Record 17122025 · 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
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.
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
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
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
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
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.
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
Being Charlie is a 2015 American drama film directed by Rob Reiner and written by his son, Nick Reiner, alongside Matt Elisofon. The film stars Nick Robinson, Common, Cary Elwes, Devon Bostick, Morgan Saylor, Susan Misner, and Ricardo Chavira.
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
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.
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
Rachael Anna-Maie Carpani was an Australian actress best known for her role as Jodi Fountain-McLeod in McLeod's Daughters.
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
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
Killing of Rob and Michele Reiner
On December 14, 2025, American filmmaker Rob Reiner and his wife, photographer and producer Michele Singer Reiner, were found dead with multiple sharp force injuries at their home in Brentwood, a neighborhood in Los Angeles, California. Their son, Nick Reiner,
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
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
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
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
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.
Stand by Me is a 1986 American coming-of-age drama film directed by Rob Reiner. Based on Stephen King's 1982 novella The Body, the film is set in the fictional town of Castle Rock, Oregon, in 1959. Stand by Me stars Wil Wheaton, River Phoenix, Corey Feldman, a
Estelle Reiner was an American actress and singer, described by The New York Times as "matriarch of one of the leading families in American comedy". She was the wife of Carl Reiner and the mother of Rob Reiner, Lucas Reiner, and Annie Reiner.
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
Trump derangement syndrome (TDS) is a pejorative term used to describe negative reactions to U.S. president Donald Trump in order to characterize them as irrational and disconnected from Trump's actual policy positions. The term has mainly been used by Trump s
This Is Spinal Tap is a 1984 American mockumentary comedy film directed by Rob Reiner in his feature directorial debut. Christopher Guest, Michael McKean, and Harry Shearer play members of the parody heavy metal band Spinal Tap, while Reiner plays Martin "Mart
The Princess Bride is a 1987 American fantasy-adventure-comedy film directed and co-produced by Rob Reiner and starring Cary Elwes, Robin Wright, Mandy Patinkin, André the Giant, Chris Sarandon, Christopher Guest, Wallace Shawn, Peter Falk, Fred Savage, Billy
2026 PDC World Darts Championship
The 2026 PDC World Darts Championship was a professional darts tournament that took place from 11 December 2025 to 3 January 2026 at Alexandra Palace in London, England. The 33rd World Darts Championship organised by the Professional Darts Corporation (PDC), i
The Port Arthur massacre was a mass shooting that occurred on 28 April 1996 at Port Arthur, a tourist town in the Australian state of Tasmania. The perpetrator, Martin Bryant, murdered 35 people and wounded 23 others, in the deadliest massacre in modern Austra
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Qualitative reasoning involves expressing and deriving knowledge based on qualitative terms such as natural language expressions, rather than strict mathematical quantities. Well over 40 qualitative calculi have been proposed so far, mostly in the spatial and temporal domains, with several practical applications such as naval traffic monitoring, warehouse process optimisation and robot manipulation. Even if a number
Holistic Generalized Linear Models
Holistic linear regression extends the classical best subset selection problem by adding additional constraints designed to improve the model quality. These constraints include sparsity-inducing constraints, sign-coherence constraints and linear constraints. The $\textsf{R}$ package $\texttt{holiglm}$ provides functionality to model and fit holistic generalized linear models. By making use of state-of-the-art conic m
On Non-Linear operators for Geometric Deep Learning
This work studies operators mapping vector and scalar fields defined over a manifold $\mathcal{M}$, and which commute with its group of diffeomorphisms $\text{Diff}(\mathcal{M})$. We prove that in the case of scalar fields $L^p_ω(\mathcal{M,\mathbb{R}})$, those operators correspond to point-wise non-linearities, recovering and extending known results on $\mathbb{R}^d$. In the context of Neural Networks defined over $
Meta-Reinforcement Learning for Building Energy Management System
The building sector is one of the largest contributors to global energy consumption. Improving its energy efficiency is essential for reducing operational costs and greenhouse gas emissions. Energy management systems (EMS) play a key role in monitoring and controlling building appliances efficiently and reliably. With the increasing integration of renewable energy, intelligent EMS solutions have received growing atte
Reformulation Techniques for Automated Planning: A Systematic Review
Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, i.e. the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesise a solution plan. Such a separation
While the increased use of AI in the manufacturing sector has been widely noted, there is little understanding on the risks that it may raise in a manufacturing organisation. Although various high level frameworks and definitions have been proposed to consolidate potential risks, practitioners struggle with understanding and implementing them. This lack of understanding exposes manufacturing to a multitude of risks,
The early identification and intervention of latent depression are of significant societal importance for mental health governance. While current automated detection methods based on social media have shown progress, their decision-making processes often lack a clinically interpretable framework, particularly in capturing the duration and dynamic evolution of depressive symptoms. To address this, this study introduce
Ensembling is among the most popular tools in machine learning (ML) due to its effectiveness in minimizing variance and thus improving generalization. Most ensembling methods for black-box base learners fall under the umbrella of "stacked generalization," namely training an ML algorithm that takes the inferences from the base learners as input. While stacking has been widely applied in practice, its theoretic
General Formulation and PCL-Analysis for Restless Bandits with Limited Observability
In this paper, we consider a general observation model for restless multi-armed bandit problems. The operation of the player is based on the past observation history that is limited (partial) and error-prone due to resource constraints or environmental or intrinsic noises. By establishing a general probabilistic model for dynamics of the observation process, we formulate the problem as a restless bandit with an infin
Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability
Adversarial examples (AEs) for DNNs have been shown to be transferable: AEs that successfully fool white-box surrogate models can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable AEs, many of these findings lack explanations and even lead to inconsistent advice. In this paper, we take a further step towards
Global Deep Forecasting with Patient-Specific Pharmacokinetics
Forecasting healthcare time series data is vital for early detection of adverse outcomes and patient monitoring. However, it can be challenging in practice due to variable medication administration and unique pharmacokinetic (PK) properties of each patient. To address these challenges, we propose a novel hybrid global-local architecture and a PK encoder that informs deep learning models of patient-specific treatment
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness
Vision Transformers (ViTs) have emerged as a fundamental architecture and serve as the backbone of modern vision-language models. Despite their impressive performance, ViTs exhibit notable vulnerability to evasion attacks, necessitating the development of specialized Adversarial Training (AT) strategies tailored to their unique architecture. While a direct solution might involve applying existing AT methods to ViTs,
Question Answering Over Spatio-Temporal Knowledge Graph
Spatio-temporal knowledge graphs (STKGs) enhance traditional KGs by integrating temporal and spatial annotations, enabling precise reasoning over questions with spatio-temporal dependencies. Despite their potential, research on spatio-temporal knowledge graph question answering (STKGQA) remains limited. This is primarily due to the lack of datasets that simultaneously contain spatio-temporal information, as well as m
Differentially Private Knowledge Distillation via Synthetic Text Generation
Large Language models (LLMs) are achieving state-of-the-art performance in many different downstream tasks. However, the increasing urgency of data privacy puts pressure on practitioners to train LLMs with Differential Privacy (DP) on private data. Concurrently, the exponential growth in parameter size of LLMs necessitates model compression before deployment of LLMs on resource-constrained devices or latency-sensitiv
I-Diff: Structural Regularization for High-Fidelity Diffusion Models
Denoising Diffusion Probabilistic Models (DDPMs) have significantly advanced generative AI, achieving impressive results in high-quality image and data generation. However, enhancing fidelity without compromising semantic content remains a key challenge in the field. Recent diffusion research in multiple disciplines has introduced objectives and architectural refinements that tighten the match between generated and r
Diversity optimization is the class of optimization problems in which we aim to find a diverse set of good solutions. One of the frequently-used approaches to solve such problems is to use evolutionary algorithms that evolve a desired diverse population. This approach is called evolutionary diversity optimization (EDO). In this paper, we analyze EDO on a three-objective function LOTZ$_k$, which is a modification of t
Automated Model Selection for Generalized Linear Models
In this paper, we show how mixed-integer conic optimization can be used to combine feature subset selection with holistic generalized linear models to fully automate the model selection process. Concretely, we directly optimize for the Akaike and Bayesian information criteria while imposing constraints designed to deal with multicollinearity in the feature selection task. Specifically, we propose a novel pairwise cor
Coupling Light with Matter for Identifying Dominant Subnetworks
We introduce DOMINO, a light-matter computing platform that exploits the full complex amplitude of coupled condensate networks to solve maximum-weight clique problems and reveal hidden indirect correlations in large graphs. By embedding network structure directly into a gain-controlled polaritonic (or photonic) oscillator array, DOMINO performs analog optimization, directly solving the maximum-weight clique problem v
REP: Resource-Efficient Prompting for Rehearsal-Free Continual Learning
Recent rehearsal-free continual learning (CL) methods guided by prompts achieve strong performance on vision tasks with non-stationary data but remain resource-intensive, hindering real-world edge deployment. We introduce resource-efficient prompting (REP), which improves the computational and memory efficiency of prompt-based rehearsal-free continual learning methods while minimizing accuracy trade-offs. Our approac
Exploring brain activity in relation to visual perception provides insights into the biological representation of the world. While functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) have enabled effective image classification and reconstruction, their high cost and bulk limit practical use. Electroencephalography (EEG), by contrast, offers low cost and excellent temporal resolution, but its
LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection Systems in Autonomous Driving
Comprehensive perception of the vehicle's environment and correct interpretation of the environment are crucial for the safe operation of autonomous vehicles. The perception of surrounding objects is the main component for further tasks such as trajectory planning. However, safe trajectory planning requires not only object detection, but also the detection of drivable areas and lane corridors. While first approac
Continuous glucose monitoring (CGM) devices provide real-time glucose monitoring and timely alerts for glycemic excursions, improving glycemic control among patients with diabetes. However, identifying rare events like hypoglycemia and hyperglycemia remain challenging due to their infrequency. Moreover, limited access to sensitive patient data hampers the development of robust machine learning models. Our objective i
Estimating Privacy Leakage of Augmented Contextual Knowledge in Language Models
Language models (LMs) rely on their parametric knowledge augmented with relevant contextual knowledge for certain tasks, such as question answering. However, the contextual knowledge can contain private information that may be leaked when answering queries, and estimating this privacy leakage is not well understood. A straightforward approach of directly comparing an LM's output to the contexts can overestimate t
COMMA: A Communicative Multimodal Multi-Agent Benchmark
The rapid advances of multimodal agents built on large foundation models have largely overlooked their potential for language-based communication between agents in collaborative tasks. This oversight presents a critical gap in understanding their effectiveness in real-world deployments, particularly when communicating with humans. Existing agentic benchmarks fail to address key aspects of inter-agent communication an
QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design
Quantum computing is an emerging field recognized for the significant speedup it offers over classical computing through quantum algorithms. However, designing and implementing quantum algorithms pose challenges due to the complex nature of quantum mechanics and the necessity for precise control over quantum states. Despite the significant advancements in AI, there has been a lack of datasets specifically tailored fo
Notable events recorded on this day and month across all years.