Record 10032026 · captured 2026-08-25
The world looked up Mojtaba Khamenei. 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.
Mojtaba Hosseini Khamenei is an Iranian Shia cleric and politician who has served as the third supreme leader of Iran since 2026. A member of the Khamenei family and the second son of second supreme leader Ali Khamenei, he previously served as Vakil of the Sup
Jennifer Runyon was an American actress. She made her feature-film debut in the slasher film To All a Goodnight (1980), and had supporting roles in the comedies Up the Creek (1984) and Ghostbusters (1984). She played the role of Gwendolyn Pierce in the 1984 si
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
Ali Hosseini Khamenei was an Iranian politician and Shia cleric who served as the second supreme leader of Iran from 1989 until his assassination in 2026. A member of the Khamenei family who held the title Grand Ayatollah, he previously served as the third pre
The ICC Men's T20 World Cup, formerly the ICC World Twenty20, is a biennial world cup for cricket in Twenty20 International (T20I) format, organised by the International Cricket Council (ICC). It was held in every odd year from 2007 to 2009, and since 2010 has
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
The Muppets are an ensemble group of comedic puppet characters originally created by Jim Henson. The Muppets have appeared in multiple television series, films, and other media appearances since the 1950s. The majority of the characters listed here originated
War Machine is a 2026 military science fiction action film directed, co-produced, and co-written by Patrick Hughes. It stars Alan Ritchson, Dennis Quaid, Stephan James, Jai Courtney, Esai Morales, Keiynan Lonsdale, and Daniel Webber, and follows a staff sergea
Iran, officially the Islamic Republic of Iran, and historically known as Persia, is a country in West Asia. It borders Iraq to the west, Turkey, Azerbaijan, and Armenia to the northwest, the Caspian Sea to the north, Turkmenistan to the northeast, Afghanistan
Sanju Viswanath Samson is an Indian cricketer who plays for the India national cricket team in the T20I format. He was part of the 2024 and 2026 T20 world cup winning teams, including a Player of the Tournament performance in 2026. He plays for Chennai Super K
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
Rahma Haruna was a 19-year-old Nigerian teenager who became famous for a viral photo taken by photographer Sani Maikatanga.
On 4 August 2002, two 10-year-old girls, Holly Marie Wells and Jessica Amiee Chapman, were lured into the home of a local resident and school caretaker, Ian Huntley, in Soham, Cambridgeshire, England. Both children were murdered – most likely by asphyxiation –
The Bride! is a 2026 American gothic romance film directed and written by Maggie Gyllenhaal and starring Jessie Buckley, Christian Bale, Peter Sarsgaard, Annette Bening, Jake Gyllenhaal, and Penélope Cruz. The film draws inspiration from the 1935 film Bride of
Ruhollah Mostafavi Khomeini was an Iranian politician and Shia cleric who served as the first supreme leader of Iran from 1979 until his death in 1989. He was the leader of the Iranian Revolution, which overthrew Mohammad Reza Pahlavi, ended the Pahlavi era, a
The 2026 World Baseball Classic was an international professional baseball tournament between 20 national baseball teams, and the sixth iteration of the World Baseball Classic (WBC). It ran from March 5 to 17, 2026. The pool-play rounds were played in LoanDepo
John Fitzgerald Kennedy Jr., also referred to as JFK Jr., was an American businessman, attorney, magazine publisher, and journalist. He was the son of the 35th U.S. president John F. Kennedy, and First Lady Jacqueline Kennedy.
The supreme leader of the Islamic Republic of Iran, officially styled as the leader of the Islamic Revolution or the leadership of the Islamic jurist by the Iranian Constitution, is the highest political and religious authority in Iran, taking precedence above
Carolyn Jeanne Bessette-Kennedy was an American fashion publicist. Raised in Greenwich, Connecticut, she graduated from Boston University and joined Calvin Klein, where she rose from a sales position in Boston to publicity and show-production roles in New York
Havana syndrome, also known as anomalous health incidents (AHIs), is a disputed medical condition. Starting in 2016 in about a dozen overseas locations, U.S. and Canadian government officials and their families reported symptoms associated with a perceived loc
The 2027 ICC Men's Cricket World Cup will be the 14th edition of the Cricket World Cup, the quadrennial international men's cricket championship contested by the national teams of the member associations of ICC. It is scheduled to be played in South Africa, Zi
Hoppers is a 2026 American animated science fiction comedy film directed by Daniel Chong from a screenplay by Jesse Andrews and a story by Chong and Andrews. Produced by Pixar Animation Studios for Walt Disney Pictures, the film stars the voices of Piper Curda
Gorillaz are an English virtual band formed in 1998 by the musician Damon Albarn and the artist Jamie Hewlett. The band primarily consists of four fictional members: 2-D, Murdoc Niccals, Noodle and Russel Hobbs (drums). Their universe is presented in media suc
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
Peter Brian Hegseth is an American government official, veteran, and former television personality who has served as the 29th United States secretary of defense since 2025.
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.
Jasprit Jasbirsingh Bumrah is an Indian International cricketer who plays for the Indian national cricket team in all formats of this game and has captained India in Tests and T20Is. He is widely regarded as one of the greatest fast bowlers of his generation.
Jeffrey Edward Epstein was an American financier and child sex offender. He began his career as a math teacher at the Dalton School in New York City, before entering the banking and finance sector. Over several decades, he made much of his fortune providing ta
The World Baseball Classic (WBC), also referred to as The Classic, is a quadrennial international baseball tournament sanctioned by the World Baseball Softball Confederation (WBSC), the sport's global governing body, and organized by World Baseball Classic Inc
International Women's Day (IWD) is celebrated on 8 March, commemorating women's fight for equality and liberation along with the women's rights movement. International Women's Day gives focus to issues such as gender equality, reproductive rights, and violence
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Online Neural Networks for Change-Point Detection
Moments when a time series changes its behavior are called change points. Occurrence of change point implies that the state of the system is altered and its timely detection might help to prevent unwanted consequences. In this paper, we present two change-point detection approaches based on neural networks and online learning. These algorithms demonstrate linear computational complexity and are suitable for change-po
Accounting for shared covariates in semi-parametric Bayesian additive regression trees
We propose some extensions to semi-parametric models based on Bayesian additive regression trees (BART). In the semi-parametric BART paradigm, the response variable is approximated by a linear predictor and a BART model, where the linear component is responsible for estimating the main effects and BART accounts for non-specified interactions and non-linearities. Previous semi-parametric models based on BART have assu
In this paper, a white-Box support vector machine (SVM) framework and its swarm-based optimization is presented for supervision of toothed milling cutter through characterization of real-time spindle vibrations. The anomalous moments of vibration evolved due to in-process tool failures (i.e., flank and nose wear, crater and notch wear, edge fracture) have been investigated through time-domain response of acceleration
Differentiable Microscopy Designs an All Optical Phase Retrieval Microscope
Designing new optical systems from the ground up for microscopy imaging tasks such as phase retrieval, requires substantial scientific expertise and creativity. To augment the traditional design process, we propose differentiable microscopy ($\partialμ$), which introduces a top-down design approach. Using all optical phase retrieval as an illustrative example, we demonstrate the effectiveness of data-driven microscop
Explainable classification of astronomical uncertain time series
Exploring the expansion history of the universe, understanding its evolutionary stages, and predicting its future evolution are important goals in astrophysics. Today, machine learning tools are used to help achieving these goals by analyzing transient sources, which are modeled as uncertain time series. Although black-box methods achieve appreciable performance, existing interpretable time series methods failed to o
Empirical Asset Pricing via Ensemble Gaussian Process Regression
We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and lends itself to general online learning tasks. We conduct an empirical analysis on a large cross-section of US stocks from 1
Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part I
We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system. We pursue a cost-driven approach, where a dynamic model in some latent state space is learned by predicting the costs without predicting the observations or actions. In particular, we focus on an intuitive cost-driven state representation learning met
A Robust Multi-Item Auction Design with Statistical Learning
We propose a novel statistical learning method for multi-item auctions that incorporates credible intervals. Our approach employs nonparametric density estimation to estimate credible intervals for bidder types based on historical data. We introduce two new strategies that leverage these credible intervals to reduce the time cost of implementing auctions. The first strategy screens potential winners' value region
Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation
Current methods of blended targets domain adaptation (BTDA) usually infer or consider domain label information but underemphasize hybrid categorical feature structures of targets, which yields limited performance, especially under the label distribution shift. We demonstrate that domain labels are not directly necessary for BTDA if categorical distributions of various domains are sufficiently aligned even facing the
An Embedding-based Approach to Inconsistency-tolerant Reasoning with Inconsistent Ontologies
Inconsistency handling is an important issue in knowledge management. Especially in ontology engineering, logical inconsistencies may occur during ontology construction. A natural way to reason with an inconsistent ontology is to utilize the maximal consistent subsets of the ontology. However, previous studies on selecting maximum consistent subsets have rarely considered the semantics of the axioms, which may result
altiro3D: Scene representation from single image and novel view synthesis
We introduce altiro3D, a free extended library developed to represent reality starting from a given original RGB image or flat video. It allows to generate a light-field (or Native) image or video and get a realistic 3D experience. To synthesize N-number of virtual images and add them sequentially into a Quilt collage, we apply MiDaS models for the monocular depth estimation, simple OpenCV and Telea inpainting techni
Utility Theory based Cognitive Modeling in the Application of Robotics: A Survey
Cognitive modeling, which explores the essence of cognition, including motivation, emotion, and perception, has been widely applied in the artificial intelligence (AI) agent domains, such as robotics. From the computational perspective, various cognitive functionalities have been developed through utility theory to provide a detailed and process-based understanding for specifying corresponding computational models of
Temporal Smoothness Regularisers for Neural Link Predictors
Most algorithms for representation learning and link prediction on relational data are designed for static data. However, the data to which they are applied typically evolves over time, including online social networks or interactions between users and items in recommender systems. This is also the case for graph-structured knowledge bases -- knowledge graphs -- which contain facts that are valid only for specific po
Automated guided vehicles (AGVs) are widely used in various industries, and scheduling and routing them in a conflict-free manner is crucial to their efficient operation. We propose a loop-based algorithm that solves the online, conflict-free scheduling and routing problem for AGVs with any capacity and ordered jobs in loop-based graphs. The proposed algorithm is compared against an exact method, a greedy heuristic a
CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification
Person re-identification (re-ID) is a challenging task that aims to learn discriminative features for person retrieval. In person re-ID, Jaccard distance is a widely used distance metric, especially in re-ranking and clustering scenarios. However, we discover that camera variation has a significant negative impact on the reliability of Jaccard distance. In particular, Jaccard distance calculates the distance based on
On the Impact of Sampling on Deep Sequential State Estimation
State inference and parameter learning in sequential models can be successfully performed with approximation techniques that maximize the evidence lower bound to the marginal log-likelihood of the data distribution. These methods may be referred to as Dynamical Variational Autoencoders, and our specific focus lies on the deep Kalman filter. It has been shown that the ELBO objective can oversimplify data representatio
DivCon: Divide and Conquer for Complex Numerical and Spatial Reasoning in Text-to-Image Generation
Diffusion-driven text-to-image (T2I) generation has achieved remarkable advancements in recent years. To further improve T2I models' capability in numerical and spatial reasoning, layout is employed as an intermedium to bridge large language models and layout-based diffusion models. However, these methods often rely on closed-source, large-scale LLMs for layout prediction, limiting accessibility and scalability.
Deepfake Generation and Detection: A Benchmark and Survey
Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions, which has significant application potential in fields such as entertainment, movie production, digital human creation, to name a few. With the advancements in deep learning, techniques primarily represented by Variational Autoencoders and Generative Adversarial Networks have achieved impressive generati
Simulating Non-Markovian Open Quantum Dynamics with Neural Quantum States
Reducing computational scaling for simulating non-Markovian dissipative dynamics using artificial neural networks is both a major focus and formidable challenge in open quantum systems. To enable neural quantum states (NQSs), we encode environmental memory in dissipatons (quasiparticles with characteristic lifetimes), yielding the dissipaton-embedded quantum master equation (DQME). The resulting NQS-DQME framework ac
LoRA-Ensemble: Efficient Uncertainty Modelling for Self-Attention Networks
Numerous real-world decisions rely on machine learning algorithms and require calibrated uncertainty estimates. However, modern methods often yield overconfident, uncalibrated predictions. The dominant approach to quantifying the uncertainty inherent in the model is to train an ensemble of separate predictors and measure their empirical variance. In an explicit implementation, the ensemble has a high computational co
Goldilocks Test Sets for Face Verification
Reported face verification accuracy has reached a plateau on current well-known test sets. As a result, some difficult test sets have been assembled by reducing the image quality or adding artifacts to the image. However, we argue that test sets can be challenging without artificially reducing the image quality because the face recognition (FR) models suffer from correctly recognizing 1) the pairs from the same ident
Fast Explanations via Policy Gradient-Optimized Explainer
The challenge of delivering efficient explanations is a critical barrier that prevents the adoption of model explanations in real-world applications. Existing approaches often depend on extensive model queries for sample-level explanations or rely on expert's knowledge of specific model structures that trade general applicability for efficiency. To address these limitations, this paper introduces a novel framewor
Few-shot fine-tuning of Diffusion Models (DMs) is a key advancement, significantly reducing training costs and enabling personalized AI applications. However, we explore the training dynamics of DMs and observe an unanticipated phenomenon: during the training process, image fidelity initially improves, then unexpectedly deteriorates with the emergence of noisy patterns, only to recover later with severe overfitting.
ProAct: Progressive Training for Hybrid Clipped Activation Function to Enhance Resilience of DNNs
Deep Neural Networks (DNNs) are extensively employed in safety-critical applications where ensuring hardware reliability is a primary concern. To enhance the reliability of DNNs against hardware faults, activation restriction techniques significantly mitigate the fault effects at the DNN structure level, irrespective of accelerator architectures. State-of-the-art methods offer either neuron-wise or layer-wise clippin
Rolling bearing fault detection has developed rapidly in the field of fault diagnosis technology, and it occupies a very important position in this field. Deep learning-based bearing fault diagnosis models have achieved significant success. At the same time, with the continuous improvement of new signal processing technologies such as Fourier transform, wavelet transform and empirical mode decomposition, the fault di
Notable events recorded on this day and month across all years.