Record 30012026 · captured 2026-08-25
The world looked up Ajit Pawar. 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.
Ajit Anantrao Pawar was an Indian politician who served as Maharashtra's longest-serving deputy chief minister for more than eight years, between 2010 and his death in 2026, for six terms. He held the office under various governments, including the cabinets of
Onika Tanya Maraj-Petty, known professionally as Nicki Minaj, is a Trinidadian rapper, singer, and songwriter. Dubbed the "Queen of Rap" and one of the most influential rappers of all time, she is noted for her dynamic rap flow, witty lyrics, musical versatili
Border 2 is a 2026 Indian Hindi-language epic war film co-written and directed by Anurag Singh. A sequel to J. P. Dutta's 1997 film Border, it was produced by Bhushan Kumar, Krishan Kumar, J. P. Dutta, and Nidhi Dutta under the banners of T-Series Films and J.
Elena Andreyevna Rybakina is a Russian-born Kazakhstani professional tennis player. She is currently ranked world No. 2 in women's singles by the Women's Tennis Association (WTA). Rybakina has won 13 WTA Tour-level singles titles, including two majors at the 2
On January 24, 2026, Alex Jeffrey Pretti, a 37-year-old American intensive care nurse for the United States Department of Veterans Affairs, was shot multiple times and killed by two United States Customs and Border Protection officers in Minneapolis, Minnesota
Aryna Siarhiejeŭna Sabalenka is a Belarusian professional tennis player. She is the current world No. 1 in women's singles by the WTA and is a former No. 1 in doubles. Sabalenka has won 24 career singles titles, including four majors—two each at the Australian
The 2025–26 UEFA Champions League was the 71st season of Europe's premier club football tournament organised by UEFA, and the 34th season since it was rebranded from the European Cup to the UEFA Champions League.
Melania is a 2026 American film directed and produced by Brett Ratner. It revolves around the experiences of Melania Trump, the first lady of the United States, in the 20 days before her husband Donald's second presidential inauguration. It was released in the
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
Wonder Man is an American television series created by Destin Daniel Cretton and Andrew Guest for the streaming service Disney+, based on the Marvel Comics character Simon Williams / Wonder Man. It is the 17th television series in the Marvel Cinematic Universe
Bridgerton is an American alternative history, Regency romance television series created by Chris Van Dusen for Netflix. Based on the book series of the same name by Julia Quinn, it is Shondaland's first scripted show for Netflix. The series stars an ensemble
Nipah virus is a bat-borne, zoonotic virus that causes Nipah virus infection in humans and other animals, a disease with a very high case fatality rate (40–75%). Numerous disease outbreaks caused by the Nipah virus have occurred in India, Malaysia, and Singapo
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
Send Help is a 2026 American survival horror film directed and co-produced by Sam Raimi and written by Damian Shannon and Mark Swift. The film stars Rachel McAdams and Dylan O'Brien as an employee and her boss, respectively, who become stranded on a desert isl
Odessa Zion Segall Adlon, known professionally as Odessa A'zion, is an American actress. On television, she is known for her roles in the CBS series Fam (2019), the Netflix series Grand Army (2020) and the HBO series I Love LA (2025). For her performance in th
Kristi Lynn Arnold Noem is an American politician serving as the United States special envoy for the Shield of the Americas since 2026. From 2025 to 2026, she served as the eighth United States secretary of homeland security. A member of the Republican Party,
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
Ilhan Abdullahi Omar is an American politician serving as the U.S. representative for Minnesota's 5th congressional district since 2019. The district includes all of Minneapolis and some of its first-ring suburbs. From 2017 to 2019, Omar served in the Minnesot
Sharadchandra Govindrao Pawar is an Indian politician who has served as a Member of Parliament, Rajya Sabha since 2014. Prior to 2014 he served as a member of Lok Sabha, as a member of the Nationalist Congress Party (NCP). He has served four terms as the Chief
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
José Mário dos Santos Mourinho Félix is a Portuguese professional football manager and former player who is the head coach of La Liga club Real Madrid. Nicknamed "The Special One", he is one of the most decorated managers of all time. Mourinho has won league c
The Wrecking Crew is a 2026 American buddy cop action comedy film directed by Ángel Manuel Soto and written by Jonathan Tropper. It stars Dave Bautista, Jason Momoa, Claes Bang, Temuera Morrison, Jacob Batalon, Frankie Adams, Miyavi, Stephen Root, and Morena B
Wonder Man is a character appearing in American comic books published by Marvel Comics. Created by writer Stan Lee and artists Don Heck and Jack Kirby, he first appeared in The Avengers #9. The character, who was initially introduced as a supervillain imbued w
The 2026 Royal Rumble, also promoted as Royal Rumble: Riyadh, was a professional wrestling pay-per-view (PPV) and livestreaming event produced by the American company WWE. It was the 39th annual Royal Rumble and took place on January 31, 2026, at Riyadh Season
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 UEFA Champions League, commonly known as the Champions League, is an annual club association football competition organised by the Union of European Football Associations (UEFA) that is contested by top-division European clubs. The competition begins with
Katherine LaNasa is an American actress. Since 2025, she has portrayed Nurse Dana Evans in the HBO Max medical drama The Pitt (2025–present), for which she earned a Primetime Emmy Award, a Critics' Choice Award, and an Actor Award.
Thomas Douglas Homan is an American law enforcement officer. In November 2024, Donald Trump designated Homan as "border czar" for his second presidency. Homan also served during the Obama administration and the first Trump administration. He served as acting d
Jessica Pegula is an American professional tennis player. She has a career-high rankings in singles of world No. 3, achieved in October 2022, and in doubles of world No. 1, achieved in September 2023. Pegula has won 11 singles titles and seven doubles titles o
David Michael Bautista Jr. is an American actor and retired professional wrestler. Regarded as one of the most prolific professional wrestlers of the Ruthless Aggression Era, he rose to fame for his multiple stints in WWE between 2002 and 2019.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a large diversity of goals, aiming to discover what is controllable in their environments, and what is not. Because some goals might prove easy and some impossible, agents must actively select which goal to practice at any moment, to maximize
Building autonomous machines that can explore open-ended environments, discover possible interactions and build repertoires of skills is a general objective of artificial intelligence. Developmental approaches argue that this can only be achieved by $autotelic$ $agents$: intrinsically motivated learning agents that can learn to represent, generate, select and solve their own problems. In recent years, the convergence
Active Inference Tree Search in Large POMDPs
The ability to plan ahead efficiently is key for both living organisms and artificial systems. Model-based planning and prospection are widely studied in cognitive neuroscience and artificial intelligence (AI), but from different perspectives--and with different desiderata in mind (biological realism versus scalability) that are difficult to reconcile. Here, we introduce a novel method to plan in POMDPs--Active Infer
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a subset of them) are being modified. We show that with small-to-medium training data, applying BitFit on pre-trained BERT models is competitive with (and sometimes better than) fine-tuning the entire model. For larger data, the method is competitive with other sparse fine-tuning methods. Besides their practical utility, these
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information
Transformer-based language models usually treat texts as linear sequences. However, most texts also have an inherent hierarchical structure, i.e., parts of a text can be identified using their position in this hierarchy. In addition, section titles usually indicate the common topic of their respective sentences. We propose a novel approach to formulate, extract, encode and inject hierarchical structure information ex
Divergence Results and Convergence of a Variance Reduced Version of ADAM
Stochastic optimization algorithms using exponential moving averages of the past gradients, such as ADAM, RMSProp and AdaGrad, have been having great successes in many applications, especially in training deep neural networks. ADAM in particular stands out as efficient and robust. Despite of its outstanding performance, ADAM has been proved to be divergent for some specific problems. We revisit the divergent question
Automated Search for Conjectures on Mathematical Constants using Analysis of Integer Sequences
Formulas involving fundamental mathematical constants had a great impact on various fields of science and mathematics, for example aiding in proofs of irrationality of constants. However, the discovery of such formulas has historically remained scarce, often perceived as an act of mathematical genius by great mathematicians such as Ramanujan, Euler, and Gauss. Recent efforts to automate the discovery of formulas for
Gaussian kernels on non-simply-connected closed Riemannian manifolds are never positive definite
We show that the Gaussian kernel $\exp\left\{-λd_g^2(\bullet, \bullet)\right\}$ on any non-simply-connected closed Riemannian manifold $(\mathcal{M},g)$, where $d_g$ is the geodesic distance, is not positive definite for any $λ> 0$, combining analyses in the recent preprint~[9] by Da Costa--Mostajeran--Ortega and classical comparison theorems in Riemannian geometry.
Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models
In this perspective paper, we first comprehensively review existing evaluations of Large Language Models (LLMs) using both standardized tests and ability-oriented benchmarks. We pinpoint several problems with current evaluation methods that tend to overstate the capabilities of LLMs. We then articulate what artificial general intelligence should encompass beyond the capabilities of LLMs. We propose four characteristi
Machine learning for option pricing: an empirical investigation of network architectures
We consider the supervised learning problem of learning the price of an option or the implied volatility given appropriate input data (model parameters) and corresponding output data (option prices or implied volatilities). The majority of articles in this literature considers a (plain) feed forward neural network architecture in order to connect the neurons used for learning the function mapping inputs to outputs. I
Low-redundancy Distillation for Continual Learning
Continual learning (CL) aims to learn new tasks without erasing previous knowledge. However, current CL methods primarily emphasize improving accuracy while often neglecting training efficiency, which consequently restricts their practical application. Drawing inspiration from the brain's contextual gating mechanism, which selectively filters neural information and continuously updates past memories, we propose L
ACES: Generating Diverse Programming Puzzles with with Autotelic Generative Models
The ability to invent novel and interesting problems is a remarkable feature of human intelligence that drives innovation, art, and science. We propose a method that aims to automate this process by harnessing the power of state-of-the-art generative models to produce a diversity of challenging yet solvable problems, here in the context of Python programming puzzles. Inspired by the intrinsically motivated literature
Fair Graph Machine Learning under Adversarial Missingness Processes
Graph Neural Networks (GNNs) have achieved state-of-the-art results in many relevant tasks where decisions might disproportionately impact specific communities. However, existing work on fair GNNs often assumes that either sensitive attributes are fully observed or they are missing completely at random. We show that an adversarial missingness process can inadvertently disguise a fair model through the imputation, lea
Texture modeling and synthesis are essential for enhancing the realism of virtual environments. Methods that directly synthesize textures in 3D offer distinct advantages to the UV-mapping-based methods as they can create seamless textures and align more closely with the ways textures form in nature. We propose Mesh Neural Cellular Automata (MeshNCA), a method that directly synthesizes dynamic textures on 3D meshes wi
Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurring
Self-supervised methods have recently proved to be nearly as effective as supervised ones in various imaging inverse problems, paving the way for learning-based approaches in scientific and medical imaging applications where ground truth data is hard or expensive to obtain. These methods critically rely on invariance to translations and/or rotations of the image distribution to learn from incomplete measurement data
Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video
Generating dynamic 3D object from a single-view video is challenging due to the lack of 4D labeled data. An intuitive approach is to extend previous image-to-3D pipelines by transferring off-the-shelf image generation models such as score distillation sampling.However, this approach would be slow and expensive to scale due to the need for back-propagating the information-limited supervision signals through a large pr
Scaling Laws for Downstream Task Performance of Large Language Models
Scaling laws provide important insights that can guide the design of large language models (LLMs). Existing work has primarily focused on studying scaling laws for pretraining (upstream) loss. However, in transfer learning settings, in which LLMs are pretrained on an unsupervised dataset and then finetuned on a downstream task, we often also care about the downstream performance. In this work, we study the scaling be
Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling
Point cloud processing methods leverage local and global point features %at the feature level to cater to downstream tasks, yet they often overlook the task-level context inherent in point clouds during the encoding stage. We argue that integrating task-level information into the encoding stage significantly enhances performance. To that end, we propose SMTransformer which incorporates task-level information into a v
Towards Identifiable Latent Additive Noise Models
Causal representation learning (CRL) offers the promise of uncovering the underlying causal model by which observed data was generated, but the practical applicability of existing methods remains limited by the strong assumptions required for identifiability and by challenges in applying them to real-world settings. Most current approaches are applicable only to relatively restrictive model classes, such as linear or
Enhancing Multiple Object Tracking Accuracy via Quantum Annealing
Multiple object tracking (MOT), a key task in image recognition, presents a persistent challenge in balancing processing speed and tracking accuracy. This study introduces a novel approach that leverages quantum annealing (QA) to expedite computation speed, while enhancing tracking accuracy through the ensembling of object tracking processes. A method to improve the matching integration process is also proposed. By u
Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound
Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited. To address this, we introduce a generic framework designed for deploying image-based AI models in such settings. Using this framework, we deployed a trained model for fetal ultrasound standard plane detection, and evaluated it in real-time sessions with both novice and expert users.
NoiseNCA: Noisy Seed Improves Spatio-Temporal Continuity of Neural Cellular Automata
Neural Cellular Automata (NCA) is a class of Cellular Automata where the update rule is parameterized by a neural network that can be trained using gradient descent. In this paper, we focus on NCA models used for texture synthesis, where the update rule is inspired by partial differential equations (PDEs) describing reaction-diffusion systems. To train the NCA model, the spatio-temporal domain is discretized, and Eul
Emergent Dynamics in Neural Cellular Automata
Neural Cellular Automata (NCA) models are trainable variations of traditional Cellular Automata (CA). Emergent motion in the patterns created by NCA has been successfully applied to synthesize dynamic textures. However, the conditions required for an NCA to display dynamic patterns remain unexplored. Here, we investigate the relationship between the NCA architecture and the emergent dynamics of the trained models. Sp
Hyperspectral imaging technology has a wide range of applications, including forest management, mineral resource exploration, and Earth surface monitoring. A key step in utilizing this technology is endmember extraction, which aims to identify the spectral signatures of materials in observed scenes. Theoretical studies suggest that self-dictionary methods using linear programming (LP), known as Hottopixx methods, are
The increasing volume of electronic health records (EHRs) presents the opportunity to improve the accuracy and robustness of models in clinical prediction tasks. Unlike traditional centralized approaches, federated learning enables training on data from multiple institutions while preserving patient privacy and complying with regulatory constraints. In practice, healthcare institutions (i.e., hosts) often need to bui
Notable events recorded on this day and month across all years.