Record 05032026 · 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.
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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
James Dell Talarico is an American politician and educator who has served since 2018 as a member of the Texas House of Representatives. He is the Democratic nominee in the 2026 U.S. Senate election in Texas.
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
IRIS Dena (75) was a Moudge-class frigate in the Southern Fleet of the Islamic Republic of Iran Navy. She was named after Mount Dena, and was commissioned into the navy in 2021.
Jasmine Felicia Crockett is an American politician serving as the U.S. representative for Texas's 30th congressional district since 2023. A member of the Democratic Party, she represented the 100th district in the Texas House of Representatives from 2021 to 20
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
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 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
The Yakovlev Yak-130 is a subsonic, two-seat, advanced jet trainer and light combat aircraft.
Steven Hixson Toth is an American businessman, pastor, and politician serving as a member of the Texas House of Representatives from District 15, The Woodlands area. Toth is the Republican nominee for Texas's 2nd congressional district, having defeated incumbe
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 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
Louis Leo Holtz was an American college football coach. He served as the head football coach at the College of William & Mary (1969–1971), North Carolina State University (1972–1975), the New York Jets (1976), the University of Arkansas (1977–1983), the Univer
The Islamic Republic of Iran Navy, also referred to as the Iranian Navy or the Artesh, is the naval warfare branch of Iran's regular Islamic Republic of Iran Armed Forces. The Islamic Revolutionary Guard Corps, formed after the 1979 revolution, also has a Navy
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.
Daniel Reed Crenshaw is an American politician and former United States Navy SEAL officer serving as the U.S. representative for Texas's 2nd congressional district since 2019. He is a member of the Republican Party.
2026 United States Senate election in Texas
The 2026 United States Senate election in Texas will be held on November 3, 2026, to elect a member of the United States Senate to represent the state of Texas. Republican state attorney general Ken Paxton and Democratic state representative James Talarico are
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
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 Epstein files are a partially released collection of millions of documents, images, videos, and emails related to the activities of American financier and convicted child sex offender Jeffrey Epstein, including his social circle of public figures, politici
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
On 28 February 2026, Ali Khamenei, the supreme leader of Iran, was assassinated in Tehran as part of a series of Israeli airstrikes aimed at high-ranking Iranian officials. Khamenei's death was confirmed by the Iranian government on 1 March. His death occurred
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.
The Mark 48 and its improved Advanced Capability (ADCAP) variant are American heavyweight submarine-launched torpedoes. They were designed to sink deep-diving nuclear-powered submarines and high-performance surface ships.
Mohammad Reza Pahlavi was the last Shah of Iran, reigning from 1941 to 1979. He succeeded his father Reza Shah and ruled the Imperial State of Iran until he was overthrown in the Islamic Revolution led by Ruhollah Khomeini, which abolished the Iranian monarchy
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
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
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 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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Schrödinger's Camera: First Steps Towards a Quantum-Based Privacy Preserving Camera
Privacy-preserving vision must overcome the dual challenge of utility and privacy. Too much anonymity renders the images useless, but too little privacy does not protect sensitive data. We propose a novel design for privacy preservation, where the imagery is stored in quantum states. In the future, this will be enabled by quantum imaging cameras, and, currently, storing very low resolution imagery in quantum states i
Recurrent Action Transformer with Memory
Transformers have become increasingly popular in offline reinforcement learning (RL) due to their ability to treat agent trajectories as sequences, reframing policy learning as a sequence modeling task. However, in partially observable environments (POMDPs), effective decision-making depends on retaining information about past events -- something that standard transformers struggle with due to the quadratic complexit
Crystal-GFN: sampling crystals with desirable properties and constraints
The discovery of novel solid-state materials, such as electrocatalysts, super-ionic conductors, or photovoltaic materials, plays a critical role in addressing various global challenges. It has, for instance, the potential to significantly improve the efficiency of renewable energy production and storage, thereby making substantial contributions to climate crisis mitigation strategies. In this paper, we introduce Crys
GeoTop: Advancing Image Classification with Geometric-Topological Analysis
A fundamental challenge in diagnostic imaging is the phenomenon of topological equivalence, where benign and malignant structures share global topology but differ in critical geometric detail, leading to diagnostic errors in both conventional and deep learning models. We introduce GeoTop, a mathematically principled framework that unifies Topological Data Analysis (TDA) and Lipschitz-Killing Curvatures (LKCs) to reso
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity
We study the problem of hypothesis selection under the constraint of local differential privacy. Given a class $\mathcal{F}$ of $k$ distributions and a set of i.i.d. samples from an unknown distribution $h$, the goal of hypothesis selection is to pick a distribution $\hat{f}$ whose total variation distance to $h$ is comparable with the best distribution in $\mathcal{F}$ (with high probability). We devise an $\varepsi
Catch Me If You Can Describe Me: Open-Vocabulary Camouflaged Instance Segmentation with Diffusion
Text-to-image diffusion techniques have shown exceptional capabilities in producing high-quality, dense visual predictions from open-vocabulary text. This indicates a strong correlation between visual and textual domains in open concepts and that diffusion-based text-to-image models can capture rich and diverse information for computer vision tasks. However, we found that those advantages do not hold for learning of
Graph Neural Networks in EEG-based Emotion Recognition: A Survey
Compared to other modalities, EEG-based emotion recognition can intuitively respond to the emotional patterns in the human brain and, therefore, has become one of the most concerning tasks in the brain-computer interfaces field. Since dependencies within brain regions are closely related to emotion, a significant trend is to develop Graph Neural Networks (GNNs) for EEG-based emotion recognition. However, brain region
Learning to Generate Conditional Tri-plane for 3D-aware Expression Controllable Portrait Animation
In this paper, we present Export3D, a one-shot 3D-aware portrait animation method that is able to control the facial expression and camera view of a given portrait image. To achieve this, we introduce a tri-plane generator with an effective expression conditioning method, which directly generates a tri-plane of 3D prior by transferring the expression parameter of 3DMM into the source image. The tri-plane is then deco
FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Matching
The paper proposes FireANTs, a multi-scale Adaptive Riemannian Optimization algorithm for dense diffeomorphic image matching. Existing state-of-the-art methods for diffeomorphic image matching are slow due to inefficient implementations and slow convergence due to the ill-conditioned nature of the optimization problem. Deep learning methods offer fast inference but require extensive training time, substantial inferen
A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving
Reinforcement learning has emerged as an important approach for autonomous driving. A reward function is used in reinforcement learning to establish the learned skill objectives and guide the agent toward the optimal policy. Since autonomous driving is a complex domain with partly conflicting objectives with varying degrees of priority, developing a suitable reward function represents a fundamental challenge. This pa
RAEE: A Robust Retrieval-Augmented Early Exit Framework for Efficient Inference
Deploying large language model inference remains challenging due to their high computational overhead. Early exit optimizes model inference by adaptively reducing the number of inference layers. Current methods typically train internal classifiers or use heuristic methods to determine the exit layer. However, those methods either introduce significant training overheads or lead to performance degradation. To address
Leveraging Large Language Models for Semantic Query Processing in a Scholarly Knowledge Graph
The proposed research aims to develop an innovative semantic query processing system that enables users to obtain comprehensive information about research works produced by Computer Science (CS) researchers at the Australian National University (ANU). The system integrates Large Language Models (LLMs) with the ANU Scholarly Knowledge Graph (ASKG), a structured repository of all research-related artifacts produced at
Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset
The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previous state-of-the-art approaches for automated analysis leverage vision-language models (VLMs) that jointly model images and radiology reports. However, current medical VLMs are generally limited to 2D images and short reports. Here to overcom
Tracking solutions of time-varying variational inequalities
Tracking the solution of time-varying variational inequalities is an important problem with applications in game theory, optimization, and machine learning. Existing work considers time-varying games or time-varying optimization problems. For strongly convex optimization problems or strongly monotone games, these results provide tracking guarantees under the assumption that the variation of the time-varying problem i
Grammatical rules in natural languages are often characterized by exceptions. How do language learners learn these exceptions to otherwise general patterns? Here, we study this question through the case study of English passivization. While passivization is in general quite productive, there are cases where it cannot apply (cf. the following sentence is ungrammatical: *One hour was lasted by the meeting). Using neura
Black Box Meta-Learning Intrinsic Rewards
The broader application of reinforcement learning (RL) is limited by challenges including data efficiency, generalization capability, and ability to learn in sparse-reward environments. Meta-learning has emerged as a promising approach to address these issues by optimizing components of the learning algorithm to meet desired characteristics. Additionally, a different line of work has extensively studied the use of in
AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithm
We present AuToMATo, a novel clustering algorithm based on persistent homology. While AuToMATo is not parameter-free per se, we provide default choices for its parameters that make it into an out-of-the-box clustering algorithm that performs well across the board. AuToMATo combines the existing ToMATo clustering algorithm with a bootstrapping procedure in order to separate significant peaks of an estimated density fu
Detection of correlation in a pair of random graphs is a fundamental statistical and computational problem that has been extensively studied in recent years. In this work, we consider a pair of correlated (sparse) stochastic block models $\mathcal{S}(n,\tfracλ{n};k,ε;s)$ that are subsampled from a common parent stochastic block model $\mathcal S(n,\tfracλ{n};k,ε)$ with $k=O(1)$ symmetric communities, average degree $
As humans can explore and understand the world through active touch, similar capability is desired for robots. In this paper, we address the problem of active tactile object recognition, pose estimation and shape transfer learning, where a customized particle filter (PF) and Gaussian process implicit surface (GPIS) is combined in a unified Bayesian framework. Upon new tactile input, the customized PF updates the join
FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models
The training of diffusion models is computationally intensive, making effective pre-training essential. However, real-world deployments often demand models of variable sizes due to diverse memory and computational constraints, posing challenges when corresponding pre-trained versions are unavailable. To address this, we propose FINE, a novel pre-training method whose resulting model can flexibly factorize its knowled
Diffusion & Adversarial Schrödinger Bridges via Iterative Proportional Markovian Fitting
The Iterative Markovian Fitting (IMF) procedure, which iteratively projects onto the space of Markov processes and the reciprocal class, successfully solves the Schrödinger Bridge (SB) problem. However, an efficient practical implementation requires a heuristic modification -- alternating between fitting forward and backward time diffusion at each iteration. This modification is crucial for stabilizing training and a
Scaling Laws For Diffusion Transformers
Diffusion transformers (DiT) have already achieved appealing synthesis and scaling properties in content recreation, e.g., image and video generation. However, scaling laws of DiT are less explored, which usually offer precise predictions regarding optimal model size and data requirements given a specific compute budget. Therefore, experiments across a broad range of compute budgets, from 1e17 to 6e18 FLOPs are condu
TextMaster: A Unified Framework for Realistic Text Editing via Glyph-Style Dual-Control
In image editing tasks, high-quality text editing capabilities can significantly reduce both human and material resource costs. Existing methods, however, face significant limitations in terms of stroke accuracy for complex text and controllability of generated text styles. To address these challenges, we propose TextMaster, a solution capable of accurately editing text across various scenarios and image regions, whi
Offline-to-Online Reinforcement Learning has emerged as a powerful paradigm, leveraging offline data for initialization and online fine-tuning to enhance both sample efficiency and performance. However, most existing research has focused on single-agent settings, with limited exploration of the multi-agent extension, i.e., Offline-to-Online Multi-Agent Reinforcement Learning (O2O MARL). In O2O MARL, two critical chal
Toward Reasoning on the Boundary: A Mixup-based Approach for Graph Anomaly Detection
While GNN-based detection methods excel at identifying overt outliers, they often struggle with boundary anomalies -- subtly camouflaged nodes that are difficult to distinguish from normal instances. This limitation highlights a fundamental gap in the reasoning capabilities of existing methods. We attribute this issue to the reliance of standard Graph Contrastive Learning (GCL) on easy negatives, which fosters the le
Notable events recorded on this day and month across all years.