Record 18122025 · captured 2026-08-25
The world looked up Rob Reiner. 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.
Robert Reiner was an American filmmaker, actor, and political activist. He directed a series of acclaimed studio films in a career that spanned comedy, drama, romance, and documentary. Reiner received numerous accolades, including winning two Primetime Emmy Aw
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
Google Chrome is a cross-platform web browser developed by Google. It was launched in September 2008 for Microsoft Windows and was built with free software components from Apple WebKit and Mozilla Firefox. Versions for Linux, macOS, iOS, iPadOS, and Android we
Disclosure Day is a 2026 American science fiction thriller film directed and produced by Steven Spielberg from a screenplay by David Koepp, based on a story by Spielberg. The film stars an ensemble cast, including Emily Blunt, Josh O'Connor, Colin Firth, Eve H
Gilbert Cyril Gerard was an American actor, whose roles include that of Captain William "Buck" Rogers in the 1979–1981 television series Buck Rogers in the 25th Century.
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
Tracy Reiner is an American former actress. She is known for her roles in When Harry Met Sally..., Masque of the Red Death, A League of Their Own, and Apollo 13.
On 14 December 2025, an antisemitic and Islamic State (IS)-inspired terrorist attack occurred at the Archer Park area of Bondi Beach in Sydney, New South Wales, Australia, during a celebration of the Jewish holiday of Hanukkah attended by around 1,000 people.
Carole Penny Marshall was an American actress, film director, and producer. She starred as Laverne DeFazio on the television sitcom Laverne & Shirley from 1976 to 1983, and received three nominations for the Golden Globe Award for Best Actress – Television Ser
Susan L. Wiles is an American Republican political consultant and lobbyist who has served as the 32nd White House chief of staff since January 2025.
This is a list of lists of deaths of significant people, organized by year. New deaths articles are added to their respective month and then linked below.
Carl Reiner was an American actor, author, comedian, director, and screenwriter whose career spanned seven decades. His awards and honors include 12 Primetime Emmy Awards, a Grammy Award, and the Mark Twain Prize for American Humor. He was inducted into the Te
Wake Up Dead Man is a 2025 American mystery film written and directed by Rian Johnson. It is the third film in the Knives Out series. The film stars Daniel Craig, who reprises his role as master detective Benoit Blanc as he investigates the death of a Catholic
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
It: Welcome to Derry is an American supernatural horror television series based on Stephen King's 1986 novel It. Serving as a prequel to the films It (2017) and It Chapter Two (2019), the series was developed by Andy Muschietti, Barbara Muschietti and Jason Fu
Nuno Filipe Gomes Loureiro was a Portuguese plasma physicist. He was the Herman Feshbach Professor of Physics at the Massachusetts Institute of Technology (MIT) and director of the MIT Plasma Science and Fusion Center from 2024 until his murder in 2025.
Sardar Abdul Rehman Baloch, known by the alias Rehman Dakait, was a Pakistani gangster based in Karachi's Lyari neighbourhood who formed the Peoples' Aman Committee which was affiliated with the Pakistan People's Party. The Government of Sindh had set a reward
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
Anthony Geary was an American actor. His career spanned more than four decades, and began in episodic television. He appeared as a guest on several primetime series and transitioned into a career predominantly in the soap opera genre. His first soap role was D
2025 Brown University shooting
On December 13, 2025, a mass shooting occurred at Brown University in Providence, Rhode Island, United States, during the second day of final examination week for the fall semester. The shooter, Cláudio Manuel Neves Valente, entered the Barus and Holley Buildi
Joshua Mathias O'Connor is an English actor. From 2016 to 2019, he had a major role portraying Larry Durrell in ITV's The Durrells. He had his breakthrough playing the lead role of a gay sheep farmer in Francis Lee's romantic drama God's Own Country (2017), fo
Pluribus is an American post-apocalyptic science fiction television series created by Vince Gilligan for Apple TV. Set and filmed primarily in Albuquerque, New Mexico, the series follows novelist Carol Sturka, who finds herself isolated after an alien virus tr
Odessa Zion Segall Adlon, known professionally as Odessa A'zion, is an American actress. On television, she is known for her roles in the CBS series Fam (2019), the Netflix series Grand Army (2020) and the HBO series I Love LA (2025). For her performance in th
The NBA Cup is an annual National Basketball Association (NBA) tournament that occurs during the regular season. The tournament was officially announced on July 8, 2023, and it debuted during the 2023–24 NBA season. The first edition of the event was called th
The 2025 SEA Games, officially called the 33rd SEA Games was an international multi-sport event sanctioned by the Southeast Asian Games Federation (SEAGF). The event took place in December 2025 from 9 to 20 December and was held across the Bangkok Metropolitan
6-7 is an Internet meme, slang term, and gesture that became popular in 2025 on TikTok and Instagram Reels. It has no fixed meaning.
Fallout is an American post-apocalyptic drama television series created by Graham Wagner and Geneva Robertson-Dworet for Amazon Prime Video. Based on the role-playing video game franchise created by Tim Cain and Leonard Boyarsky, the series is set two centurie
Terence Allan "Bud" Crawford is an American former professional boxer who competed from 2008 to 2025. He retired with an undefeated record and won 18 different major world championships in five weight classes from lightweight to super middleweight, including t
Macaulay Macaulay Culkin Culkin is an American actor and musician. Considered one of the most successful child actors of the 1990s, Culkin has received several accolades including a Golden Globe Award nomination. In 2005, he was ranked second on VH1's list of
Michele Singer Reiner was an American photographer, political activist, and film producer. Reiner was the second wife of filmmaker and actor Rob Reiner. She was originally a photographer, taking the cover picture of The Art of the Deal (1987). While working on
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Robust Tensor Principal Component Analysis: Exact Recovery via Deterministic Model
Tensor, also known as multi-dimensional array, arises from many applications in signal processing, manufacturing processes, healthcare, among others. As one of the most popular methods in tensor literature, Robust tensor principal component analysis (RTPCA) is a very effective tool to extract the low rank and sparse components in tensors. In this paper, a new method to analyze RTPCA is proposed based on the recently
tensorflow-riemopt: A Library for Optimization on Riemannian Manifolds
This paper presents tensorflow-riemopt, a Python library for geometric machine learning in TensorFlow. The library provides efficient implementations of neural network layers with manifold-constrained parameters, geometric operations on Riemannian manifolds, and stochastic optimization algorithms for non-Euclidean spaces. Designed for integration with TensorFlow Extended, it supports both research prototyping and pro
Myoelectric pattern recognition is one of the important aspects in the design of the control strategy for various applications including upper-limb prostheses and bio-robotic hand movement systems. The current work has proposed an approach to design an energy-efficient EMG-based controller by considering a kernelized SVM classifier for decoding the information of surface electromyography (sEMG) signals to infer the u
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension
A classical result in online learning characterizes the optimal mistake bound achievable by deterministic learners using the Littlestone dimension (Littlestone '88). We prove an analogous result for randomized learners: we show that the optimal expected mistake bound in learning a class $\mathcal{H}$ equals its randomized Littlestone dimension, which is the largest $d$ for which there exists a tree shattered by $
SketchOGD: Memory-Efficient Continual Learning
When machine learning models are trained continually on a sequence of tasks, they are often liable to forget what they learned on previous tasks--a phenomenon known as catastrophic forgetting. Proposed solutions to catastrophic forgetting tend to involve storing information about past tasks, meaning that memory usage is a chief consideration in determining their practicality. This paper develops a memory-efficient so
Enigma: Application-Layer Privacy for Quantum Optimization on Untrusted Computers
The Early Fault-Tolerant (EFT) era is emerging, where modest Quantum Error Correction (QEC) can enable quantum utility before full-scale fault tolerance. Quantum optimization is a leading candidate for early applications, but protecting these workloads is critical since they will run on expensive cloud services where providers could learn sensitive problem details. Experience with classical computing systems has show
Large language models (LLMs) have opened up new possibilities for intelligent agents, endowing them with human-like thinking and cognitive abilities. In this work, we delve into the potential of large language models (LLMs) in autonomous driving (AD). We introduce DriveMLM, an LLM-based AD framework that can perform close-loop autonomous driving in realistic simulators. To this end, (1) we bridge the gap between the
Variational Continual Test-Time Adaptation
Continual Test-Time Adaptation (CTTA) task investigates effective domain adaptation under the scenario of continuous domain shifts during testing time. Due to the utilization of solely unlabeled samples, there exists significant uncertainty in model updates, leading CTTA to encounter severe error accumulation issues. In this paper, we introduce VCoTTA, a variational Bayesian approach to measure uncertainties in CTTA.
Since coral reef ecosystems face threats from human activities and climate change, coral conservation programs are implemented worldwide. Monitoring coral health provides references for guiding conservation activities. However, current labor-intensive methods result in a backlog of unsorted images, highlighting the need for automated classification. Few studies have simultaneously utilized accurate annotations along
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning
Exemplar-free class-incremental learning (EFCIL) aims to mitigate catastrophic forgetting in class-incremental learning (CIL) without available historical training samples as exemplars. Compared with its exemplar-based CIL counterpart that stores exemplars, EFCIL suffers more from forgetting issues. Recently, a new EFCIL branch named Analytic Continual Learning (ACL) introduces a gradient-free paradigm via Recursive
NeRF-based methods reconstruct 3D scenes by building a radiance field with implicit or explicit representations. While NeRF-based methods can perform novel view synthesis (NVS) at arbitrary scale, the performance in high-resolution novel view synthesis (HRNVS) with low-resolution (LR) optimization often results in oversmoothing. On the other hand, single-image super-resolution (SR) aims to enhance LR images to HR cou
Imbalances in Neurosymbolic Learning: Characterization and Mitigating Strategies
We study one of the most popular problems in **neurosymbolic learning** (NSL), that of learning neural classifiers given only the result of applying a symbolic component $σ$ to the gold labels of the elements of a vector $\mathbf x$. The gold labels of the elements in $\mathbf x$ are unknown to the learner. We make multiple contributions, theoretical and practical, to address a problem that has not been studied so fa
A Survey of Accessible Explainable Artificial Intelligence Research
The increasing integration of Artificial Intelligence (AI) into everyday life makes it essential to explain AI-based decision-making in a way that is understandable to all users, including those with disabilities. Accessible explanations are crucial as accessibility in technology promotes digital inclusion and allows everyone, regardless of their physical, sensory, or cognitive abilities, to use these technologies ef
Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning
Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration
While modern Autonomous Vehicle (AV) systems can develop reliable driving policies under regular traffic conditions, they frequently struggle with safety-critical traffic scenarios. This difficulty primarily arises from the rarity of such scenarios in driving datasets and the complexities associated with predictive modeling of multiple vehicles. Effectively simulating safety-critical traffic situations is therefore a
Visually Rich Documents (VRDs), comprising elements such as charts, tables, and paragraphs, convey complex information across diverse domains. However, extracting key information from these documents remains labour-intensive, particularly for scanned formats with inconsistent layouts and domain-specific requirements. Despite advances in pretrained models for VRD understanding, their dependence on large annotated data
Reasoning and linguistic skills form the cornerstone of human intelligence, facilitating problem-solving and decision-making. Recent advances in Large Language Models (LLMs) have led to impressive linguistic capabilities and emergent reasoning behaviors, fueling widespread adoption across application domains. However, LLMs still struggle with complex reasoning tasks, highlighting their systemic limitations. In this w
Multi-Task Dynamic Pricing in Credit Market with Contextual Information
We study the dynamic pricing problem faced by a broker seeking to learn prices for a large number of credit market securities, such as corporate bonds, government bonds, loans, and other credit-related securities. A major challenge in pricing these securities stems from their infrequent trading and the lack of transparency in over-the-counter (OTC) markets, which leads to insufficient data for individual pricing. Nev
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models
Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most existing work treats diffusion models as a standalone component for perception tasks, employing them either solely for off-the-shelf data augmentation or as mere feature extractors. In contrast to these isolated and thus sub-optimal efforts, we introduce a unified, versatile
Cascaded Dual Vision Transformer for Accurate Facial Landmark Detection
Facial landmark detection is a fundamental problem in computer vision for many downstream applications. This paper introduces a new facial landmark detector based on vision transformers, which consists of two unique designs: Dual Vision Transformer (D-ViT) and Long Skip Connections (LSC). Based on the observation that the channel dimension of feature maps essentially represents the linear bases of the heatmap space,
Toward Robust and Accurate Adversarial Camouflage Generation against Vehicle Detectors
Adversarial camouflage is a widely used physical attack against vehicle detectors for its superiority in multi-view attack performance. One promising approach involves using differentiable neural renderers to facilitate adversarial camouflage optimization through gradient back-propagation. However, existing methods often struggle to capture environmental characteristics during the rendering process or produce adversa
Humans can visualize new and unknown concepts from their natural language description, based on their experience and previous knowledge. Insipired by this, we present a way to extend this ability to Vision-Language Models (VLMs), teaching them novel concepts by only using a textual description. We refer to this approach as Knowledge Transfer (KT). Our hypothesis is that the knowledge of a pre-trained VLM can be re-us
From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning
Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, traditional ultrasound diagnostics relies heavily on physician expertise and is often hampered by suboptimal image quality, leading to potential diagnostic errors. While artificial intelligence (AI) offers a promising solution to enhance clinical diagnosis by detecting abnormalities across vario
The use of machine learning and AI on electronic health records (EHRs) holds substantial potential for clinical insight. However, this approach faces challenges due to data heterogeneity, sparsity, temporal misalignment, and limited labeled outcomes. In this context, we leverage a linked EHR dataset of approximately one million de-identified individuals from Bristol, North Somerset, and South Gloucestershire, UK, to
KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment
Wireless sensing has recently found widespread applications in diverse environments, including homes, offices, and public spaces. By analyzing patterns in channel state information (CSI), it is possible to infer human actions for tasks such as person identification, gesture recognition, and fall detection. However, CSI is highly sensitive to environmental changes, where even minor alterations can significantly distor
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