Record 28012026 · captured 2026-08-25
The world looked up Gregory Bovino. 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.
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.
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.
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,
Learner Tien is an American professional tennis player. He has a career-high ATP singles ranking of world No. 12 achieved on August 10, 2026 and doubles ranking of No. 298 achieved on May 25, 2026. Tien has won two ATP Tour singles titles, as well as the 2025
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
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
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
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
Joseph Brady may refer to:
Alex de Miñaur Román is an Australian professional tennis player. He has a career-high ATP singles ranking of world No. 5 achieved on 13 July 2026 and a doubles ranking of No. 58 achieved on 12 October 2020. He is the current No. 1 Australian singles player.
The 2026 Royal Rumble, also promoted as Royal Rumble: Riyadh, was a professional wrestling pay-per-view (PPV) and livestreaming event produced by the American company WWE. It was the 39th annual Royal Rumble and took place on January 31, 2026, at Riyadh Season
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.
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
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
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
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
Nipah virus is a bat-borne, zoonotic virus that causes Nipah virus infection in humans and other animals, a disease with a very high case fatality rate (40–75%). Numerous disease outbreaks caused by the Nipah virus have occurred in India, Malaysia, and Singapo
Elina Mykhailivna Svitolina is a Ukrainian professional tennis player. She has career-high rankings of world No. 3 in singles and No. 108 in doubles from the WTA. Svitolina has won 20 WTA Tour singles titles, including the 2018 WTA Finals and five WTA 1000-lev
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.
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
Carol Anne Kirkwood is a retired Scottish weather presenter. She was trained by the Met Office and worked for the BBC between 1998 and 2026. She is best known for being the main weather presenter for BBC Breakfast for over 25 years. She is also a published aut
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
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
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.
Iva Jovic is an American professional tennis player. She has a career-high WTA singles ranking of No. 14 achieved on August 24, 2026, and a best doubles ranking of No. 64 reached on August 24, 2026. She has won one WTA Tour title, at the 2025 Guadalajara Open.
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
Michael John McCarthy is an American professional football coach who is the head coach for the Pittsburgh Steelers of the National Football League (NFL). Previously, he served as the head coach of the Dallas Cowboys and Green Bay Packers. In 2011, McCarthy led
António Luís Santos da Costa is a Goan-Portuguese lawyer and politician who has served as president of the European Council since 2024. He previously served as prime minister of Portugal from 2015 to 2024 and secretary-general of the Socialist Party from 2014
Limonene is a slightly yellow-green liquid aliphatic hydrocarbon classified as a cyclic monoterpene, and is the major component in the fragrance and essential oil of citrus fruit peels, taking its name from Italian limone ("lemon").
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Artificial Neural Network in Cosmic Landscape
In this paper we propose that artificial neural network, the basis of machine learning, is useful to generate the inflationary landscape from a cosmological point of view. Traditional numerical simulations of a global cosmic landscape typically need an exponential complexity when the number of fields is large. However, a basic application of artificial neural network could solve the problem based on the universal app
Image deblurring based on lightweight multi-information fusion network
Recently, deep learning based image deblurring has been well developed. However, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from high computational burden. To solve this problem, we propose a lightweight multiinformation fusion network (LMFN) for image deblurring. The proposed LMFN is designed as an encoder-decode
A Bi-Encoder LSTM Model For Learning Unstructured Dialogs
Creating a data-driven model that is trained on a large dataset of unstructured dialogs is a crucial step in developing Retrieval-based Chatbot systems. This paper presents a Long Short Term Memory (LSTM) based architecture that learns unstructured multi-turn dialogs and provides results on the task of selecting the best response from a collection of given responses. Ubuntu Dialog Corpus Version 2 was used as the cor
Differential privacy for symmetric log-concave mechanisms
Adding random noise to database query results is an important tool for achieving privacy. A challenge is to minimize this noise while still meeting privacy requirements. Recently, a sufficient and necessary condition for $(ε, δ)$-differential privacy for Gaussian noise was published. This condition allows the computation of the minimum privacy-preserving scale for this distribution. We extend this work and provide a
Movement speed data from urban road networks, computed from ridesharing vehicles or taxi trajectories, is often high-dimensional, sparse, and nonstationary (e.g., exhibiting seasonality). To address these challenges, we propose a Nonstationary Temporal Matrix Factorization (NoTMF) model that leverages matrix factorization to project high-dimensional and sparse movement speed data into low-dimensional latent spaces. T
Chaotic Hedging with Iterated Integrals and Neural Networks
In this paper, we derive an $L^p$-chaos expansion based on iterated Stratonovich integrals with respect to a given exponentially integrable continuous semimartingale. By omitting the orthogonality of the expansion, we show that every $p$-integrable functional, $p \in [1,\infty)$, can be approximated by a finite sum of iterated Stratonovich integrals. Using (possibly random) neural networks as integrands, we therefere
Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting
Deep learning-based multivariate and multistep-ahead traffic forecasting models are typically trained with the mean squared error (MSE) or mean absolute error (MAE) as the loss function in a sequence-to-sequence setting, simply assuming that the errors follow an independent and isotropic Gaussian or Laplacian distributions. However, such assumptions are often unrealistic for real-world traffic forecasting tasks, wher
CNN-based IoT Device Identification: A Comparative Study on Payload vs. Fingerprint
The proliferation of the Internet of Things (IoT) has introduced a massive influx of devices into the market, bringing with them significant security vulnerabilities. In this diverse ecosystem, robust IoT device identification is a critical preventive measure for network security and vulnerability management. This study proposes a deep learning-based method to identify IoT devices using the Aalto dataset. We employ C
Hedonic Prices and Quality Adjusted Price Indices Powered by AI
We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices. To achieve this, we generate abstract product attributes (or ``features'') from descriptions and images using deep neural networks. These attributes are then used to estimate the hedonic price function. To demonstrat
An efficient, provably optimal algorithm for the 0-1 loss linear classification problem
Algorithms for solving the linear classification problem have a long history, dating back at least to 1936 with linear discriminant analysis. For linearly separable data, many algorithms can obtain the exact solution to the corresponding 0-1 loss classification problem efficiently, but for data which is not linearly separable, it has been shown that this problem, in full generality, is NP-hard. Alternative approaches
Beyond Classical Attention: Quantum Attention for Scalable Computation
As large language models (LLMs) demonstrate outstanding performance across various tasks, attention-driven models have profoundly transformed the field of machine learning. Since attention computations account for the primary computational overhead in both model inference and training, efficiently computing attention matrices has become one of the core challenges in accelerating large language models. It is well-know
PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images
For the past few years, deep generative models have increasingly been used in biological research for a variety of tasks. Recently, they have proven to be valuable for uncovering subtle cell phenotypic differences that are not directly discernible to the human eye. However, current methods employed to achieve this goal mainly rely on Generative Adversarial Networks (GANs). While effective, GANs encompass issues such
Large Language Models (LLMs) have demonstrated strong performance across a wide range of NLP tasks. However, they often exhibit suboptimal behaviors and inconsistencies when exposed to unfamiliar external information, underscoring their limitations in effectively leveraging such knowledge. Inspired by constructivist learning theory, we propose ThinkNote, a novel framework that enhances the external knowledge utilizat
Watermark-based Attribution of AI-Generated Content
Several companies have deployed watermark-based detection to identify AI-generated content. However, attribution--the ability to trace back to the user of a generative AI (GenAI) service who created a given AI-generated content--remains largely unexplored despite its growing importance. In this work, we aim to bridge this gap by conducting the first systematic study on watermark-based, user-level attribution of AI-ge
Accelerating Ill-conditioned Hankel Matrix Recovery via Structured Newton-like Descent
This paper studies the robust Hankel recovery problem, which simultaneously removes the sparse outliers and fulfills missing entries from the partial observation. We propose a novel non-convex algorithm, coined Hankel Structured Newton-Like Descent (HSNLD), to tackle the robust Hankel recovery problem. HSNLD is highly efficient with linear convergence, and its convergence rate is independent of the condition number o
A simple algorithm for output range analysis for deep neural networks
This paper presents a novel approach for the output range estimation problem in Deep Neural Networks (DNNs) by integrating a Simulated Annealing (SA) algorithm tailored to operate within constrained domains and ensure convergence towards global optima. The method effectively addresses the challenges posed by the lack of local geometric information and the high non-linearity inherent to DNNs, making it applicable to a
Link Representation Learning for Probabilistic Travel Time Estimation
Travel time estimation is a key task in navigation apps and web mapping services. Existing deterministic and probabilistic methods, based on the assumption of trip independence, predominantly focus on modeling individual trips while overlooking trip correlations. However, real-world conditions frequently introduce strong correlations between trips, influenced by external and internal factors such as weather and the t
Epistemological Bias As a Means for the Automated Detection of Injustices in Text
Injustices in text are often subtle since implicit biases or stereotypes frequently operate unconsciously due to the pervasive nature of prejudice in society. This makes automated detection of injustices more challenging which leads to them being often overlooked. We introduce a novel framework that combines knowledge from epistemology to enhance the detection of implicit injustices in text using NLP models to addres
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
Kernel ridge regression (KRR) is a fundamental computational tool, appearing in problems that range from computational chemistry to health analytics, with a particular interest due to its starring role in Gaussian process regression. However, full KRR solvers are challenging to scale to large datasets: both direct (i.e., Cholesky decomposition) and iterative methods (i.e., PCG) incur prohibitive computational and sto
Large language models demonstrate impressive performance on downstream tasks, yet they require extensive resource consumption when fully fine-tuning all parameters. To mitigate this, Parameter Efficient Fine-Tuning (PEFT) strategies, such as LoRA, have been developed. In this paper, we delve into the concept of task-specific directions (TSDs), which are critical for transitioning large models from pretrained states t
Joint Diffusion for Universal Hand-Object Grasp Generation
Predicting and generating human hand grasp over objects is critical for animation and robotic tasks. In this work, we focus on generating both the hand and objects in a grasp by a single diffusion model. Our proposed Joint Hand-Object Diffusion (JHOD) models the hand and object in a unified latent representation. It uses the hand-object grasping data to learn to accommodate hand and object to form plausible grasps. A
D2Vformer: A Flexible Time Series Prediction Model Based on Time Position Embedding
Time position embeddings capture the positional information of time steps, often serving as auxiliary inputs to enhance the predictive capabilities of time series models. However, existing models exhibit limitations in capturing intricate time positional information and effectively utilizing these embeddings. To address these limitations, this paper proposes a novel model called D2Vformer. Unlike typical prediction m
Token Caching for Diffusion Transformer Acceleration
Diffusion transformers have gained substantial interest in diffusion generative modeling due to their outstanding performance. However, their computational demands, particularly the quadratic complexity of attention mechanisms and multi-step inference processes, present substantial bottlenecks that limit their practical applications. To address these challenges, we propose TokenCache, a novel acceleration method that
Pseudo-Nonlinear Data Augmentation: A Constrained Energy Minimization Viewpoint
We propose a simple yet novel data augmentation method for general data modalities based on energy-based modeling and principles from information geometry. Unlike most existing learning-based data augmentation methods, which rely on learning latent representations with generative models, our proposed framework enables an intuitive construction of a geometrically aware latent space that represents the structure of the
A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research
Deep Generative Models (DGMs) have rapidly advanced in recent years, becoming essential tools in various fields due to their ability to learn complex data distributions and generate synthetic data. Their importance in transportation research is increasingly recognized, particularly for applications like traffic data generation, prediction, and feature extraction. This paper offers a comprehensive introduction and tut
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