Record 16012026 · captured 2026-08-25
The world looked up Charlie Kirk. 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.
Charles James Kirk was an American right-wing political activist, entrepreneur, and media personality. He co‑founded the conservative student organization Turning Point USA (TPUSA) in 2012 and served as its executive director until his assassination in 2025. 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
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.
28 Years Later: The Bone Temple
28 Years Later: The Bone Temple is a 2026 post-apocalyptic horror film directed by Nia DaCosta and written by Alex Garland. It is the direct sequel to 28 Years Later (2025) and the fourth instalment in the 28 Days Later film series. It stars Ralph Fiennes, Jac
Robert Edward Jenrick is an English politician who has been Member of Parliament (MP) for Newark since the 2014 by-election. Elected as a member of the Conservative Party, he joined Reform UK in January 2026.
The Insurrection Act of 1807, or just the Insurrection Act, is the U.S. federal law that empowers the president of the United States to nationally deploy the Armed Forces and to federalize the National Guard units of the individual states in specific circumsta
Timothy Busfield is an American actor and director. He played Arnold Poindexter in the first two Revenge of the Nerds films, Elliot Weston on the television series Thirtysomething, Mark in Field of Dreams, and Danny Concannon on the television series The West
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
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
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
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
John William Harbaugh is an American professional football coach who is the head coach for the New York Giants of the National Football League (NFL). He previously coached the defensive backs for the Philadelphia Eagles and served as their special teams coach
Scott Raymond Adams was an American cartoonist, author, and commentator. He is best known as the creator of the Dilbert comic strip and nonfiction works of business, self-improvement, commentary, and satire.
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
His & Hers is an American mystery thriller limited series starring Tessa Thompson, Jon Bernthal, Pablo Schreiber, Marin Ireland, Sunita Mani, Rebecca Rittenhouse, Chris Bauer, Poppy Liu and Crystal Fox. It is an adaptation of the 2020 novel of the same name by
2026 Brihanmumbai Municipal Corporation election
The 2026 Brihanmumbai Municipal Corporation election took place on 15 January 2026 to elect members to the Brihanmumbai Municipal Corporation of Greater Mumbai, the second largest city in India. Voting lasted one day and the results were released on 16 January
"XXX" is a song by American rapper Kendrick Lamar, from his fourth studio album Damn, released on April 14, 2017. The eleventh track on the album, the song was written by Lamar, Mike Will Made It, DJ Dahi, Mark Spears a.k.a. Sounwave, Anthony Tiffith, Bono, th
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
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
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
The RajaSaab is a 2026 Indian Telugu-language fantasy horror comedy film written and directed by Maruthi, and produced by People Media Factory and IVY Entertainment. The film stars Prabhas, alongside Sanjay Dutt, Nidhhi Agerwal, Malavika Mohanan, Riddhi Kumar
The 2025–2026 Iranian protests were a series of nationwide demonstrations against the government of Iran that began on 28 December 2025 amid a deepening economic crisis. The unrest followed a sharp depreciation of the Iranian rial, rising inflation, and widesp
Taskaree: The Smuggler's Web is a 2026 Indian Hindi-language crime thriller television series created by Neeraj Pandey and written by Vipul K Rawal along with Neeraj Pandey for Netflix, produced under the banner of Friday Storytellers. The series follows an In
Melissa Ellen Gilbert is an American actress. Gilbert began her career as a child actress in the late 1960s, appearing in numerous commercials and guest-starring roles on television. From 1974 to 1983, she starred as Laura Ingalls Wilder, the second-oldest dau
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
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
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
Venezuela, officially the Bolivarian Republic of Venezuela, is a country on the northern coast of South America, consisting of a continental landmass and various islands and islets in the Caribbean Sea. It comprises an area of 912,050 km2 (352,140 sq mi), with
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
Todd Anthony Bridges is an American actor. He portrayed Willis Jackson on the sitcom Diff'rent Strokes and had a recurring role as Monk on the sitcom Everybody Hates Chris. Bridges worked as a commentator on the television series World's Dumbest... from 2008 t
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
Convolutional neural networks (CNNs) are usually built by stacking convolutional operations layer-by-layer. Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored. These relationships are important to learn semantic objects with strong shape priors but weak appearance coherences, s
Data-Driven Feature Tracking for Event Cameras With and Without Frames
Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios. Existing feature tracking methods for event cameras are either handcrafted or derived from first principles but require extensive parameter tuning, are sensitive to noise, and do not generali
This paper brings an in detail Genetic Algorithm (GA) based combinatorial optimization method used for the optimal design of the water distribution network (WDN) of Gurudeniya Service Zone, Sri Lanka. Genetic Algorithm (GA) mimics the survival of the fittest principle of nature to develop a search process. Methodology employs fuzzy combinations of pipe diameters to check their suitability to be considered as the cost
Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoising
Recently, the mainstream practice for training low-light raw image denoising methods has shifted towards employing synthetic data. Noise modeling, which focuses on characterizing the noise distribution of real-world sensors, profoundly influences the effectiveness and practicality of synthetic data. Currently, physics-based noise modeling struggles to characterize the entire real noise distribution, while learning-ba
Generative language models are usually pretrained on large text corpus via predicting the next token (i.e., sub-word/word/phrase) given the previous ones. Recent works have demonstrated the impressive performance of large generative language models on downstream tasks. However, existing generative language models generally neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequ
A Kolmogorov metric embedding for live cell microscopy signaling patterns
We present a metric embedding that captures spatiotemporal patterns of cell signaling dynamics in 5-D $(x,y,z,channel,time)$ live cell microscopy movies. The embedding uses a metric distance called the normalized information distance (NID) based on Kolmogorov complexity theory, an absolute measure of information content between digital objects. The NID uses statistics of lossless compression to compute a theoreticall
Tuning-Free Adaptive Style Incorporation for Structure-Consistent Text-Driven Style Transfer
In this work, we target the task of text-driven style transfer in the context of text-to-image (T2I) diffusion models. The main challenge is consistent structure preservation while enabling effective style transfer effects. The past approaches in this field directly concatenate the content and style prompts for a prompt-level style injection, leading to unavoidable structure distortions. In this work, we propose a no
SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning
In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are trained and communicated each ro
Jump-teaching: Combating Sample Selection Bias via Temporal Disagreement
Sample selection is a straightforward technique to combat noisy labels, aiming to prevent mislabeled samples from degrading the robustness of neural networks. However, existing methods mitigate compounding selection bias either by leveraging dual-network disagreement or additional forward propagations, leading to multiplied training overhead. To address this challenge, we introduce $\textit{Jump-teaching}$, an effici
Social Determinants of Health (SDOH), also known as Health-Related Social Needs (HSRN), play a significant role in patient health outcomes. The Centers for Disease Control and Prevention (CDC) introduced a subset of ICD-10 codes called Z-codes to recognize and measure SDOH. However, Z-codes are infrequently coded in a patient's Electronic Health Record (EHR), and instead, in many cases, need to be inferred from c
Instance-level quantitative saliency in multiple sclerosis lesion segmentation
Explainable artificial intelligence (XAI) methods have been proposed to interpret model decisions in classification and, more recently, in semantic segmentation. However, instance-level XAI for semantic segmentation, namely explanations focused on a single object among multiple instances of the same class, remains largely unexplored. Such explanations are particularly important in multi-lesional diseases to understan
AITTI: Learning Adaptive Inclusive Token for Text-to-Image Generation
Despite the high-quality results of text-to-image generation, stereotypical biases have been spotted in their generated contents, compromising the fairness of generative models. In this work, we propose to learn adaptive inclusive tokens to shift the attribute distribution of the final generative outputs. Unlike existing de-biasing approaches, our method requires neither explicit attribute specification nor prior kno
Machine Unlearning Fails to Remove Data Poisoning Attacks
We revisit the efficacy of several practical methods for approximate machine unlearning developed for large-scale deep learning. In addition to complying with data deletion requests, one often-cited potential application for unlearning methods is to remove the effects of poisoned data. We experimentally demonstrate that, while existing unlearning methods have been demonstrated to be effective in a number of settings,
Mathematical theory of deep learning
This book provides an introduction to the mathematical analysis of deep learning. It covers fundamental results in approximation theory, optimization theory, and statistical learning theory, which are the three main pillars of deep neural network theory. Serving as a guide for students and researchers in mathematics and related fields, the book aims to equip readers with foundational knowledge on the topic. It priori
Fairness Definitions in Language Models Explained
Language Models (LMs) have demonstrated exceptional performance across various Natural Language Processing (NLP) tasks. Despite these advancements, LMs can inherit and amplify societal biases related to sensitive attributes such as gender and race, limiting their adoption in real-world applications. Therefore, fairness has been extensively explored in LMs, leading to the proposal of various fairness notions. However,
Persistent Homology via Ellipsoids
Persistent homology is one of the most popular methods in topological data analysis. An initial step in its use involves constructing a nested sequence of simplicial complexes. There is an abundance of different complexes to choose from, with Čech, Rips, alpha, and witness complexes being popular choices. In this manuscript, we build a novel type of geometrically informed simplicial complex, called a Rips-type ellips
Large image collections generated from camera traps offer valuable insights into species richness, occupancy, and activity patterns, significantly aiding biodiversity monitoring. However, the manual processing of these datasets is time-consuming, hindering analytical processes. To address this, deep neural networks have been widely adopted to automate image labelling, but the impact of classification error on key eco
Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors
Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective therapies by tailoring treatment based on biomarkers, beyond cancer type. Unstructured data that requires manual curation hinders efficient use of biomarker profiling for th
Machine Learning and Theory Ladenness -- A Phenomenological Account
We provide an analysis of theory ladenness in machine learning in science, where "theory", that we call "domain theory", refers to the domain knowledge of the scientific discipline where ML is used. By constructing an account of ML models based on a comparison with phenomenological models, we show, against recent trends in philosophy of science, that ML model-building is mostly indifferent to domain t
Debiased Orthogonal Boundary-Driven Efficient Noise Mitigation
Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical, as obtaining entirely clean or human-annotated samples for large-scale pre-training tasks is often impractical. Nonetheless, existing noise mitigation methods often encounter limitations in practical applications due to their task-specific design, model dependency, and significant computational overhead. In th
Permissive Information-Flow Analysis for Large Language Models
Large Language Models (LLMs) are rapidly becoming commodity components of larger software systems. This poses natural security and privacy problems: poisoned data retrieved from one component can change the model's behavior and compromise the entire system, including coercing the model to spread confidential data to untrusted components. One promising approach is to tackle this problem at the system level via dyn
CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning
Mathematical reasoning remains a significant challenge for large language models (LLMs), despite progress in prompting techniques such as Chain-of-Thought (CoT). We present **Chain of Mathematically Annotated Thought (CoMAT)**, which enhances reasoning through two stages: *Symbolic Conversion* (converting natural language queries into symbolic form) and *Reasoning Execution* (deriving answers from symbolic representa
Topology meets Machine Learning: An Introduction using the Euler Characteristic Transform
This overview article makes the case for how topological concepts can enrich research in machine learning. Using the Euler Characteristic Transform (ECT), a geometrical-topological invariant, as a running example, I present different use cases that result in more efficient models for analyzing point clouds, graphs, and meshes. Moreover, I outline a vision for how topological concepts could be used in the future, comp
In this paper we propose the Hatching-Box, a novel imaging and analysis system to automatically monitor and quantify the developmental behavior of Drosophila in standard rearing vials and during regular rearing routines, rendering explicit experiments obsolete. This is achieved by combining custom tailored imaging hardware with dedicated detection and tracking algorithms, enabling the quantification of larvae, filled
VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction
In-Context Operator Networks (ICONs) have demonstrated the ability to learn operators across diverse partial differential equations using few-shot, in-context learning. However, existing ICONs process each spatial point as an individual token, severely limiting computational efficiency when handling dense data in higher spatial dimensions. We propose Vision In-Context Operator Networks (VICON), which integrates visio
Notable events recorded on this day and month across all years.