Record 05012026 · captured 2026-08-25
The world looked up Nicolás Maduro. 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.
Nicolás Maduro Moros is a Venezuelan politician and former union leader who served as the 53rd president of Venezuela from 2013 until his capture during the United States intervention in Venezuela in 2026 for alleged drug trafficking to which he pleaded not gu
Venezuela, officially the Bolivarian Republic of Venezuela, is a country on the northern coast of South America, consisting of a continental landmass and various islands and islets in the Caribbean Sea. It comprises an area of 912,050 km2 (352,140 sq mi), with
2026 United States intervention in Venezuela
On 3 January 2026, the United States launched a military strike in Venezuela and captured incumbent Venezuelan president Nicolás Maduro and his wife, Cilia Flores. The US operation, codenamed Operation Absolute Resolve, began around 2 a.m. local time, when exp
Delcy Eloína Rodríguez Gómez is a Venezuelan lawyer, politician and diplomat who has been interim president of Venezuela since 2026, following the United States intervention in Venezuela. She is the first woman in Venezuelan history to perform the duties of th
Hugo Rafael Chávez Frías was a Venezuelan politician, revolutionary, and military officer who was the president of Venezuela from 1999 until his death in 2013. Chávez changed the country's name from the Republic of Venezuela to the Bolivarian Republic of Venez
Cilia Adela Flores de Maduro is a Venezuelan lawyer and politician who served as the first lady of Venezuela from 2013 to 2026, as the wife of Nicolás Maduro, the 53rd president of Venezuela. A member of the United Socialist Party of Venezuela, she served as p
Dhurandhar is a 2025 Indian Hindi-language spy action thriller film written and directed by Aditya Dhar. It is produced by Aditya Dhar, Lokesh Dhar and Jyoti Deshpande under Jio Studios and B62 Studios. The film features an ensemble cast consisting of Ranveer
Avatar: Fire and Ash is a 2025 American epic science fiction film directed by James Cameron from a screenplay he co-wrote with Rick Jaffa and Amanda Silver. Produced by Lightstorm Entertainment, it is the third installment in the Avatar film series and the seq
Manuel Antonio Noriega Moreno was a Panamanian military officer and politician who was the de facto ruler of Panama from 1983 to 1989. He never officially served as president of Panama, instead ruling as an unelected military dictator through puppet presidents
Stranger Things is an American television series created by the Duffer Brothers for Netflix. Produced by Monkey Massacre Productions and 21 Laps Entertainment, the first season was released on Netflix on July 15, 2016. The second and third seasons followed in
María Corina Machado Parisca is a Venezuelan politician, activist, and prominent leader of the opposition to the administrations of Hugo Chávez, Nicolás Maduro, and Delcy Rodríguez. She served as a member of the National Assembly of Venezuela from 2011 to 2014
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
Samuel Richard Darnold is an American professional football quarterback for the Seattle Seahawks of the National Football League (NFL). He played college football for the USC Trojans, becoming the first freshman to win the Archie Griffin Award.
Run Away is a British television miniseries made for streaming service Netflix, adapted from a novel by Harlan Coben. The series premiered on 1 January 2026 and it stars James Nesbitt, Alfred Enoch, Ruth Jones, Minnie Driver, and Ellie de Lange.
Millie Bonnie Bongiovi, known professionally as Millie Bobby Brown, is a British actress and film producer. She gained international recognition for playing Eleven in the Netflix science fiction series Stranger Things (2016–2025), for which she received nomina
Marco Antonio Rubio is an American politician, attorney, and diplomat serving as the 72nd United States secretary of state since 2025. He is also the acting national security advisor. A member of the Republican Party, Rubio represented Florida in the United St
2024 Venezuelan presidential election
Presidential elections were held in Venezuela on 28 July 2024 to choose a president for a six-year term beginning on 10 January 2025. The election was contentious, with international monitors calling it neither free nor fair, citing the incumbent Maduro admini
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
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
The 1st Special Forces Operational Detachment–Delta, also known as Delta Force, Combat Applications Group (CAG), or within Joint Special Operations Command (JSOC) as Task Force Green, is a special operations force of the United States Army under the operationa
Marty Supreme is a 2025 American sports comedy-drama film directed by Josh Safdie, who co-wrote it with Ronald Bronstein. Set in the 1950s, it stars Timothée Chalamet as table tennis player Marty Mauser and follows his quest to become world champion. Gwyneth P
Riley James Leonard is an American professional football quarterback for the Indianapolis Colts of the National Football League (NFL). He played college football for the Duke Blue Devils and Notre Dame Fighting Irish. Leonard was selected by the Colts in the s
The Housemaid is a 2025 American erotic psychological thriller film directed by Paul Feig and written by Rebecca Sonnenshine. It is based on the 2022 novel by Freida McFadden, and stars Sydney Sweeney and Amanda Seyfried. In the film, Millie Calloway, a young
Nicolás Ernesto Maduro Guerra, also referred to as Nicolás Maduro Jr., Maduro Jr., or Nicolasito, is a Venezuelan politician and economist and the son of the President of Venezuela, Nicolás Maduro. Maduro Jr. has served as a deputy in the Venezuelan National A
USS Iwo Jima (LHD-7) is a Wasp-class amphibious assault ship of the United States Navy. The ship was named for the Battle of Iwo Jima of World War II. The ship was commissioned in 2001 and is in service.
Joseph David Keery, also known by his musical stage name Djo, is an American actor, singer, musician, and songwriter. He rose to international prominence for his role as Steve Harrington in the sci-fi horror series Stranger Things (2016–2025). He has also star
The fifth and final season of the American science fiction horror drama television series Stranger Things, marketed as Stranger Things 5, was released on the streaming service Netflix in two volumes and the finale. The first set of four episodes was released o
Jana Nayagan is a 2026 Indian Tamil-language political action drama film directed by H. Vinoth and produced by Venkat K. Narayana under KVN Productions. The film stars C. Joseph Vijay, Bobby Deol, Pooja Hegde, and Mamitha Baiju in the lead role alongside Nassa
Luke Littler is an English professional darts player who competes in Professional Darts Corporation (PDC) events, where he is ranked world number one. Nicknamed "the Nuke", Littler has won two PDC World Darts Championships, in 2025 and 2026, and is the younges
The Monroe Doctrine is a United States foreign policy position that opposes any foreign interference in the Western Hemisphere. Originally concerned with European colonialism, it holds that any intervention in the political affairs of the Americas by foreign p
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Sensing systems powered by energy harvesting have traditionally been designed to tolerate long periods without energy. As the Internet of Things (IoT) evolves towards a more transient and opportunistic execution paradigm, reducing energy storage costs will be key for its economic and ecologic viability. However, decreasing energy storage in harvesting systems introduces reliability issues. Transducers only produce in
MCD: Marginal Contrastive Discrimination for conditional density estimation
We consider the problem of conditional density estimation, which is a major topic of interest in the fields of statistical and machine learning. Our method, called Marginal Contrastive Discrimination, MCD, reformulates the conditional density function into two factors, the marginal density function of the target variable and a ratio of density functions which can be estimated through binary classification. Like noise
Density-Based Algorithms for Corruption-Robust Contextual Search and Convex Optimization
We study the problem of contextual search, a generalization of binary search in higher dimensions, in the adversarial noise model. Let $d$ be the dimension of the problem, $T$ be the time horizon and $C$ be the total amount of adversarial noise in the system. We focus on the $ε$-ball and the symmetric loss. For the $ε$-ball loss, we give a tight regret bound of $O(C + d \log(1/ε))$ improving over the $O(d^3 \log(1/ε)
Distributed Sparse Linear Regression under Communication Constraints
In multiple domains, statistical tasks are performed in distributed settings, with data split among several end machines that are connected to a fusion center. In various applications, the end machines have limited bandwidth and power, and thus a tight communication budget. In this work we focus on distributed learning of a sparse linear regression model, under severe communication constraints. We propose several two
Efficient Multi-Task Scene Analysis with RGB-D Transformers
Scene analysis is essential for enabling autonomous systems, such as mobile robots, to operate in real-world environments. However, obtaining a comprehensive understanding of the scene requires solving multiple tasks, such as panoptic segmentation, instance orientation estimation, and scene classification. Solving these tasks given limited computing and battery capabilities on mobile platforms is challenging. To addr
Sparse-Input Neural Network using Group Concave Regularization
Simultaneous feature selection and non-linear function estimation is challenging in modeling, especially in high-dimensional settings where the number of variables exceeds the available sample size. In this article, we investigate the problem of feature selection in neural networks. Although the group least absolute shrinkage and selection operator (LASSO) has been utilized to select variables for learning with neura
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is a crucial task when training data is not accessible due to various concerns, eg, data privacy, yet it is challenging since the models need to generalize to anomalies across different domains where the appearance of foreground objects, abnormal regions, an
Simple and Effective Input Reformulations for Translation
Foundation language models learn from their finetuning input context in different ways. In this paper, we reformulate inputs during finetuning for challenging translation tasks, leveraging model strengths from pretraining in novel ways to improve downstream performance. These reformulations are simple data level modifications, require no additional collection of training data or modification of data at inference time
Probabilistic Reduced-Dimensional Vector Autoregressive Modeling with Oblique Projections
In this paper, we propose a probabilistic reduced-dimensional vector autoregressive (PredVAR) model to extract low-dimensional dynamics from high-dimensional noisy data. The model utilizes an oblique projection to partition the measurement space into a subspace that accommodates the reduced-dimensional dynamics and a complementary static subspace. An optimal oblique decomposition is derived for the best predictabilit
Improving the efficiency of state-of-the-art methods in semantic segmentation requires overcoming the increasing computational cost as well as issues such as fusing semantic information from global and local contexts. Based on the recent success and problems that convolutional neural networks (CNNs) encounter in semantic segmentation, this research proposes an encoder-decoder architecture with a unique efficient resi
Recent LLMs have hundreds of billions of parameters consuming vast resources. Furthermore, the so called "AI scaling law" for transformers suggests that the number of parameters must scale linearly with the size of the data. In response, we inquire into efficient LLMs, i.e. those with the fewest parameters that achieve the desired accuracy on a training corpus. Specifically, by comparing theoretical and empir
Current gait analysis faces challenges in various aspects, including limited and poorly labeled data within existing wearable electronics databases, difficulties in collecting patient data due to privacy concerns, and the inadequacy of the Zero-Velocity Update Technique (ZUPT) in accurately analyzing pathological gait patterns. To address these limitations, we introduce GaitMotion, a novel machine-learning framework
From Transformers to LLMs: A Systematic Survey of Efficiency Considerations in NLP
The emergence of Transformer-based Large Language Models (LLMs) has substantially augmented the capabilities of Natural Language Processing (NLP), thereby intensifying the demand for computational resources. Therefore, enhancing efficiency based on factors like computational requirements, energy consumption, carbon footprint and financial cost has become a vital area of research. This motivates us to conduct a system
Test-time generative augmentation for medical image segmentation
Medical image segmentation is critical for clinical diagnosis, treatment planning, and monitoring, yet segmentation models often struggle with uncertainties stemming from occlusions, ambiguous boundaries, and variations in imaging devices. Traditional test-time augmentation (TTA) techniques typically rely on predefined geometric and photometric transformations, limiting their adaptability and effectiveness in complex
LeanQuant: Accurate and Scalable Large Language Model Quantization with Loss-error-aware Grid
Large language models (LLMs) have shown immense potential across various domains, but their high memory requirements and inference costs remain critical challenges for deployment. Post-training quantization (PTQ) has emerged as a promising technique to reduce memory requirements and decoding latency. However, recent accurate quantization methods often depend on specialized computations or custom data formats to achie
Neural architecture search (NAS) has emerged as a powerful paradigm that enables researchers to automatically explore vast search spaces and discover efficient neural networks. However, NAS suffers from a critical bottleneck, i.e. the evaluation of numerous architectures during the search process demands substantial computing resources and time. In order to improve the efficiency of NAS, a series of methods have been
CIC: Circular Image Compression
Learned image compression (LIC) is currently the cutting-edge method. However, the inherent difference between testing and training images of LIC results in performance degradation to some extent. Especially for out-of-sample, out-of-distribution, or out-of-domain testing images, the performance of LIC degrades significantly. Classical LIC is a serial image compression (SIC) approach that utilizes an open-loop archit
Background: Gliomas are among the most common malignant brain tumors and exhibit substantial heterogeneity, complicating accurate detection and segmentation. Although multi-modal MRI is the clinical standard for glioma imaging, variability across modalities and high computational demands hamper effective automated segmentation. Methods: We propose UKAN-EP, a novel 3D extension of the original 2D U-KAN model for multi
EXAONE 3.0 7.8B Instruction Tuned Language Model
We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model sizes, we publicly release the 7.8B instruction-tuned model to promote open research and innovations. Through extensive evaluations across a wide range of public and in-house benchmarks, EXAONE 3.0 demonstrates highly competitive real-world per
Survey of Data-driven Newsvendor: Unified Analysis and Spectrum of Achievable Regrets
In the Newsvendor problem, the goal is to guess the number that will be drawn from some distribution, with asymmetric consequences for guessing too high vs. too low. In the data-driven version, the distribution is unknown, and one must work with samples from the distribution. Data-driven Newsvendor has been studied under many variants: additive vs. multiplicative regret, high probability vs. expectation bounds, and d
Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model
For reasons such as privacy, there are use cases for language models at the edge. This has given rise to small language models targeted for deployment in resource-constrained devices where energy efficiency is critical. Spiking neural networks (SNNs) offer a promising solution due to their energy efficiency, and there are already works on realizing transformer-based models on SNNs. However, key operations like softma
CloudTrack: Scalable UAV Tracking with Cloud Semantics
Nowadays, unmanned aerial vehicles (UAVs) are commonly used in search and rescue scenarios to gather information in the search area. The automatic identification of the person searched for in aerial footage could increase the autonomy of such systems, reduce the search time, and thus increase the missed person's chances of survival. In this paper, we present a novel approach to perform semantically conditioned op
Mitigating optimistic bias in entropic risk estimation and optimization
The entropic risk measure is widely used in high-stakes decision-making across economics, management science, finance, and safety-critical control systems because it captures tail risks associated with uncertain losses. However, when data are limited, the empirical entropic risk estimator, formed by replacing the expectation in the risk measure with a sample average, underestimates true risk. We show that this negati
Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture
Large Language Models (LLMs) utilize extensive knowledge databases and show powerful text generation ability. However, their reliance on high-quality copyrighted datasets raises concerns about copyright infringements in generated texts. Current research often employs prompt engineering or semantic classifiers to identify copyrighted content, but these approaches have two significant limitations: (1) Challenging to id
Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation
Adapting pre-trained large language models (LLMs) is crucial but challenging due to their enormous size. Parameter-efficient fine-tuning (PEFT) techniques typically employ additive adapters applied to frozen model weights. To further reduce memory usage, model weights are often compressed through quantization. However, existing PEFT methods often yield suboptimal model quality because they rely on restrictive assumpt
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