Record 27012026 · captured 2026-08-25
The world looked up Sam Darnold. 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.
Samuel Richard Darnold is an American professional football quarterback for the Seattle Seahawks of the National Football League (NFL). He played college football for the USC Trojans, becoming the first freshman to win the Archie Griffin Award.
On January 24, 2026, Alex Jeffrey Pretti, a 37-year-old American intensive care nurse for the United States Department of Veterans Affairs, was shot multiple times and killed by two United States Customs and Border Protection officers in Minneapolis, Minnesota
Border 2 is a 2026 Indian Hindi-language epic war film co-written and directed by Anurag Singh. A sequel to J. P. Dutta's 1997 film Border, it was produced by Bhushan Kumar, Krishan Kumar, J. P. Dutta, and Nidhi Dutta under the banners of T-Series Films and J.
Republic Day is a national holiday in India commemorating the adoption of the Constitution of the Republic of India and the country's transition to a republic which came into effect on 26 January 1950.
Jaxon Smith-Njigba, also known by his initials JSN, is an American professional football wide receiver for the Seattle Seahawks of the National Football League (NFL). He played college football for the Ohio State Buckeyes, setting school records for receptions
Gregory Kent Bovino is a United States Border Patrol officer who served as the commander-at-large of the Border Patrol from October 2025 to January 2026.
Super Bowl LX was an American football game played to determine the champion of the National Football League (NFL) for the 2025 season. The National Football Conference (NFC) champion Seattle Seahawks defeated the American Football Conference (AFC) champion Ne
Michael Macdonald is an American professional football coach who is the head coach for the Seattle Seahawks of the National Football League (NFL). He began his career with the Baltimore Ravens in 2014, serving as a defensive assistant. In 2021, Macdonald left
Alexander J Honnold is an American rock climber best known for his free solo ascents of big wall climbing routes. Honnold rose to worldwide fame in June 2017 when he became the first person to free solo a full route on El Capitan in Yosemite National Park via
The Super Bowl is the annual American football game that determines the champion of the National Football League (NFL). The game culminates a season that begins in the previous calendar year, and is the conclusion of the NFL playoffs. The winner receives the V
Sue-Ellen Cassiana "Suella" Braverman is a British politician and barrister who twice served as Home Secretary from 6 September 2022 to 19 October 2022, and again from 25 October 2022 to 13 November 2023.
The Seattle Seahawks are a professional American football team based in Seattle. The Seahawks compete in the National Football League (NFL) as a member of the National Football Conference (NFC) West division. They have played their home games at Lumen Field in
Drake Lee Maye is an American professional football quarterback for the New England Patriots of the National Football League (NFL). He played college football for the North Carolina Tar Heels, winning the Shaun Alexander Award and ACC Football Player of the Ye
John Matthew Stafford is an American professional football quarterback for the Los Angeles Rams of the National Football League (NFL). He played college football for the Georgia Bulldogs, receiving first-team All-American honors in 2008, and was selected first
Michael George Vrabel is an American professional football coach and former linebacker who is the head coach for the New England Patriots of the National Football League (NFL). Vrabel previously played in the NFL for 14 seasons, most notably with the Patriots.
A Knight of the Seven Kingdoms (TV series)
A Knight of the Seven Kingdoms is an American fantasy drama television series created by Ira Parker and George R. R. Martin. A prequel to Game of Thrones (2011–2019), it is the third television series in Martin's A Song of Ice and Fire franchise and is an adap
Cooper Douglas Kupp is an American professional football wide receiver for the Seattle Seahawks of the National Football League (NFL). He played college football for the Eastern Washington Eagles, winning the Walter Payton Award in 2015 and setting the NCAA Di
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
Kyle Howard Rittenhouse is an American man who gained national attention at age 17 for shooting three men in Kenosha, Wisconsin, two fatally, amid protests and riots in response to the police shooting of Jacob Blake in 2020.
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,
Makea "Puka" Nacua is an American professional football wide receiver for the Los Angeles Rams of the National Football League (NFL). He played college football for the Washington Huskies and BYU Cougars and was selected by the Rams in the fifth round of the 2
Super Bowl XLIX was an American football game played to determine the champion of the National Football League (NFL) for the 2014 season. The American Football Conference (AFC) champion New England Patriots defeated the National Football Conference (NFC) champ
Elizabeth Ann Gilmour is an American child safety activist and commentator for ABC News. She was put into the national spotlight in 2002 at age 14 when she was abducted from her home in Salt Lake City by Brian David Mitchell. Mitchell and his wife, Wanda Barze
Thomas Douglas Homan is an American law enforcement officer. In November 2024, Donald Trump designated Homan as "border czar" for his second presidency. Homan also served during the Obama administration and the first Trump administration. He served as acting d
On February 22, 2024, Laken Riley, a 22-year-old Augusta University nursing student, was attacked and murdered while she was jogging at the University of Georgia (UGA) in Athens, Georgia, United States. Her body was found in Oconee Forest Park near Lake Herric
Sinners is a 2025 American horror film produced, written, and directed by Ryan Coogler. Set in 1932 in the Mississippi Delta, it stars Michael B. Jordan in dual roles as criminal twin brothers who return to their hometown in the Jim Crow South, where they are
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
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
Sean Patrick McVay is an American professional football coach who is the head coach for the Los Angeles Rams of the National Football League (NFL). He became the youngest NFL head coach in the modern era when he was hired by the Rams in 2017 at the age of 30 y
The Rip is a 2026 American action thriller film written and directed by Joe Carnahan, who developed the story with Michael McGrale. The film stars Matt Damon and Ben Affleck as police officers in the Miami-Dade Police Department narcotics unit. It also stars S
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Clustering is an essential data mining tool for analyzing and grouping similar objects. In big data applications, however, many clustering algorithms are infeasible due to their high memory requirements and/or unfavorable runtime complexity. In contrast, Contraction Clustering (RASTER) is a single-pass algorithm for identifying density-based clusters with linear time complexity. Due to its favorable runtime and the f
Energy-Aware DNN Graph Optimization
Unlike existing work in deep neural network (DNN) graphs optimization for inference performance, we explore DNN graph optimization for energy awareness and savings for power- and resource-constrained machine learning devices. We present a method that allows users to optimize energy consumption or balance between energy and inference performance for DNN graphs. This method efficiently searches through the space of equ
Inferring manifolds using Gaussian processes
It is often of interest to infer lower-dimensional structure underlying complex data. As a flexible class of non-linear structures, it is common to focus on Riemannian manifolds. Most existing manifold learning algorithms replace the original data with lower-dimensional coordinates without providing an estimate of the manifold or using the manifold to denoise the original data. This article proposes a new methodology
Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them approximately sample in proportion to a given reward function. In this paper, we show a number of additional theoretical properties of GFlowNets. They can be used to estimate joint probability distributions and the corresponding marginal d
Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0
Stuttering is a varied speech disorder that harms an individual's communication ability. Persons who stutter (PWS) often use speech therapy to cope with their condition. Improving speech recognition systems for people with such non-typical speech or tracking the effectiveness of speech therapy would require systems that can detect dysfluencies while at the same time being able to detect speech techniques acquired
PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images
We present PyMAF-X, a regression-based approach to recovering parametric full-body models from monocular images. This task is very challenging since minor parametric deviation may lead to noticeable misalignment between the estimated mesh and the input image. Moreover, when integrating part-specific estimations into the full-body model, existing solutions tend to either degrade the alignment or produce unnatural wris
CloSET: Modeling Clothed Humans on Continuous Surface with Explicit Template Decomposition
Creating animatable avatars from static scans requires the modeling of clothing deformations in different poses. Existing learning-based methods typically add pose-dependent deformations upon a minimally-clothed mesh template or a learned implicit template, which have limitations in capturing details or hinder end-to-end learning. In this paper, we revisit point-based solutions and propose to decompose explicit garme
Structured and Fast Optimization: The Kronecker SGD Algorithm
Stochastic gradient descent (SGD) now acts as a fundamental part of optimization in current machine learning. Meanwhile, deep learning architectures have shown outstanding performance in a wide range of fields, such as natural language processing, bioinformatics, and computer vision. Nevertheless, as the parameter size $d$ increases, these models encounter serious efficiency challenges. Previous studies show that the
Patient Outcome Predictions Improve Operations at a Large Hospital Network
Problem definition: Access to accurate predictions of patients' outcomes can enhance medical staff's decision-making, which ultimately benefits all stakeholders in the hospitals. A large hospital network in the US has been collaborating with academics and consultants to predict short-term and long-term outcomes for all inpatients across their seven hospitals. Methodology/results: We develop machine learning m
Near-Optimal Partially Observable Reinforcement Learning with Partial Online State Information
Partially observable Markov decision processes (POMDPs) are a general framework for sequential decision-making under latent state uncertainty, yet learning in POMDPs is intractable in the worst case. Motivated by sensing and probing constraints in practice, we study how much online state information (OSI) is sufficient to enable efficient learning guarantees. We formalize a model in which the learner can query only p
Myocardial infarction (MI) is a severe case of coronary artery disease (CAD) and ultimately, its detection is substantial to prevent progressive damage to the myocardium. In this study, we propose a novel view-fusion model named self-attention fusion network (SAF-Net) to detect MI from multi-view echocardiography recordings. The proposed framework utilizes apical 2-chamber (A2C) and apical 4-chamber (A4C) view echoca
ELIP: Efficient Discriminative Language-Image Pre-training with Fewer Vision Tokens
Learning a versatile language-image model is computationally prohibitive under a limited computing budget. This paper delves into the \emph{efficient language-image pre-training}, an area that has received relatively little attention despite its importance in reducing computational cost and footprint. To that end, we propose a vision token pruning and merging method ELIP, to remove less influential tokens based on th
Saliency-Bench: A Comprehensive Benchmark for Evaluating Visual Explanations
Explainable AI (XAI) has gained significant attention for providing insights into the decision-making processes of deep learning models, particularly for image classification tasks through visual explanations visualized by saliency maps. Despite their success, challenges remain due to the lack of annotated datasets and standardized evaluation pipelines. In this paper, we introduce Saliency-Bench, a novel benchmark su
The graduated optimization approach is a method for finding global optimal solutions for nonconvex functions by using a function smoothing operation with stochastic noise. This paper makes three contributions regarding graduated optimization. First, we extend the definition of function smoothing that is traditionally achieved through convolution with Gaussian noise and characterize for the first time function smoothi
Stochastic-Constrained Stochastic Optimization with Markovian Data
This paper considers stochastic-constrained stochastic optimization where the stochastic constraint is to satisfy that the expectation of a random function is below a certain threshold. In particular, we study the setting where data samples are drawn from a Markov chain and thus are not independent and identically distributed. We generalize the drift-plus-penalty framework, a primal-dual stochastic gradient method de
Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference
Surrogate models are statistical or conceptual approximations for more complex simulation models. In this context, it is crucial to propagate the uncertainty induced by limited simulation budget and surrogate approximation error to predictions, inference, and subsequent decision-relevant quantities. However, quantifying and then propagating the uncertainty of surrogates is usually limited to special analytic cases or
Inconsistency Masks: Harnessing Model Disagreement for Stable Semi-Supervised Segmentation
A primary challenge in semi-supervised learning (SSL) for segmentation is the confirmation bias from noisy pseudo-labels, which destabilizes training and degrades performance. We propose Inconsistency Masks (IM), a framework that reframes model disagreement not as noise to be averaged away, but as a valuable signal for identifying uncertainty. IM leverages an ensemble of teacher models to generate a mask that explici
Sound event localization and classification using WASN in Outdoor Environment
Deep learning-based sound event localization and classification is an emerging research area within wireless acoustic sensor networks. However, current methods for sound event localization and classification typically rely on a single microphone array, making them susceptible to signal attenuation and environmental noise, which limits their monitoring range. Moreover, methods using multiple microphone arrays often fo
Existing brain tumor segmentation methods usually utilize multiple Magnetic Resonance Imaging (MRI) modalities in brain tumor images for segmentation, which can achieve better segmentation performance. However, in clinical applications, some modalities are often missing due to resource constraints, resulting in significant performance degradation for methods that rely on complete modality segmentation. In this paper,
The softmax activation function plays a crucial role in the success of large language models (LLMs), particularly in the self-attention mechanism of the widely adopted Transformer architecture. However, the underlying learning dynamics that contribute to the effectiveness of softmax remain largely unexplored. As a step towards better understanding, this paper provides a theoretical study of the optimization and gener
Adversarial Robustness Guarantees for Quantum Classifiers
Despite their ever more widespread deployment throughout society, machine learning algorithms remain critically vulnerable to being spoofed by subtle adversarial tampering with their input data. The prospect of near-term quantum computers being capable of running {quantum machine learning} (QML) algorithms has therefore generated intense interest in their adversarial vulnerability. Here we show that quantum propertie
Training Tensor Attention Efficiently: From Cubic to Almost Linear Time
Tensor Attention, a multi-view attention that is able to capture high-order correlations among multiple modalities, can overcome the representational limitations of classical matrix attention. However, the $O(n^3)$ time complexity of tensor attention poses a significant obstacle to its utilization in transformers, where $n$ is the input sequence length. In this work, we prove that the backward gradient of tensor atte
Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA
Long-form videos that span across wide temporal intervals are highly information redundant and contain multiple distinct events or entities that are often loosely related. Therefore, when performing long-form video question answering (LVQA), all information necessary to generate a correct response can often be contained within a small subset of frames. Recent literature leverage large language models (LLMs) in LVQA b
Exploring LGBTQ+ Bias in Generative AI Answers across Different Country and Religious Contexts
Previous discussions have highlighted the need for generative AI tools to become more culturally sensitive, yet often neglect the complexities of handling content about minorities, who are perceived differently across cultures and religions. Our study examined how two generative AI systems respond to homophobic statements with varying cultural and religious context information. Findings showed ChatGPT 3.5's repli
FeatureSORT: Essential Features for Effective Tracking
We introduce FeatureSORT, a simple yet effective online multiple object tracker that reinforces the DeepSORT baseline with a redesigned detector and additional feature cues. In contrast to conventional detectors that only provide bounding boxes, our modified YOLOX architecture is extended to output multiple appearance attributes, including clothing color, clothing style, and motion direction, alongside the bounding b
Notable events recorded on this day and month across all years.