Record 22012026 · captured 2026-08-25
The world looked up Nicola Peltz. 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.
Nicola Anne Peltz Beckham is an American actress. She is known for her roles as Katara in the film The Last Airbender (2010), Bradley Martin in the A&E drama series Bates Motel (2013–2015) and Tessa Yeager in the film Transformers: Age of Extinction (2014).
Greenland is an autonomous territory of the Kingdom of Denmark and is the largest of the kingdom's three constituent parts by land area, the others being Denmark proper and the Faroe Islands. Citizens of Greenland are citizens of Denmark. They are thus citizen
Fernando Gabriel Mendoza V is an American professional football quarterback for the Las Vegas Raiders of the National Football League (NFL). Mendoza played college football for the California Golden Bears for three seasons before transferring to the Indiana Ho
Nelson Peltz is an American billionaire businessman and investor. He is a founding partner, together with Peter W. May and Edward P. Garden, of Trian Partners, an alternative investment management fund based in New York. He is a former director of Heinz, Monde
Usha Bala Vance is an American lawyer and second lady of the United States since 2025, being the wife of JD Vance, the 50th vice president of the United States. She is the first Indian-American second lady.
Elizabeth Ann Gilmour is an American child safety activist and commentator for ABC News. She was put into the national spotlight in 2002 at age 14 when she was abducted from her home in Salt Lake City by Brian David Mitchell. Mitchell and his wife, Wanda Barze
The Rip is a 2026 American action thriller film written and directed by Joe Carnahan, who developed the story with Michael McGrale. The film stars Matt Damon and Ben Affleck as police officers in the Miami-Dade Police Department narcotics unit. It also stars S
Donald John Trump is an American politician, media personality, and businessman who is the 47th president of the United States. A member of the Republican Party, he served as the 45th president from 2017 to 2021.
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
Brooklyn Joseph Peltz Beckham is the eldest child of former professional footballer David Beckham and fashion designer and former Spice Girls member Victoria Beckham.
"XXX" is a song by American rapper Kendrick Lamar, from his fourth studio album Damn, released on April 14, 2017. The eleventh track on the album, the song was written by Lamar, Mike Will Made It, DJ Dahi, Mark Spears a.k.a. Sounwave, Anthony Tiffith, Bono, th
Anna's Archive is an open source search engine for shadow libraries that was launched by the pseudonymous Anna shortly after law enforcement efforts to shut down Z-Library in 2022. The site aggregates records from Z-Library, Sci-Hub, and Library Genesis (LibGe
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
A Knight of the Seven Kingdoms (TV series)
A Knight of the Seven Kingdoms is an American fantasy drama television series created by Ira Parker and George R. R. Martin. A prequel to Game of Thrones (2011–2019), it is the third television series in Martin's A Song of Ice and Fire franchise and is an adap
Rachel Anne McAdams is a Canadian actress. A graduate of York University in 2001 with a BFA in theatre, she is known for her starring roles in comedy and drama films as well as her work in television and theater. Her accolades include nominations for an Academ
Since 2025, the second Donald Trump administration of the United States has sought to annex Greenland, an autonomous territory of Denmark, triggering an ongoing international diplomatic crisis. This escalated in early 2026 after Trump refused to rule out the u
Curtis John Cignetti is an American college football coach who is the head football coach at Indiana University Bloomington. He previously served as the head coach at Indiana University of Pennsylvania (IUP) from 2011 to 2016, Elon University from 2017 to 2018
Mark Joseph Carney is a Canadian politician and economist who became the 24th prime minister of Canada in 2025. Carney was also elected as leader of the Liberal Party and the member of Parliament (MP) for Nepean in 2025. He was previously Governor of the Bank
The Board of Peace (BoP), or the Peace Board, is an international organization with the stated purpose of promoting peacebuilding around the world. Established by President Donald Trump and led by the government of the United States, the board is named in Unit
Abella Danger is an American former pornographic film actress and director.
28 Years Later: The Bone Temple
28 Years Later: The Bone Temple is a 2026 post-apocalyptic horror film directed by Nia DaCosta and written by Alex Garland. It is the direct sequel to 28 Years Later (2025) and the fourth instalment in the 28 Days Later film series. It stars Ralph Fiennes, Jac
Dhurandhar is a 2025 Indian Hindi-language spy action thriller film written and directed by Aditya Dhar. It is produced by Aditya Dhar, Lokesh Dhar and Jyoti Deshpande under Jio Studios and B62 Studios. The film features an ensemble cast consisting of Ranveer
James David Vance is an American politician, author, and venture capitalist serving as the 50th vice president of the United States. A member of the Republican Party, he represented the state of Ohio in the United States Senate from 2023 to 2025.
The kidnapping of Elizabeth Smart occurred on June 5, 2002, when 14-year-old American girl Elizabeth Ann Smart was kidnapped from her home in Salt Lake City, Utah by Brian David Mitchell.
Timothy Busfield is an American actor and director. He played Arnold Poindexter in the first two Revenge of the Nerds films, Elliot Weston on the television series Thirtysomething, Mark in Field of Dreams, and Danny Concannon on the television series The West
Proposed United States acquisition of Greenland
The United States has discussed obtaining Greenland from Denmark since the 19th century. There were talks within the US federal government about purchasing Greenland in 1867, advocated by Secretary of State William H. Seward, and again in 1910. However, in 191
Avatar: Fire and Ash is a 2025 American epic science fiction film directed by James Cameron from a screenplay he co-wrote with Rick Jaffa and Amanda Silver. Produced by Lightstorm Entertainment, it is the third installment in the Avatar film series and the seq
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
Nitin Nabin is an Indian politician, political organiser, and activist who has been serving as the 16th national president of the Bharatiya Janata Party (BJP) since January 2026 and an MP in the upper chamber of the Indian Parliament, the Rajya Sabha since Apr
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Learning from Discriminatory Training Data
Supervised learning systems are trained using historical data and, if the data was tainted by discrimination, they may unintentionally learn to discriminate against protected groups. We propose that fair learning methods, despite training on potentially discriminatory datasets, shall perform well on fair test datasets. Such dataset shifts crystallize application scenarios for specific fair learning methods. For insta
Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic
Linear temporal logic (LTL) is a specification language for finite sequences (called traces) widely used in program verification, motion planning in robotics, process mining, and many other areas. We consider the problem of learning LTL formulas for classifying traces; despite a growing interest of the research community, existing solutions suffer from two limitations: they do not scale beyond small formulas, and the
Continual Learning for Monolingual End-to-End Automatic Speech Recognition
Adapting Automatic Speech Recognition (ASR) models to new domains results in a deterioration of performance on the original domain(s), a phenomenon called Catastrophic Forgetting (CF). Even monolingual ASR models cannot be extended to new accents, dialects, topics, etc. without suffering from CF, making them unable to be continually enhanced without storing all past data. Fortunately, Continual Learning (CL) methods,
Impartial Games: A Challenge for Reinforcement Learning
AlphaZero-style reinforcement learning (RL) algorithms have achieved superhuman performance in many complex board games such as Chess, Shogi, and Go. However, we showcase that these algorithms encounter significant and fundamental challenges when applied to impartial games, a class where players share game pieces and optimal strategy often relies on abstract mathematical principles. Specifically, we utilise the game
Inverse wave scattering aims at determining the properties of an object using data on how the object scatters incoming waves. In order to collect information, sensors are put in different locations to send and receive waves from each other. The choice of sensor positions and incident wave frequencies determines the reconstruction quality of scatterer properties. This paper introduces reinforcement learning to develop
Deep learning has demonstrated significant potential in medical imaging; however, the opacity of "black-box" models hinders clinical trust, while segmentation tasks typically necessitate labourious, hard-to-obtain pixel-wise annotations. To address these challenges simultaneously, this paper introduces a framework for three inherently explainable classifiers (GP-UNet, GP-ShuffleUNet, and GP-ReconResNet). By i
Semantic Image Synthesis via Diffusion Models
Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks compared with Generative Adversarial Nets (GANs). Recent work on semantic image synthesis mainly follows the de facto GAN-based approaches, which may lead to unsatisfactory quality or diversity of generated images. In this paper, we propose a novel framework based on DDPM for semantic image synthesis. U
Machine Learning Decoder for 5G NR PUCCH Format 0
5G cellular systems depend on the timely exchange of feedback control information between the user equipment and the base station. Proper decoding of this control information is necessary to set up and sustain high throughput radio links. This paper makes the first attempt at using Machine Learning techniques to improve the decoding performance of the Physical Uplink Control Channel Format 0. We use fully connected n
Adapting a trained Automatic Speech Recognition (ASR) model to new tasks results in catastrophic forgetting of old tasks, limiting the model's ability to learn continually and to be extended to new speakers, dialects, languages, etc. Focusing on End-to-End ASR, in this paper, we propose a simple yet effective method to overcome catastrophic forgetting: weight averaging. By simply taking the average of the previou
Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent
With the fast development of big data, learning the optimal decision rule by recursively updating it and making online decisions has been easier than before. We study the online statistical inference of model parameters in a contextual bandit framework of sequential decision-making. We propose a general framework for an online and adaptive data collection environment that can update decision rules via weighted stocha
Reassessing feature-based Android malware detection in a contemporary context
We report the findings of a reimplementation of 18 foundational studies in feature-based machine learning for Android malware detection, published during the period 2013-2023. These studies are reevaluated on a level playing field using a contemporary Android environment and a balanced dataset of 124,000 applications. Our findings show that feature-based approaches can still achieve detection accuracies beyond 98%, d
Finite Expression Methods for Discovering Physical Laws from Data
Nonlinear dynamics is a pervasive phenomenon observed in scientific and engineering disciplines. However, the task of deriving analytical expressions to describe nonlinear dynamics from limited data remains challenging. In this paper, we shall present a novel deep symbolic learning method called the "finite expression method" (FEX) to discover governing equations within a function space containing a finite se
Benchmarking the Influence of Pre-training on Explanation Performance in MR Image Classification
Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improved performance when training data for the task are scarce. The resulting models are highly complex and typically do not provide any insight into their predictive mechanisms, motivating the field of "explainable" artificial intelligen
A Finite Expression Method for Solving High-Dimensional Committor Problems
Transition path theory (TPT) is a mathematical framework for quantifying rare transition events between a pair of selected metastable states $A$ and $B$. Central to TPT is the committor function, which describes the probability to hit the metastable state $B$ prior to $A$ from any given starting point of the phase space. Once the committor is computed, the transition channels and the transition rate can be readily fo
Learning minimal representations of stochastic processes with variational autoencoders
Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena. Due to their intrinsic randomness and uncertainty, they are, however, difficult to characterize. Here, we introduce an unsupervised machine learning approach to determine the minimal set of parameters required to effectively describe the dynamics of a stochastic process. Our method build
We propose that future AI transparency and accountability regulations are based on an open global standard for exchanging information about AI systems, which allows co-existence of potentially conflicting local regulations. Then, we discuss key components of a lightweight and effective AI transparency and/or accountability regulation. To prevent overregulation, the proposed approach encourages collaboration between r
GSINA: Improving Subgraph Extraction for Graph Invariant Learning via Graph Sinkhorn Attention
Graph invariant learning (GIL) seeks invariant relations between graphs and labels under distribution shifts. Recent works try to extract an invariant subgraph to improve out-of-distribution (OOD) generalization, yet existing approaches either lack explicit control over compactness or rely on hard top-$k$ selection that shrinks the solution space and is only partially differentiable. In this paper, we provide an in-d
Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching
Point cloud matching, a crucial technique in computer vision, medical and robotics fields, is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios, emphasizing local differences is crucial for accurately identifying a correct match, thereby enhancing the overall robustness and reliability of the matching process. Commonly used shape descriptors have sev
Visual instruction tuning is a key training stage of large multimodal models. However, when learning multiple visual tasks simultaneously, this approach often results in suboptimal and imbalanced overall performance due to latent knowledge conflicts across tasks. To mitigate this issue, we propose a novel Adaptive Task Balancing approach tailored for visual instruction tuning (VisATB). Specifically, we measure two cr
Sora as a World Model? A Complete Survey on Text-to-Video Generation
The evolution of video generation from text, from animating MNIST to simulating the world with Sora, has progressed at a breakneck speed. Here, we systematically discuss how far text-to-video generation technology supports essential requirements in world modeling. We curate 250+ studies on text-based video synthesis and world modeling. We then observe that recent models increasingly support spatial, action, and strat
Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code
The rise of Large Language Models (LLMs) has significantly advanced various applications on software engineering tasks, particularly in code generation. Despite the promising performance, LLMs are prone to generate hallucinations, which means LLMs might produce outputs that deviate from users' intent, exhibit internal inconsistencies, or misaligned with the real-world knowledge, making the deployment of LLMs pote
Dynamic angular synchronization under smoothness constraints
Given an undirected measurement graph $\mathcal{H} = ([n], \mathcal{E})$, the classical angular synchronization problem consists of recovering unknown angles $θ_1^*,\dots,θ_n^*$ from a collection of noisy pairwise measurements of the form $(θ_i^* - θ_j^*) \mod 2π$, for all $\{i,j\} \in \mathcal{E}$. This problem arises in a variety of applications, including computer vision, time synchronization of distributed networ
Human cognition excels at transcending sensory input and forming latent representations that structure our understanding of the world. While Large Language Model (LLM) agents demonstrate emergent reasoning and decision-making abilities, they lack a principled framework for capturing latent structures and modeling uncertainty. In this work, we explore for the first time how to bridge LLM agents with probabilistic grap
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations
Large pre-trained language models have become a crucial backbone for many downstream tasks in natural language processing (NLP), and while they are trained on a plethora of data containing a variety of biases, such as gender biases, it has been shown that they can also inherit such biases in their weights, potentially affecting their prediction behavior. However, it is unclear to what extent these biases also affect
Targeted Deep Learning System Boundary Testing
Evaluating the behavioral boundaries of deep learning (DL) systems is crucial for understanding their reliability across diverse, unseen inputs. Existing solutions fall short as they rely on untargeted random, model- or latent-based perturbations, due to difficulties in generating controlled input variations. In this work, we introduce Mimicry, a novel black-box test generator for fine-grained, targeted exploration o
Notable events recorded on this day and month across all years.