Record 09012026 · captured 2026-08-25
The world looked up Killing of Renée Good. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
Complete record JSON
What the most people looked up, ranked by Wikipedia pageviews for that day.
On January 7, 2026, Renée Nicole Macklin Good, a 37-year-old American woman, was fatally shot by United States Immigration and Customs Enforcement (ICE) agent Jonathan Ross in Minneapolis, Minnesota, during Operation Metro Surge. Good was in her car stopped si
Jacob Lawrence Frey is an American politician and attorney who has served since 2018 as the 48th mayor of Minneapolis. A member of the Minnesota Democratic–Farmer–Labor Party, he served on the Minneapolis City Council from 2014 to 2018 and was elected mayor of
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
Toxic: A Fairy Tale for Grown-Ups is a 2026 Indian gangster film directed by Geetu Mohandas and jointly produced by Venkat K. Narayana and Yash through KVN Productions and Monster Mind Creations LLP respectively. It stars Yash in a dual role, alongside Kiara A
Greenland is an autonomous territory of the Kingdom of Denmark and is the largest of the kingdom's three constituent parts by land area, the others being Denmark proper and the Faroe Islands. Citizens of Greenland are citizens of Denmark. They are thus citizen
Terence Charles Yorath was a Welsh professional football player and manager at both club and international level.
Kristi Lynn Arnold Noem is an American politician serving as the United States special envoy for the Shield of the Americas since 2026. From 2025 to 2026, she served as the eighth United States secretary of homeland security. A member of the Republican Party,
Gayatri Das, better known by her stage name Geetu Mohandas, is an Indian actress and director known for her works in Malayalam cinema. In 2013, she directed the socio political film Liar's Dice which has received two National Film Awards, was premiered at Sund
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
The following notable deaths occurred in 2026. Names are reported under the date of death, in alphabetical order. A typical entry reports information in the following sequence:Name, age, country of citizenship at birth, subsequent nationality, what subject was
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
Anna's Archive is an open source search engine for shadow libraries that was launched by the pseudonymous Anna shortly after law enforcement efforts to shut down Z-Library in 2022. The site aggregates records from Z-Library, Sci-Hub, and Library Genesis (LibGe
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
Rayford Trae Young is an American professional basketball player for the Washington Wizards of the National Basketball Association (NBA). He played college basketball for the Oklahoma Sooners, where in his one season in 2017–18, he tied the then NCAA Division
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
United States Immigration and Customs Enforcement
United States Immigration and Customs Enforcement (ICE) is a federal law enforcement agency under the United States Department of Homeland Security. Its stated mission is to conduct criminal investigations, enforce immigration laws, preserve national security,
Hudson Williams is a Canadian actor. He rose to prominence for his breakout role as Shane Hollander in the Crave original television series, Heated Rivalry (2025–present), for which he won the Canadian Screen Award for Best Leading Performance in a Drama Serie
Nicolás Maduro Moros is a Venezuelan politician and former union leader who served as the 53rd president of Venezuela from 2013 until his capture during the United States intervention in Venezuela in 2026 for alleged drug trafficking to which he pleaded not gu
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
Michael Lee McDaniel is an American professional football coach who is the offensive coordinator for the Los Angeles Chargers of the National Football League (NFL). A branch of the Shanahan coaching tree, McDaniel began his NFL coaching career as an intern for
Anil Agarwal, is an Indian billionaire businessman who is the founder and chairman of Vedanta Resources Limited. He controls Vedanta Resources through Volcan Investments, a holding vehicle with a 100% stake in the business.
Edward Henrik Larsson is a Swedish professional football coach and former player who played 106 times for the Swedish national team. A striker, Larsson began his career with Högaborgs BK. In 1992, he moved to Helsingborg IF where in his first campaign he helpe
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
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
Jana Nayagan is a 2026 Indian Tamil-language political action drama film directed by H. Vinoth and produced by Venkat K. Narayana under KVN Productions. The film stars C. Joseph Vijay, Bobby Deol, Pooja Hegde, and Mamitha Baiju in the lead role alongside Nassa
Marty Supreme is a 2025 American sports comedy-drama film directed by Josh Safdie, who co-wrote it with Ronald Bronstein. Set in the 1950s, it stars Timothée Chalamet as table tennis player Marty Mauser and follows his quest to become world champion. Gwyneth P
Ethan Green Hawke is an American actor, author, and filmmaker whose career on both stage and screen has spanned four decades. Known for his versatility across a range of roles and collaborations with director Richard Linklater, he has worked in both independen
Donald John Trump is an American politician, media personality, and businessman who is the 47th president of the United States. A member of the Republican Party, he served as the 45th president from 2017 to 2021.
Gabrielle Nicole Logan is a Welsh television and radio presenter, and a former rhythmic gymnast who represented Wales and Great Britain. She hosted Final Score for BBC Sport from 2009 until 2013. She has also presented live sports events for the BBC, including
2026 United States intervention in Venezuela
On 3 January 2026, the United States launched a military strike in Venezuela and captured incumbent Venezuelan president Nicolás Maduro and his wife, Cilia Flores. The US operation, codenamed Operation Absolute Resolve, began around 2 a.m. local time, when exp
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Calculating Ultra-Strong and Extended Solutions for Nine Men's Morris, Morabaraba, and Lasker Morris
The strong solutions of Nine Men's Morris and its variant, Lasker Morris are well-known results (the starting positions are draws). We re-examined both of these games, and calculated extended strong solutions for them. By this we mean the game-theoretic values of all possible game states that could be reached from certain starting positions where the number of stones to be placed by the players is different from
Compressed sensing has been a very successful high-dimensional signal acquisition and recovery technique that relies on linear operations. However, the actual measurements of signals have to be quantized before storing or processing. 1(One)-bit compressed sensing is a heavily quantized version of compressed sensing, where each linear measurement of a signal is reduced to just one bit: the sign of the measurement. Onc
Topology-Informed Graph Transformer
Transformers have revolutionized performance in Natural Language Processing and Vision, paving the way for their integration with Graph Neural Networks (GNNs). One key challenge in enhancing graph transformers is strengthening the discriminative power of distinguishing isomorphisms of graphs, which plays a crucial role in boosting their predictive performances. To address this challenge, we introduce 'Topology-In
Pelican Soup Framework: A Theoretical Framework for Language Model Capabilities
In this work, we propose a simple theoretical framework, Pelican Soup, aiming to better understand how pretraining allows LLMs to (1) generalize to unseen instructions and (2) perform in-context learning, even when the verbalizers are irrelevant to the task. To this end, in our framework, we introduce the notion of "knowledge base" and "reference-sense association" and a simple formalism for natural l
GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks
Graph Neural Networks (GNNs) have demonstrated impressive performance across various tasks, leading to their increased adoption in high-stakes decision-making systems. However, concerns have arisen about GNNs potentially generating unfair decisions for underprivileged groups or individuals when lacking fairness constraints. This work addresses this issue by introducing GraphGini, a novel approach that incorporates th
Metasql: A Generate-then-Rank Framework for Natural Language to SQL Translation
The Natural Language Interface to Databases (NLIDB) empowers non-technical users with database access through intuitive natural language (NL) interactions. Advanced approaches, utilizing neural sequence-to-sequence models or large-scale language models, typically employ auto-regressive decoding to generate unique SQL queries sequentially. While these translation models have greatly improved the overall translation ac
Controllable Generation with Text-to-Image Diffusion Models: A Survey
In the rapidly advancing realm of visual generation, diffusion models have revolutionized the landscape, marking a significant shift in capabilities with their impressive text-guided generative functions. However, relying solely on text for conditioning these models does not fully cater to the varied and complex requirements of different applications and scenarios. Acknowledging this shortfall, a variety of studies a
Extreme Solar Flare Prediction Using Residual Networks with HMI Magnetograms and Intensitygrams
Solar flares, especially C, M, and X class, pose significant risks to satellite operations, communication systems, and power grids. We present a novel approach for predicting extreme solar flares using HMI intensitygrams and magnetograms. By detecting sunspots from intensitygrams and extracting magnetic field patches from magnetograms, we train a Residual Network (ResNet) to classify extreme class flares. Our model d
A Counterfactual Analysis of the Dishonest Casino
The dishonest casino is a well-known hidden Markov model (HMM) often used in education to introduce HMMs and graphical models. A sequence of die rolls is observed with the casino switching between a fair and a loaded die. Instead of recovering the latent regime through filtering, smoothing, or the Viterbi algorithm, we ask a counterfactual question: how much of the gambler's winnings are caused by the casino'
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions
Autoregressive language models have demonstrated a remarkable ability to extract latent structure from text. The embeddings from large language models have been shown to capture aspects of the syntax and semantics of language. But what should embeddings represent? We connect the autoregressive prediction objective to the idea of constructing predictive sufficient statistics to summarize the information contained in a
Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributions
Federated Learning (FL) enables decentralized machine learning while preserving data privacy, making it ideal for sensitive applications where data cannot be shared. While FL has been widely studied in supervised contexts, its application to unsupervised learning remains underdeveloped. This work introduces FedCRef, a novel unsupervised federated learning method designed to uncover all underlying data distributions a
BOP-Distrib: Revisiting 6D Pose Estimation Benchmarks for Better Evaluation under Visual Ambiguities
6D pose estimation aims at determining the object pose that best explains the camera observation. The unique solution for non-ambiguous objects can turn into a multi-modal pose distribution for symmetrical objects or when occlusions of symmetry-breaking elements happen, depending on the viewpoint. Currently, 6D pose estimation methods are benchmarked on datasets that consider, for their ground truth annotations, visu
Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems
Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations. Such characteristics render them an effective approach for simulating the time evolution of quantum wavefunctions, which is a computationally challenging, yet coveted task for studying quantum systems. In this manuscript, we use FNOs to model the evolution of quantum spin systems, so ch
Accurate estimates of surface solar irradiance (SSI) are essential for solar resource assessments and solar energy forecasts in grid integration and building control applications. SSI estimates for spatially extended regions can be retrieved from geostationary satellites such as Meteosat. Traditional SSI satellite retrievals like Heliosat rely on physical radiative transfer modelling. We introduce the first machine-l
ChakmaNMT: Machine Translation for a Low-Resource and Endangered Language via Transliteration
We present the first systematic study of machine translation for Chakma, an endangered and extremely low-resource Indo-Aryan language, with the goal of supporting language access and preservation. We introduce a new Chakma-Bangla parallel and monolingual dataset, along with a trilingual Chakma-Bangla-English benchmark for evaluation. To address script mismatch and data scarcity, we propose a character-level translite
Generating speech-driven 3D talking heads presents numerous challenges; among those is dealing with varying mesh topologies where no point-wise correspondence exists across the meshes the model can animate. While previous literature works assume fixed mesh structures, in this work we present the first framework capable of animating 3D faces in arbitrary topologies, including real scanned data. Our approach leverages
Explainable Binary Classification of Separable Shape Ensembles
Scientists, engineers, biologists, and technology specialists universally leverage image segmentation to extract shape ensembles containing many thousands of curves representing patterns in observations and measurements. These large curve ensembles facilitate inferences about important changes when comparing and contrasting images. We introduce novel pattern recognition formalisms combined with inference methods over
BraVE: Offline Reinforcement Learning for Discrete Combinatorial Action Spaces
Offline reinforcement learning in high-dimensional, discrete action spaces is challenging due to the exponential scaling of the joint action space with the number of sub-actions and the complexity of modeling sub-action dependencies. Existing methods either exhaustively evaluate the action space, making them computationally infeasible, or factorize Q-values, failing to represent joint sub-action effects. We propose B
Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically significant cancers independent of radiologists. In this study, we propose to develop deep learning models that improve the overall cancer diagnostic accuracy, by classifying radiologist-identified patients or lesions (i.e. radiologist-positive
$π_0$: A Vision-Language-Action Flow Model for General Robot Control
Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot
Grounding Natural Language to SQL Translation with Data-Based Self-Explanations
Natural Language Interfaces for Databases empower non-technical users to interact with data using natural language (NL). Advanced approaches, utilizing either neural sequence-to-sequence or more recent sophisticated large-scale language models, typically implement NL to SQL (NL2SQL) translation in an end-to-end fashion. However, like humans, these end-to-end translation models may not always generate the best SQL out
Feature selection is critical for improving the performance and interpretability of machine learning models, particularly in high-dimensional spaces where complex feature interactions can reduce accuracy and increase computational demands. Existing approaches often rely on static feature subsets or manual intervention, limiting adaptability and scalability. However, dynamic, per-instance feature selection methods and
SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection
Developing robust drone detection systems is often constrained by the limited availability of large-scale annotated training data and the high costs associated with real-world data collection. However, leveraging synthetic data generated via game engine-based simulations provides a promising and cost-effective solution to overcome this issue. Therefore, we present SynDroneVision, a synthetic dataset specifically desi
Meta-Learning Objectives for Preference Optimization
Evaluating preference optimization (PO) algorithms on LLM alignment is a challenging task that presents prohibitive costs, noise, and several variables like model size and hyper-parameters. In this work, we show that it is possible to gain insights on the efficacy of PO algorithm on simpler benchmarks. We design a diagnostic suite of MuJoCo tasks and datasets, which we use to systematically evaluate PO algorithms, es
AI red-teaming is a sociotechnical problem: on values, labor, and harms
As generative AI technologies find more and more real-world applications, the importance of testing their performance and safety seems paramount. "Red-teaming" has quickly become the primary approach to test AI models--prioritized by AI companies, and enshrined in AI policy and regulation. Members of red teams act as adversaries, probing AI systems to test their safety mechanisms and uncover vulnerabilities.
Notable events recorded on this day and month across all years.