Record 13032026 · captured 2026-08-25
The world looked up 2026 Iran war. 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.
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
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
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 Strait of Hormuz is a waterway between the Persian Gulf and the Gulf of Oman. On the north coast lies Iran, and on the south coast lies the Musandam Peninsula under the Musandam Governorate of Oman, with a portion of the southwest of the peninsula under th
Scarpetta is an American crime drama television series developed by Liz Sarnoff and based on the book series of the same name by Patricia Cornwell. It stars Nicole Kidman as the titular character as Dr. Kay Scarpetta, a forensic pathologist who uses forensic t
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
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
One Piece is a fantasy adventure television series developed by Matt Owens and Steven Maeda for Netflix. The series is a live-action adaptation of the 1997 Japanese manga series One Piece by Eiichiro Oda, who also serves as a creative consultant. It is produce
Edrice Femi "Bam" Adebayo is an American professional basketball player for the Miami Heat of the National Basketball Association (NBA). He played college basketball for the Kentucky Wildcats before being selected by the Heat with the 14th overall pick in the
Anton Viktorovich Yelchin was an American actor. Born in the Soviet Union to a Russian Jewish family, he emigrated to the United States with his parents at the age of six months. He began his career as a child actor, appearing as the lead of the mystery drama
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
Gaurav Kapur, also spelled as Gaurav Kapoor is an Indian actor, television and cricket presenter known for being the host of the pre-match Indian Premier League show, Extraaa Innings T20, Cricbuzz Live and his YouTube show Breakfast with Champions.
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
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
Kritika Kamra is an Indian actress. She began her career in television, with the soap operas Kitani Mohabbat Hai (2009–2011), Kuch Toh Log Kahenge (2011–2013), and Reporters (2015). Kamra has also featured in Anubhav Sinha's film Bheed (2023), and the Amazon P
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.
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
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
Vincent Joseph Pasquantino, is an American professional baseball first baseman and designated hitter for the Kansas City Royals of Major League Baseball (MLB). Nicknamed "Pasquatch", he made his MLB debut in 2022. In international baseball competitions he play
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
Pleural thickening is an increase in the bulkiness of one or both of the pulmonary pleurae. A severe form of the condition is known as fibrothorax.
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.
Ghukas Karnetsi was Catholicos of All Armenians in Etchmiadzin from 1780 to 1799. He succeeded his former teacher Simeon of Yerevan and continued many of his policies. He was catholicos during a time of economic hardship and conflict in Iranian Armenia and app
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
Federico Santiago Valverde Dipetta is a Uruguayan professional footballer who plays as a central midfielder for La Liga club Real Madrid and the Uruguay national team. Considered one of the best midfielders in the world, he is known for his versatility, work r
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.
Null may refer to:
Hermann Wilhelm Göring was a German politician, aviator, military commander, and convicted war criminal. He was one of the most powerful figures in the Nazi Party, which controlled Germany from 1933 to 1945. He also served as Oberbefehlshaber der Luftwaffe, a
Malik Edward Stanley is an American football wide receiver who plays for the Munich Ravens of the European League of Football (ELF). He played college football at South Alabama and Louisiana Tech.
33-61 Emerson Place Row is a set of historic rowhouses located at Buffalo in Erie County, New York. It is one of a rare surviving group of speculative multi-unit frame residences designed to resemble rowhouses in the city of Buffalo. It was built in 1893, by l
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
The COVID-19 pandemic has caused millions of cases and deaths and the AI-related scientific community, after being involved with detecting COVID-19 signs in medical images, has been now directing the efforts towards the development of methods that can predict the progression of the disease. This task is multimodal by its very nature and, recently, baseline results achieved on the publicly available AIforCOVID dataset
Multimodal Explainability via Latent Shift applied to COVID-19 stratification
We are witnessing a widespread adoption of artificial intelligence in healthcare. However, most of the advancements in deep learning in this area consider only unimodal data, neglecting other modalities. Their multimodal interpretation necessary for supporting diagnosis, prognosis and treatment decisions. In this work we present a deep architecture, which jointly learns modality reconstructions and sample classificat
Group-equivariant convolutional neural networks (G-CNN) heavily rely on parameter sharing to increase CNN's data efficiency and performance. However, the parameter-sharing strategy greatly increases the computational burden for each added parameter, which hampers its application to deep neural network models. In this paper, we address these problems by proposing a non-parameter-sharing approach for group equivari
A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Values
In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical domain, our primary objective is to develop an AI model capable of dynamically handling this missing data. Additionally, we aim to leverage all accessible data, effectively analyzing both uncensored p
A Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography
Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first needs intravenously administration of an iodinated contrast medium; then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image. The two scans are then combined to get a recombined image showing contrast enhancement. Despite CESM diagnostic advantages for breast cancer di
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
State-of-the-art methods for conditional average treatment effect (CATE) estimation make widespread use of representation learning. Here, the idea is to reduce the variance of the low-sample CATE estimation by a (potentially constrained) low-dimensional representation. However, low-dimensional representations can lose information about the observed confounders and thus lead to bias, because of which the validity of r
Onflow: a model free, online portfolio allocation algorithm robust to transaction fees
We introduce Onflow, a reinforcement learning method for optimizing portfolio allocation via gradient flows. Our approach dynamically adjusts portfolio allocations to maximize expected log returns while accounting for transaction costs. Using a softmax parameterization, Onflow updates allocations through an ordinary differential equation derived from gradient flow methods. This algorithm belongs to the large class of
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings, resulting in clinical dataset with highly unbalanced therapy representation. To tackle this issue, we introduce
Domain-Independent Dynamic Programming
For combinatorial optimization problems, model-based paradigms such as mixed-integer programming (MIP) and constraint programming (CP) aim to decouple modeling and solving a problem: the `holy grail' of declarative problem solving. We propose domain-independent dynamic programming (DIDP), a novel model-based paradigm based on dynamic programming (DP). While DP is not new, it has typically been implemented as a pr
Estimating Canopy Height at Scale
We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the re
Preserving Full Degradation Details for Blind Image Super-Resolution
The performance of image super-resolution relies heavily on the accuracy of degradation information, especially under blind settings. Due to the absence of true degradation models in real-world scenarios, previous methods learn distinct representations by distinguishing different degradations in a batch. However, the most significant degradation differences may provide shortcuts for the learning of representations su
Not Another Imputation Method: A Transformer-based Model for Missing Values in Tabular Datasets
Handling missing values in tabular datasets presents a significant challenge in training and testing artificial intelligence models, an issue usually addressed using imputation techniques. Here we introduce "Not Another Imputation Method" (NAIM), a novel transformer-based model specifically designed to address this issue without the need for traditional imputation techniques. NAIM's ability to avoid the n
A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications
Deep learning has revolutionized biomedical research by providing sophisticated methods to handle complex, high-dimensional data. Multimodal deep learning (MDL) further enhances this capability by integrating diverse data types such as imaging, textual data, and genetic information, leading to more robust and accurate predictive models. In MDL, differently from early and late fusion methods, intermediate fusion stand
Multi-agent Reinforcement Learning (MARL) is emerging as a key framework for various sequential decision-making and control tasks. Unlike their single-agent counterparts, multi-agent systems necessitate successful cooperation among the agents. The deployment of these systems in real-world scenarios often requires decentralized training, a diverse set of agents, and learning from infrequent environmental reward signal
Understanding how real data is distributed in high dimensional spaces is the key to many tasks in machine learning. We want to provide a natural geometric structure on the space of data employing a ReLU neural network trained as a classifier. Through the Data Information Matrix (DIM), a variation of the Fisher information matrix, the model will discern a singular foliation structure on the space of data. We show that
This paper introduces Llettuce, an open-source tool designed to address the complexities of converting medical terms into OMOP standard concepts. Unlike existing solutions such as the Athena database search and Usagi, which struggle with semantic nuances and require substantial manual input, Llettuce leverages advanced natural language processing, including large language models and fuzzy matching, to automate and en
Stein Variational Evolution Strategies
Stein Variational Gradient Descent (SVGD) is a highly efficient method to sample from an unnormalized probability distribution. However, the SVGD update relies on gradients of the log-density, which may not always be available. Existing gradient-free versions of SVGD make use of simple Monte Carlo approximations or gradients from surrogate distributions, both with limitations. To improve gradient-free Stein variation
CARROT: A Learned Cost-Constrained Retrieval Optimization System for RAG
Large Language Models (LLMs) have demonstrated impressive ability in generation and reasoning tasks but struggle with handling up-to-date knowledge, leading to inaccuracies or hallucinations. Retrieval-Augmented Generation (RAG) mitigates this by retrieving and incorporating external knowledge into input prompts. In particular, due to LLMs' context window limitations and long-context hallucinations, only the most
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
Estimating causal quantities from observational data is crucial for understanding the safety and effectiveness of medical treatments. However, to make reliable inferences, medical practitioners require not only estimating averaged causal quantities, such as the conditional average treatment effect, but also understanding the randomness of the treatment effect as a random variable. This randomness is referred to as al
In this work, we address unconstrained finite-sum optimization problems, with particular focus on instances originating in large scale deep learning scenarios. Our main interest lies in the exploration of the relationship between recent line search approaches for stochastic optimization in the overparametrized regime and momentum directions. First, we point out that combining these two elements with computational ben
Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models
We address the issue of the testability of instrumental variables derived from observational data. Most existing testable implications are centered on scenarios where the treatment is a discrete variable, e.g., instrumental inequality (Pearl, 1995), or where the effect is assumed to be constant, e.g., instrumental variables condition based on the principle of independent mechanisms (Burauel, 2023). However, treatment
Finance-Informed Neural Network: Learning the Geometry of Option Pricing
We propose a Finance-Informed Neural Network (FINN) for option pricing and hedging that integrates financial theory directly into machine learning. Instead of training on observed option prices, FINN is learned through a self-supervised replication objective based on dynamic hedging, ensuring economic consistency by construction. We show theoretically that minimizing replication error recovers the arbitrage-free pric
MARIA: a Multimodal Transformer Model for Incomplete Healthcare Data
In healthcare, the integration of multimodal data is pivotal for developing comprehensive diagnostic and predictive models. However, managing missing data remains a significant challenge in real-world applications. We introduce MARIA (Multimodal Attention Resilient to Incomplete datA), a novel transformer-based deep learning model designed to address these challenges through an intermediate fusion strategy. Unlike co
RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning
Network simulation is pivotal in network modeling, assisting with tasks ranging from capacity planning to performance estimation. Traditional approaches such as Discrete Event Simulation (DES) face limitations in terms of computational cost and accuracy. This paper introduces RouteNet-Gauss, a novel integration of a testbed network with a Machine Learning (ML) model to address these challenges. By using the testbed a
Accurately knowing uncertainties in appearance-based gaze tracking is critical for ensuring reliable downstream applications. Due to the lack of individual uncertainty labels, current uncertainty-aware approaches adopt probabilistic models to acquire uncertainties by following distributions in the training dataset. Without regulations, this approach lets the uncertainty model build biases and overfits the training da
Notable events recorded on this day and month across all years.