Record 20032026 · captured 2026-08-25
The world looked up Dhurandhar: The Revenge. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
Complete record JSON
What the most people looked up, ranked by Wikipedia pageviews for that day.
Dhurandhar: The Revenge is a 2026 Indian Hindi-language spy action-thriller film written and directed by Aditya Dhar. It is produced by Dhar, Lokesh Dhar, and Jyoti Deshpande under Jio Studios and B62 Studios. It is a sequel to the 2025 film Dhurandhar and the
Cesario Estrada "Cesar" Chavez was an American labor unionist and political activist. Along with Dolores Huerta and Gilbert Padilla, he co-founded the National Farm Workers Association (NFWA), which later merged with the Agricultural Workers Organizing Committ
Since 28 February 2026, the United States and Israel have been at war with Iran and its regional allies. Hostilities broke out after US–Israeli airstrikes killed several Iranian officials, including Supreme Leader Ali Khamenei. The strikes were launched amid o
Joseph Clay Kent is an American politician, former United States Army warrant officer, and former Central Intelligence Agency paramilitary officer who served as the director of the National Counterterrorism Center from 2025 to 2026. A member of the Republican
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
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
Joseph Edgar Foreman, known by his stage name Afroman, is an American rapper, singer, and musician. His fourth album, The Good Times (2001), featured the singles "Because I Got High" and "Crazy Rap". "Because I Got High" was nominated for a Grammy Award in 200
Dawood Ibrahim Kaskar is an Indian gangster, mob boss, drug lord and narcoterrorist. He is the leader of the organised crime syndicate D-Company, which he founded in Mumbai in the 1970s. Dawood is wanted on multiple charges of murder, extortion, targeted killi
Benjamin Netanyahu, nicknamed "Bibi", is an Israeli politician and diplomat who has served as Prime Minister of Israel since 2022. Having previously held office from 1996 to 1999 and from 2009 to 2021, Netanyahu is Israel's longest-serving prime minister.
Dolores Huerta is an American labor leader and feminist activist. After working for several years with the Community Service Organization (CSO), she co-founded the National Farm Workers Association (NFWA) with fellow activists Cesar Chavez and Gilbert Padilla,
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
Tulsi Gabbard is an American politician and U.S. Army officer who served as the eighth Director of National Intelligence (DNI) from 2025 to 2026. She previously served as U.S. representative for Hawaii's 2nd congressional district from 2013 to 2021 and in the
One Battle After Another is a 2025 American action thriller film written, directed, and produced by Paul Thomas Anderson. Inspired by the 1990 novel Vineland by Thomas Pynchon, the film's ensemble cast includes Leonardo DiCaprio, Sean Penn, Benicio del Toro, R
Project Hail Mary is a 2026 American science fiction film produced and directed by Phil Lord and Christopher Miller and written by Drew Goddard, based on the 2021 novel of the same name by Andy Weir. It stars Ryan Gosling, who also produced the film, as Ryland
Sinners is a 2025 American horror film produced, written, and directed by Ryan Coogler. Set in 1932 in the Mississippi Delta, it stars Michael B. Jordan in dual roles as criminal twin brothers who return to their hometown in the Jim Crow South, where they are
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
South Pars/North Dome Gas-Condensate field
The South Pars/North Dome field is a natural-gas condensate field located in the Persian Gulf. It is by far the world's largest natural gas field, with ownership of the field shared between Iran and Qatar. According to the International Energy Agency (IEA), th
Eid al-Fitr is the first of the two main festivals in Islam, the other being Eid al-Adha. The holiday falls on the first day of Shawwal, the tenth month of the Islamic calendar. One of the most important Islamic celebrations, Eid al-Fitr is celebrated by Musli
Stockbridge Town Hall, Hampshire
Stockbridge Town Hall is a municipal building in the High Street in Stockbridge, Hampshire, England. The structure, which is used as the meeting place of Stockbridge Parish Council, is a Grade II* listed building.
The Port of Liverpool is a major port on the River Mersey in Liverpool, England, extending from Brunswick Dock in Liverpool to Seaforth Dock in Seaforth, and including the Birkenhead Docks on the western side of the estuary.
Markwayne Mullin is an American politician and businessman who has served since 2026 as the ninth United States secretary of homeland security. A member of the Republican Party, Mullin served from 2023 to 2026 as the junior United States senator from Oklahoma
Aditya Dhar is an Indian filmmaker who works in Hindi cinema. Having previously worked as a lyricist, Dhar made his directorial debut with the 2019 war film Uri: The Surgical Strike, a commercially successful venture which earned him the National Film Award fo
Atiq Ahmed, was a notorious Indian gangster, criminal and politician. He served as a member of the Indian Parliament and the Uttar Pradesh Legislative Assembly from the Samajwadi Party. Ahmed had more than 160 criminal cases registered against him and conteste
Michael Bakari Jordan is an American actor, producer, and director. His accolades include an Academy Award, three Actor Awards, and a Producers Guild Award, in addition to nominations for a British Academy Film Award, a Golden Globe Award and two Emmy Awards.
Crimson Desert is a 2026 action-adventure game developed and published by Pearl Abyss. Originally planned as a prequel to Black Desert Online, the game evolved into a standalone title during development. It was released for macOS, PlayStation 5, Windows, and X
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
Wellington R. Burt was an American lumber baron from Saginaw, Michigan. At the time of his death, his wealth was estimated to be between $40 and $90 million. For a time in the early 1900s, Burt ranked as one of the eight wealthiest men in the United States. He
Circa is an album by the American jazz pianist and composer Michael Cain recorded in August 1996 and released on ECM the following year. The trio features trumpeter Ralph Alessi and saxophonist Peter Epstein.
Ugādi, Yugādi or also known as Saṁvatsarādi, is the first day of the year on the Hindu calendar. It is traditionally celebrated by the Kannadigas and Telugu people in the Indian states of Andhra Pradesh, Karnataka and Telangana, in some parts of Tamil Nadu and
Joshua James Duggar is an American convicted sex offender and former reality television personality. The eldest of Michelle and Jim Bob Duggar's nineteen children, Duggar and his family gained fame as the focus of the TLC series 19 Kids and Counting, which spu
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Exploring AI in Fashion: A Review of Aesthetics, Personalization, Virtual Try-On, and Forecasting
Fashion-focused artificial intelligence has rapidly advanced in recent years, driven by deep learning and its deployment in recommender systems, detection, retrieval, and analytics. Yet several consumer-facing domains remain comparatively under-surveyed despite their practical impact. This work provides a comprehensive review of methods, datasets, and evaluation metrics across four such domains: aesthetics, personali
Improved Learning Rates for Stochastic Optimization
Stochastic optimization is a cornerstone of modern machine learning. This paper studies the generalization performance of two classical stochastic optimization algorithms: stochastic gradient descent (SGD) and Nesterov's accelerated gradient (NAG). We establish new learning rates for both algorithms, with improved guarantees in some settings or comparable rates under weaker assumptions in others. We also provide
Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces. For contextualized embeddings, alignment becomes more complex as we additionally take context into consideration. In this work, we propose using Optimal Transport (OT) as an alignment objective during fine-tuning to further i
"Calibeating": Beating Forecasters at Their Own Game
In order to identify expertise, forecasters should not be tested by their calibration score, which can always be made arbitrarily small, but rather by their Brier score. The Brier score is the sum of the calibration score and the refinement score; the latter measures how good the sorting into bins with the same forecast is, and thus attests to "expertise." This raises the question of whether one can gain cali
Inverse classification with logistic and softmax classifiers: efficient optimization
In recent years, a certain type of problems have become of interest where one wants to query a trained classifier. Specifically, one wants to find the closest instance to a given input instance such that the classifier's predicted label is changed in a desired way. Examples of these "inverse classification" problems are counterfactual explanations, adversarial examples and model inversion. All of them are
Algebras of actions in an agent's representations of the world
In this paper, we propose a framework to extract the algebra of the transformations of worlds from the perspective of an agent. As a starting point, we use our framework to reproduce the symmetry-based representations from the symmetry-based disentangled representation learning (SBDRL) formalism proposed by [1]; only the algebra of transformations of worlds that form groups can be described using symmetry-based repre
BarcodeBERT: Transformers for Biodiversity Analysis
In the global challenge of understanding and characterizing biodiversity, short species-specific genomic sequences known as DNA barcodes play a critical role, enabling fine-grained comparisons among organisms within the same kingdom of life. Although machine learning algorithms specifically designed for the analysis of DNA barcodes are becoming more popular, most existing methodologies rely on generic supervised trai
Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Dataset
We present PD-REAL, a novel large-scale dataset for unsupervised anomaly detection (AD) in the 3D domain. It is motivated by the fact that 2D-only representations in the AD task may fail to capture the geometric structures of anomalies due to uncertainty in lighting conditions or shooting angles. PD-REAL consists entirely of Play-Doh models for 15 object categories and focuses on the analysis of potential benefits fr
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
In the era of fast-paced precision medicine, observational studies play a major role in properly evaluating new treatments in clinical practice. Yet, unobserved confounding can significantly compromise causal conclusions drawn from non-randomized data. We propose a novel strategy that leverages randomized trials to quantify unobserved confounding. First, we design a statistical test to detect unobserved confounding w
Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
In the past several years, the last-iterate convergence of the Stochastic Gradient Descent (SGD) algorithm has triggered people's interest due to its good performance in practice but lack of theoretical understanding. For Lipschitz convex functions, different works have established the optimal $O(\log(1/δ)\log T/\sqrt{T})$ or $O(\sqrt{\log(1/δ)/T})$ high-probability convergence rates for the final iterate, where
We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts, such as the conditional average treatment effect and conditional relative risk. The EP-learning framework enjoys the same oracle efficiency as Neyman-orthogonal learning strategies, such as DR-learning and R-learning, while addressing some of their primary drawbacks: (i) their practical applicability
On Minimal Depth in Neural Networks
Understanding the relationship between the depth of a neural network and its representational capacity is a central problem in deep learning theory. In this work, we develop a geometric framework to analyze the expressivity of ReLU networks with the notion of depth complexity for convex polytopes. The depth of a polytope recursively quantifies the number of alternating convex hull and Minkowski sum operations require
Heuristic Multiobjective Discrete Optimization using Restricted Decision Diagrams
Decision diagrams (DDs) have emerged as a state-of-the-art method for exact multiobjective integer linear programming. When the DD is too large to fit into memory or the decision-maker prefers a fast approximation to the Pareto frontier, the complete DD must be restricted to a subset of its states (or nodes). We introduce new node-selection heuristics for constructing restricted DDs that produce a high-quality approx
SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Models
Large Language Models (LLMs) have emerged as a powerful tool in advancing the Text-to-SQL task, significantly outperforming traditional methods.Nevertheless, as a nascent research field, there is still no consensus on the optimal prompt templates and design frameworks. Additionally, existing benchmarks inadequately explore the performance of LLMs across the various sub-tasks of the Text-to-SQL process, which hinders
A multiscale cavity method for sublinear-rank symmetric matrix factorization
We consider a statistical model for symmetric matrix factorization with additive Gaussian noise in the high-dimensional regime, where the rank of the signal matrix to infer $M$ scales with its size $N$ as $M=\mathrm{o}(\sqrt{\ln N})$. Allowing for an $N$-dependent rank offers new challenges and requires new methods. Working in the Bayes-optimal setting, we show that whenever the signal has i.i.d. entries, the limitin
CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions
Examining Drug-Drug Interactions (DDIs) is a pivotal element in the process of drug development. DDIs occur when one drug's properties are affected by the inclusion of other drugs. Detecting favorable DDIs has the potential to pave the way for creating and advancing innovative medications applicable in practical settings. However, existing DDI prediction models continue to face challenges related to generalizatio
Improved Convex Decomposition with Ensembling and Negative Primitives
Describing a scene in terms of primitives -- geometrically simple shapes that offer a parsimonious but accurate abstraction of structure -- is an established and difficult fitting problem. Different scenes require different numbers of primitives, and these primitives interact strongly. Existing methods are evaluated by comparing predicted depth, normals, and segmentation against ground truth. The state of the art met
$μ$LO: Compute-Efficient Meta-Generalization of Learned Optimizers
Learned optimizers (LOs) have the potential to significantly reduce the wall-clock training time of neural networks. However, they can struggle to optimize unseen tasks (meta-generalize), especially when training networks wider than those seen during meta-training. To address this, we derive the Maximal Update Parametrization ($μ$P) for two state-of-the-art learned optimizer architectures and propose a simple meta-tr
This study introduces the Perception Latency Mitigation Network (PLM-Net), a modular deep learning framework designed to mitigate perception latency in vision-based imitation-learning lane-keeping systems. Perception latency, defined as the delay between visual sensing and steering actuation, can degrade lateral tracking performance and steering stability. While delay compensation has been extensively studied in clas
Automated Explanation Selection for Scientific Discovery
Automated reasoning is a key technology in the young but rapidly growing field of Explainable Artificial Intelligence (XAI). Explanability helps build trust in artificial intelligence systems beyond their mere predictive accuracy and robustness. In this paper, we propose a cycle of scientific discovery that combines machine learning with automated reasoning for the generation and the selection of explanations. We pre
Modeling Inverse Ellipsometry Problem via Flow Matching with a Large-Scale Dataset
Inverse ellipsometry, i.e., reconstructing optical constants and film thickness from the measured phase difference $Δ$ and amplitude ratio $Ψ$, is a fundamentally ill-posed problem. Traditional solutions rely on slow, expert-driven iterative fitting, while the development of machine learning approaches has been severely limited by the lack of large-scale, physically consistent datasets. To address this gap, we introd
A Review of Pseudo-Labeling for Computer Vision
Deep neural models have achieved state of the art performance on a wide range of problems in computer science, especially in computer vision. However, deep neural networks often require large datasets of labeled samples to generalize effectively, and an important area of active research is semi-supervised learning, which attempts to instead utilize large quantities of (easily acquired) unlabeled samples. One family o
Latent Causal Modeling for 3D Brain MRI Counterfactuals
The number of samples in structural brain MRI studies is often too small to properly train deep learning models. Generative models show promise in addressing this issue by effectively learning the data distribution and generating high-fidelity MRI. However, they struggle to produce diverse, high-quality data outside the distribution defined by the training data. One way to address this issue is to use causal models d
ODE-Constrained Generative Modeling of Cardiac Dynamics for 12-Lead ECG Synthesis
Generating realistic training data for supervised learning remains a significant challenge in artificial intelligence, particularly in domains where large, expert-labeled datasets are scarce or costly to obtain. This is especially true for electrocardiograms (ECGs), where privacy constraints, class imbalance, and the need for physician annotation limit the availability of labeled 12-lead recordings, motivating the de
Biased AI can Influence Political Decision-Making
As modern large language models (LLMs) become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. While bias in models are well-documented, less is known about how these biases influence human decisions. This paper presents two interactive experiments investigating the effects of partisan bias in LLMs on political opinions and decision-mak
Notable events recorded on this day and month across all years.