Record 19122025 · captured 2026-08-25
The world looked up Greg Biffle. 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.
Gregory Jack Biffle, nicknamed "the Biff", was an American professional stock car racing driver. He most notably raced from 2002 to 2022 in the NASCAR Cup Series, most notably driving the No. 16 Ford for Roush Fenway Racing from 2002 to 2016 and last competed
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
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
Sprites or red sprites are large-scale electric discharges that occur in the mesosphere, high above thunderstorm clouds, or cumulonimbus, giving rise to a varied range of visual shapes flickering in the night sky. They are usually triggered by the discharges o
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.
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
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
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
William Edward Rush was an English actor and singer-songwriter. He was best known for portraying the role of Josh Stevenson in the BBC school-based drama series Waterloo Road (2009–2013). In 2016, Rush appeared as a contestant on the thirteenth series of The X
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.
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
Survivor 49 is the forty-ninth season of the American competitive reality television series Survivor. It premiered on September 24, 2025, on CBS in the United States. It was the seventeenth consecutive season to be filmed in the Mamanuca Islands in Fiji. The s
Danielle Riley Keough is an American actress. Born into the Presley family, she is the eldest daughter of Lisa Marie Presley and the eldest grandchild of Elvis Presley and Priscilla Presley. She began her career as a model from 2004 to 2008 before transitionin
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
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
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
1989 Tiananmen Square protests and massacre
Protests led by students and workers, known in China as the June Fourth Incident, were held in Tiananmen Square in Beijing, China, from 15 April to 4 June 1989. After weeks of unsuccessful attempts between the demonstrators and the Chinese government to find a
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
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
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 Oluwafemi Olaseni "AJ" Joshua is a British professional boxer. He held the unified heavyweight championship twice between 2017 and 2021. He also held the International Boxing Organization (IBO) title during his reigns as champion. At regional level, he
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.
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
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
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.
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
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Learning-Driven Exploration for Reinforcement Learning
Effective and intelligent exploration has been an unresolved problem for reinforcement learning. Most contemporary reinforcement learning relies on simple heuristic strategies such as $ε$-greedy exploration or adding Gaussian noise to actions. These heuristics, however, are unable to intelligently distinguish the well explored and the unexplored regions of state space, which can lead to inefficient use of training ti
Neural networks for dengue forecasting: a systematic review
Background: Early forecasts of dengue are an important tool for disease mitigation. Neural networks are powerful predictive models that have made contributions to many areas of public health. In this study, we reviewed the application of neural networks in the dengue forecasting literature, with the objective of informing model design for future work. Methods: Following PRISMA guidelines, we conducted a systematic se
Optimization with Access to Auxiliary Information
We investigate the fundamental optimization question of minimizing a target function $f$, whose gradients are expensive to compute or have limited availability, given access to some auxiliary side function $h$ whose gradients are cheap or more available. This formulation captures many settings of practical relevance, such as i) re-using batches in SGD, ii) transfer learning, iii) federated learning, iv) training with
Low-Resolution Action Recognition for Tiny Actions Challenge
Tiny Actions Challenge focuses on understanding human activities in real-world surveillance. Basically, there are two main difficulties for activity recognition in this scenario. First, human activities are often recorded at a distance, and appear in a small resolution without much discriminative clue. Second, these activities are naturally distributed in a long-tailed way. It is hard to alleviate data bias for such
The Emergence of Chunking Structures with Hierarchical RNN
In Natural Language Processing (NLP), predicting linguistic structures, such as parsing and chunking, has mostly relied on manual annotations of syntactic structures. This paper introduces an unsupervised approach to chunking, a syntactic task that involves grouping words in a non-hierarchical manner. We present a Hierarchical Recurrent Neural Network (HRNN) designed to model word-to-chunk and chunk-to-sentence compo
Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning
Exploring exoplanets has transformed our understanding of the universe by revealing many planetary systems that defy our current understanding. To study their atmospheres, spectroscopic observations are used to infer essential atmospheric properties that are not directly measurable. Estimating atmospheric parameters that best fit the observed spectrum within a specified atmospheric model is a complex problem that is
Open-world video recognition is challenging since traditional networks are not generalized well on complex environment variations. Alternatively, foundation models with rich knowledge have recently shown their generalization power. However, how to apply such knowledge has not been fully explored for open-world video recognition. To this end, we propose a generic knowledge transfer pipeline, which progressively exploi
MALTO at SemEval-2024 Task 6: Leveraging Synthetic Data for LLM Hallucination Detection
In Natural Language Generation (NLG), contemporary Large Language Models (LLMs) face several challenges, such as generating fluent yet inaccurate outputs and reliance on fluency-centric metrics. This often leads to neural networks exhibiting "hallucinations". The SHROOM challenge focuses on automatically identifying these hallucinations in the generated text. To tackle these issues, we introduce two key compo
Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review
The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable. In particular, the challenge of identifying thyroid cancer (TC) has seen progress with the use of machine learning (ML) and big data analysis, incorporating Transformers to evaluate TC prognosis and determine the risk of malignancy in individuals. This review art
Houston we have a Divergence: A Subgroup Performance Analysis of ASR Models
The Fearless Steps APOLLO Community Resource provides unparalleled opportunities to explore the potential of multi-speaker team communications from NASA Apollo missions. This study focuses on discovering the characteristics that make Apollo recordings more or less intelligible to Automatic Speech Recognition (ASR) methods. We extract, for each audio recording, interpretable metadata on recordings (signal-to-noise rat
Four-hour thunderstorm nowcasting using a deep diffusion model of satellite data
Convection (thunderstorm) develops rapidly within hours and is highly destructive, posing a significant challenge for nowcasting and resulting in substantial losses to infrastructure and society. After the emergence of artificial intelligence (AI)-based methods, convection nowcasting has experienced rapid advancements, with its performance surpassing that of physics-based numerical weather prediction and other conven
Online Bandits with (Biased) Offline Data: Adaptive Learning under Distribution Mismatch
Traditional online learning models are typically initialized from scratch. By contrast, contemporary real-world applications often have access to historical datasets that can potentially enhanced the online learning processes. We study how offline data can be leveraged to facilitate online learning in stochastic multi-armed bandits and combinatorial bandits. In our study, the probability distributions that govern the
A Sparse Tensor Generator with Efficient Feature Extraction
Sparse tensor operations are increasingly important in diverse applications such as social networks, deep learning, diagnosis, crime, and review analysis. However, a major obstacle in sparse tensor research is the lack of large-scale sparse tensor datasets. Another challenge lies in analyzing sparse tensor features, which are essential not only for understanding the nonzero pattern but also for selecting the most sui
Sparse-Tuning: Adapting Vision Transformers with Efficient Fine-tuning and Inference
Parameter-efficient fine-tuning (PEFT) has emerged as a popular solution for adapting pre-trained Vision Transformer (ViT) models to downstream applications by updating only a small subset of parameters. While current PEFT methods have achieved fine-tuning efficiency, they overlook the efficiency of computation and GPU memory during inference, falling short of practical requirements. To address this limitation, we pr
Models That Prove Their Own Correctness
How can we trust the correctness of a learned model on a particular input of interest? Model accuracy is typically measured on average over a distribution of inputs, giving no guarantee for any fixed input. This paper proposes a theoretically-founded solution to this problem: to train Self-Proving models that prove the correctness of their output to a verification algorithm $V$ via an Interactive Proof. Self-Proving
PILA: Physics-Informed Low Rank Augmentation for Interpretable Earth Observation
Physically meaningful representations are essential for Earth Observation (EO), yet existing physical models are often simplified and incomplete. This leads to discrepancies between simulation and observations that hinder reliable forward model inversion. Common approaches to EO inversion either ignored this incompleteness or relied on case-specific preprocessing. More recent methods use physics-informed autoencoders
MMRel: Benchmarking Relation Understanding in Multi-Modal Large Language Models
Though Multi-modal Large Language Models (MLLMs) have recently achieved significant progress, they often struggle to understand diverse and complicated inter-object relations. Specifically, the lack of large-scale and high-quality relation data has greatly hindered the progress of MLLMs in various vision-language perception tasks. We attempt to address this challenge by contributing the Multi-Modal Relation Understan
Bandits with Preference Feedback: A Stackelberg Game Perspective
Bandits with preference feedback present a powerful tool for optimizing unknown target functions when only pairwise comparisons are allowed instead of direct value queries. This model allows for incorporating human feedback into online inference and optimization and has been employed in systems for fine-tuning large language models. The problem is well understood in simplified settings with linear target functions or
Perception systems of autonomous vehicles are susceptible to occlusion, especially when examined from a vehicle-centric perspective. Such occlusion can lead to overlooked object detections, e.g., larger vehicles such as trucks or buses may create blind spots where cyclists or pedestrians could be obscured, accentuating the safety concerns associated with such perception system limitations. To mitigate these challenge
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs
Dynamic graph modeling aims to uncover evolutionary patterns in real-world systems, enabling accurate social recommendation and early detection of cancer cells. Inspired by the success of recent state space models in efficiently capturing long-term dependencies, we propose DyG-Mamba by translating dynamic graph modeling into a long-term sequence modeling problem. Specifically, inspired by Ebbinghaus' forgetting c
Unsupervised discovery of the shared and private geometry in multi-view data
Studying complex real-world phenomena often involves data from multiple views (e.g. sensor modalities or brain regions), each capturing different aspects of the underlying system. Within neuroscience, there is growing interest in large-scale simultaneous recordings across multiple brain regions. Understanding the relationship between views (e.g., the neural activity in each region recorded) can reveal fundamental ins
Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review
Recent technological advancements in multimodal machine learning--including the rise of large language models (LLMs)--have improved our ability to collect, process, and analyze diverse multimodal data such as speech, video, and eye gaze in learning and training contexts. While prior reviews have addressed individual components of the multimodal pipeline (e.g., conceptual models, data fusion), a comprehensive review o
WildFit: Autonomous In-situ Model Adaptation for Resource-Constrained IoT Systems
Resource-constrained IoT devices increasingly rely on deep learning models, however, these models experience significant accuracy drops due to domain shifts when encountering variations in lighting, weather, and seasonal conditions. While cloud-based retraining can address this issue, many IoT deployments operate with limited connectivity and energy constraints, making traditional fine-tuning approaches impractical.
Enhancing Long-term RAG Chatbots with Psychological Models of Memory Importance and Forgetting
While Retrieval-Augmented Generation (RAG) has shown promise in enhancing long-term conversations, the increasing memory load as conversations progress degrades retrieval accuracy. Drawing on psychological insights, we propose LUFY, a simple yet effective method that focuses on emotionally arousing memories and retains less than 10% of the conversation. In the user experiment, participants interacted with three types
Matérn Kernels for Tunable Implicit Surface Reconstruction
We propose to use the family of Matérn kernels for implicit surface reconstruction, building upon the recent success of kernel methods for 3D reconstruction of oriented point clouds. As we show from a theoretical and practical perspective, Matérn kernels have some appealing properties which make them particularly well suited for surface reconstruction -- outperforming state-of-the-art methods based on the arc-cosine
Notable events recorded on this day and month across all years.