Record 03032026 · 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
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
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
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.
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
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
A company is a legal entity representing an association of legal persons with a shared objective, such as generating profit or benefiting society. Depending on the jurisdiction, companies can take on various forms, including voluntary associations, nonprofit o
Harrison Ford is an American actor. Regarded as a cinematic cultural icon, Ford's accolades include nominations for an Academy Award, a British Academy Film Award, two Emmy Awards, five Golden Globes and two SAG-AFTRA's Actor Awards. He is also the recipient o
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
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.
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
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.
The 32nd Actor Awards, honoring the best achievements in film and television performances for the year 2025, were presented on March 1, 2026, at the Shrine Auditorium and Expo Hall in Los Angeles, California. For the third year in a row, the ceremony streamed
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
Calista Kay Flockhart is an American actress. She is best known for her role as the title character on the television series Ally McBeal (1997–2002), for which she received a Golden Globe Award in 1998 and was thrice nominated for the Primetime Emmy Award for
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
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
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.
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.
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
Thomas Stanley Holland is a British actor. His accolades include a BAFTA Award as well as two Critics' Choice Awards nominations. Holland's films as a leading actor have grossed over $14.9 billion worldwide, making him the Fourth highest-grossing actor of all
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.
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.
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
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
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 Interim Leadership Council is a body that temporarily handles the duties of the supreme leader of Iran when the office is vacant. The council consists of the president of Iran, the chief justice of Iran, and a member of the Guardian Council selected by the
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Towards Accurate One-Stage Object Detection with AP-Loss
One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background class imbalance issue due to the large number of anchors. This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (A
A Survey Mobility Management in 5G Networks
For the next wireless communication systems, the major challenges are to provide ubiquitous wireless access abilities and maintain the quality of service and seamless mobility management for mobile communication devices in heterogeneous networks. Due to the rapid growth of mobile users, this demand becomes more challenging where the users always require seamless connectivity while they move to other places at any tim
AP-Loss for Accurate One-Stage Object Detection
One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background class imbalance issue due to the large number of anchors. This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (A
OmniTracker: Unifying Object Tracking by Tracking-with-Detection
Visual Object Tracking (VOT) aims to estimate the positions of target objects in a video sequence, which is an important vision task with various real-world applications. Depending on whether the initial states of target objects are specified by provided annotations in the first frame or the categories, VOT could be classified as instance tracking (e.g., SOT and VOS) and category tracking (e.g., MOT, MOTS, and VIS) t
FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead
We present FengWu, an advanced data-driven global medium-range weather forecast system based on Artificial Intelligence (AI). Different from existing data-driven weather forecast methods, FengWu solves the medium-range forecast problem from a multi-modal and multi-task perspective. Specifically, a deep learning architecture equipped with model-specific encoder-decoders and cross-modal fusion Transformer is elaboratel
RFAConv: Receptive-Field Attention Convolution for Improving Convolutional Neural Networks
In the realm of deep learning, spatial attention mechanisms have emerged as a vital method for enhancing the performance of convolutional neural networks. However, these mechanisms possess inherent limitations that cannot be overlooked. This work delves into the mechanism of spatial attention and reveals a new insight. It is that the mechanism essentially addresses the issue of convolutional parameter sharing. By add
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances
In recent years, Variational Quantum Algorithms (VQAs) have emerged as a promising approach for solving optimization problems on quantum computers in the NISQ era. However, one limitation of VQAs is their reliance on fixed-structure circuits, which may not be taylored for specific problems or hardware configurations. A leading strategy to address this issue are Adaptative VQAs, which dynamically modify the circuit st
Impression-Aware Recommender Systems
Novel data sources bring new opportunities to improve the quality of recommender systems and serve as a catalyst for the creation of new paradigms on personalized recommendations. Impressions are a novel data source containing the items shown to users on their screens. Past research focused on providing personalized recommendations using interactions, and occasionally using impressions when such a data source was ava
Biomedical knowledge is growing in an astounding pace with a majority of this knowledge is represented as scientific publications. Text mining tools and methods represents automatic approaches for extracting hidden patterns and trends from this semi structured and unstructured data. In Biomedical Text mining, Literature Based Discovery (LBD) is the process of automatically discovering novel associations between medic
Factuality Challenges in the Era of Large Language Models
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention. These incredibly useful, natural-sounding tools mark significant advances in natural language generation, yet they exhibit a propensity to generate false, erroneous, or misleading content -- commonly referred to as "hallucinations."
Velocity Disambiguation for Video Frame Interpolation
Existing video frame interpolation (VFI) methods blindly predict where each object is at a specific timestep t ("time indexing"), which struggles to predict precise object movements. Given two images of a baseball, there are infinitely many possible trajectories: accelerating or decelerating, straight or curved. This often results in blurry frames as the method averages out these possibilities. Instead of for
Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning
Directed Acyclic Graphs (DAGs) are a standard tool in causal modeling, but their suitability for capturing the complexity of large-scale multimodal data is questionable. In practice, real-world multimodal datasets are often collected from heterogeneous generative processes that do not conform to a single DAG. Instead, they may involve multiple, and even opposing, DAG structures with inverse causal directions. To addr
Towards Precision Cardiovascular Analysis in Zebrafish: The ZACAF Paradigm
Quantifying cardiovascular parameters like ejection fraction in zebrafish as a host of biological investigations has been extensively studied. Since current manual monitoring techniques are time-consuming and fallible, several image processing frameworks have been proposed to automate the process. Most of these works rely on supervised deep-learning architectures. However, supervised methods tend to be overfitted on
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization
Zero-shot anomaly detection (ZSAD) methods entail detecting anomalies directly without access to any known normal or abnormal samples within the target item categories. Existing approaches typically rely on the robust generalization capabilities of multimodal pretrained models, computing similarities between manually crafted textual features representing "normal" or "abnormal" semantics and image feat
Can we modify the training data distribution to encourage the underlying optimization method toward finding solutions with superior generalization performance on in-distribution data? In this work, we approach this question for the first time by comparing the inductive bias of gradient descent (GD) with that of sharpness-aware minimization (SAM). By studying a two-layer CNN, we rigorously prove that SAM learns differ
VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data
Effective clustering of biomedical data is crucial in precision medicine, enabling accurate stratifiction of patients or samples. However, the growth in availability of high-dimensional categorical data, including `omics data, necessitates computationally efficient clustering algorithms. We present VICatMix, a variational Bayesian finite mixture model designed for the clustering of categorical data. The use of variat
Towards Camera Open-set 3D Object Detection for Autonomous Driving Scenarios
Conventional camera-based 3D object detectors in autonomous driving are limited to recognizing a predefined set of objects, which poses a safety risk when encountering novel or unseen objects in real-world scenarios. To address this limitation, we present OS-Det3D, a two-stage training framework designed for camera-based open-set 3D object detection. In the first stage, our proposed 3D object discovery network (ODN3D
We address the task of identifying distracted driving by analyzing in-car videos using efficient transformers. Although transformer models have achieved outstanding performance in human action recognition tasks, their high computational costs limit their application onboard a vehicle. We introduce POGUISE+, a multi-task video transformer that, given an input clip, predicts the distracted driving action, the driver
MSSPlace: Multi-Sensor Place Recognition with Visual and Text Semantics
Place recognition is a challenging task in computer vision, crucial for enabling autonomous vehicles and robots to navigate previously visited environments. While significant progress has been made in learnable multimodal methods that combine onboard camera images and LiDAR point clouds, the full potential of these methods remains largely unexplored in localization applications. In this paper, we study the impact of
A Dataset for Crucial Object Recognition in Blind and Low-Vision Individuals' Navigation
This paper introduces a dataset for improving real-time object recognition systems to aid blind and low-vision (BLV) individuals in navigation tasks. The dataset comprises 21 videos of BLV individuals navigating outdoor spaces, and a taxonomy of 90 objects crucial for BLV navigation, refined through a focus group study. We also provide object labeling for the 90 objects across 31 video segments created from the 21 vi
Analyzing the Effectiveness of Quantum Annealing with Meta-Learning
The field of Quantum Computing has gathered significant popularity in recent years and a large number of papers have studied its effectiveness in tackling many tasks. We focus in particular on Quantum Annealing (QA), a meta-heuristic solver for Quadratic Unconstrained Binary Optimization (QUBO) problems. It is known that the effectiveness of QA is dependent on the task itself, as is the case for classical solvers, bu
With the growing adoption of electric vehicles (EVs), understanding user charging behavior has become critical for grid stability and transportation planning. This study investigates the behavioral heterogeneity of EV taxi drivers by analyzing the interaction between psychological traits and situational triggers within dynamic travel contexts. Leveraging large language models (LLMs) as a core simulation tool, a novel
Large Language Model Agent in Financial Trading: A Survey
Trading is a highly competitive task that requires a combination of strategy, knowledge, and psychological fortitude. With the recent success of large language models(LLMs), it is appealing to apply the emerging intelligence of LLM agents in this competitive arena and understanding if they can outperform professional traders. In this survey, we provide a comprehensive review of the current research on using LLMs as a
Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey
The rapid development of artificial intelligence has constantly reshaped the field of intelligent healthcare and medicine. As a vital technology, multimodal learning has increasingly garnered interest due to data complementarity, comprehensive modeling form, and great application potential. Currently, numerous researchers are dedicating their attention to this field, conducting extensive studies and constructing abun
Reinforcement Learning for Variational Quantum Circuits Design
Variational Quantum Algorithms have emerged as promising tools for solving optimization problems on quantum computers. These algorithms leverage a parametric quantum circuit called ansatz, where its parameters are adjusted by a classical optimizer with the goal of optimizing a certain cost function. However, a significant challenge lies in designing effective circuits for addressing specific problems. In this study,
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