Record 02032026 · captured 2026-08-25
The world looked up Ali Khamenei. 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.
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
List of attacks during the 2026 Iran war
This is a list of airstrikes and bombardments carried out during the 2026 Iran war. The strikes began on 28 February 2026, when Israel and the United States launched attacks on targets across Iran, codenamed Operation Roaring Lion in Israel and Operation Epic
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
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
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
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
Masoud Pezeshkian is an Iranian politician and heart surgeon who has served as the ninth president of Iran since 2024. A member of the reformist faction, he is the oldest person to serve in this position, taking office at the age of 69.
The 2026 Elimination Chamber, also promoted as Elimination Chamber: Chicago, was a professional wrestling pay-per-view (PPV) and livestreaming event produced by WWE. It was the 16th Elimination Chamber event and took place on February 28, 2026, at United Cente
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
Mahmoud Ahmadinejad is an Iranian politician who served as the sixth president of Iran from 2005 to 2013. Ideologically a principlist and nationalist, he was a member of the Expediency Discernment Council and a strong proponent of Iran's nuclear program. He wa
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
Mansoureh Khojasteh Bagherzadeh
Mansoureh Khojasteh Bagherzadeh is the widow of Ali Khamenei, the third president of Iran from 1981 to 1989 and the second supreme leader of Iran from 1989 until his assassination in 2026.
Alireza Arafi is an Iranian Shia cleric and politician who has served as a member of the Guardian Council since 2019, and a member of the Assembly of Experts since 2022.
Connor Storrie is an American actor. He is best known for his breakout role as Ilya Rozanov in the sports romance series Heated Rivalry (2025–present). He hosted an episode of Saturday Night Live in 2026, for which he received a Primetime Emmy Award nomination
The president of the Islamic Republic of Iran is the head of government and the second-highest ranking official of Iran, after the supreme leader. While the president is also Iran's head of state, the system of government of the Islamic Revolution provides tha
Mostafa Hosseini Khamenei is an Iranian Shia cleric. A member of the Khamenei family, he is the eldest son of the second Iranian supreme leader Ali Khamenei, and the elder brother of the third and current supreme leader Mojtaba Khamenei.
Reza Pahlavi is an Iranian political activist and the former Crown Prince of the Pahlavi dynasty of Iran. He is the eldest son of Mohammad Reza Pahlavi, the last Shah of Iran, and his wife, Empress Farah. He lives in the United States as a dissident in exile.
The Iran–Israel conflict is a long-standing geopolitical and military confrontation between the Islamic Republic of Iran and the State of Israel, involving proxy hostilities since 1985 and direct clashes since 2024.
Donovan Andrew Danhausen, better known mononymously as Danhausen, is an American professional wrestler. As of February 2026, he is signed to WWE. He is also known for his tenures in All Elite Wrestling (AEW) and Ring of Honor (ROH), as well as his appearances
James Eugene Carrey is a Canadian and American actor and comedian. Known primarily for his energetic slapstick performances, he is regarded as one of the most prominent comedic actors of his generation. He has received two Golden Globe Awards, in addition to n
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
2026 Iranian supreme leader election
An election for the third supreme leader of Iran by the Assembly of Experts was held from 3 to 8 March 2026, following the assassination of Ali Khamenei on 28 February during the 2026 Iran war. Mojtaba Khamenei, son of Ali Khamenei, was announced as the new su
Masoud Hosseini Khamenei, also known as Mohsen Khamenei is an Iranian Twelver Shia cleric. A member of the Khamenei family, he is the third son of the second Iranian supreme leader Ali Khamenei, and the younger brother of the third and current supreme leader,
Neil Sedaka was an American singer, songwriter, and pianist. Beginning his music career in 1957, he sold millions of records worldwide and wrote or co-wrote over 500 songs for himself and other artists, collaborating mostly with lyricists Howard Greenfield and
Ali Ardashir Larijani was an Iranian politician, military officer, and philosopher who served as the secretary of the Supreme National Security Council from 2025 until his assassination in 2026. He had previously served in the position from 2005 to 2007. From
Scream 7 is a 2026 American slasher film directed by Kevin Williamson and written by Williamson and Guy Busick. It is the sequel to Scream VI (2023) and the seventh installment in the Scream film series. The film stars Neve Campbell, Jasmin Savoy Brown, Mason
The Iranian Revolution, also known as the Islamic Revolution, culminated in the overthrow of the Pahlavi dynasty in 1979. The revolution led to the replacement of the Imperial State of Iran by the Islamic Republic of Iran, as the monarchical government of Shah
Ayatollah is a title for high-ranking Twelver Shia clergy. It came into widespread usage in the 20th century. Those who hold this title must be men and specialists in Islamic sciences such as jurisprudence (fiqh) and principles (usul), often teaching in semina
World War III, also known as the Third World War, is a hypothetical future global conflict subsequent to World War I (1914–1918) and World War II (1939–1945). It is widely predicted that such a war would involve all of the great powers, like its two predecesso
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Machine learning driven object detection and classification within non-visible imagery has an important role in many fields such as night vision, all-weather surveillance and aviation security. However, such applications often suffer due to the limited quantity and variety of non-visible spectral domain imagery, in contrast to the high data availability of visible-band imagery that readily enables contemporary deep l
Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding
We study the problem of efficiently generating high-quality and diverse content in games. Previous work on automated deckbuilding in Hearthstone shows that the quality diversity algorithm MAP-Elites can generate a collection of high-performing decks with diverse strategic gameplay. However, MAP-Elites requires a large number of expensive evaluations to discover a diverse collection of decks. We propose assisting MAP-
Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation
A high-resolution network exhibits remarkable capability in extracting multi-scale features for human pose estimation, but fails to capture long-range interactions between joints and has high computational complexity. To address these problems, we present a Dynamic lightweight High-Resolution Network (Dite-HRNet), which can efficiently extract multi-scale contextual information and model long-range spatial dependency
CO^3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving
Unsupervised contrastive learning for indoor-scene point clouds has achieved great successes. However, unsupervised learning point clouds in outdoor scenes remains challenging because previous methods need to reconstruct the whole scene and capture partial views for the contrastive objective. This is infeasible in outdoor scenes with moving objects, obstacles, and sensors. In this paper, we propose CO^3, namely Coope
Gaussian processes are arguably the most important class of spatiotemporal models within machine learning. They encode prior information about the modeled function and can be used for exact or approximate Bayesian learning. In many applications, particularly in physical sciences and engineering, but also in areas such as geostatistics and neuroscience, invariance to symmetries is one of the most fundamental forms of
ANFIS-based prediction of power generation for combined cycle power plant
This paper presents the application of an adaptive neuro-fuzzy inference system (ANFIS) to predict the generated electrical power in a combined cycle power plant. The ANFIS architecture is implemented in MATLAB through a code that utilizes a hybrid algorithm that combines gradient descent and the least square estimator to train the network. The Model is verified by applying it to approximate a nonlinear equation with
Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally. However, improving the efficiency of a solar PV system remains a significant challenge. To enhance the robustness of the solar system, this paper proposes a trained convolutional neural network (CNN) based fault detection scheme to divide the images of photovoltaic modules. For binary classification
Assessment of Spatio-Temporal Predictors in the Presence of Missing and Heterogeneous Data
Deep learning methods achieve remarkable predictive performance in modeling complex, large-scale data. However, assessing the quality of derived models has become increasingly challenging, as more classical statistical assumptions may no longer apply. These difficulties are particularly pronounced for spatio-temporal data, which exhibit dependencies across both space and time and are often characterized by nonlinear
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
Learning transferable representations from unlabeled time series is crucial for improving performance in data-scarce classification. Existing self-supervised methods often operate at the point level and rely on unidirectional encoding, leading to low semantic density and a mismatch between pre-training and downstream optimization. In this paper, we propose TimeMAE, a self-supervised framework that reformulates masked
In this paper, we provide a novel analytical perspective on the theoretical understanding of gradient-based learning algorithms by interpreting consensus-based optimization (CBO), a recently proposed multi-particle derivative-free optimization method, as a stochastic relaxation of gradient descent. Remarkably, we observe that through communication of the particles, CBO exhibits a stochastic gradient descent (SGD)-lik
DRL-ORA: Distributional Reinforcement Learning with Online Risk Adaption
One of the main challenges in reinforcement learning (RL) is that the agent has to make decisions that would influence the future performance without having complete knowledge of the environment. Dynamically adjusting the level of epistemic risk during the learning process can help to achieve reliable policies in safety-critical settings with better efficiency. In this work, we propose a new framework, Distributional
Less is more -- the Dispatcher/ Executor principle for multi-task Reinforcement Learning
Humans instinctively know how to neglect details when it comes to solve complex decision making problems in environments with unforeseeable variations. This abstraction process seems to be a vital property for most biological systems and helps to 'abstract away' unnecessary details and boost generalisation. In this work we introduce the dispatcher/ executor principle for the design of multi-task Reinforcement
Scalable Mechanism Design for Multi-Agent Path Finding
Multi-Agent Path Finding (MAPF) involves determining paths for multiple agents to travel simultaneously and collision-free through a shared area toward given goal locations. This problem is computationally complex, especially when dealing with large numbers of agents, as is common in realistic applications like autonomous vehicle coordination. Finding an optimal solution is often computationally infeasible, making th
Guidance Graph Optimization for Lifelong Multi-Agent Path Finding
We study how to use guidance to improve the throughput of lifelong Multi-Agent Path Finding (MAPF). Previous studies have demonstrated that, while incorporating guidance, such as highways, can accelerate MAPF algorithms, this often results in a trade-off with solution quality. In addition, how to generate good guidance automatically remains largely unexplored, with current methods falling short of surpassing manually
A Hormetic Approach to the Value-Loading Problem: Preventing the Paperclip Apocalypse?
The value-loading problem is a significant challenge for researchers aiming to create artificial intelligence (AI) systems that align with human values and preferences. This problem requires a method to define and regulate safe and optimal limits of AI behaviors. In this work, we propose HALO (Hormetic ALignment via Opponent processes), a regulatory paradigm that uses hormetic analysis to regulate the behavioral patt
Bridging the gap between diffusion models and human preferences is crucial for their integration into practical generative workflows. While optimizing downstream reward models has emerged as a promising alignment strategy, concerns arise regarding the risk of excessive optimization with learned reward models, which potentially compromises ground-truth performance. In this work, we confront the reward overoptimization
A blockchain-based intelligent recommender system framework for enhancing supply chain resilience
Applying advanced digital technologies such as artificial intelligence (AI), blockchain (BLC), bigdata analytics (BDA) and digital twin (DT)/simulations to enhance supply chain resilience (SCRes) has been widely discussed in light of the global pandemic, regional conflicts, and the technology revolution such as Industry 4.0 and 5.0. Previous studies are limited at the conceptual level as the proactive SCRes measure w
Multi-Agent Path Finding (MAPF) is the problem of moving multiple agents from starts to goals without collisions. Lifelong MAPF (LMAPF) extends MAPF by continuously assigning new goals to agents. We present our winning approach to the 2023 League of Robot Runners LMAPF competition, which leads us to several interesting research challenges and future directions. In this paper, we outline three main research challenges
Integrative analysis of multiple heterogeneous datasets has become standard practice in many research fields, especially in single-cell genomics and medical informatics. Existing approaches oftentimes suffer from limited power in capturing nonlinear structures, insufficient account of noisiness and effects of high-dimensionality, lack of adaptivity to signals and sample sizes imbalance, and their results are sometime
Uni-ISP: Toward Unifying the Learning of ISPs from Multiple Mobile Cameras
Modern end-to-end image signal processors (ISPs) can learn complex mappings from RAW/XYZ data to sRGB (and vice versa), opening new possibilities in image processing. However, the growing diversity of camera models, particularly in mobile devices, renders the development of individual ISPs unsustainable due to their limited versatility and adaptability across varied camera systems. In this paper, we introduce Uni-ISP
Spectral-Stimulus Information for Self-Supervised Stimulus Encoding
Mammalian spatial navigation relies on specialized neurons, such as place and grid cells, which encode position based on self-motion and environmental cues. While extensive research has explored the computational role of grid cells, the principles underlying efficient place cell coding remain less understood. Existing spatial information rate measures primarily assess single-neuron encoding, limiting insights into po
As Artificial Intelligence (AI) models are increasingly integrated into critical systems, the need for a robust framework to establish the trustworthiness of AI is increasingly paramount. While collaborative efforts have established conceptual foundations for such a framework, there remains a significant gap in developing concrete, technically robust methods for assessing AI model quality and performance. This paper
R2GenCSR: Mining Contextual and Residual Information for LLMs-based Radiology Report Generation
Inspired by the tremendous success of Large Language Models (LLMs), existing Radiology report generation methods attempt to leverage large models to achieve better performance. They usually adopt a Transformer to extract the visual features of a given X-ray image, and then, feed them into the LLM for text generation. How to extract more effective information for the LLMs to help them improve final results is an urgen
Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal Image Fusion
Multi-modal image fusion integrates complementary information from different modalities to produce enhanced and informative images. Although State-Space Models, such as Mamba, are proficient in long-range modeling with linear complexity, most Mamba-based approaches use fixed scanning strategies, which can introduce biased prior information. To mitigate this issue, we propose a novel Bayesian-inspired scanning strateg
We use the Quality Diversity (QD) algorithm with Neural Cellular Automata (NCA) to automatically evaluate Multi-Agent Path Finding (MAPF) algorithms by generating diverse maps. Previously, researchers typically evaluate MAPF algorithms on a set of specific, human-designed maps at their initial stage of algorithm design. However, such fixed maps may not cover all scenarios, and algorithms may overfit to the small set
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