{"date":"2026-01-28","schema_version":1,"captured_at":"2026-08-25T23:25:43.894Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":17},"attention":[{"title":"Gregory Bovino","views":517121,"rank":3,"extract":"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.","url":"https://en.wikipedia.org/wiki/Gregory_Bovino","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/c/ce/2026_Gregory_Bovino_of_U.S._Immigration_and_Customs_Enforcement_%28ICE%29_Enforcement_and_Removal_Operations%27_%28ERO%29_%28cropped%29.jpg/330px-2026_Gregory_Bovino_of_U.S._Immigration_and_Customs_Enforcement_%28ICE%29_Enforcement_and_Removal_Operations%27_%28ERO%29_%28cropped%29.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Killing of Alex Pretti","views":340470,"rank":4,"extract":"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","url":"https://en.wikipedia.org/wiki/Killing_of_Alex_Pretti","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/d/d1/Alex_Pretti_-_VA_portrait_%28enhanced_deluxe%29.jpg/330px-Alex_Pretti_-_VA_portrait_%28enhanced_deluxe%29.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Border 2","views":306085,"rank":5,"extract":"Border 2 is a 2026 Indian Hindi-language epic war film co-written and directed by Anurag Singh. 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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","authors":["Liu, Junyu"],"url":"https://arxiv.org/abs/1707.02800","arxiv_id":"1707.02800","published":"2026-01-28"},{"title":"Image deblurring based on lightweight multi-information fusion network","abstract":"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","authors":["Zhang, Yanni","Liu, Yiming","Li, Qiang","Qi, Miao","Xu, Dahong","Kong, Jun"],"url":"https://arxiv.org/abs/2101.05403","arxiv_id":"2101.05403","published":"2026-01-28"},{"title":"A Bi-Encoder LSTM Model For Learning Unstructured Dialogs","abstract":"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","authors":["Brahman, Danny","Negi, Pooran S.","Mahoor, Mohammad"],"url":"https://arxiv.org/abs/2104.12269","arxiv_id":"2104.12269","published":"2026-01-28"},{"title":"Differential privacy for symmetric log-concave mechanisms","abstract":"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 ","authors":["Vinterbo, Staal A."],"url":"https://arxiv.org/abs/2202.11393","arxiv_id":"2202.11393","published":"2026-01-28"},{"title":"Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorization","abstract":"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","authors":["Chen, Xinyu","Zhang, Chengyuan","Zhao, Xi-Le","Saunier, Nicolas","Sun, Lijun"],"url":"https://arxiv.org/abs/2203.10651","arxiv_id":"2203.10651","published":"2026-01-28"},{"title":"Chaotic Hedging with Iterated Integrals and Neural Networks","abstract":"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","authors":["Neufeld, Ariel","Schmocker, Philipp"],"url":"https://arxiv.org/abs/2209.10166","arxiv_id":"2209.10166","published":"2026-01-28"},{"title":"Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting","abstract":"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","authors":["Choi, Seongjin","Saunier, Nicolas","Zheng, Vincent Zhihao","Trepanier, Martin","Sun, Lijun"],"url":"https://arxiv.org/abs/2212.06653","arxiv_id":"2212.06653","published":"2026-01-28"},{"title":"CNN-based IoT Device Identification: A Comparative Study on Payload vs. Fingerprint","abstract":"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","authors":["Kostas, Kahraman"],"url":"https://arxiv.org/abs/2304.13894","arxiv_id":"2304.13894","published":"2026-01-28"},{"title":"Hedonic Prices and Quality Adjusted Price Indices Powered by AI","abstract":"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","authors":["Bajari, Patrick","Cen, Zhihao","Chernozhukov, Victor","Manukonda, Manoj","Vijaykumar, Suhas","Wang, Jin"],"url":"https://arxiv.org/abs/2305.00044","arxiv_id":"2305.00044","published":"2026-01-28"},{"title":"An efficient, provably optimal algorithm for the 0-1 loss linear classification problem","abstract":"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","authors":["He, Xi","Little, Max A."],"url":"https://arxiv.org/abs/2306.12344","arxiv_id":"2306.12344","published":"2026-01-28"},{"title":"Beyond Classical Attention: Quantum Attention for Scalable Computation","abstract":"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","authors":["Guo, Xuyang","Song, Zhao","Yang, Xin","Zhang, Ruizhe"],"url":"https://arxiv.org/abs/2307.08045","arxiv_id":"2307.08045","published":"2026-01-28"},{"title":"PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images","abstract":"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 ","authors":["Bourou, Anis","Boyer, Thomas","Daupin, Kévin","Dubreuil, Véronique","De Thonel, Aurélie","Mezger, Valérie"],"url":"https://arxiv.org/abs/2312.08290","arxiv_id":"2312.08290","published":"2026-01-28"},{"title":"ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling","abstract":"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","authors":["Xu, Zhipeng","Liu, Zhenghao","Yan, Yukun","Wang, Shuo","Yu, Shi","Zeng, Zheni"],"url":"https://arxiv.org/abs/2402.13547","arxiv_id":"2402.13547","published":"2026-01-28"},{"title":"Watermark-based Attribution of AI-Generated Content","abstract":"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","authors":["Jiang, Zhengyuan","Guo, Moyang","Hu, Yuepeng","Wang, Yupu","Gong, Neil Zhenqiang"],"url":"https://arxiv.org/abs/2404.04254","arxiv_id":"2404.04254","published":"2026-01-28"},{"title":"Accelerating Ill-conditioned Hankel Matrix Recovery via Structured Newton-like Descent","abstract":"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","authors":["Cai, HanQin","Huang, Longxiu","Lu, Xiliang","You, Juntao"],"url":"https://arxiv.org/abs/2406.07409","arxiv_id":"2406.07409","published":"2026-01-28"},{"title":"A simple algorithm for output range analysis for deep neural networks","abstract":"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","authors":["Rojas, Helder","Rojas, Nilton","B., Espinoza J.","Huamanchumo, Luis"],"url":"https://arxiv.org/abs/2407.02700","arxiv_id":"2407.02700","published":"2026-01-28"},{"title":"Link Representation Learning for Probabilistic Travel Time Estimation","abstract":"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","authors":["Xu, Chen","Wang, Qiang","Sun, Lijun"],"url":"https://arxiv.org/abs/2407.05895","arxiv_id":"2407.05895","published":"2026-01-28"},{"title":"Epistemological Bias As a Means for the Automated Detection of Injustices in Text","abstract":"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","authors":["Andrews, Kenya","Chiazor, Lamogha"],"url":"https://arxiv.org/abs/2407.06098","arxiv_id":"2407.06098","published":"2026-01-28"},{"title":"Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression","abstract":"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","authors":["Rathore, Pratik","Frangella, Zachary","Yang, Jiaming","Dereziński, Michał","Udell, Madeleine"],"url":"https://arxiv.org/abs/2407.10070","arxiv_id":"2407.10070","published":"2026-01-28"},{"title":"Task-Specific Directions: Definition, Exploration, and Utilization in Parameter Efficient Fine-Tuning","abstract":"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","authors":["Si, Chongjie","Shi, Zhiyi","Zhang, Shifan","Yang, Xiaokang","Pfister, Hanspeter","Shen, Wei"],"url":"https://arxiv.org/abs/2409.01035","arxiv_id":"2409.01035","published":"2026-01-28"},{"title":"Joint Diffusion for Universal Hand-Object Grasp Generation","abstract":"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","authors":["Cao, Jinkun","Liu, Jingyuan","Kitani, Kris","Zhou, Yi"],"url":"https://arxiv.org/abs/2409.04560","arxiv_id":"2409.04560","published":"2026-01-28"},{"title":"D2Vformer: A Flexible Time Series Prediction Model Based on Time Position Embedding","abstract":"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","authors":["Song, Xiaobao","Wang, Hao","Deng, Liwei","He, Yuxin","Cao, Wenming","Leungc, Chi-Sing"],"url":"https://arxiv.org/abs/2409.11024","arxiv_id":"2409.11024","published":"2026-01-28"},{"title":"Token Caching for Diffusion Transformer Acceleration","abstract":"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","authors":["Lou, Jinming","Luo, Wenyang","Liu, Yufan","Li, Bing","Ding, Xinmiao","Hu, Weiming"],"url":"https://arxiv.org/abs/2409.18523","arxiv_id":"2409.18523","published":"2026-01-28"},{"title":"Pseudo-Nonlinear Data Augmentation: A Constrained Energy Minimization Viewpoint","abstract":"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","authors":["Hu, Pingbang","Sugiyama, Mahito"],"url":"https://arxiv.org/abs/2410.00718","arxiv_id":"2410.00718","published":"2026-01-28"},{"title":"A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research","abstract":"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","authors":["Choi, Seongjin","Jin, Zhixiong","Ham, Seung Woo","Kim, Jiwon","Sun, Lijun"],"url":"https://arxiv.org/abs/2410.07066","arxiv_id":"2410.07066","published":"2026-01-28"}],"history":{"events":[{"year":1986,"text":"The Space Shuttle Challenger disintegrated 73 seconds into its tenth mission (video featured), killing all seven crew members.","url":"https://en.wikipedia.org/wiki/Space_Shuttle_Challenger_disaster"},{"year":1984,"text":"Tropical Storm Domoina made landfall in southern Mozambique, causing some of the most severe flooding recorded in the region.","url":"https://en.wikipedia.org/wiki/Tropical_Storm_Domoina"},{"year":1981,"text":"U.S. president Ronald Reagan lifted price controls from petroleum products, contributing to the 1980s oil glut.","url":"https://en.wikipedia.org/wiki/Ronald_Reagan"},{"year":1964,"text":"Three U.S. Air Force pilots aboard an unarmed T-39 Sabreliner were killed when the aircraft was shot down over Erfurt, East Germany, by a Soviet MiG-19.","url":"https://en.wikipedia.org/wiki/North_American_Sabreliner"},{"year":1961,"text":"In Gitarama, Rwanda, local and other government officials initiated a coup to overthrow the monarchy and establish a republic.","url":"https://en.wikipedia.org/wiki/Muhanga"},{"year":1958,"text":"The Lego Group, a Danish toy company, filed a patent in Denmark for the design of Lego bricks.","url":"https://en.wikipedia.org/wiki/The_Lego_Group"},{"year":1941,"text":"World War II: About three hours after Thai bombers raided Sisophon, a ceasefire paused hostilities in the Franco-Thai War.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1933,"text":"Choudhry Rahmat Ali published a pamphlet in which he called for the creation of a Muslim state in north-western India that he termed \"Pakstan\".","url":"https://en.wikipedia.org/wiki/Choudhry_Rahmat_Ali"},{"year":1922,"text":"The largest recorded snowstorm in the history of Washington, D.C., collapsed the Knickerbocker Theatre (damage pictured), killing 98 people.","url":"https://en.wikipedia.org/wiki/Knickerbocker_storm"},{"year":1916,"text":"The province of Manitoba passed a law that first granted some Canadian women the right to vote.","url":"https://en.wikipedia.org/wiki/Manitoba"},{"year":1754,"text":"The word serendipity, derived from the Persian fairy tale The Three Princes of Serendip, was coined by Horace Walpole (pictured) in a letter to a friend.","url":"https://en.wikipedia.org/wiki/Serendipity"},{"year":1671,"text":"Anglo-Spanish War: In pursuit of retreating Spanish troops, English soldiers sacked the city of Panama.","url":"https://en.wikipedia.org/wiki/Anglo-Spanish_War_(1654%E2%80%931660)"},{"year":1568,"text":"Delegates of the Three Nations of Transylvania adopted the Edict of Torda, allowing local communities to elect their preachers freely, in an unprecedented act of religious tolerance.","url":"https://en.wikipedia.org/wiki/Unio_Trium_Nationum"},{"year":1547,"text":"Nine-year-old Edward VI, the first English monarch to be raised as a Protestant, became king.","url":"https://en.wikipedia.org/wiki/Edward_VI"},{"year":1521,"text":"Charles V and the estates of the Holy Roman Empire met at the Diet of Worms to discuss Martin Luther and the effects of the Protestant Reformation.","url":"https://en.wikipedia.org/wiki/Charles_V%2C_Holy_Roman_Emperor"},{"year":1393,"text":"King Charles VI of France (pictured) was nearly killed when several other dancers' costumes caught fire during a masquerade ball in Paris.","url":"https://en.wikipedia.org/wiki/Charles_VI_of_France"},{"year":1069,"text":"Robert de Comines, Earl of Northumbria, was killed in Durham, causing William the Conqueror to embark on a campaign to subjugate northern England.","url":"https://en.wikipedia.org/wiki/Robert_de_Comines"}],"births":[{"year":2007,"text":"That Girl Lay Lay, American rapper","url":"https://en.wikipedia.org/wiki/Alaya_High"},{"year":2006,"text":"Beckett Sennecke, Canadian ice hockey player","url":"https://en.wikipedia.org/wiki/Beckett_Sennecke"},{"year":2004,"text":"Emoni Bates, American basketball player","url":"https://en.wikipedia.org/wiki/Emoni_Bates"},{"year":2004,"text":"Liam Öhgren, Swedish ice hockey player","url":"https://en.wikipedia.org/wiki/Liam_%C3%96hgren"},{"year":2003,"text":"Whitney Peak, Ugandan-Canadian actress","url":"https://en.wikipedia.org/wiki/Whitney_Peak_(actress)"},{"year":2003,"text":"Carson Hocevar, American professional stock car racing driver","url":"https://en.wikipedia.org/wiki/Carson_Hocevar"},{"year":2002,"text":"Tabyana Ali, American actress and author","url":"https://en.wikipedia.org/wiki/Tabyana_Ali"},{"year":2002,"text":"Yoo Seon-ho, South Korean actor","url":"https://en.wikipedia.org/wiki/Yoo_Seon-ho"}],"deaths":[{"year":2026,"text":"Ajit Pawar, Indian politician, former Deputy Chief Minister of Maharashtra (born 1959)","url":"https://en.wikipedia.org/wiki/Ajit_Pawar"},{"year":2021,"text":"Cicely Tyson, American actress (born 1924)","url":"https://en.wikipedia.org/wiki/Cicely_Tyson"},{"year":2019,"text":"Pepe Smith, Filipino rock musician (born 1947)","url":"https://en.wikipedia.org/wiki/Pepe_Smith"},{"year":2017,"text":"Alexander Chancellor, British journalist (born 1940)","url":"https://en.wikipedia.org/wiki/Alexander_Chancellor"},{"year":2017,"text":"Geoff Nicholls, British musician (born 1948)","url":"https://en.wikipedia.org/wiki/Geoff_Nicholls"},{"year":2016,"text":"Signe Toly Anderson, American singer (born 1941)","url":"https://en.wikipedia.org/wiki/Signe_Toly_Anderson"},{"year":2016,"text":"Paul Kantner, American singer-songwriter and guitarist (born 1941)","url":"https://en.wikipedia.org/wiki/Paul_Kantner"},{"year":2016,"text":"Franklin Gene Bissell, American football player and coach (born 1926)","url":"https://en.wikipedia.org/wiki/Gene_Bissell"}]},"census":null,"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","licence":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","licence":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"licence":"Records are CC BY-SA 4.0, inheriting from Wikipedia. Every item links to its source."}