Record 13072026 · captured 2026-08-25
The world looked up Lindsey Graham. 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.
Lindsey Olin Graham was an American politician, attorney, and military officer who represented South Carolina in the United States Senate from 2003 until his death in 2026. A member of the Republican Party, he represented South Carolina's 3rd congressional dis
Jude Victor William Bellingham is an English professional footballer who plays as a midfielder for La Liga club Real Madrid and the England national team. Regarded as one of the best players in the world, he is known for his athleticism and ball-winning abilit
Jannik Sinner is an Italian professional tennis player. He is currently ranked world No.1 by Association of Tennis Professionals (ATP), and was the year-end No. 1 in 2024. Sinner has won 30 ATP Tour-level singles titles, including five majors, ten Masters and
Alexander "Sascha" Zverev is a German professional tennis player. He has a career-high singles ranking of world No. 2 by the ATP achieved in June 2022. Zverev has won 25 ATP Tour singles titles, including the 2026 French Open, a gold medal at the 2020 Tokyo Ol
The 2026 FIFA World Cup was the 23rd FIFA World Cup, the quadrennial international men's soccer championship contested by the national teams of the member associations of FIFA. The tournament began on June 11, 2026, and concluded on July 19 with Spain winning
Erling Braut Haaland is a Norwegian professional footballer who plays as a striker for Premier League club Manchester City and the Norway national team. Regarded as one of the best players in the world and the greatest Norwegian player of all time, he is known
Conor Anthony McGregor is an Irish professional mixed martial artist. He is a former Ultimate Fighting Championship (UFC) Featherweight and Lightweight Champion, becoming the first fighter to hold UFC championships in two weight classes simultaneously. He is a
Addison Mitchell McConnell III is an American politician and attorney who has been a United States senator from Kentucky since 1985 and has been Kentucky's senior U.S. senator since 1999. A member of the Republican Party, McConnell is in his seventh Senate ter
Lionel Andrés "Leo" Messi is an Argentine professional footballer who plays as a forward for and captains both Major League Soccer (MLS) club Inter Miami and the Argentina national team. Widely regarded as one of the greatest players in history, Messi has set
UFC 329: McGregor vs. Holloway 2 was a mixed martial arts event produced by the Ultimate Fighting Championship that took place on July 11, 2026, at the T-Mobile Arena in Paradise, Nevada, part of the Las Vegas Valley, United States.
Sistla Janaki was an Indian playback singer and occasional music composer. Fondly known as Janaki Amma and described as the "Nightingale of South India", Janaki is considered one of the greatest and most influential singers in the history of Indian music. She
Ann Noreen Widdecombe was a British politician and media personality. As a member of the Conservative Party, she was the member of Parliament (MP) for Maidstone and The Weald, previously Maidstone, from 1987 to 2010. She joined the Brexit Party – later Reform
Jayden Oswin Adams was a South African professional soccer player who played as a defensive midfielder. He played for Stellenbosch, Mamelodi Sundowns, and the South African national team.
The FIFA Men's World Ranking is a ranking system for men's national teams in association football, first introduced in December 1992. The men's teams of the member nations of FIFA, football's world governing body, are ranked based on their game results with th
Vinod Khosla is an Indian-American billionaire, entrepreneur, and venture capitalist. He is a co-founder of Sun Microsystems and the founder of Khosla Ventures. As of July 11, 2026, he entered into a binding agreement to purchase the Seattle Seahawks. Khosla m
The FIFA World Cup is an international association football competition contested by the senior men's national teams of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The championship has been awarded every fou
The FIFA World Cup is an international association football competition among the senior men's national teams of the members of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The tournament has been held every
Jerome Max Keliʻi Holloway is an American professional mixed martial artist. He currently competes in the Lightweight division of the Ultimate Fighting Championship (UFC), where he is a former UFC Featherweight Champion and former symbolic UFC "BMF" titleholde
Alexis Mac Allister is an Argentine professional footballer who plays as a midfielder for Premier League club Liverpool and the Argentina national team. He has been praised for his passing range, tactical awareness, and ability to control the tempo of play.
Patrick Mark Pimblett is an English professional mixed martial artist. A professional since 2012, Pimblett is a former Cage Warriors Featherweight Champion. He currently competes in the Lightweight division of the Ultimate Fighting Championship (UFC). As of 11
Thomas Tuchel is a German professional football manager and former player who is the manager of the England national team.
The Odyssey is a 2026 epic action fantasy film written and directed by Christopher Nolan, who produced it with his wife Emma Thomas. An adaptation of Homer's ancient Greek epic poem the Odyssey, it stars an ensemble cast including Matt Damon, Tom Holland, Anne
Obsession is a 2025 American supernatural horror film written, directed, and edited by Curry Barker. The film follows Bear, a music store employee who buys a supernatural toy that grants his wish for his friend Nikki to fall in love with him, which makes her b
Argentina–England football rivalry
The Argentina–England football rivalry is a sports rivalry between the national football teams of Argentina and England. Exacerbated by historical political tensions, it is widely considered one of the most intense rivalries in international sport. Fixtures be
Carlos Alcaraz Garfia is a Spanish professional tennis player. He has been ranked world No. 1 in men's singles by the Association of Tennis Professionals (ATP) for 66 weeks, and finished as the year-end No. 1 in 2022 and 2025. Alcaraz has won 26 ATP Tour–level
Murat Yakin is a Swiss football coach and former player who is currently the manager of the Switzerland national team.
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
Breel Donald Embolo is a professional footballer who plays as a forward for Major League Soccer club Atlanta United and the Switzerland national team.
Cristiano Ronaldo dos Santos Aveiro is a Portuguese professional footballer who plays as a forward for and captains the Saudi Pro League club Al-Nassr and the Portugal national team. Nicknamed CR7, he is widely regarded as one of the greatest players in histor
Harry Edward Kane is an English professional footballer who plays as a striker for Bundesliga club Bayern Munich and captains the England national team. He is regarded as one of the best players in the world, one of the best strikers of his generation, and one
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
The Quantification Horizon Theory of Consciousness
To make nature mathematically tractable, the scientific model of the world omits qualia--colors, sounds, tastes, sensations--leaving only what admits of numerical characterization. The "hard problem" of consciousness--the enigma of why and how physical processing gives rise to felt experience--remains unsolved. The Quantification Horizon Theory of Consciousness (QHT) proposes that this enigma reflects a struc
A Fourier analytique approach to Gaussian mixture learning
Suppose that we are given independent, identically distributed random samples $x_1,\cdots,x_n$ from a mixture at most $k$ many $d$-dimensional spherical Gaussian distributions $μ_1,\cdots,μ_{k_0}$ of identical and known variance $σ^2$ in each coordinate, such that the minimum $\ell^2$ distance between two distinct centers $y_l$ and $y_j$ is greater than $2Δσ\min\{\sqrt{d},\sqrt k\}$, where $Δ>C_0$, and $C_0$ is a
Ruby: Unmasking Unsafe Rust in Stripped Binaries via Machine Learning
Rust, as an emerging system programming language, introduces $\texttt{unsafe}$ to allow developers to bypass safety checks during compilation. As a result, memory safety bugs are typically confined to the $\texttt{unsafe}$ regions, which have been the primary focus of Rust bug-finding tools. However, such tools rely on the presence of the $\texttt{unsafe}$ keyword in Rust source code; there are no tools available tha
The entity alignment of science and technology patents aims to link the equivalent entities in the knowledge graph of different science and technology patent data sources. Most entity alignment methods only use graph neural network to obtain the embedding of graph structure or use attribute text description to obtain semantic representation, ignoring the process of multi-information fusion in science and technology p
Relation Extraction Model Based on Semantic Enhancement Mechanism
Relational extraction is one of the basic tasks related to information extraction in the field of natural language processing, and is an important link and core task in the fields of information extraction, natural language understanding, and information retrieval. None of the existing relation extraction methods can effectively solve the problem of triple overlap. The CasAug model proposed in this paper based on the
Topic model based on co-occurrence word networks for unbalanced short text datasets
We propose a straightforward solution for detecting scarce topics in unbalanced short-text datasets. Our approach, named CWUTM (Topic model based on co-occurrence word networks for unbalanced short text datasets), addresses the challenge of sparse and unbalanced short text topics by mitigating the effects of incidental word co-occurrence. This allows our model to prioritize the identification of scarce topics (low-fr
Implicit neural representation has demonstrated promising results in 3D reconstruction on various scenes. However, existing approaches either struggle to model fast-moving objects or are incapable of handling large-scale camera ego-motions in urban environments. This leads to low-quality synthesized views of the large-scale urban scenes. In this paper, we aim to jointly solve the problems caused by large-scale scenes
Language Modeling on a SpiNNaker 2 Neuromorphic Chip
As large language models continue to scale in size rapidly, so too does the computational power required to run them. Event-based networks on neuromorphic devices offer a potential way to reduce energy consumption for inference significantly. However, to date, most event-based networks that can run on neuromorphic hardware, including spiking neural networks (SNNs), have not achieved task performance even on par with
Contrastive Learning on Multimodal Analysis of Electronic Health Records
Electronic health record (EHR) systems capture a wealth of multimodal clinical data, encompassing both structured clinical codes and unstructured clinical notes. Yet, many EHR-focused studies have traditionally examined these modalities in isolation or combined them using simplistic methods, overlooking the intrinsic synergy between them. In reality, these modalities are deeply interconnected, each containing clinica
Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization
We present in this paper novel accelerated fully first-order methods in \emph{Bilevel Optimization} (BLO). Firstly, for BLO under the assumption that the lower-level functions admit the typical strong convexity assumption, the \emph{(Perturbed) Restarted Accelerated Fully First-order methods for Bilevel Approximation} (\texttt{PRAF${}^2$BA}) algorithm leveraging \emph{fully} first-order oracles is proposed, whereas t
LDPKiT: Superimposing Remote Queries for Privacy-Preserving Distillation
To protect privacy in regulated domains such as healthcare and finance, model owners may allow only remote API access while keeping both the training data and model parameters private. However, model users performing inference on such remotely hosted models may be required to transmit potentially sensitive inputs, raising privacy concerns. In this work, we present LDPKiT, a framework for non-adversarial, privacy-pres
Projection Methods for Operator Learning and Universal Approximation
We obtain a new universal approximation theorem for continuous (possibly nonlinear) operators on arbitrary Banach spaces using the Leray-Schauder mapping. Moreover, we introduce and study a method for operator learning in Banach spaces $L^p$ of functions with multiple variables, based on orthogonal projections on polynomial bases. We derive a universal approximation result for operators where we learn a linear projec
Accelerating Large Language Model Inference with Self-Supervised Early Exits
This paper presents a modular approach to accelerate inference in large language models (LLMs) by adding early exit heads at intermediate transformer layers. Each head is trained in a self-supervised manner to mimic the main model's predictions, allowing computation to stop early when a calibrated confidence threshold is reached. We evaluate several confidence metrics and show that entropy provides the most relia
Purpose: A major barrier to the implementation of artificial intelligence for medical applications is automated CNNs' lack of explainability and high confidence for incorrect decisions, specifically with out-of-domain samples. We propose a generalization of image translation networks for image classification and demonstrate translation networks' potential as a more interpretable alternative to conventional bl
Zero-shot 3D General Obstacle Detection via Multimodal Foundation Models and Geometry
Detecting general obstacles is critical for autonomous driving, especially in long-tail scenarios with rare or unseen objects. Existing methods rely on supervision or predefined categories, limiting generalization. We propose a training-free approach that combines multimodal foundation models with geometric reasoning for 3D obstacle detection. Our key idea is to detect obstacles as deviations from the road surface, s
IFAR: Multi-Perspective and Multi-Level Causal Discovery with LLMs
Large language models (LLMs) have developed rapidly, and their reasoning capabilities have become a hot research topic. However, there is still limited exploration of abductive reasoning. The multi-perspective and multi-level of causes is one of the core challenges of abductive reasoning, which cannot be solved well by existing methods. We construct a specialized dataset named DeepAbduction, which is designed for tra
Multi-Layer Gaussian Splatting for Immersive Anatomy Visualization
In medical image visualization, path tracing of volumetric medical data like CT scans produces lifelike three-dimensional visualizations. Immersive VR displays can further enhance the understanding of complex anatomies. Going beyond the diagnostic quality of traditional 2D slices, they enable interactive 3D evaluation of anatomies, supporting medical education and planning. Rendering high-quality visualizations in re
Human Vision Constrained Super-Resolution
Modern deep-learning super-resolution (SR) techniques process images and videos independently of the underlying content and viewing conditions. However, the sensitivity of the human visual system (HVS) to image details changes depending on the underlying image characteristics, such as spatial frequency, luminance, color, contrast, or motion; as well viewing condition aspects such as ambient lighting and distance to t
Basic Research, Lethal Effects: Military AI Research Funding as Enlistment
In the context of unprecedented U.S. Department of Defense (DoD) budgets, this paper examines the recent history of DoD funding for academic research in algorithmically based warfighting. We draw from a corpus of DoD grant solicitations from 2007 to 2023, focusing on those addressed to researchers in the field of artificial intelligence (AI). Considering the implications of DoD funding for academic research, the pape
Prototypical Few-Shot Medical Image Semantic Segmentation with Background Fusion
Few-shot Semantic Segmentation (FSS) aims to adapt a pre-trained model to new classes with as few as a single labeled training sample per class. The existing prototypical work used in natural image scenarios biasedly focus on capturing foreground's discrimination while employing a simplistic representation for background, grounded on the inherent observation separation between foreground and background. However,
Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs
Neural Jump ODEs model the conditional expectation between observations by neural ODEs and jump at arrival of new observations. They have demonstrated effectiveness for fully data-driven online forecasting in settings with irregular and partial observations, operating under weak regularity assumptions. This work extends the framework to input-output systems, enabling direct applications in online filtering and classi
On Motion Blur and Deblurring in Visual Place Recognition
Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion blur is relatively unexplored despite its relevance not only in rapid motion scenarios but also in lo
LPRnet: A self-supervised registration network for LiDAR and photogrammetric point clouds
LiDAR and photogrammetry are active and passive remote sensing techniques for point cloud acquisition, respectively, offering complementary advantages and heterogeneous. Due to the fundamental differences in sensing mechanisms, spatial distributions and coordinate systems, their point clouds exhibit significant discrepancies in density, precision, noise, and overlap. Coupled with the lack of ground truth for large-sc
Vertebral Landmarks Localization in Dual-Energy X-ray Absorptiometry based Lateral Spine Imaging plays a critical role in evaluating spinal alignment, Vertebral Fracture Assessment, and facilitating intervertebral guide placement for Abdominal Aortic Calcification quantification. While lateral spine DXA scans offer advantages such as reduced cost and lower radiation exposure, its analysis remains challenging due to a
Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation
Current state-of-the-art segmentation models encode entire images before focusing on specific objects. This wastes computational resources. We introduce FLIP (Fovea-Like Input Patching), a parameter-efficient vision model that realizes object segmentation through biologically-inspired top-down attention. FLIP selectively samples multi-resolution patches centered on objects of interest from the input. As a result, it
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