Record 01122025 · captured 2026-08-25
The world looked up Survivor Series: WarGames (2025). 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.
Survivor Series: WarGames (2025)
The 2025 Survivor Series: WarGames, also promoted as Survivor Series: WarGames San Diego, was a professional wrestling pay-per-view (PPV) and livestreaming event produced by WWE. It was the 39th annual Survivor Series and took place on November 29, 2025, at Pe
1989 Tiananmen Square protests and massacre
Protests led by students and workers, known in China as the June Fourth Incident, were held in Tiananmen Square in Beijing, China, from 15 April to 4 June 1989. After weeks of unsuccessful attempts between the demonstrators and the Chinese government to find a
Lane Monte Kiffin is an American football coach who is the head coach of the LSU Tigers. He served as the head coach of the Oakland Raiders from 2007 to 2008, the University of Tennessee in 2009, USC from 2010 to 2013, Florida Atlantic from 2017 to 2019, and O
The fifth and final season of the American science fiction horror drama television series Stranger Things, marketed as Stranger Things 5, was released on the streaming service Netflix in two volumes and the finale. The first set of four episodes was released o
Google Chrome is a cross-platform web browser developed by Google. It was launched in September 2008 for Microsoft Windows and was built with free software components from Apple WebKit and Mozilla Firefox. Versions for Linux, macOS, iOS, iPadOS, and Android we
Stranger Things is an American television series created by the Duffer Brothers for Netflix. Produced by Monkey Massacre Productions and 21 Laps Entertainment, the first season was released on Netflix on July 15, 2016. The second and third seasons followed in
Lina Marcela Medina de Jurado is a Peruvian woman who became the youngest confirmed mother in history when she gave birth to her son Gerardo on 14 May 1939 when she was five years, seven months, and 21 days of age. Based on the medical assessments of her pregn
Sir Tom Stoppard was a British playwright and screenwriter. He wrote for film, radio, stage, and television, finding prominence with plays. His work covered the themes of human rights, censorship, and political freedom, often delving into the deeper philosophi
Millie Bonnie Bongiovi, known professionally as Millie Bobby Brown, is a British actress and film producer. She gained international recognition for playing Eleven in the Netflix science fiction series Stranger Things (2016–2025), for which she received nomina
Zootopia 2 is a 2025 American animated buddy cop comedy film produced by Walt Disney Animation Studios, the second film in the series and a sequel to Zootopia (2016). Directed by Jared Bush and Byron Howard and written by Bush, the film stars Ginnifer Goodwin,
2025 Formula One World Championship
The 2025 FIA Formula One World Championship was a motor racing championship for Formula One cars and the 76th running of the Formula One World Championship. It was recognised by the Fédération Internationale de l'Automobile (FIA), the governing body of interna
William Arthur Bonds was an English professional footballer and manager, who was most often associated with West Ham United with whom he spent 27 years as player and manager. He played 799 first-team games for West Ham in a career spanning 21 seasons, winning
Tere Ishk Mein is a 2025 Indian Hindi-language romantic drama film directed by Aanand L. Rai from a screenplay written by Himanshu Sharma and Neeraj Yadav. Billed as a spiritual sequel to Raanjhanaa (2013), the film stars Dhanush and Kriti Sanon. It follows Sh
This is a list of lists of deaths of significant people, organized by year. New deaths articles are added to their respective month and then linked below.
Bruce Lee was a Hong Kong and American martial artist, actor, and filmmaker. He was the founder of Jeet Kune Do, a hybrid martial arts philosophy, which was formed from his experiences in unarmed fighting and self-defense—as well as eclectic, Zen Buddhist, and
Aleksey Golesh is a Russian-American college football coach who is currently the head football coach at Auburn University. He previously served as the head football coach at the University of South Florida. Prior to that role he was the offensive coordinator a
Heated Rivalry is a Canadian sports romance television series created, written, and directed by Jacob Tierney for Crave. Based on the Game Changers book series by Rachel Reid, the show takes its title from the 2019 second installment. It stars Hudson Williams
Stephen Thomas "Pete" Golding is an American football coach who is currently the head football coach at the University of Mississippi. He previously served as the defensive coordinator and the inside linebackers coach at Ole Miss from 2023 to 2025. Golding was
Noah Cameron Schnapp is an American actor. He made his acting debut in 2015 with his portrayal of Charlie Brown in the animated film The Peanuts Movie and his supporting role in Steven Spielberg's Bridge of Spies. Schnapp gained international recognition for h
List of Stranger Things episodes
Stranger Things is an American science fiction, horror, mystery, and drama television series created by the Duffer Brothers for the streaming service Netflix. The brothers, as well as Karl Gajdusek in the first season only, are the program's showrunners. They
Virat Kohli is an Indian international cricketer and former all-format captain of the Indian national cricket team. He is a right-handed batter and an occasional right-arm medium-pace bowler. Considered one of the greatest batters in limited-overs cricket, he
Jonathan Edward Sumrall is an American college football coach who is the head football coach at the University of Florida. He previously served as the head coach at Troy University from 2022 to 2023 and Tulane University from 2024 to 2025.
Wicked: For Good is a 2025 musical fantasy film directed by Jon M. Chu and written by Winnie Holzman and Dana Fox. The sequel to Wicked (2024), it adapts the second act of the 2003 stage musical by Stephen Schwartz and Holzman, which was loosely based on Grego
Pluribus is an American post-apocalyptic science fiction television series created by Vince Gilligan for Apple TV. Set and filmed primarily in Albuquerque, New Mexico, the series follows novelist Carol Sturka, who finds herself isolated after an alien virus tr
Monkey D. Luffy , also known as "Straw Hat" Luffy, is a fictional character and the main protagonist of the manga series One Piece, created by Eiichiro Oda, as well as the central character of the franchise generated from it. Luffy made his debut as a young bo
Thomas Maxwell Brosmer is an American professional football quarterback for the Minnesota Vikings of the National Football League (NFL). He played college football for the New Hampshire Wildcats and Minnesota Golden Gophers.
Natalia Danielle Dyer is an American actress. She is best known for her role as Nancy Wheeler in the Netflix science fiction horror series Stranger Things (2016–2025). She has also appeared in the Peacock comedy thriller series Based on a True Story (2023) and
Maya Ray Thurman Hawke is an American actress and singer-songwriter. The daughter of Ethan Hawke and Uma Thurman, she began her career in modeling and subsequently made her screen debut as Jo March in the 2017 BBC adaptation of Little Women. She gained interna
Joseph David Keery, also known by his musical stage name Djo, is an American actor, singer, musician, and songwriter. He rose to international prominence for his role as Steve Harrington in the sci-fi horror series Stranger Things (2016–2025). He has also star
Charles Ross Heaton is an English actor and musician. He is best known for his role as Jonathan Byers in the science fiction series Stranger Things (2016–2025). He has also starred in films such as As You Are (2016), Marrowbone (2017), The New Mutants (2020),
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
A Trio Neural Model for Dynamic Entity Relatedness Ranking
Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in static settings and an unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity-relations are very dynamic over time. In this work, we propose a neural networkbased approach for
Electromagnetic Navigation Systems (eMNS) can be used to control a variety of multiscale devices within the human body for remote surgery. Accurate modeling of the magnetic fields generated by the electromagnets of an eMNS is crucial for the precise control of these devices. Existing methods assume a linear behavior of these systems, leading to significant modeling errors within nonlinear regions exhibited at higher
Total Least Square Optimal Analytic Signal by Structure Tensor for N-D images
We produce the analytic signal by using the Structure Tensor, which provides Total Least Squares optimal vectors for estimating orientation and scale locally. Together, these vectors represent N-D frequency components that determine adaptive, complex probing filters. The N-D analytic signal is obtained through scalar products of adaptive filters with image neighborhoods. It comprises orientation, scale, phase, and am
Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection
The number of Earth observation satellites carrying optical sensors with similar characteristics is constantly growing. Despite their similarities and the potential synergies among them, derived satellite products are often developed for each sensor independently. Differences in retrieved radiances lead to significant drops in accuracy, which hampers knowledge and information sharing across sensors. This is particula
One-shot Transfer Learning for Population Mapping
Fine-grained population distribution data is of great importance for many applications, e.g., urban planning, traffic scheduling, epidemic modeling, and risk control. However, due to the limitations of data collection, including infrastructure density, user privacy, and business security, such fine-grained data is hard to collect and usually, only coarse-grained data is available. Thus, obtaining fine-grained populat
Fast Gradient Methods for Data-Consistent Local Super-Resolution of Medical Images
In this work, we propose a new paradigm of iterative model-based reconstruction algorithms for providing real-time solution for zooming-in and refining a region of interest in medical and clinical tomographic images. This algorithmic framework is tailored for a clinical need in medical imaging practice that after a reconstruction of the full tomographic image, the clinician may believe that some critical parts of the
Humans can flexibly generalize knowledge across domains by leveraging structured relational representations. While prior research has shown how such representations support analogical reasoning, less is known about how they are recruited to guide adaptive behavior. We address this gap by introducing the Relational Regression Tree Learner (RRTL), a model that incrementally builds policies over structured relational in
New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography
Diabetes has a long asymptomatic period which can often remain undiagnosed for multiple years. In this study, we trained a deep learning model to detect new-onset diabetes using 12-lead ECG and readily available demographic information. To do so, we used retrospective data where patients have both a hemoglobin A1c and ECG measured. However, such patients may not be representative of the complete patient population. A
Continual Learning with Global Alignment
Continual learning aims to sequentially learn new tasks without forgetting previous tasks' knowledge (catastrophic forgetting). One factor that can cause forgetting is the interference between the gradients on losses from different tasks. When the gradients on the current task's loss are in opposing directions to those on previous tasks' losses, updating the model for the current task may cause performanc
CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis
Due to the large accumulation of patients requiring hospitalization, the COVID-19 pandemic disease caused a high overload of health systems, even in developed countries. Deep learning techniques based on medical imaging data can help in the faster detection of COVID-19 cases and monitoring of disease progression. Regardless of the numerous proposed solutions for lung X-rays, none of them is a product that can be used
High fidelity design evaluation processes such as Computational Fluid Dynamics and Finite Element Analysis are often replaced with data driven surrogates to reduce computational cost in engineering design optimization. However, building accurate surrogate models still requires a large number of expensive simulations. To address this challenge, we introduce epsilon HQS, a scalable active learning strategy that leverag
We introduce a notion of self-concordant smoothing for minimizing the sum of two convex functions, one of which is smooth and the other nonsmooth. The key highlight is a natural property of the resulting problem's structure that yields a variable metric selection method and a step length rule especially suited to proximal quasi-Newton algorithms. Also, we efficiently handle specific structures promoted by the non
Convergence Analysis of Decentralized ASGD
Over the last decades, Stochastic Gradient Descent (SGD) has been intensively studied by the Machine Learning community. Despite its versatility and excellent performance, the optimization of large models via SGD still is a time-consuming task. To reduce training time, it is common to distribute the training process across multiple devices. Recently, it has been shown that the convergence of asynchronous SGD (ASGD) w
AutoHall: Automated Factuality Hallucination Dataset Generation for Large Language Models
Large language models (LLMs) have gained broad applications across various domains but still struggle with hallucinations. Currently, hallucinations occur frequently in the generation of factual content and pose a great challenge to trustworthy LLMs. However, hallucination detection is hindered by the laborious and expensive manual annotation of hallucinatory content. Meanwhile, as different LLMs exhibit distinct typ
A Sampling-Based Domain Generalization Study with Diffusion Generative Models
In this work, we investigate the domain generalization capabilities of diffusion models in the context of synthesizing images that are distinct from the training data. Instead of fine-tuning, we tackle this challenge from a sampling-based perspective using frozen, pre-trained diffusion models. Specifically, we demonstrate that arbitrary out-of-domain (OOD) images establish Gaussian priors in the latent spaces of a gi
We demonstrate a multiplication method based on numbers represented as set of polynomial radix 2 indices stored as an integer list. The 'polynomial integer index multiplication' method is a set of algorithms implemented in python code. We demonstrate the method to be faster than both the Number Theoretic Transform (NTT) and Karatsuba for multiplication within a certain bit range. Also implemented in python co
Source-free Video Domain Adaptation by Learning from Noisy Labels
Despite the progress seen in classification methods, current approaches for handling videos with distribution shifts in source and target domains remain source-dependent as they require access to the source data during the adaptation stage. In this paper, we present a self-training based source-free video domain adaptation approach to address this challenge by bridging the gap between the source and the target domain
Learning from (procedural) videos has increasingly served as a pathway for embodied agents to acquire skills from human demonstrations. To do this, video understanding models must be able to obtain structured understandings, such as the temporal segmentation of a demonstration into sequences of actions and skills, and to generalize the understandings to novel environments, tasks, and problem domains. In pursuit of th
Instance segmentation in remote sensing images is a long-standing challenge. Since horizontal bounding boxes introduce many interference objects, oriented bounding boxes (OBBs) are usually used for instance identification. However, based on ``segmentation within bounding box'' paradigm, current instance segmentation methods using OBBs are overly dependent on bounding box detection performance. To tackle this
Learning Contrastive Feature Representations for Facial Action Unit Detection
For the Facial Action Unit (AU) detection task, accurately capturing the subtle facial differences between distinct AUs is essential for reliable detection. Additionally, AU detection faces challenges from class imbalance and the presence of noisy or false labels, which undermine detection accuracy. In this paper, we introduce a novel contrastive learning framework aimed for AU detection that incorporates both self-s
Aspect-based Sentiment Analysis (ABSA) aims to determine sentiment polarity toward specific aspects in text. Existing methods enrich semantic and syntactic representations through external knowledge or GNNs, but the growing diversity of linguistic features increases model complexity and lacks a unified, extensible framework. We propose an Extensible Multi-Granularity Fusion Network (EMGF) that integrates dependency s
Fine-grained and Explainable Factuality Evaluation for Multimodal Summarization
Multimodal summarization aims to generate a concise summary based on the input text and image. However, the existing methods potentially suffer from unfactual output. To evaluate the factuality of multimodal summarization models, we propose two fine-grained and explainable evaluation frameworks (FALLACIOUS) for different application scenarios, i.e. reference-based factuality evaluation framework and reference-free fa
Infrared and Visible Image Fusion with Language-Driven Loss in CLIP Embedding Space
Infrared-visible image fusion (IVIF) has attracted much attention owing to the highly-complementary properties of the two image modalities. Due to the lack of ground-truth fused images, the fusion output of current deep-learning based methods heavily depends on the loss functions defined mathematically. As it is hard to well mathematically define the fused image without ground truth, the performance of existing fusio
Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology. This approach leverages the rich, multifaceted descriptions of biomolecules contained within textual data sources to enhance our fundamental understanding and enable downstream computational tasks such as biomolecule property predi
Configurable Fairness: Direct Optimization of Parity Metrics via Vision-Language Models
Performance disparities of image recognition across demographic groups are known to exist in deep learning-based models, due to imbalanced group representations or spurious correlation between group and target labels. Previous work has addressed such challenges without relying on expensive group labels, typically by upweighting high-loss samples or balancing discovered clusters. However, these heuristic strategies la
Notable events recorded on this day and month across all years.