Record 28042026 · captured 2026-08-25
The world looked up Michael (2026 film). 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.
Michael is a 2026 biographical film directed by Antoine Fuqua and written by John Logan. It follows the early life of the American singer Michael Jackson, from his time with the Jackson 5 in the 1960s to the Bad World Tour in the late 1980s. Jackson is portray
Michael Joseph Jackson was an American singer, songwriter, dancer, and philanthropist. Dubbed the "King of Pop", he is widely regarded as one of the most culturally significant figures of the 20th century. His musical achievements broke American racial barrier
Jaafar Jeremiah Jackson is an American actor and singer. A member of the Jackson family, he released his debut single "Got Me Singing" in 2019 but gained popularity for portraying his uncle Michael Jackson in the record-breaking biographical film Michael (2026
Euphoria is an American psychological drama television series created and written by Sam Levinson for HBO. Based on the Israeli miniseries of the same name created by Ron Leshem, the series stars Zendaya as drug-addicted teenager Rue Bennett, who also serves a
Limonene is a slightly yellow-green liquid aliphatic hydrocarbon classified as a cyclic monoterpene, and is the major component in the fragrance and essential oil of citrus fruit peels, taking its name from Italian limone ("lemon").
Apex is a 2026 survival thriller film directed by Baltasar Kormákur, written by Jeremy Robbins, and starring Charlize Theron and Taron Egerton. It tells the story of a rock climber and kayaker who finds herself being hunted in the wilds of Australia.
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
Nahui Ollin is a 16th-century concept in Aztec/Mexica cosmology with a variety of meanings. Nahui translates to "four," and Ollin translates to "movement" or "motion." Ollin was primarily portrayed in Aztec codices as two interlaced lines, each portrayed with
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 West Bengal Legislative Assembly election
Legislative Assembly elections were held in West Bengal to elect all 294 members of the West Bengal Legislative Assembly in two phases on 23 and 29 April 2026, with the votes counted and results for 293 seats released on 4 May 2026. The election saw the defeat
Jermaine LaJuane Jacksun is an American musician. A member of the Jackson family, he was second vocalist after his brother Michael of the Jackson 5 from 1964 to 1975, and played bass guitar. In 1983, he rejoined the group, which had been renamed the Jacksons;
Suicide is the act of intentionally causing one's own death. Risk factors for suicide include mental disorders, neurodevelopmental disorders, physical disorders, and substance abuse. Some suicides are impulsive acts driven by stress, relationship problems, or
Sabastian Kimaru Sawe is a Kenyan long-distance runner who holds the world record in the marathon. Sawe made his debut at the 2024 Valencia Marathon, winning in 2:02:05. At the 2026 London Marathon, he became the first person to run a sub-two-hour marathon in
List of highest-grossing Indian films
This ranking lists the highest-grossing Indian films produced by Indian cinema, based on conservative global box office estimates as reported by organisations classified as green by Wikipedia. The figures are not adjusted for inflation. However, there is no of
Dhurandhar: The Revenge is a 2026 Indian Hindi-language spy action-thriller film written and directed by Aditya Dhar. It is produced by Dhar, Lokesh Dhar, and Jyoti Deshpande under Jio Studios and B62 Studios. It is a sequel to the 2025 film Dhurandhar and the
List of The Simpsons characters
Along with the Simpson family, The Simpsons includes a large array of characters: co-workers, teachers, family friends, extended relatives, townspeople, as well as local celebrities. The creators originally intended many of these characters as one-time jokeste
Terry Gene Bollea, better known by his ring name Hulk Hogan, was an American professional wrestler and media personality. Widely regarded as one of the greatest and most recognized wrestlers of all time, Hogan won multiple championships worldwide, most notably
Kitione Takitau Lavemai, who fought under the name Kitione Lave, was a Tongan heavyweight boxer from the island of Hunga, Vava'u and Kotu, Ha'apai. At the age of 18 he became the South Pacific heavyweight champion and in 1956 he, unsuccessfully, challenged Joe
Katherine Esther Jackson is the matriarch of the Jackson family of entertainers that includes her children Michael and Janet Jackson. Michael dedicated his sixth studio album Thriller (1982) to her. Janet did the same with her fourth studio album Rhythm Nation
Diego Pavia is an American professional football quarterback. He played college football for the New Mexico Military Broncos, New Mexico State Aggies, and Vanderbilt Commodores before signing with the Baltimore Ravens as an undrafted free agent in 2026 and was
Joseph Walter Jackson was an American talent manager and patriarch of the Jackson family of entertainers. He was inducted into the Rhythm and Blues Music Hall of Fame in 2014.
Regina Rene King is an American actress, director and producer. She has received various accolades, including an Academy Award, a Golden Globe Award, and four Primetime Emmy Awards. In 2019, Time magazine named her one of the 100 most influential people in the
Benjamin Eric Sasse is an American politician and academic administrator. He represented Nebraska in the United States Senate from 2015 to 2023, resigning to become the president of the University of Florida. He is a member of the Republican Party. A critic of
The Jackson family is an American family of entertainers from Gary, Indiana. Many of the children of Joseph Walter "Joe" and Katherine Esther Jackson were successful musicians, notably the brothers that formed the Motown boy band the Jackson 5. Several of the
Charlize Theron is a South African and American actress and producer. One of the world's highest-paid actresses, her accolades include an Academy Award and a Golden Globe Award, in addition to nominations for three BAFTAs and two Emmy Awards.
Project Hail Mary is a 2026 American science fiction film produced and directed by Phil Lord and Christopher Miller and written by Drew Goddard, based on the 2021 novel of the same name by Andy Weir. It stars Ryan Gosling, who also produced the film, as Ryland
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.
List of United States presidential assassination attempts and plots
Assassination attempts and plots on the president of the United States have been numerous, ranging from the early 19th century to the present day. This article lists assassinations and assassination attempts on incumbent and former presidents and presidents-el
Hunter Schafer is an American actress, model and activist. Born in Trenton, New Jersey, and raised in Raleigh, North Carolina, Schafer was assigned male at birth and transitioned as a child. She came to public attention after joining a 2016 lawsuit against Nor
Attempted assassination of Donald Trump in Pennsylvania
On July 13, 2024, Donald Trump, then a former president of the United States and the presumptive nominee of the Republican Party in the 2024 presidential election, survived an assassination attempt while speaking at an open-air campaign rally near Butler, Penn
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization
Scaling Bayesian optimisation (BO) to high-dimensional search spaces is a active and open research problems particularly when no assumptions are made on function structure. The main reason is that at each iteration, BO requires to find global maximisation of acquisition function, which itself is a non-convex optimization problem in the original search space. With growing dimensions, the computational budget for this
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
Bayesian optimisation is a popular method for efficient optimisation of expensive black-box functions. Traditionally, BO assumes that the search space is known. However, in many problems, this assumption does not hold. To this end, we propose a novel BO algorithm which expands (and shifts) the search space over iterations based on controlling the expansion rate thought a hyperharmonic series. Further, we propose anot
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bounds ranging from $\mathcal{O}(\frac{logN}{\sqrt{N}})$ to $\mathcal O(e^{-\sqrt{N}})$, where $N$ is the number of evaluations. This paper explores the possibility of improving the regret bound in the noiseless setting by intertwining concept
Multitask Learning for Grapheme-to-Phoneme Conversion of Anglicisms in German Speech Recognition
Anglicisms are a challenge in German speech recognition. Due to their irregular pronunciation compared to native German words, automatically generated pronunciation dictionaries often include faulty phoneme sequences for Anglicisms. In this work, we propose a multitask sequence-to-sequence approach for grapheme-to-phoneme conversion to improve the phonetization of Anglicisms. We extended a grapheme-to-phoneme model w
Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition
Sensor-based human activity recognition (HAR) is a paramount technology in the Internet of Things services. HAR using representation learning, which automatically learns a feature representation from raw data, is the mainstream method because it is difficult to interpret relevant information from raw sensor data to design meaningful features. Ensemble learning is a robust approach to improve generalization performanc
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
The expected improvement (EI) algorithm is one of the most popular strategies for optimization under uncertainty due to its simplicity and efficiency. Despite its popularity, the theoretical aspects of this algorithm have not been properly analyzed. In particular, whether in the noisy setting, the EI strategy with a standard incumbent converges is still an open question of the Gaussian process bandit optimization pro
Learning Gradient-based Mixup with Extrapolation toward Flatter Minima for Domain Generalization
To address distribution shifts between training and test data, domain generalization (DG) leverages multiple source domains to learn a model that generalizes well to unseen domains. However, existing DG methods often overfit to the source domains, partly due to the limited coverage of the expected region in feature space. Motivated by this, we propose performing mixup with data interpolation and extrapolation to cove
Introduction to Online Control
This text presents an introduction to an emerging paradigm in control of dynamical systems and differentiable reinforcement learning called online nonstochastic control. The new approach applies techniques from online convex optimization and convex relaxations to obtain new methods with provable guarantees for classical settings in optimal and robust control. The primary distinction between online nonstochastic contr
Cloudless-Training: A Framework to Improve Efficiency of Geo-Distributed ML Training
Geo-distributed ML training can benefit many emerging ML scenarios (e.g., large model training, federated learning) with multi-regional cloud resources and wide area network. However, its efficiency is limited due to 2 challenges. First, efficient elastic scheduling of multi-regional cloud resources is usually missing, affecting resource utilization and performance of training. Second, training communication on WAN i
Seer: Language Instructed Video Prediction with Latent Diffusion Models
Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning. To tackle this task and empower robots with the ability to foresee the future, we propose a sample and computation-efficient model, named \textbf{Seer}, by inflating the pretrained text-to-i
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Large language models (LLMs) have transformed numerous AI applications. On-device LLM is becoming increasingly important: running LLMs locally on edge devices can reduce the cloud computing cost and protect users' privacy. However, the astronomical model size and the limited hardware resource pose significant deployment challenges. We propose Activation-aware Weight Quantization (AWQ), a hardware-friendly approac
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities
With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated but with unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult
Consistency of Lloyd's Algorithm Under Perturbations
In the context of unsupervised learning, Lloyd's algorithm is one of the most widely used clustering algorithms. It has inspired a plethora of work investigating the correctness of the algorithm under various settings with ground truth clusters. In particular, in 2016, Lu and Zhou have shown that the mis-clustering rate of Lloyd's algorithm on $n$ independent samples from a sub-Gaussian mixture is exponential
Text Tells the Cost: Predicting and Analyzing Repayment Effort of Self-Admitted Technical Debt
Technical debt refers to the consequences of sub-optimal decisions made during software development that prioritize short-term benefits over long-term maintainability. Self-Admitted Technical Debt (SATD) is a specific form of technical debt, explicitly documented by developers within software artifacts such as source code comments and commit messages. As SATD can hinder software development and maintenance, it is cru
Cluster Exploration using Informative Manifold Projections
Dimensionality reduction (DR) is one of the key tools for the visual exploration of high-dimensional data and uncovering its cluster structure in two- or three-dimensional spaces. The vast majority of DR methods in the literature do not take into account any prior knowledge a practitioner may have regarding the dataset under consideration. We propose a novel method to generate informative embeddings which not only fa
CROSS-Net: Region-Agnostic Taxi-Demand Prediction Using Feature Disentanglement
The growing demand for ride-hailing services has led to an increasing need for accurate taxi demand prediction. Existing systems are limited to specific regions, lacking generality to unseen areas. This paper presents a novel taxi demand prediction system, harnessing the strengths of multiview graph neural networks to capture spatial-temporal dependencies and patterns in urban environments. Additionally, the proposed
We introduce a Banach space-valued extension of random feature learning, a data-driven supervised machine learning technique for large-scale kernel approximation. By randomly initializing the feature maps, only the linear readout needs to be trained, which reduces the computational complexity substantially. Viewing random feature models as Banach space-valued random variables, we prove a universal approximation resul
Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review
Technical debt (TD) refers to the long-term costs associated with suboptimal design or code decisions in software development, often made to meet short-term delivery goals. Self-Admitted Technical Debt (SATD) occurs when developers explicitly acknowledge these trade-offs in the codebase, typically through comments or annotations. SATD detection has become an increasingly important research area, particularly with the
Fast gradient-free activation maximization for neurons in spiking neural networks
Elements of neural networks, both biological and artificial, can be described by their selectivity for specific cognitive features. Understanding these features is important for understanding the inner workings of neural networks. For a living system, such as a neuron, whose response to a stimulus is unknown and not differentiable, the only way to reveal these features is through a feedback loop that exposes it to a
Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference
Anomaly localization in images -- identifying regions that deviate from normal patterns -- is vital in applications such as medical diagnosis and industrial inspection. A recent trend is the use of image generation models in anomaly localization, where these models generate normal-looking counterparts of anomalous images, thereby allowing flexible and adaptive anomaly localization. However, these methods inherit the
Survey in Characterizing Semantic Change
Live languages continuously evolve to integrate the cultural change of human societies. This evolution manifests through neologisms (new words) or \textbf{semantic changes} of words (new meaning to existing words). Understanding the meaning of words is vital for interpreting texts coming from different cultures (regionalism or slang), domains (e.g., technical terms), or periods. In computer science, these words are r
Data Collaboration Analysis with Orthonormal Basis Selection and Alignment
Data Collaboration (DC) enables multiple parties to jointly train a model by sharing only linear projections of their private datasets. The core challenge in DC is to align the bases of these projections without revealing each party's secret basis. While existing theory suggests that any target basis spanning the common subspace should suffice, in practice, the choice of basis can substantially affect both accura
ANCHOR: LLM-driven Subject Conditioning for Text-to-Image Synthesis
Text-to-image (T2I) models have achieved remarkable progress in high-quality image synthesis, yet most benchmarks rely on simple, self-contained prompts, failing to capture the complexity of real-world captions. Human-written captions often involve multiple interacting subjects, rich contextual references, and abstractive phrasing, conditions under which current image-text encoders like CLIP struggle. To systematical
Breast cancer is the most common cancer type in women worldwide. Early detection and appropriate treatment can significantly reduce its impact. While histopathology examinations play a vital role in rapid and accurate diagnosis, they often require experienced medical experts for proper recognition and cancer grading. Automated image retrieval systems have the potential to assist pathologists in identifying cancerous
"Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood
Noise Contrastive Estimation (NCE) has fueled major breakthroughs in representation learning and generative modeling. Yet a long-standing challenge remains: accurately estimating ratios between distributions that differ substantially, which significantly limits the applicability of NCE on modern high-dimensional and multimodal datasets. We revisit this problem from a less explored perspective: the magnitude of the no
Notable events recorded on this day and month across all years.