Record 22122025 · captured 2026-08-25
The world looked up James Ransone. 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.
James Finley Ransone III was an American actor. Known for his roles in horror and drama, he played Ziggy Sobotka in the second season of the drama series The Wire, Cpl. Josh Ray Person in the war drama miniseries Generation Kill (2008), Deputy "So-and-So" in t
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
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
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
Jeffrey Edward Epstein was an American financier and child sex offender. He began his career as a math teacher at the Dalton School in New York City, before entering the banking and finance sector. Over several decades, he made much of his fortune providing ta
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
Jake Joseph Paul is an American professional boxer, influencer, and former actor. He began his career posting videos on Vine in September 2013 and had amassed 5.3 million followers and 2 billion views before the app was discontinued. He launched his YouTube ch
The Epstein files are a partially released collection of millions of documents, images, videos, and emails related to the activities of American financier and convicted child sex offender Jeffrey Epstein, including his social circle of public figures, politici
Chase Douglas DeMoor is an American professional boxer, actor, and former college football player. He currently holds the MFB Heavyweight Title and is most known for his role in Too Hot to Handle and defeating Andrew Tate at Misfits Boxing.
Malik Antonio Willis is an American professional football quarterback for the Miami Dolphins of the National Football League (NFL). He played college football for the Auburn Tigers and Liberty Flames, winning the 2020 Dudley Award with the latter. Willis was s
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
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.
Emory Andrew Tate III is an American and British social media personality and former professional kickboxer who built a webcam pornography business before gaining notoriety for promoting various highly controversial positions in the manosphere. His commentary
The Great Flood (Korean: 대홍수) is a 2025 South Korean science fiction disaster film co-written and directed by Kim Byung-woo. Starring Kim Da-mi and Park Hae-soo, the film depicts the desperate struggle of those who have pinned their hopes on humanity's last da
One Battle After Another is a 2025 American action thriller film written, directed, and produced by Paul Thomas Anderson. Inspired by the 1990 novel Vineland by Thomas Pynchon, the film's ensemble cast includes Leonardo DiCaprio, Sean Penn, Benicio del Toro, R
Cher is an American singer and actress. Dubbed the "Goddess of Pop", she is known for her androgynous, contralto voice, bold fashion, elaborate stagecraft and multifaceted career. Her screen roles often reflect her public image as a strong-willed, outspoken wo
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
Avatar is a 2009 epic science fiction film written and directed by James Cameron. It features an ensemble cast including Sam Worthington, Zoe Saldaña, Stephen Lang, Michelle Rodriguez, and Sigourney Weaver. It is the first installment in the Avatar film series
Sreenivasan was an Indian actor, screenwriter, film director and producer who predominantly worked in Malayalam cinema. He starred in over 225 films. Sreenivasan wrote the screenplays of films such as Odaruthammava Aalariyam (1984), Sanmanassullavarkku Samadha
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
Avatar: The Way of Water is a 2022 American epic science fiction film directed by James Cameron and written by Cameron, Rick Jaffa and Amanda Silver. It is the second installment in the Avatar film series and the sequel to Avatar (2009). Sam Worthington, Zoe S
Karen Julia Carney is an English sports journalist and former professional footballer who played as a winger and midfielder. Carney has been a regular broadcaster for live football on TNT Sports, Sky Sports, ITV and Amazon Prime, including Women's Super League
Heated Rivalry is a Canadian sports romance television series created, written, and directed by Jacob Tierney for Crave. Based on the Game Changers book series by Rachel Reid, the show takes its title from the 2019 second installment. It stars Hudson Williams
Ghislaine Noelle Marion Maxwell is a British convicted child sex trafficker and former socialite. In 2021, she was convicted of child sex trafficking, and in 2022 was sentenced to 20 years in prison.
Bigg Boss (Telugu TV series) season 9
Bigg Boss 9, also known as Bigg Boss 9: Ranarangam, is a reality show and the ninth season of the Indian Telugu-language reality television series Bigg Boss, produced by Banijay. Nagarjuna hosts the show for the seventh time in a row. The season premiered on 7
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
Neil "Razor" Ruddock is an English former professional footballer and television personality who is a club director at Enfield F.C.
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 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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Self-Organization and Artificial Life
Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines,
Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, it is required to compare its functions before and after mutations. However, this sometimes takes a high computational cost as the network size grows. Here we develop a predictive method to estimate the robustness and evolvability of biological networks without
Even when concepts similar to emergence have been used since antiquity, we lack an agreed definition. However, emergence has been identified as one of the main features of complex systems. Most would agree on the statement ``life is complex''. Thus, understanding emergence and complexity should benefit the study of living systems. It can be said that life emerges from the interactions of complex molecules. Bu
ROI: A method for identifying organizations receiving personal data
Many studies have exposed the massive collection of personal data in the digital ecosystem through, for instance, websites, mobile apps, or smart devices. This fact goes unnoticed by most users, who are also unaware that the collectors are sharing their personal data with many different organizations around the globe. This paper assesses techniques available in the state of the art to identify the organizations recei
Language statistics at different spatial, temporal, and grammatical scales
Statistical linguistics has advanced considerably in recent decades as data has become available. This has allowed researchers to study how statistical properties of languages change over time. In this work, we use data from Twitter to explore English and Spanish considering the rank diversity at different scales: temporal (from 3 to 96 hour intervals), spatial (from 3km to 3000+km radii), and grammatical (from monog
Learning Task-preferred Inference Routes for Gradient De-conflict in Multi-output DNNs
Multi-output deep neural networks(MONs) contain multiple task branches, and these tasks usually share partial network filters that lead to the entanglement of different task inference routes. Due to the inconsistent optimization objectives, the task gradients used for training MONs will interfere with each other on the shared routes, which will decrease the overall model performance. To address this issue, we propose
Modern machine learning models deployed in the wild can encounter both covariate and semantic shifts, giving rise to the problems of out-of-distribution (OOD) generalization and OOD detection respectively. While both problems have received significant research attention lately, they have been pursued independently. This may not be surprising, since the two tasks have seemingly conflicting goals. This paper provides a
Quaternion Convolutional Neural Networks: Current Advances and Future Directions
Since their first applications, Convolutional Neural Networks (CNNs) have solved problems that have advanced the state-of-the-art in several domains. CNNs represent information using real numbers. Despite encouraging results, theoretical analysis shows that representations such as hyper-complex numbers can achieve richer representational capacities than real numbers, and that Hamilton products can capture intrinsic i
An AI-driven Assessment of Bone Density as a Biomarker Leading to the Aging Law
As global population aging intensifies, there is growing interest in the study of biological age. Bones have long been used to evaluate biological age, and the decline in bone density with age is a well-recognized phenomenon in adults. However, the pattern of this decline remains controversial, making it difficult to serve as a reliable indicator of the aging process. Here we present a novel AI-driven statistical met
Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies
Current imitation learning approaches, predominantly based on deep neural networks (DNNs), offer efficient mechanisms for learning driving policies from real-world datasets. However, they suffer from inherent limitations in interpretability and generalizability--issues of critical importance in safety-critical domains such as autonomous driving. In this paper, we introduce Symbolic Imitation Learning (SIL), a novel f
Differentially private Bayesian tests
Differential privacy has emerged as an significant cornerstone in the realm of scientific hypothesis testing utilizing confidential data. In reporting scientific discoveries, Bayesian tests are widely adopted since they effectively circumnavigate the key criticisms of P-values, namely, lack of interpretability and inability to quantify evidence in support of the competing hypotheses. We present a novel differentially
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
In this paper, we analyze the sample and communication complexity of the federated linear stochastic approximation (FedLSA) algorithm. We explicitly quantify the effects of local training with agent heterogeneity. We show that the communication complexity of FedLSA scales polynomially with the inverse of the desired accuracy $ε$. To overcome this, we propose SCAFFLSA a new variant of FedLSA that uses control variates
Resilience of Rademacher chaos of low degree
The {\em resilience} of a Rademacher chaos is the maximum number of adversarial sign-flips that the chaos can sustain without having its largest atom probability significantly altered. Inspired by probabilistic lower-bound guarantees for the resilience of linear Rademacher chaos (aka. resilience of the Littlewood-Offord problem), obtained by Bandeira, Ferber, and Kwan (Advances in Mathematics, Vol. $319$, $2017$), we
Sparse, Efficient and Explainable Data Attribution with DualXDA
Data Attribution (DA) is an emerging approach in the field of eXplainable Artificial Intelligence (XAI), aiming to identify influential training datapoints which determine model outputs. It seeks to provide transparency about the model and individual predictions, e.g. for model debugging, identifying data-related causes of suboptimal performance. However, existing DA approaches suffer from prohibitively high computat
A Feature-based Generalizable Prediction Model for Both Perceptual and Abstract Reasoning
A hallmark of human intelligence is the ability to infer abstract rules from limited experience and apply these rules to unfamiliar situations. This capacity is widely studied in the visual domain using the Raven's Progressive Matrices. Recent advances in deep learning have led to multiple artificial neural network models matching or even surpassing human performance. However, while humans can identify and expres
LN3DIFF++: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation
The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework called LN3Diff++ to address this gap and enable fast, high-quality, and generic conditional 3D generation. Our approach harnesses a 3D-aware a
HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs
Neural networks with sub-microsecond inference latency are required by many critical applications. Targeting such applications deployed on FPGAs, we present High Granularity Quantization (HGQ), a quantization-aware training framework that optimizes parameter bit-widths through gradient descent. Unlike conventional methods, HGQ determines the optimal bit-width for each parameter independently, making it suitable for h
MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction
Computed Tomography (CT) technology reduces radiation haz-ards to the human body through sparse sampling, but fewer sampling angles pose challenges for image reconstruction. Score-based generative models are widely used in sparse-view CT re-construction, performance diminishes significantly with a sharp reduction in projection angles. Therefore, we propose an ultra-sparse view CT reconstruction method utilizing multi
Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications
Large language models (LLMs) significantly enhance the performance of various applications, but they are computationally intensive and energy-demanding. This makes it challenging to deploy them on devices with limited resources, such as personal computers and mobile/wearable devices, and results in substantial inference costs in resource-rich environments like cloud servers. To extend the use of LLMs, we introduce a
Large Language Models: A New Approach for Privacy Policy Analysis at Scale
The number and dynamic nature of web and mobile applications presents significant challenges for assessing their compliance with data protection laws. In this context, symbolic and statistical Natural Language Processing (NLP) techniques have been employed for the automated analysis of these systems' privacy policies. However, these techniques typically require labor-intensive and potentially error-prone manually
Deep generative models like VAEs and diffusion models have advanced various generation tasks by leveraging latent variables to learn data distributions and generate high-quality samples. Despite the field of explainable AI making strides in interpreting machine learning models, understanding latent variables in generative models remains challenging. This paper introduces LatentExplainer, a framework for automatically
Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy
Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation, and long-term strategic planning. Previous AI agents have demonstrated their ability to handle multi-step games and large action spaces in multi-agent tasks. However, diplomacy involves a staggering magnitude of decision spaces, especially
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Many machine learning models require setting a parameter that controls their size before training, e.g. number of neurons in DNNs, or inducing points in GPs. Increasing capacity typically improves performance until all the information from the dataset is captured. After this point, computational cost keeps increasing, without improved performance. This leads to the question "How big is big enough?" We investi
Privacy Bias in Language Models: A Contextual Integrity-based Auditing Metric
As large language models (LLMs) are integrated into sociotechnical systems, it is crucial to examine the privacy biases they exhibit. We define privacy bias as the appropriateness value of information flows in responses from LLMs. A deviation between privacy biases and expected values, referred to as privacy bias delta, may indicate privacy violations. As an auditing metric, privacy bias can help (a) model trainers e
FAMOUS: Flexible Accelerator for the Attention Mechanism of Transformer on UltraScale+ FPGAs
Transformer neural networks (TNNs) are being applied across a widening range of application domains, including natural language processing (NLP), machine translation, and computer vision (CV). Their popularity is largely attributed to the exceptional performance of their multi-head self-attention blocks when analyzing sequential data and extracting features. To date, there are limited hardware accelerators tailored f
Notable events recorded on this day and month across all years.