Record 23022026 · captured 2026-08-25
The world looked up Johnny Gaudreau. 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.
John Michael Gaudreau was an American professional ice hockey player. A left winger, he played 11 seasons in the National Hockey League (NHL). He played college ice hockey for the Boston College Eagles in NCAA Division I for three seasons beginning in 2011 and
Jack Rowden Hughes is an American professional ice hockey player who is a center and alternate captain for the New Jersey Devils of the National Hockey League (NHL). A product of the U.S. National Development Team, Hughes was drafted first overall by the Devil
Rondale DaSean Moore was an American professional football player who was a wide receiver in the National Football League (NFL). He played college football for the Purdue Boilermakers, earning consensus All-American honors as a freshman. Moore was selected by
Alysa Liu is an American figure skater. She is the 2026 Olympic champion in both the women's singles and team events, the 2025 World champion, the 2022 World bronze medalist, the 2025–26 Grand Prix Final champion, a two-time Grand Prix medalist, a four-time Ch
Connor Charles Hellebuyck is an American professional ice hockey player who is a goaltender for the Winnipeg Jets of the National Hockey League (NHL).
Eileen Feng Gu, also known by her Chinese name Gu Ailing (谷爱凌), is a Chinese-American freestyle skier and model. She has represented China in halfpipe, slopestyle, and big air events since the 2018–19 season. With three gold and three silver medals, Gu is the
Eric William Dane was an American actor. After multiple television roles in the 1990s and 2000s, including his recurring role as Jason Dean on Charmed, he was cast as Dr. Mark Sloan on the ABC medical drama Grey's Anatomy. He went on to appear in films such as
Nemesio Rubén Oseguera Cervantes, commonly referred to by his alias "El Mencho", was a Mexican drug lord and head of the Jalisco New Generation Cartel (CJNG), an organized crime group based in Jalisco. He was the most wanted person in Mexico and one of the mos
List of Olympic medalists in ice hockey
Ice hockey is a sport that is contested at the Winter Olympic Games. A men's ice hockey tournament has been held every Winter Olympics ; an ice hockey tournament was also held at the 1920 Summer Olympics. From 1920 to 1968, the Olympics also acted as the Ice H
The 2026 Winter Olympics, officially the XXV Olympic Winter Games and commonly known as Milano Cortina 2026, were an international winter multi-sport event held from 6 to 22 February 2026, at multiple sites across Lombardy, Veneto and Trentino-Alto Adige/Südti
Quintin Jerome Hughes is an American professional ice hockey player who is a defenseman for the Minnesota Wild of the National Hockey League (NHL). Hughes was drafted seventh overall by the Vancouver Canucks in the 2018 NHL entry draft and played his first sev
Rebecca Gayheart is an American actress and model. Gayheart began her career as a teen model in the 1980s before becoming an advertising spokeswoman. She has been active as an actress since 1990.
Zoe Atkin is a British freestyle skier. She is a two-time X Games champion. In 2025, she became the world champion competing for Great Britain in the halfpipe.
United States men's national ice hockey team
The United States men's national ice hockey team, also known as Team USA, represents the United States in men's international ice hockey. The team is controlled by USA Hockey, the governing body for organized ice hockey in the United States. As of June 2026, t
Ilia Malinin is an American figure skater. He is a 2026 Olympic Games team event gold medalist, three-time World champion, three-time Grand Prix Final champion, seven-time Grand Prix gold medalist, four-time Challenger Series gold medalist, and four-time U.S.
Robert Michael Aramayo is an English actor. From 2016 to 2017, he played the role of young Eddard Stark in the sixth and seventh seasons of the HBO series Game of Thrones. In 2021, he starred in the Netflix psychological thriller miniseries Behind Her Eyes. Si
Jeffrey Edward Epstein was an American financier and child sex offender. He began his career as a math teacher at the Dalton School in New York City, before entering the banking and finance sector. Over several decades, he made much of his fortune providing ta
The 2030 Winter Olympics, officially known as the XXVI Olympic Winter Games and branded as Alpes 2030, is an upcoming international winter multi-sport event scheduled to take place from 1 to 17 February 2030 in the French Alps region as main hosts.
Ice hockey at the Olympic Games
Ice hockey tournaments have been staged at the Olympic Games since 1920. The men's tournament was introduced at the 1920 Summer Olympics and was transferred permanently to the Winter Olympic Games program in 1924. The women's tournament was first held at the 1
Sidney Patrick Crosby is a Canadian professional ice hockey player who is a centre and captain for the Pittsburgh Penguins of the National Hockey League (NHL). Nicknamed "Sid the Kid" and dubbed "The Next One", he was selected first overall by the Penguins in
Ryan Garcia is an American professional boxer. He has held the World Boxing Council (WBC) welterweight title since 2026. He previously held the WBC interim lightweight title in 2021.
The "Miracle on Ice" was an ice hockey game during the 1980 Winter Olympics in Lake Placid, New York. It was played between the hosting United States and the Soviet Union on February 22, 1980, during the medal round of the men's ice hockey tournament. Although
Matthew Edward Boldy is an American professional ice hockey player who is a forward for the Minnesota Wild of the National Hockey League (NHL). He was drafted 12th overall by the Wild in the first round of the 2019 NHL entry draft.
John Fitzgerald Kennedy Jr., also referred to as JFK Jr., was an American businessman, attorney, magazine publisher, and journalist. He was the son of the 35th U.S. president John F. Kennedy, and First Lady Jacqueline Kennedy.
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
Carolyn Jeanne Bessette-Kennedy was an American fashion publicist. Raised in Greenwich, Connecticut, she graduated from Boston University and joined Calvin Klein, where she rose from a sales position in Boston to publicity and show-production roles in New York
Johannes Høsflot Klæbo is a Norwegian cross-country skier who represents Byåsen IL. He holds multiple records, among them for being the youngest skier in history to win the FIS Cross-Country World Cup, the Tour de Ski, a World Championship event, and an Olympi
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
List of Olympic Games host cities
The modern Olympics began in 1896. Since then, summer and winter games have usually celebrated a four-year period known as an Olympiad. From the inaugural Winter Games in 1924 until 1992, winter and summer Games were held in the same year. Since 1994, summer a
Macklin Richard Celebrini is a Canadian professional ice hockey player who is a centre and alternate captain for the San Jose Sharks of the National Hockey League (NHL). Selected first overall by the Sharks in the 2024 NHL entry draft, Celebrini made his NHL d
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
SUNLayer: Stable denoising with generative networks
Deep neural networks are often used to implement powerful generative models for real-world data. Notable applications include image denoising, as well as other classical inverse problems like compressed sensing and super-resolution. To provide a rigorous but simplified analysis of generative models, in this work, we introduce an elegant theoretical framework based on spherical harmonics, namely \textbf{SUNLayer}. Our
As a proposal-free approach, instance segmentation through pixel embedding learning and clustering is gaining more emphasis. Compared with bounding box refinement approaches, such as Mask R-CNN, it has potential advantages in handling complex shapes and dense objects. In this work, we propose a simple, yet highly effective, architecture for object-aware embedding learning. A distance regression module is incorporated
Object Detection Based Handwriting Localization
We present an object detection based approach to localize handwritten regions from documents, which initially aims to enhance the anonymization during the data transmission. The concatenated fusion of original and preprocessed images containing both printed texts and handwritten notes or signatures are fed into the convolutional neural network, where the bounding boxes are learned to detect the handwriting. Afterward
Multiscale Softmax Cross Entropy for Fovea Localization on Color Fundus Photography
Fovea localization is one of the most popular tasks in ophthalmic medical image analysis, where the coordinates of the center point of the macula lutea, i.e. fovea centralis, should be calculated based on color fundus images. In this work, we treat the localization problem as a classification task, where the coordinates of the x- and y-axis are considered as the target classes. Moreover, the combination of the softma
Fair Community Detection and Structure Learning in Heterogeneous Graphical Models
Inference of community structure in probabilistic graphical models may not be consistent with fairness constraints when nodes have demographic attributes. Certain demographics may be over-represented in some detected communities and under-represented in others. This paper defines a novel $\ell_1$-regularized pseudo-likelihood approach for fair graphical model selection. In particular, we assume there is some communit
Convergence of gradient descent for deep neural networks
We give a simple local Polyak-Lojasiewicz (PL) criterion that guarantees linear (exponential) convergence of gradient flow and gradient descent to a zero-loss solution of a nonnegative objective. We then verify this criterion for the squared training loss of a feedforward neural network with smooth, strictly increasing activation functions, in a regime that is complementary to the usual over-parameterized analyses: t
Perceptually Optimized Color Selection for Visualization
We propose an approach, called the Equilibrium Distribution Model (EDM), for automatically selecting colors with optimum perceptual contrast for scientific visualization. Given any number of features that need to be emphasized in a visualization task, our approach derives evenly distributed points in the CIELAB color space to assign colors to the features so that the minimum Euclidean Distance among the colors are op
A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation
We propose a neural network-based framework to optimize the perceptions simulated by the in silico retinal implant model pulse2percept. The overall pipeline consists of a trainable encoder, a pre-trained retinal implant model and a pre-trained evaluator. The encoder is a U-Net, which takes the original image and outputs the stimulus. The pre-trained retinal implant model is also a U-Net, which is trained to mimic the
This study uses domain randomization to generate a synthetic RGB-D dataset for training multimodal instance segmentation models, aiming to achieve colour-agnostic hand localization in cluttered industrial environments. Domain randomization is a simple technique for addressing the "reality gap" by randomly rendering unrealistic features in a simulation scene to force the neural network to learn essential domai
From deciding on a PhD program to buying a new camera, unfamiliar decisions--decisions without domain knowledge--are frequent and significant. The complexity and uncertainty of such decisions demand unique approaches to information seeking, understanding, and decision-making. Our formative study highlights that users want to start by discovering broad and relevant domain information evenly and simultaneously, quickly
Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation
Modern biomedical image analysis using deep learning often encounters the challenge of limited annotated data. To overcome this issue, deep generative models can be employed to synthesize realistic biomedical images. In this regard, we propose an image synthesis method that utilizes denoising diffusion probabilistic models (DDPMs) to automatically generate retinal optical coherence tomography (OCT) images. By providi
Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical laws and able to accurately simulate them. However, today's PINNs are often trained for a single physics task and require computationally expensive re-training for each new task, even for tasks from similar physics domains. To address this
Learning Performance Maximizing Ensembles with Explainability Guarantees
In this paper we propose a method for the optimal allocation of observations between an intrinsically explainable glass box model and a black box model. An optimal allocation being defined as one which, for any given explainability level (i.e. the proportion of observations for which the explainable model is the prediction function), maximizes the performance of the ensemble on the underlying task, and maximizes perf
A Unified Framework for Analyzing Meta-algorithms in Online Convex Optimization
In this paper, we analyze the problem of online convex optimization in different settings, including different feedback types (full-information/semi-bandit/bandit/etc) in either stochastic or non-stochastic setting and different notions of regret (static adversarial regret/dynamic regret/adaptive regret). This is done through a framework which allows us to systematically propose and analyze meta-algorithms for the va
Governance of Generative Artificial Intelligence for Companies
Generative Artificial Intelligence (GenAI), specifically large language models (LLMs) like ChatGPT, has swiftly entered organizations without adequate governance, posing both opportunities and risks. Despite extensive debate on GenAI's transformative potential and emerging regulatory measures, limited research addresses organizational governance from both technical and business perspectives. While frameworks for
On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysis
A major challenge in Explainable AI is in correctly interpreting activations of hidden neurons: accurate interpretations would help answer the question of what a deep learning system internally detects as relevant in the input, demystifying the otherwise black-box nature of deep learning systems. The state of the art indicates that hidden node activations can, in some cases, be interpretable in a way that makes sense
Topic Modeling with Fine-tuning LLMs and Bag of Sentences
Large language models (LLMs) are increasingly used for topic modeling, outperforming classical topic models such as LDA. Commonly, pre-trained LLM encoders such as BERT are used out-of-the-box despite the fact that fine-tuning is known to improve LLMs considerably. The challenge lies in obtaining a suitable labeled dataset for fine-tuning. In this paper, we build on the recent idea of using bags of sentences as the e
Physics-informed graph neural networks for flow field estimation in carotid arteries
Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology such as atherosclerosis. Non-invasive, in-vivo measurement of these quantities can only be performed using a select number of modalities that are not widely available, such as 4D flow magnetic resonance imaging (MRI). In this work, we create a surrogate model for hemodynamic flow field estimation, powered by machine learning. We
Beyond Mimicry to Contextual Guidance: Knowledge Distillation for Interactive AI
As large language models increasingly mediate firm - customer interactions, firms face a tradeoff: the most capable models perform well but are costly and difficult to control at scale. Existing knowledge distillation methods address this challenge by training weaker, deployable models to imitate frontier outputs; however, such open-loop approaches are poorly suited to interactive, multi-turn settings where responses
Expressiveness of Multi-Neuron Convex Relaxations in Neural Network Certification
Neural network certification methods heavily rely on convex relaxations to provide robustness guarantees. However, these relaxations are often imprecise: even the most accurate single-neuron relaxation is incomplete for general ReLU networks, a limitation known as the *single-neuron convex barrier*. While multi-neuron relaxations have been heuristically applied to address this issue, two central questions arise: (i)
Visual Fixation-Based Retinal Prosthetic Simulation
This study proposes a retinal prosthetic simulation framework driven by visual fixations, inspired by the saccade mechanism, and assesses performance improvements through end-to-end optimization in a classification task. Salient patches are predicted from input images using the self-attention map of a vision transformer to mimic visual fixations. These patches are then encoded by a trainable U-Net and simulated using
GIFT: A Framework Towards Global Interpretable Faithful Textual Explanations of Vision Classifiers
Understanding the decision processes of deep vision models is essential for their safe and trustworthy deployment in real-world settings. Existing explainability approaches, such as saliency maps or concept-based analyses, often suffer from limited faithfulness, local scope, or ambiguous semantics. We introduce GIFT, a post-hoc framework that aims to derive Global, Interpretable, Faithful, and Textual explanations fo
SAMa: Material-aware 3D Selection and Segmentation
Decomposing 3D assets into material parts is a common task for artists, yet remains a highly manual process. In this work, we introduce Select Any Material (SAMa), a material selection approach for in-the-wild objects in arbitrary 3D representations. Building on SAM2's video prior, we construct a material-centric video dataset that extends it to the material domain. We propose an efficient way to lift the model&#
TrapFlow: Controllable Website Fingerprinting Defense via Dynamic Backdoor Learning
Website fingerprinting (WF) attacks, which covertly monitor user communications to identify the web pages they visit, pose a serious threat to user privacy. Existing WF defenses attempt to reduce attack accuracy by disrupting traffic patterns, but attackers can retrain their models to adapt, making these defenses ineffective. Meanwhile, their high overhead limits deployability. To overcome these limitations, we intro
Data-Efficient Inference of Neural Fluid Fields via SciML Foundation Model
Recent developments in 3D vision have enabled significant progress in inferring neural fluid fields and realistic rendering of fluid dynamics. However, these methods require dense captures of real-world flows, which demand specialized laboratory setups, making the process costly and challenging. Scientific machine learning (SciML) foundation models, pretrained on extensive simulations of partial differential equation
Notable events recorded on this day and month across all years.