Record 07012026 · captured 2026-08-25
The world looked up Liam Rosenior. 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.
Liam James Rosenior is an English professional football manager and former player who is the head coach of Ligue 1 club Paris FC.
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
Greenland is an autonomous territory of the Kingdom of Denmark and is the largest of the kingdom's three constituent parts by land area, the others being Denmark proper and the Faroe Islands. Citizens of Greenland are citizens of Denmark. They are thus citizen
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
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
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
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
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
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
Douglas Lee LaMalfa was an American politician and businessman who served as the U.S. representative for California's 1st congressional district from 2013 until his death in 2026. A member of the Republican Party, his district covered nearly all of interior No
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
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
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
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
John William Harbaugh is an American professional football coach who is the head coach for the New York Giants of the National Football League (NFL). He previously coached the defensive backs for the Philadelphia Eagles and served as their special teams coach
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
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
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
Jayne Marie Trcka was an American bodybuilder, fitness model and actress.
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
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.
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
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
Epiphany, also known as Theophany in Eastern Christian tradition, is a Christian feast day commemorating the visit of the Magi, the baptism of Jesus, and the wedding at Cana.
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
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
Stephen N. Miller is an American political advisor serving as White House deputy chief of staff for policy and homeland security advisor since 2025. He previously served as senior advisor to the president and director of speechwriting from 2017 to 2021 during
Leroy De Graft Rosenior is a football coach and pundit. He is a former footballer whose clubs included Fulham, Queens Park Rangers, Bristol City and West Ham United. Rosenior represented his birthplace England as a youth international, before formally switchin
Alfred Charles Sharpton Jr. is an American civil rights and social justice activist, Baptist minister, radio talk show host, and TV personality, who is also the founder of the National Action Network civil rights organization. In 2004, he was a candidate for t
The 2025 Africa Cup of Nations was the 35th edition of the Africa Cup of Nations, a biennial football tournament contested by the men's national teams of the Confederation of African Football (CAF). It was hosted by Morocco — the second time the country had ho
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Inference and forecast problems of the nonlinear dynamical system have arisen in a variety of contexts. Reservoir computing and deep sequential models, on the one hand, have demonstrated efficient, robust, and superior performance in modeling simple and chaotic dynamical systems. However, their innate deterministic feature has partially detracted their robustness to noisy system, and their inability to offer uncertai
We present TEyeD, the world's largest unified public data set of eye images taken with head-mounted devices. TEyeD was acquired with seven different head-mounted eye trackers. Among them, two eye trackers were integrated into virtual reality (VR) or augmented reality (AR) devices. The images in TEyeD were obtained from various tasks, including car rides, simulator rides, outdoor sports activities, and daily indoo
Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis
Intraoral 3D scanning is now widely adopted in modern dentistry and plays a central role in supporting key tasks such as tooth segmentation, detection, labeling, and dental landmark identification. Accurate analysis of these scans is essential for orthodontic and restorative treatment planning, as it enables automated workflows and minimizes the need for manual intervention. However, the development of robust learnin
Federated learning (FL) is a popular distributed machine learning (ML) technique in Internet of Things (IoT) networks, where resource-constrained devices collaboratively train ML models while preserving data privacy. However, implementation of FL over 5G-and-beyond wireless networks faces key challenges caused by (i) dynamics of the wireless network conditions and (ii) the coexistence of multiple FL-services in the s
Accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon-prone areas. Indoor radon concentration at the national scale is usually estimated on the basis of extensive measurement campaigns. However, characteristics of the sampled households often differ from the characteristics of the target population owing to the large number of relevant factors that
MAST: Model-Agnostic Sparsified Training
We introduce a novel optimization problem formulation that departs from the conventional way of minimizing machine learning model loss as a black-box function. Unlike traditional formulations, the proposed approach explicitly incorporates an initially pre-trained model and random sketch operators, allowing for sparsification of both the model and gradient during training. We establish the insightful properties of the
Generating time series data is a promising approach to address data deficiency problems. However, it is also challenging due to the complex temporal properties of time series data, including local correlations as well as global dependencies. Most existing generative models have failed to effectively learn both the local and global properties of time series data. To address this open problem, we propose a novel time s
CSAI: Conditional Self-Attention Imputation for Healthcare Time-series
We introduce the Conditional Self-Attention Imputation (CSAI) model, a novel recurrent neural network architecture designed to address the challenges of complex missing data patterns in multivariate time series derived from hospital electronic health records (EHRs). CSAI extends state-of-the-art neural network-based imputation by introducing key modifications specific to EHR data: a) attention-based hidden state init
Urban mobility is on the cusp of transformation with the emergence of shared, connected, and cooperative automated vehicles. Yet, for them to be accepted by customers, trust in their punctuality is vital. Many pilot initiatives operate without a fixed schedule, enhancing the importance of reliable arrival time (AT) predictions. This study presents an AT prediction system for automated shuttles, utilizing separate mod
Learning mirror maps in policy mirror descent
Policy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a unifying perspective that encompasses numerous algorithms. These algorithms are derived through the selection of a mirror map and enjoy finite-time convergence guarantees. Despite its popularity, the exploration of PMD's full potential is limited, with the majority of research focusing on a particular mirror map -- namely,
A Method For Bounding Tail Probabilities
We present a method for upper and lower bounding the right and the left tail probabilities of continuous random variables (RVs). For the right tail probability of RV $X$ with probability density function $f (x)$, this method requires first setting a continuous, positive, and strictly decreasing function $g (x)$ such that $-f (x)/g' (x)$ is a decreasing and increasing function, $\forall x>x_0$, which results in
Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions
In this paper, we focus on the decentralized stochastic subgradient-based methods in minimizing nonsmooth nonconvex functions without Clarke regularity, especially in the decentralized training of nonsmooth neural networks. We propose a general framework that unifies various decentralized subgradient-based methods, such as decentralized stochastic subgradient descent (DSGD), DSGD with gradient-tracking technique (DSG
Data-fusion networks have shown significant promise for RGB-thermal scene parsing. However, the majority of existing studies have relied on symmetric duplex encoders for heterogeneous feature extraction and fusion, paying inadequate attention to the inherent differences between RGB and thermal modalities. Recent progress in vision foundation models (VFMs) trained through self-supervision on vast amounts of unlabeled
Myopically Verifiable Probabilistic Certificates for Safe Control and Learning
This paper addresses the design of safety certificates for stochastic systems, with a focus on ensuring long-term safety through fast real-time control. In stochastic environments, set invariance-based methods that restrict the probability of risk events in infinitesimal time intervals may exhibit significant long-term risks due to cumulative uncertainties/risks. On the other hand, reachability-based approaches that
Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning
Existing SFDA methods struggle to fully use pre-trained knowledge and often rely on a single model's predictions or handcrafted prompts, limiting robustness under domain shift. Multimodal Large Language Models (MLLMs) offer a promising alternative: they encode rich visual-semantic knowledge and generalize well without task-specific tuning. However, their use in SFDA is hindered by instruction-following failures,
Topological Perspectives on Optimal Multimodal Embedding Spaces
Recent strides in multimodal model development have ignited a paradigm shift in the realm of text-to-image generation. Among these advancements, CLIP stands out as a remarkable achievement which is a sophisticated autoencoder adept at encoding both textual and visual information within a unified latent space. This paper delves into a comparative analysis between CLIP and its recent counterpart, CLOOB. To unravel the
A Survey on Failure Analysis and Fault Injection in AI Systems
The rapid advancement of Artificial Intelligence (AI) has led to its integration into various areas, especially with Large Language Models (LLMs) significantly enhancing capabilities in Artificial Intelligence Generated Content (AIGC). However, the complexity of AI systems has also exposed their vulnerabilities, necessitating robust methods for failure analysis (FA) and fault injection (FI) to ensure resilience and r
Limits to Predicting Online Speech Using Large Language Models
Our paper studies the predictability of online speech -- that is, how well language models learn to model the distribution of user generated content on X (previously Twitter). We define predictability as a measure of the model's uncertainty, i.e. its negative log-likelihood. As the basis of our study, we collect 10M tweets for ``tweet-tuning'' base models and a further 6.25M posts from more than five thou
Artificial Neural Networks on Graded Vector Spaces
This paper presents a transformative framework for artificial neural networks over graded vector spaces, tailored to model hierarchical and structured data in fields like algebraic geometry and physics. By exploiting the algebraic properties of graded vector spaces, where features carry distinct weights, we extend classical neural networks with graded neurons, layers, and activation functions that preserve structural
Quantum-secure multiparty deep learning
Secure multiparty computation enables the joint evaluation of multivariate functions across distributed users while ensuring the privacy of their local inputs. This field has become increasingly urgent due to the exploding demand for computationally intensive deep learning inference. These computations are typically offloaded to cloud computing servers, leading to vulnerabilities that can compromise the security of t
An Uncertainty-Aware Generalization Framework for Cardiovascular Image Segmentation
Deep learning models have achieved significant success in segmenting cardiovascular structures, but there is a growing need to improve their generalization and robustness. Current methods often face challenges such as overfitting and limited accuracy, largely due to their reliance on large annotated datasets and limited optimization techniques. This paper introduces the UU-Mamba model, an extension of the U-Mamba arc
Conformal Prediction for Dose-Response Models with Continuous Treatments
Understanding the dose-response relation between a continuous treatment and the outcome for an individual can greatly drive decision-making, particularly in areas like personalized drug dosing and personalized healthcare interventions. Point estimates are often insufficient in these high-risk environments, highlighting the need for uncertainty quantification to support informed decisions. Conformal prediction, a dist
Large Language Models can Achieve Social Balance
Large Language Models (LLMs) can be deployed in situations where they process positive/negative interactions with other agents. We study how this is done under the sociological framework of social balance, which explains the emergence of one faction or multiple antagonistic ones among agents. Across different LLM models, we find that balance depends on the (i) type of interaction, (ii) update mechanism, and (iii) pop
How Many Images Does It Take? Estimating Imitation Thresholds in Text-to-Image Models
Text-to-image models are trained using large datasets of image-text pairs collected from the internet. These datasets often include copyrighted and private images. Training models on such datasets enables them to generate images that might violate copyright laws and individual privacy. This phenomenon is termed imitation -- generation of images with content that has recognizable similarity to its training images. In
Communication Compression for Tensor Parallel LLM Inference
Large Language Models (LLMs) have pushed the frontier of artificial intelligence but are comprised of hundreds of billions of parameters and operations. For faster inference latency, LLMs are deployed on multiple hardware accelerators through various Model Parallelism strategies. Our paper looks into the details on one such strategy - Tensor Parallel - and proposes to reduce latency by compressing inter-accelerator c
Notable events recorded on this day and month across all years.