Ethical Decision Modeling
Applied ethics applies ethical decision-making to domain-specific contexts using contextual information such as agents, actions, temporal and spatial settings, and theoretical constructs such as utility, virtues, rights, and duties. However, representing an ethical decision is challenging as it may be abstract, context-sensitive, and semantically heterogeneous. Nevertheless, important ethical and contextual factors c
Transfer Learning of CATE with Kernel Ridge Regression
The proliferation of data has sparked significant interest in leveraging findings from one study to estimate treatment effects in a different target population without direct outcome observations. However, the transfer learning process is frequently hindered by substantial covariate shift and limited overlap between (i) the source and target populations, as well as (ii) the treatment and control groups within the sou
GCDance: Genre-Controlled Music-Driven 3D Full Body Dance Generation
Music-driven dance generation is a challenging task as it requires strict adherence to genre-specific choreography while ensuring physically realistic and precisely synchronized dance sequences with the music's beats and rhythm. Although significant progress has been made in music-conditioned dance generation, most existing methods struggle to convey specific stylistic attributes in generated dance. To bridge thi
Partial Excitation in Parameter Learning
This paper investigates parameter learning problems under Partial Persistent Excitation (PPE). The PPE condition is a rank-deficient, and therefore, a more general evolution of the well-known Persistent Excitation (PE) condition. Under the PPE condition, a proposed online algorithm is able to calculate the PE and non-PE subspaces, and finally gives an optimal parameter estimate in the sense of least squares. In parti
Can Small Language Models Reliably Resist Jailbreak Attacks? A Comprehensive Evaluation
Small language models (SLMs) have emerged as promising alternatives to large language models (LLMs) due to their low computational demands, enhanced privacy guarantees, and comparable performance in specific domains. Deploying SLMs on edge devices, such as smartphones and smart vehicles, has become a growing trend. However, the security implications of SLMs have not received as much attention as those of LLMs, partic
STGDPM:Vessel Trajectory Prediction with Spatio-Temporal Graph Diffusion Probabilistic Model
Vessel trajectory prediction is a critical component for ensuring maritime traffic safety and avoiding collisions. Due to the inherent uncertainty in vessel behavior, trajectory prediction systems must adopt a multimodal approach to accurately model potential future motion states. However, existing vessel trajectory prediction methods lack the ability to comprehensively model behavioral multi-modality. To better capt
Notable events recorded on this day and month across all years.