Record 07082026 · 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
Abdulrahman Mohamed El-Sayed, commonly known as Abdul El-Sayed, is an American politician and epidemiologist who is the Democratic nominee in the 2026 United States Senate election in Michigan. A progressive member of the Democratic Party, El-Sayed was a candi
Mario Armando Lavandeira Jr., known professionally as Perez Hilton, is an American blogger, columnist, and media personality. His blog is known for posts covering gossip items about celebrities, and for posting tabloid photos over which he has added his own ca
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
Jason Atta Kwei Arday was a British academic who was a professor of sociology of education at the University of Cambridge from 2023 to 2026. Arday received international attention and resigned amid accusations of plagiarism, false claims in his research, and f
Christopher Edward Hansen is an American television presenter, journalist, and YouTube personality. During his tenure as a correspondent for Dateline NBC, he hosted the program's segment To Catch a Predator (2004–2007), which revolved around catching potential
Primetime is an upcoming American thriller film directed by Lance Oppenheim in his narrative feature film debut. The film is based on Luke Dittrich's 2007 Esquire article "Tonight on Dateline This Man Will Die". It follows the production of the reality televis
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
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
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
.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
Pradeep Rawat was an Indian actor who worked predominantly in Telugu, Hindi and Tamil language films. Notable for playing villainous roles, Rawat's first appearance was in B.R. Chopra's Mahabharat as Ashwatthama.
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
To Catch a Predator is an American reality television series in the television news magazine program Dateline NBC. The program features confrontations by host Chris Hansen, partly filmed with a hidden camera, with adult men arriving at a sting house to have se
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").
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
Pornography is sexually suggestive material, such as pictures, videos, text, or audio intended for sexual arousal. Over the course of human history, pornographic depictions have taken many forms, from stone carvings in the Upper Paleolithic, to virtual reality
The Shards is an American teen thriller television series for FX and FX on Hulu. It premiered on August 5, 2026. The series was created by Ryan Murphy and Bret Easton Ellis, based on Ellis' semi-autobiographical novel.
Kit Sebastian Connor is an English actor. Connor gained recognition for starring as secondary school student Nick Nelson in the Netflix teen series Heartstopper (2022–2024) and its series finale film Heartstopper Forever (2026). He won the inaugural Children's
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
Ted Lasso is an American sports comedy-drama television series developed by Jason Sudeikis, Bill Lawrence, Brendan Hunt, and Joe Kelly. It is based on a character Sudeikis portrayed in a series of promotional media for NBC Sports' coverage of England's soccer
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
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.
Glen James Hansard was an Irish singer-songwriter and actor. From Ballymun in northern Dublin, he busked from his teens. In 1990, he co-founded the Irish rock band the Frames, which he fronted. The band released six studio albums, four of which reached the top
In multi-objective optimization, the Pareto front is the set of all Pareto efficient solutions. Informally, this means when there are many distinct objectives to consider in an optimization problem, a Pareto front represents the set of solutions where no solut
Hyperion is a coast redwood tree in California, which is the world's tallest known living tree, measured at 116.22 metres (381.3 ft) tall in 2026.
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
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
Robert Douglas Thomas Pattinson is an English actor. He is known for starring in both major studio productions and independent films, in which he often portrays eccentric characters across a diverse range of genres. Pattinson has been ranked among the world's
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Analogy as Nonparametric Bayesian Inference over Relational Systems
Our inferences in the real world are rarely naïve - we acquire experiences through our lifetime that can help us more quickly understand the structure of something new. A fundamental question in cognitive science is how we make such generalizations. Studies of analogy have explored the question of how to map information from a single familiar concept or environment to an unfamiliar one. In this paper, we examine how
This paper explores the integration of human linguistic insights into multilingual text-to-speech (TTS) systems by evaluating the Featurally Underspecified Lexicon (FUL) as a theory-driven input representation. Unlike data-intensive end-to-end models, FUL offers a compact, interpretable feature set grounded in phonological principles, enabling scalable and equitable TTS development for low-resource languages. We prov
Path Planning of Cleaning Robot with Reinforcement Learning
Recently, as the demand for cleaning robots has steadily increased, therefore household electricity consumption is also increasing. To solve this electricity consumption issue, the problem of efficient path planning for cleaning robot has become important and many studies have been conducted. However, most of them are about moving along a simple path segment, not about the whole path to clean all places. As the emerg
Latent Utility Q-Learning for Preference-Adaptive Dynamic Treatment Regimes
Optimizing individualized treatment sequences for patients who weigh multiple, competing outcomes differently poses a challenge for dynamic treatment regime (DTR) methods, which typically assume a single univariate outcome. We propose Latent Utility Q-Learning (LUQ-Learning), which estimates DTRs optimizing patient-specific preference-weighted combinations of multivariate outcomes $\mathbf{Y}\in\mathbb{R}^d$ across $
Gradient-free online learning of subgrid-scale dynamics with neural emulators
In this paper, we propose a generic algorithm to train machine learning-based subgrid parametrizations online, i.e., with $\textit{a posteriori}$ loss functions, but for non-differentiable numerical solvers. The proposed approach leverages a neural emulator to approximate the reduced state-space solver, which is then used to allow gradient propagation through temporal integration steps. We apply this methodology on a
Hybrid Bit and Semantic Communications
Semantic communication technology is regarded as a method surpassing the Shannon limit of bit transmission, capable of effectively enhancing transmission efficiency. However, current approaches that directly map content to transmission symbols are challenging to deploy in practice, imposing significant limitations on the development of semantic communication. To address this challenge, we propose a hybrid bit and sem
Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges
Text generation has become more accessible than ever, and the growing interest in these systems, especially those using large language models, has spurred a surge in related publications. We provide a systematic literature review comprising 257 papers, covering the period from January 2017 to December 2025. This review categorizes text generation contributions into five main tasks: open-ended text generation, summari
LADDER: Language-Driven Slice Discovery and Error Rectification in Vision Classifiers
Error slice discovery is crucial to diagnose and mitigate model errors. Current clustering or discrete attribute-based slice discovery methods face key limitations: 1) clustering results in incoherent slices, while assigning discrete attributes to slices leads to incomplete coverage of error patterns due to missing or insufficient attributes; 2) these methods lack complex reasoning, preventing them from fully explain
Revisiting Black-Box Model Ownership Verification through Information Theory
Modern machine learning models require substantial computational resources and data to train, making them valuable intellectual property. Model watermarking has emerged as a practical solution for black-box ownership verification, but existing methods suffer from a persistent trade-off between robustness and predictive utility. In this work, we analyze this limitation from an information-theoretic perspective and ide
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally optimize an impurity or information metric. However, the literature shows conflicting evidence on the value of ODTs, with some demonstrating superior out-of-sample performance of ODTs over greedy approaches, while others show the opposite. The
OM4OV: Leveraging Ontology Matching for Ontology Versioning
Due to the dynamics of the Semantic Web, version control is necessary to manage changes in widely used ontologies. Despite the long-standing recognition of ontology versioning (OV) as a crucial component of efficient ontology management, many approaches treat OV as similar to ontology matching (OM) and directly reuse OM systems for OV tasks. In this study, we systematically analyse similarities and differences betwee
Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning
The development of the DRL model for malware attribution involved extensive research, iterative coding, and numerous adjustments based on the insights gathered from predecessor models and contemporary research papers. This preparatory work was essential to establish a robust foundation for the model, ensuring it could adapt and respond effectively to the dynamic nature of malware threats. Initially, the model struggl
Leveraging LLM Embeddings for Cross Dataset Label Alignment and Zero Shot Music Emotion Prediction
In this work, we present a novel method for music emotion recognition that leverages Large Language Model (LLM) embeddings for label alignment across multiple datasets and zero-shot prediction on novel categories. First, we compute LLM embeddings for emotion labels and apply non-parametric clustering to group similar labels, across multiple datasets containing disjoint labels. We use these cluster centers to map musi
Tree-NET: Enhancing 2D Medical Image Segmentation Through Efficient Low-Level Feature Training
This paper introduces Tree-NET, a novel framework for medical image segmentation that leverages bottleneck supervision to enhance both segmentation accuracy and computational efficiency. While previous studies have applied bottleneck feature supervision to segmentation tasks, it has typically been limited to the training phase, offering no computational benefits during inference. To the best of our knowledge, this is
Sparse Mixture-of-Experts for Non-Uniform Noise Reduction in MRI Images
Magnetic Resonance Imaging (MRI) is an essential diagnostic tool in clinical settings, but its utility is often hindered by noise artifacts introduced during the imaging process. Effective denoising is critical for enhancing image quality while preserving anatomical structures. However, traditional denoising methods, which often assume uniform noise distributions, struggle to handle the non-uniform noise commonly pre
Explanations of Large Language Models Explain Language Representations in the Brain
Large Language Model (LLM) representations are known to align with brain activity during language processing, but it remains unclear what drives this alignment. We test whether explainable AI (XAI) can help answer this: using attribution methods, we quantify the contribution of each input word to an LLM's next-word predictions and use these explanations to predict fMRI data from participants listening to narrativ
Reward Shaping to Mitigate Reward Hacking in RLHF
Reinforcement learning from human feedback (RLHF) is widely used to align large language models (LLMs) with human preferences. However, RLHF remains vulnerable to \emph{reward hacking}, whereby a policy exploits imperfections in the reward function instead of learning the intended behavior, thereby undermining alignment. Although reward shaping can stabilize RLHF training and partially mitigate reward hacking, shapin
Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented Manipulation
Object affordance reasoning, the ability to infer object functionalities based on physical properties, is fundamental for task-oriented planning and activities in both humans and Artificial Intelligence (AI). This capability, required for planning and executing daily activities in a task-oriented manner, relies on commonsense knowledge of object physics and functionalities, extending beyond simple object recognition.
A Study of LLMs' Preferences for Libraries and Programming Languages
Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntactic validity, overlooking how LLMs make critical design choices such as which library or programming language to use. To fill this gap, we perform the first empirical study of LLMs' preferences for libraries and programming languages when generating code, covering eight dive
Visual Intention Grounding for Egocentric Assistants
Visual grounding associates textual descriptions with objects in an image. Conventional methods target third-person image inputs and named object queries. In applications such as AI assistants, the perspective shifts -- inputs are egocentric, and objects may be referred to implicitly through needs and intentions. To bridge this gap, we introduce EgoIntention, the first dataset for egocentric visual intention groundin
Estimation of discrete distributions in relative entropy, and the deviations of the missing mass
We study the problem of estimating a distribution over a finite alphabet from an i.i.d. sample, with accuracy measured in relative entropy (Kullback-Leibler divergence). While optimal bounds on the expected risk are known, high-probability guarantees remain less well-understood. First, we analyze the classical Laplace (add-one) estimator, obtaining matching upper and lower bounds on its performance and establishing i
ASAT: Adaptive Scoring and Thresholding with Human Feedback for Robust Out-of-Distribution Detection
Machine Learning (ML) models are trained on in-distribution (ID) data but often encounter out-of-distribution (OOD) inputs during deployment---posing serious risks in safety-critical domains. Recent works have focused on designing scoring functions to quantify OOD uncertainty, with score thresholds typically set based solely on ID data to achieve a target true positive rate (TPR), since OOD data is limited before dep
A Reverse-BSDE Diffusion Sampler
Diffusion-based generative models have renewed interest in stochastic differential equation methods for sampling from complex distributions. We study a setting in which the target density is known only up to a normalizing constant and reformulate the reverse-time diffusion sampler as a forward-backward stochastic differential equation (FBSDE). This formulation replaces the separate pre-estimation of the time-dependen
MoCA: Multi-modal Cross-masked Autoencoder for Time Series in Digital Health
Wearable devices enable continuous multi-modal physiological and behavioral monitoring, yet analysis of these data streams faces fundamental challenges including the lack of gold-standard labels and incomplete sensor data. While self-supervised learning approaches have shown promise for addressing these issues, existing multi-modal extensions present opportunities to better leverage the rich temporal and cross-modal
Purpose: To compare five lightweight open-weight large language models (LLMs) with a rule-based algorithm (RBA) and fine-tuned RadBERT for zero-shot labeling of chest-abdomen-pelvis (CAP) CT reports, and to examine how labeling conventions affect measured performance. Materials and Methods: In this retrospective study, 40,833 CAP CT reports from 29,540 patients examined between 2012 and 2017 were analyzed; age and se
Notable events recorded on this day and month across all years.