Record 02122025 · captured 2026-08-25
The world looked up Raj & DK. 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.
Raj Nidimoru and Krishna Dasarakothapalli, collectively credited as Raj & DK, are an Indian filmmaker duo known for their work as writers, directors, and producers in Hindi cinema. They are noted for creating, directing, and producing the Hindi-language thrill
Lane Monte Kiffin is an American football coach who is the head coach of the LSU Tigers. He served as the head coach of the Oakland Raiders from 2007 to 2008, the University of Tennessee in 2009, USC from 2010 to 2013, Florida Atlantic from 2017 to 2019, and O
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
The fifth and final season of the American science fiction horror drama television series Stranger Things, marketed as Stranger Things 5, was released on the streaming service Netflix in two volumes and the finale. The first set of four episodes was released o
Samantha Ruth Prabhu is an Indian actress who works predominantly in Tamil and Telugu films. One of South India's highest-paid actresses, Samantha is the recipient of several accolades, including four Filmfare Awards South, two Nandi Awards and a Tamil Nadu St
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
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
Zootopia 2 is a 2025 American animated buddy cop comedy film produced by Walt Disney Animation Studios, the second film in the series and a sequel to Zootopia (2016). Directed by Jared Bush and Byron Howard and written by Bush, the film stars Ginnifer Goodwin,
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.
Tere Ishk Mein is a 2025 Indian Hindi-language romantic drama film directed by Aanand L. Rai from a screenplay written by Himanshu Sharma and Neeraj Yadav. Billed as a spiritual sequel to Raanjhanaa (2013), the film stars Dhanush and Kriti Sanon. It follows Sh
Marcus Ardel Taulauniu Mariota is an American professional football quarterback for the Washington Commanders of the National Football League (NFL). He played college football for the Oregon Ducks, winning the Heisman Trophy in 2014. Mariota was selected secon
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
Stephen Thomas "Pete" Golding is an American football coach who is currently the head football coach at the University of Mississippi. He previously served as the defensive coordinator and the inside linebackers coach at Ole Miss from 2023 to 2025. Golding was
2025 Honduran general election
General elections were held in Honduras on 30 November 2025. Voters elected the President, all 128 members of the National Congress, and 20 representatives to the Central American Parliament (PARLACEN). The National Electoral Council (CNE) declared National Pa
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
Noah Cameron Schnapp is an American actor. He made his acting debut in 2015 with his portrayal of Charlie Brown in the animated film The Peanuts Movie and his supporting role in Steven Spielberg's Bridge of Spies. Schnapp gained international recognition for h
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
List of Stranger Things episodes
Stranger Things is an American science fiction, horror, mystery, and drama television series created by the Duffer Brothers for the streaming service Netflix. The brothers, as well as Karl Gajdusek in the first season only, are the program's showrunners. They
Bo Chapman Nix is an American professional football quarterback for the Denver Broncos of the National Football League (NFL). He played his first three seasons of college football for the Auburn Tigers, winning SEC Freshman of the Year in 2019. During his last
Wicked: For Good is a 2025 musical fantasy film directed by Jon M. Chu and written by Winnie Holzman and Dana Fox. The sequel to Wicked (2024), it adapts the second act of the 2003 stage musical by Stephen Schwartz and Holzman, which was loosely based on Grego
The Family Man (Indian TV series)
The Family Man is an Indian Hindi-language spy thriller streaming television series created by Raj & DK for Amazon Prime Video. It features Manoj Bajpayee as Srikant Tiwari, a middle-class man secretly working as an intelligence officer for the Threat Analysis
2025 Formula One World Championship
The 2025 FIA Formula One World Championship was a motor racing championship for Formula One cars and the 76th running of the Formula One World Championship. It was recognised by the Fédération Internationale de l'Automobile (FIA), the governing body of interna
Jacob Hurley Bongiovi is an American model and actor. He is the son of rock musician Jon Bon Jovi.
Aleksey Golesh is a Russian-American college football coach who is currently the head football coach at Auburn University. He previously served as the head football coach at the University of South Florida. Prior to that role he was the offensive coordinator a
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
Natalia Danielle Dyer is an American actress. She is best known for her role as Nancy Wheeler in the Netflix science fiction horror series Stranger Things (2016–2025). She has also appeared in the Peacock comedy thriller series Based on a True Story (2023) and
James Metcalfe Campbell Bower is an English actor, singer, and musician. He is best known for his role as Henry Creel / Vecna in the fourth (2022) and fifth (2025) seasons of the science fiction horror series Stranger Things, for which he received critical acc
Maya Ray Thurman Hawke is an American actress and singer-songwriter. The daughter of Ethan Hawke and Uma Thurman, she began her career in modeling and subsequently made her screen debut as Jo March in the 2017 BBC adaptation of Little Women. She gained interna
6-7 was an Internet meme, slang term, and gesture that became popular in 2025 on TikTok and Instagram Reels. It has no fixed meaning.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
A Neuro-inspired Theory of Joint Human-Swarm Interaction
Human-swarm interaction (HSI) is an active research challenge in the realms of swarm robotics and human-factors engineering. Here we apply a cognitive systems engineering perspective and introduce a neuro-inspired joint systems theory of HSI. The mindset defines predictions for adaptive, robust and scalable HSI dynamics and therefore has the potential to inform human-swarm loop design.
Adversarial Inverse Reinforcement Learning for Mean Field Games
Mean field games (MFGs) provide a mathematically tractable framework for modelling large-scale multi-agent systems by leveraging mean field theory to simplify interactions among agents. It enables applying inverse reinforcement learning (IRL) to predict behaviours of large populations by recovering reward signals from demonstrated behaviours. However, existing IRL methods for MFGs are powerless to reason about uncert
STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large-scale data collection, sanitization, and annotation. Moreover, the created datasets usually suffer from extremely imbalanced class distribution or partial low-quality data samples. Motivated by this, we explore the procedurally synthetic 3D data g
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4
The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy. HIPAA (Health Insurance Portability and Accountability Act) mandates removing re-identifying information before the dissemination of medical records. Thus, effective and efficient solutions for de-identifying medical data, especially those in free-text forms, are high
Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation
Post-click conversion rate (CVR) is a reliable indicator of online customers' preferences, making it crucial for developing recommender systems. A major challenge in predicting CVR is severe selection bias, arising from users' inherent self-selection behavior and the system's item selection process. To mitigate this issue, the inverse propensity score (IPS) is employed to weight the prediction error of ea
EPLKG: Efficient Prompt Learning with Knowledge Graph
Large-scale pre-trained models such as CLIP excel in transferability and robust generalization across diverse datasets. However, adapting these models to new datasets or domains is computationally costly, especially in low-resource or few-shot settings, and existing prompt-learning methods often lack interpretability. We introduce Efficient Prompt Learning with Knowledge Graph (EPLKG), which uses a knowledge graph to
DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection
The increasingly pervasive facial recognition (FR) systems raise serious concerns about personal privacy, especially for billions of users who have publicly shared their photos on social media. Several attempts have been made to protect individuals from being identified by unauthorized FR systems utilizing adversarial attacks to generate encrypted face images. However, existing methods suffer from poor visual quality
Learning by Aligning 2D Skeleton Sequences and Multi-Modality Fusion
This paper presents a self-supervised temporal video alignment framework which is useful for several fine-grained human activity understanding applications. In contrast with the state-of-the-art method of CASA, where sequences of 3D skeleton coordinates are taken directly as input, our key idea is to use sequences of 2D skeleton heatmaps as input. Unlike CASA which performs self-attention in the temporal domain only,
Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networks
Subgraph counting is a fundamental task for analyzing structural patterns in graph-structured data, with important applications in domains such as computational biology and social network analysis, where recurring motifs reveal functional and organizational properties. In this paper, we propose localized versions of the Weisfeiler-Leman (WL) algorithms to improve both expressivity and computational efficiency for thi
An Efficient Recommendation System in E-commerce using Passer learning optimization based on Bi-LSTM
Online reviews play a crucial role in shaping consumer decisions, especially in the context of e-commerce. However, the quality and reliability of these reviews can vary significantly. Some reviews contain misleading or unhelpful information, such as advertisements, fake content, or irrelevant details. These issues pose significant challenges for recommendation systems, which rely on user-generated reviews to provide
Evaluation of Deep Neural Operator Models toward Ocean Forecasting
Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the larger fluids community. The present work investigates the possible effectiveness of such deep neural operator models for reproducing and predicting classic fluid flows and simulation
Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition
We introduce a new cross-modal fusion technique designed for generative error correction in automatic speech recognition (ASR). Our methodology leverages both acoustic information and external linguistic representations to generate accurate speech transcription contexts. This marks a step towards a fresh paradigm in generative error correction within the realm of n-best hypotheses. Unlike the existing ranking-based r
Phase codes emerge in recurrent neural networks optimized for modular arithmetic
Recurrent neural networks (RNNs) can implement complex computations by leveraging a range of dynamics, such as oscillations, attractors, and transient trajectories. A growing body of work has highlighted the emergence of phase codes, a type of oscillatory activity where information is encoded in the relative phase of network activity, in RNNs trained for working memory tasks. However, these studies rely on architectu
Whisper is a multitask and multilingual speech model covering 99 languages. It yields commendable automatic speech recognition (ASR) results in a subset of its covered languages, but the model still underperforms on a non-negligible number of under-represented languages, a problem exacerbated in smaller model versions. In this work, we propose DistilWhisper, an approach able to bridge the performance gap in ASR for t
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning
In-vehicle network (IVN) is facing complex external cyber-attacks, especially the emerging masquerade attacks with extremely high difficulty of detection while serious damaging effects. In this paper, we propose the STATGRAPH, which is an effective and fine-grained intrusion detection methodology for IVN security services via multi-view statistical graph learning on in-vehicle controller area network (CAN) messages w
Extending Multilingual Machine Translation through Imitation Learning
Despite the growing variety of languages supported by existing multilingual neural machine translation (MNMT) models, most of the world's languages are still being left behind. We aim to extend large-scale MNMT models to incorporate a new language, enabling translations between this new language and all previously supported languages, even in the challenging scenario where only a parallel corpus between the new l
Forms of Understanding for XAI-Explanations
Explainability has become an important topic in computer science and artificial intelligence, leading to a subfield called Explainable Artificial Intelligence (XAI). The goal of providing or seeking explanations is to achieve (better) 'understanding' on the part of the explainee. However, what it means to 'understand' is still not clearly defined, and the concept itself is rarely the subject of scient
Japanese Tort-case Dataset for Rationale-supported Legal Judgment Prediction
This paper presents the first dataset for Japanese Legal Judgment Prediction (LJP), the Japanese Tort-case Dataset (JTD), which features two tasks: tort prediction and its rationale extraction. The rationale extraction task identifies the court's accepting arguments from alleged arguments by plaintiffs and defendants, which is a novel task in the field. JTD is constructed based on annotated 3,477 Japanese Civil C
Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time series
Real-world time series data often exhibits substantial missing values, posing challenges for advanced analysis. A common approach to addressing this issue is imputation, where the primary challenge lies in determining the appropriate values to fill in. While previous deep learning methods have proven effective for time series imputation, they often produce overconfident imputations, which poses a potentially overlook
Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolution
The effective extraction of spatial-angular features plays a crucial role in light field image super-resolution (LFSR) tasks, and the introduction of convolution and Transformers leads to significant improvement in this area. Nevertheless, due to the large 4D data volume of light field images, many existing methods opted to decompose the data into a number of lower-dimensional subspaces and perform Transformers in ea
Families of costs with zero and nonnegative MTW tensor in optimal transport and the c-divergences
We study the information geometry of $\bcc$-divergences from families of costs of the form $\mathsf{c}(x, \barx) =\mathsf{u}(x^{\mathfrak{t}}\barx)$ through the optimal transport point of view. Here, $\mathsf{u}$ is a scalar function with inverse $\mathsf{s}$, $x^{\ft}\barx$ is a nondegenerate bilinear pairing of vectors $x, \barx$ belonging to an open subset of $\mathbb{R}^n$. We compute explicitly the MTW tensor (o
Resource-efficient Layer-wise Federated Self-supervised Learning
Many studies integrate federated learning (FL) with self-supervised learning (SSL) to take advantage of raw data distributed across edge devices. However, edge devices often struggle with high computational and communication costs imposed by SSL and FL algorithms. With the deployment of more complex and large-scale models, these challenges are exacerbated. To tackle this, we propose Layer-Wise Federated Self-Supervis
Stochastic Hessian Fittings with Lie Groups
This report investigates the fitting of the Hessian or its inverse for stochastic optimizations using a Hessian fitting criterion derived from the preconditioned stochastic gradient descent (PSGD) method. This criterion is closely related to many widely used second-order and adaptive gradient optimization methods, including BFGS, the Gauss-Newton algorithm, natural gradient descent, and AdaGrad. Our analyses reveal t
LCEN: A Nonlinear, Interpretable Feature Selection and Machine Learning Algorithm
Interpretable models can have advantages over black-box models, and interpretability is essential for the application of machine learning in critical settings, such as aviation or medicine. This article introduces the LASSO-Clip-EN (LCEN) algorithm for nonlinear, interpretable feature selection and machine learning modeling. In a wide variety of artificial and empirical datasets, LCEN constructed sparse and frequentl
STC-ViT: Spatio Temporal Continuous Vision Transformer for Medium-range Global Weather Forecasting
Operational Numerical Weather Prediction (NWP) system relies on computationally expensive physics-based models. Recently, transformer models have shown remarkable potential in weather forecasting achieving state-of-the-art results. However, traditional transformers discretize spatio-temporal dimensions, limiting their ability to model continuous dynamical weather processes. Moreover, their reliance on increased depth
Notable events recorded on this day and month across all years.