Record 29012026 · 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
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.
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
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
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
Sunetra Ajit Pawar is an Indian politician serving as 11th Deputy Chief Minister of Maharashtra alongside Eknath Shinde since 2026. She is the first woman to hold the office. Sunetra also serves as the National President of the Nationalist Congress Party and h
Todd Robert Monken is an American professional football coach and former quarterback who is the head coach for the Cleveland Browns of the National Football League (NFL). He previously served as the head coach at the University of Southern Mississippi from 201
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,
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
Gregory Kent Bovino is a United States Border Patrol officer who served as the commander-at-large of the Border Patrol from October 2025 to January 2026.
Sir John Grey Gorton was an Australian politician, farmer and airman who served as the 19th prime minister of Australia from 1968 to 1971. He held office as the leader of the Liberal Party of Australia, having previously served as a senator for Victoria. He wa
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
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.
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
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
The Nationalist Congress Party (NCP) is a state-level political party in India and one of the major political parties in Maharashtra. The party has its presence in legislative assemblies of Maharashtra and Arunachal Pradesh, being in the governing coalition in
Space Shuttle Challenger disaster
On January 28, 1986, Space Shuttle Challenger broke apart 73 seconds into its flight, killing all seven crew members. The spacecraft disintegrated about 46,000 feet (14 km) above the Atlantic Ocean, off the coast of Cape Canaveral, Florida, at 16:39:13 UTC. It
The Doomsday Clock is a symbol that represents the estimated likelihood of a human-made global catastrophe, in the opinion of the nonprofit organization Bulletin of the Atomic Scientists.
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
William Stephen Belichick is an American football coach who is the head coach for the North Carolina Tar Heels. Regarded as one of the greatest head coaches of all time, he holds numerous coaching records, including the record of most Super Bowl wins (six) as
The Learjet 45 (LJ45) is a mid-size business jet aircraft produced by the Learjet Division of Bombardier Aerospace.
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
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
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
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
Alexander J Honnold is an American rock climber best known for his free solo ascents of big wall climbing routes. Honnold rose to worldwide fame in June 2017 when he became the first person to free solo a full route on El Capitan in Yosemite National Park via
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
Michael John McCarthy is an American professional football coach who is the head coach for the Pittsburgh Steelers of the National Football League (NFL). Previously, he served as the head coach of the Dallas Cowboys and Green Bay Packers. In 2011, McCarthy led
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
Kyle Howard Rittenhouse is an American man who gained national attention at age 17 for shooting three men in Kenosha, Wisconsin, two fatally, amid protests and riots in response to the police shooting of Jacob Blake in 2020.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
A Roadmap for Greater Public Use of Privacy-Sensitive Government Data: Workshop Report
Government agencies collect and manage a wide range of ever-growing datasets. While such data has the potential to support research and evidence-based policy making, there are concerns that the dissemination of such data could infringe upon the privacy of the individuals (or organizations) from whom such data was collected. To appraise the current state of data sharing, as well as learn about opportunities for stimul
FC-PINO: High Precision Physics-Informed Neural Operators via Fourier Continuation
The physics-informed neural operator (PINO) is a machine learning paradigm that has demonstrated promising results for learning solutions to partial differential equations (PDEs). It leverages the Fourier Neural Operator to learn solution operators in function spaces and leverages physics losses during training to penalize deviations from known physics laws. Spectral differentiation provides an efficient way to compu
The criminalization of poverty has been widely denounced as a collective bias against the most vulnerable. NGOs and international organizations claim that the poor are blamed for their situation, are more often associated with criminal offenses than the wealthy strata of society and even incur criminal offenses simply as a result of being poor. While no evidence has been found in the literature that correlates povert
Toward Highly Efficient and Private Submodular Maximization via Matrix-Based Acceleration
Submodular function maximization is a critical building block for diverse tasks, such as document summarization, sensor placement, and image segmentation. Yet its practical utility is often limit by the $O(knd^2)$ computational bottleneck. In this paper, we propose an integrated framework that addresses efficiency and privacy simultaneously. First, we introduce a novel matrix-based computation paradigm that accelerat
Membership Privacy Risks of Sharpness Aware Minimization
Optimization algorithms that seek flatter minima, such as Sharpness-Aware Minimization (SAM), are credited with improved generalization and robustness to noise. We ask whether such gains impact membership privacy. Surprisingly, we find that SAM is more prone to Membership Inference Attacks (MIA) than classical SGD across multiple datasets and attack methods, despite achieving lower test error. This suggests that the
This literature review aimed to compare various time-series analysis approaches utilized in forecasting COVID-19 cases in Africa. The study involved a methodical search for English-language research papers published between January 2020 and July 2023, focusing specifically on papers that utilized time-series analysis approaches on COVID-19 datasets in Africa. A variety of databases including PubMed, Google Scholar, S
Robust MAE-Driven NAS: From Mask Reconstruction to Architecture Innovation
Neural Architecture Search (NAS) relies heavily on labeled data, which is labor-intensive and time-consuming to obtain. In this paper, we propose a novel NAS method based on an unsupervised paradigm, specifically Masked Autoencoders (MAE), thereby eliminating the need for labeled data. By replacing the supervised learning objective with an image reconstruction task, our approach enables the efficient discovery of net
Diffusion Noise Feature: Accurate and Fast Generated Image Detection
Generative models now produce images with such stunning realism that they can easily deceive the human eye. While this progress unlocks vast creative potential, it also presents significant risks, such as the spread of misinformation. Consequently, detecting generated images has become a critical research challenge. However, current detection methods are often plagued by low accuracy and poor generalization. In this
LLM Multi-Agent Systems: Challenges and Open Problems
This paper explores multi-agent systems and identify challenges that remain inadequately addressed. By leveraging the diverse capabilities and roles of individual agents, multi-agent systems can tackle complex tasks through agent collaboration. We discuss optimizing task allocation, fostering robust reasoning through iterative debates, managing complex and layered context information, and enhancing memory management
LLMBind: A Unified Modality-Task Integration Framework
Despite recent progress in Multi-Modal Large Language Models (MLLMs), it remains challenging to integrate diverse tasks ranging from pixel-level perception to high-fidelity generation. Existing approaches often suffer from either restricted task extensibility or severe performance degradation due to modality interference. n this paper, we present LLMBind, an extensible framework that unifies multimodal tasks through
GenCode: A Generic Data Augmentation Framework for Boosting Deep Learning-Based Code Understanding
Pre-trained code models lead the era of code intelligence, with multiple models designed with impressive performance. However, one important problem, data augmentation for code data that automatically helps developers prepare training data lacks study in this field. In this paper, we introduce a generic data augmentation framework, GenCode, to enhance the training of code understanding models. Simply speaking, GenCod
Accurate, and effective traffic forecasting is vital for smart traffic systems, crucial in urban traffic planning and management. Current Spatio-Temporal Transformer models, despite their prediction capabilities, struggle with balancing computational efficiency and accuracy, favoring global over local information, and handling spatial and temporal data separately, limiting insight into complex interactions. We introd
GPT2MEG: Quantizing MEG for Autoregressive Generation
Foundation models trained with self-supervised objectives are increasingly applied to brain recordings, but autoregressive generation of realistic multichannel neural time series remains comparatively underexplored, particularly for Magnetoencephalography (MEG). We study (i) modified multichannel WaveNet variants and (ii) a GPT-2-style Transformer, autoregressively trained by next-step prediction on unlabelled MEG. F
This research presents an innovative use of parallel computing with the ARIMA (AutoRegressive Integrated Moving Average) model to forecast energy consumption in Peru's Puno region. The study conducts a thorough and multifaceted analysis, focusing on the execution speed, prediction accuracy, and scalability of both sequential and parallel implementations. A significant emphasis is placed on efficiently managing la
Standard natural language processing (NLP) pipelines operate on symbolic representations of language, which typically consist of sequences of discrete tokens. However, creating an analogous representation for ancient logographic writing systems is an extremely labor intensive process that requires expert knowledge. At present, a large portion of logographic data persists in a purely visual form due to the absence of
We introduce SimBench, a benchmark designed to evaluate the proficiency of simulator-oriented LLMs (S-LLMs) in generating digital twins (DTs) that can be used in simulators for virtual testing. Given a collection of S-LLMs, this benchmark ranks them according to their ability to produce high-quality DTs. We demonstrate this by comparing over 33 open- and closed-source S-LLMs. Using multi-turn interactions, SimBench e
Tokenization for Molecular Foundation Models
Text-based foundation models have become an important part of scientific discovery, with molecular foundation models accelerating advancements in material science and molecular design.However, existing models are constrained by closed-vocabulary tokenizers that capture only a fraction of molecular space. In this work, we systematically evaluate 34 tokenizers, including 19 chemistry-specific ones, and reveal significa
Improving Fine-Grained Control via Aggregation of Multiple Diffusion Models
While many diffusion models perform well when controlling particular aspects such as style, character, and interaction, they struggle with fine-grained control due to dataset limitations and intricate model architecture design. This paper introduces a novel training-free algorithm for fine-grained generation, called Aggregation of Multiple Diffusion Models (AMDM). The algorithm integrates features in the latent data
Physics-informed neural networks (PINNs) have emerged as a powerful approach for solving partial differential equations (PDEs) by training neural networks with loss functions that incorporate physical constraints. In this work, we introduce HyResPINNs, a two-level convex-gated architecture designed to maximize approximation expressiveness for a fixed number of degrees of freedom (DoF). The first level involves a trai
Event-guided Low-light Video Semantic Segmentation
Recent video semantic segmentation (VSS) methods have demonstrated promising results in well-lit environments. However, their performance significantly drops in low-light scenarios due to limited visibility and reduced contextual details. In addition, unfavorable low-light conditions make it harder to incorporate temporal consistency across video frames and thus, lead to video flickering effects. Compared with conven
LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs
We introduce LLMStinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Unlike traditional methods, which require complex prompt engineering or white-box access, LLMStinger uses a reinforcement learning (RL) loop to fine-tune an attacker LLM, generating new suffixes based on existing attacks for harmful questions from the HarmBench be
NLPrompt: Noise-Label Prompt Learning for Vision-Language Models
The emergence of vision-language foundation models, such as CLIP, has revolutionized image-text representation, enabling a broad range of applications via prompt learning. Despite its promise, real-world datasets often contain noisy labels that can degrade prompt learning performance. In this paper, we demonstrate that using mean absolute error (MAE) loss in prompt learning, named PromptMAE, significantly enhances ro
In earthwork and construction, excavators often encounter large rocks mixed with various soil conditions, requiring skilled operators. This paper presents a framework for achieving autonomous excavation using reinforcement learning (RL) through a rock excavation simulator. In the simulation, resolution can be defined by the particle size/number in the whole soil space. Fine-resolution simulations closely mimic real-w
Hierarchical Multi-Agent DRL Based Dynamic Cluster Reconfiguration for UAV Mobility Management
Multi-connectivity involves dynamic cluster formation among distributed access points (APs) and coordinated resource allocation from these APs, highlighting the need for efficient mobility management strategies for users with multi-connectivity. In this paper, we propose a novel mobility management scheme for unmanned aerial vehicles (UAVs) that uses dynamic cluster reconfiguration with energy-efficient power allocat
Are LLMs Really Not Knowledgeable? Mining the Submerged Knowledge in LLMs' Memory
Large language models (LLMs) have shown promise as parametric knowledge bases, but often underperform on question answering (QA) tasks due to hallucinations and uncertainty. While prior work attributes these failures to knowledge gaps in the model's parameters, we uncover a complementary phenomenon: LLMs frequently retain correct knowledge even when generating incorrect or "unsure" answers. By analyzing t
Notable events recorded on this day and month across all years.