{"date":"2025-12-24","schema_version":1,"captured_at":"2026-08-25T23:06:01.961Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Philip Rivers","views":687232,"rank":4,"extract":"Philip Michael Rivers is an American former professional football quarterback who played in the National Football League (NFL) for 18 seasons, primarily with the Chargers franchise. He played college football for the NC State Wolfpack, winning ACC Player of th","url":"https://en.wikipedia.org/wiki/Philip_Rivers","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/3/3d/Philip_Rivers_Indianapolis_Colts_Helmet.jpg/330px-Philip_Rivers_Indianapolis_Colts_Helmet.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Vince Zampella","views":551498,"rank":5,"extract":"Vincent Walter Zampella II was an American video game designer. 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We study a sequence of statistics called "cross-validated eigenvalues." Under a large class of random graph models, including both Poisson and Bernoulli edges, without parametric assumptions, we provide a $p$-value for each cross-validated eigenvalue. It tests ","authors":["Chen, Fan","Roch, Sebastien","Rohe, Karl","Yu, Shuqi"],"url":"https://arxiv.org/abs/2108.03336","arxiv_id":"2108.03336","published":"2025-12-24"},{"title":"Reduced-order autoregressive dynamics of a complex financial system: a PCA-based approach","abstract":"This study analyzes the dynamic interactions among the NASDAQ index, crude oil, gold, and the US dollar using a reduced-order modeling approach. Time-delay embedding and principal component analysis are employed to encode high-dimensional financial dynamics, followed by linear regression in the reduced space. Correlation and lagged regression analyses reveal heterogeneous cross-asset dependencies. Model performance, ","authors":["Khalilian, Pouriya","Azizi, Sara","Amiri, Mohammad Hossein","Firouzjaee, Javad T."],"url":"https://arxiv.org/abs/2212.12044","arxiv_id":"2212.12044","published":"2025-12-24"},{"title":"Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler","abstract":"The development of efficient sampling algorithms catering to non-Euclidean geometries has been a challenging endeavor, as discretization techniques which succeed in the Euclidean setting do not readily carry over to more general settings. We develop a non-Euclidean analog of the recent proximal sampler of [LST21], which naturally induces regularization by an object known as the log-Laplace transform (LLT) of a densit","authors":["Gopi, Sivakanth","Lee, Yin Tat","Liu, Daogao","Shen, Ruoqi","Tian, Kevin"],"url":"https://arxiv.org/abs/2302.06085","arxiv_id":"2302.06085","published":"2025-12-24"},{"title":"Fusarium head blight detection, spikelet estimation, and severity assessment in wheat using 3D convolutional neural networks","abstract":"Fusarium head blight (FHB) is one of the most significant diseases affecting wheat and other small grain cereals worldwide. The development of resistant varieties requires the laborious task of field and greenhouse phenotyping. The applications considered in this work are the automated detection of FHB disease symptoms expressed on a wheat plant, the automated estimation of the total number of spikelets and the total","authors":["Hamila, Oumaima","Henry, Christopher J.","Molina, Oscar I.","Bidinosti, Christopher P.","Henriquez, Maria Antonia"],"url":"https://arxiv.org/abs/2303.05634","arxiv_id":"2303.05634","published":"2025-12-24"},{"title":"Behavioral Machine Learning? Regularization and Forecast Bias","abstract":"Standard forecast efficiency tests interpret violations as evidence of behavioral bias. We show theoretically and empirically that rational forecasters using optimal regularization systematically violate these tests. Machine learning forecasts show near zero bias at one year horizon, but strong overreaction at two years, consistent with predictions from a model of regularization and measurement noise. We provide thre","authors":["Frank, Murray Z.","Gao, Jing","Yang, Keer"],"url":"https://arxiv.org/abs/2303.16158","arxiv_id":"2303.16158","published":"2025-12-24"},{"title":"Optimal Clustering with Dependent Costs in Bayesian Networks","abstract":"Background: Clustering of nodes in Bayesian Networks (BNs) and related graphical models such as Dynamic BNs (DBNs) has been demonstrated to enhance computational efficiency and improve model learning. It typically involves partitioning the underlying Directed Acyclic Graph (DAG) into cliques or optimising for some cost or criteria. Objectives: We focus on a critical but understudied aspect of optimal clustering invol","authors":["Wu, Paul Pao-Yen","Ruggeri, Fabrizio","Mengersen, Kerrie"],"url":"https://arxiv.org/abs/2308.03970","arxiv_id":"2308.03970","published":"2025-12-24"},{"title":"A correlation-based fuzzy cluster validity index with secondary options detector","abstract":"The optimal number of clusters is one of the main concerns when applying cluster analysis. Several cluster validity indexes have been introduced to address this problem. However, in some situations, there is more than one option that can be chosen as the final number of clusters. This aspect has been overlooked by most of the existing works in this area. In this study, we introduce a correlation-based fuzzy cluster v","authors":["Wiroonsri, Nathakhun","Preedasawakul, Onthada"],"url":"https://arxiv.org/abs/2308.14785","arxiv_id":"2308.14785","published":"2025-12-24"},{"title":"Boosted Control Functions: Distribution generalization and invariance in confounded models","abstract":"Modern machine learning methods and the availability of large-scale data have significantly advanced our ability to predict target quantities from large sets of covariates. However, these methods often struggle under distributional shifts, particularly in the presence of hidden confounding. While the impact of hidden confounding is well-studied in causal effect estimation, e.g., instrumental variables, its implicatio","authors":["Gnecco, Nicola","Peters, Jonas","Engelke, Sebastian","Pfister, Niklas"],"url":"https://arxiv.org/abs/2310.05805","arxiv_id":"2310.05805","published":"2025-12-24"},{"title":"Improving Local Training in Federated Learning via Temperature Scaling","abstract":"Federated learning is inherently hampered by data heterogeneity: non-i.i.d. training data over local clients. We propose a novel model training approach for federated learning, FLex&Chill, which exploits the Logit Chilling method. Through extensive evaluations, we demonstrate that, in the presence of non-i.i.d. data characteristics inherent in federated learning systems, this approach can expedite model convergen","authors":["Lee, Kichang","Zhang, Pei","Kim, Songkuk","Ko, JeongGil"],"url":"https://arxiv.org/abs/2401.09986","arxiv_id":"2401.09986","published":"2025-12-24"},{"title":"Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks","abstract":"Graph Neural Networks (GNNs) and Transformer-based models have been increasingly adopted to learn the complex vector representations of spatio-temporal graphs, capturing intricate spatio-temporal dependencies crucial for applications such as traffic datasets. Although many existing methods utilize multi-head attention mechanisms and message-passing neural networks (MPNNs) to capture both spatial and temporal relation","authors":["Lee, Minho","Choi, Yun Young","Park, Sun Woo","Lee, Seunghwan","Ko, Joohwan","Hong, Jaeyoung"],"url":"https://arxiv.org/abs/2401.15894","arxiv_id":"2401.15894","published":"2025-12-24"},{"title":"A Bayesian cluster validity index","abstract":"Selecting the appropriate number of clusters is a critical step in applying clustering algorithms. To assist in this process, various cluster validity indices (CVIs) have been developed. These indices are designed to identify the optimal number of clusters within a dataset. However, users may not always seek the absolute optimal number of clusters but rather a secondary option that better aligns with their specific a","authors":["Wiroonsri, Nathakhun","Preedasawakul, Onthada"],"url":"https://arxiv.org/abs/2402.02162","arxiv_id":"2402.02162","published":"2025-12-24"},{"title":"Tactile-based Object Retrieval From Granular Media","abstract":"We introduce GEOTACT, the first robotic system capable of grasping and retrieving objects of potentially unknown shapes buried in a granular environment. While important in many applications, ranging from mining and exploration to search and rescue, this type of interaction with granular media is difficult due to the uncertainty stemming from visual occlusion and noisy contact signals. To address these challenges, we","authors":["Xu, Jingxi","Jia, Yinsen","Yang, Dongxiao","Meng, Patrick","Zhu, Xinyue","Guo, Zihan"],"url":"https://arxiv.org/abs/2402.04536","arxiv_id":"2402.04536","published":"2025-12-24"},{"title":"Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes","abstract":"Dense scene reconstruction for photo-realistic view synthesis has various applications, such as VR/AR, autonomous vehicles. However, most existing methods have difficulties in large-scale scenes due to three core challenges: \\textit{(a) inaccurate depth input.} Accurate depth input is impossible to get in real-world large-scale scenes. \\textit{(b) inaccurate pose estimation.} Most existing approaches rely on accurate","authors":["Deng, Tianchen","Wang, Nailin","Wang, Chongdi","Yuan, Shenghai","Wang, Jingchuan","Wang, Hesheng"],"url":"https://arxiv.org/abs/2404.06050","arxiv_id":"2404.06050","published":"2025-12-24"},{"title":"Reinforcement Learning for Unsupervised Video Summarization with Reward Generator Training","abstract":"This paper presents a novel approach for unsupervised video summarization using reinforcement learning (RL), addressing limitations like unstable adversarial training and reliance on heuristic-based reward functions. The method operates on the principle that reconstruction fidelity serves as a proxy for informativeness, correlating summary quality with reconstruction ability. The summarizer model assigns importance s","authors":["Abbasi, Mehryar","Hadizadeh, Hadi","Saeedi, Parvaneh"],"url":"https://arxiv.org/abs/2407.04258","arxiv_id":"2407.04258","published":"2025-12-24"},{"title":"Generative Retrieval with Few-shot Indexing","abstract":"Existing generative retrieval (GR) methods rely on training-based indexing, which fine-tunes a model to memorise associations between queries and the document identifiers (docids) of relevant documents. Training-based indexing suffers from high training costs, under-utilisation of pre-trained knowledge in large language models (LLMs), and limited adaptability to dynamic document corpora. To address the issues, we pro","authors":["Askari, Arian","Meng, Chuan","Aliannejadi, Mohammad","Ren, Zhaochun","Kanoulas, Evangelos","Verberne, Suzan"],"url":"https://arxiv.org/abs/2408.02152","arxiv_id":"2408.02152","published":"2025-12-24"},{"title":"Decoding the human brain tissue response to radiofrequency excitation using a biophysical-model-free deep MRI on a chip framework","abstract":"Magnetic resonance imaging (MRI) relies on radiofrequency (RF) excitation of proton spin. Clinical diagnosis requires a comprehensive collation of biophysical data via multiple MRI contrasts, acquired using a series of RF sequences that lead to lengthy examinations. Here, we developed a vision transformer-based framework that captures the spatiotemporal magnetic signal evolution and decodes the brain tissue response ","authors":["Nagar, Dinor","Zaiss, Moritz","Perlman, Or"],"url":"https://arxiv.org/abs/2408.08376","arxiv_id":"2408.08376","published":"2025-12-24"},{"title":"TropNNC: Structured Neural Network Compression Using Tropical Geometry","abstract":"We present TropNNC, a framework for compressing neural networks with linear and convolutional layers and ReLU activations using tropical geometry. By representing a network's output as a tropical rational function, TropNNC enables structured compression via reduction of the corresponding tropical polynomials. Our method refines the geometric approximation of previous work by adaptively selecting the weights of re","authors":["Fotopoulos, Konstantinos","Maragos, Petros","Misiakos, Panagiotis"],"url":"https://arxiv.org/abs/2409.03945","arxiv_id":"2409.03945","published":"2025-12-24"},{"title":"Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Object-Oriented Programming","abstract":"Object-Oriented Programming (OOP) has become a crucial paradigm for managing the growing complexity of modern software systems, particularly in fields like machine learning, deep learning, large language models (LLM), and data analytics. This work provides a comprehensive introduction to the integration of OOP techniques within these domains, with a focus on improving code modularity, maintainability, and scalability","authors":["Wang, Tianyang","Bi, Ziqian","Chen, Keyu","Xu, Jiawei","Niu, Qian","Liu, Junyu"],"url":"https://arxiv.org/abs/2409.19916","arxiv_id":"2409.19916","published":"2025-12-24"},{"title":"Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns","abstract":"This book, Design Patterns in Machine Learning and Deep Learning: Advancing Big Data Analytics Management, presents a comprehensive study of essential design patterns tailored for large-scale machine learning and deep learning applications. The book explores the application of classical software engineering patterns, Creational, Structural, Behavioral, and Concurrency Patterns, to optimize the development, maintenanc","authors":["Chen, Keyu","Bi, Ziqian","Wang, Tianyang","Wen, Yizhu","Feng, Pohsun","Niu, Qian"],"url":"https://arxiv.org/abs/2410.03795","arxiv_id":"2410.03795","published":"2025-12-24"},{"title":"Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice","abstract":"This book provides a comprehensive introduction to the foundational concepts of machine learning (ML) and deep learning (DL). It bridges the gap between theoretical mathematics and practical application, focusing on Python as the primary programming language for implementing key algorithms and data structures. The book covers a wide range of topics, including basic and advanced Python programming, fundamental mathema","authors":["Chen, Silin","Bi, Ziqian","Liu, Junyu","Peng, Benji","Zhang, Sen","Pan, Xuanhe"],"url":"https://arxiv.org/abs/2410.19849","arxiv_id":"2410.19849","published":"2025-12-24"},{"title":"FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI","abstract":"We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most major branches of modern mathematics -- from computationally intensive problems in number theory and real analysis to abstract questions in algebraic geometry and category theory. Solving a typical problem requires multiple hours of effort","authors":["Glazer, Elliot","Erdil, Ege","Besiroglu, Tamay","Chicharro, Diego","Chen, Evan","Gunning, Alex"],"url":"https://arxiv.org/abs/2411.04872","arxiv_id":"2411.04872","published":"2025-12-24"},{"title":"Multiscale Corrections by Continuous Super-Resolution","abstract":"Finite element methods typically require a high resolution to satisfactorily approximate micro and even macro patterns of an underlying physical model. This issue can be circumvented by appropriate multiscale strategies that are able to obtain reasonable approximations on under-resolved scales. In this paper, we study the implicit neural representation and propose a continuous super-resolution network as a correction","authors":["Liu, Zhi-Song","Maier, Roland","Rupp, Andreas"],"url":"https://arxiv.org/abs/2411.07576","arxiv_id":"2411.07576","published":"2025-12-24"},{"title":"FP=xINT:Representing Neural Networks via Low-Bit Series Basis Functions","abstract":"Post-Training Quantization (PTQ) converts pre-trained Full-Precision (FP) models into quantized versions without training. While existing methods reduce size and computational costs, they also significantly degrade performance and quantization efficiency at extremely low settings due to quantization noise. We introduce a deep model series expansion framework to address this issue, enabling rapid and accurate approxim","authors":["Zhang, Boyang","Cheng, Daning","Zhang, Yunquan","Tian, Jiake","Li, Jing","Liu, Fangming"],"url":"https://arxiv.org/abs/2412.06865","arxiv_id":"2412.06865","published":"2025-12-24"},{"title":"Lossless Model Compression via Joint Low-Rank Factorization Optimization","abstract":"Low-rank factorization is a popular model compression technique that minimizes the error $δ$ between approximated and original weight matrices. Despite achieving performances close to the original models when $δ$ is optimized, a performance discrepancy remains due to the separate optimization processes for low-rank factorization and model performance, resulting in unavoidable losses. We address this issue by introduc","authors":["Zhang, Boyang","Cheng, Daning","Zhang, Yunquan","Liu, Fangming","Tian, Jiake"],"url":"https://arxiv.org/abs/2412.06867","arxiv_id":"2412.06867","published":"2025-12-24"},{"title":"Compression for Better: A General and Stable Lossless Compression Framework","abstract":"This work focus on how to stabilize and lossless model compression, aiming to reduce model complexity and enhance efficiency without sacrificing performance due to compression errors. A key challenge is effectively leveraging compression errors and defining the boundaries for lossless compression to minimize model loss. i.e., compression for better. Currently, there is no systematic approach to determining this error","authors":["Zhang, Boyang","Cheng, Daning","Zhang, Yunquan","Liu, Fangming","Chen, Wenguang"],"url":"https://arxiv.org/abs/2412.06868","arxiv_id":"2412.06868","published":"2025-12-24"}],"history":{"events":[{"year":2021,"text":"Burmese military forces killed at least 40 civilians during the Mo So massacre in Kayah State, Myanmar.","url":"https://en.wikipedia.org/wiki/Tatmadaw"},{"year":2008,"text":"The Lord's Resistance Army, a Ugandan rebel group, began attacks on several villages in the north of the Democratic Republic of the Congo, killing hundreds and committing numerous atrocities.","url":"https://en.wikipedia.org/wiki/Lord's_Resistance_Army"},{"year":1999,"text":"Jihadists linked to al-Qaeda hijacked Indian Airlines Flight 814 to force the release of Islamist figures held in prison in India.","url":"https://en.wikipedia.org/wiki/Jihadism"},{"year":1979,"text":"The Soviet government deployed troops in Afghanistan, starting the Soviet–Afghan War.","url":"https://en.wikipedia.org/wiki/Soviet%E2%80%93Afghan_War"},{"year":1973,"text":"The U.S. Congress granted home rule to Washington, D.C., allowing the residents to elect their own mayor and a city council.","url":"https://en.wikipedia.org/wiki/United_States_Congress"},{"year":1968,"text":"Piloted by Jim Lovell, Apollo 8 became the first human spaceflight to reach and orbit the Moon (Earthrise pictured).","url":"https://en.wikipedia.org/wiki/Jim_Lovell"},{"year":1964,"text":"The Viet Cong bombed the Brinks Hotel in Saigon, killing two U.S. Army officers and raising fears of an escalation of the Vietnam War.","url":"https://en.wikipedia.org/wiki/Viet_Cong"},{"year":1955,"text":"According to legend, the NORAD Tracks Santa program began after children began calling the Continental Air Defense Command Center to inquire about Santa Claus's whereabouts due to a misprinted phone number in an advertisement.","url":"https://en.wikipedia.org/wiki/NORAD_Tracks_Santa"},{"year":1953,"text":"A railway bridge at Tangiwai on New Zealand's North Island was damaged by a lahar and collapsed beneath a passenger train (wreckage pictured), killing 151 people.","url":"https://en.wikipedia.org/wiki/North_Island"},{"year":1925,"text":"Winnie-the-Pooh first appeared by name in a children's story in the London Evening News.","url":"https://en.wikipedia.org/wiki/Winnie-the-Pooh"},{"year":1918,"text":"Forces united in the Kingdom of Serbs, Croats and Slovenes defeated Hungarian forces to end the occupation of Međimurje.","url":"https://en.wikipedia.org/wiki/Kingdom_of_Yugoslavia"},{"year":1914,"text":"World War I: British and German soldiers interrupted fighting to celebrate Christmas, beginning the Christmas truce .","url":"https://en.wikipedia.org/wiki/World_War_I"},{"year":1913,"text":"Seventy-three people were crushed to death in a stampede after someone falsely yelled \"fire\" at a crowded Christmas party in Calumet, Michigan, U.S.","url":"https://en.wikipedia.org/wiki/Italian_Hall_disaster"},{"year":1871,"text":"Aida, one of Giuseppe Verdi's most popular operas, made its debut in Cairo, Egypt.","url":"https://en.wikipedia.org/wiki/Aida"},{"year":1865,"text":"Six Confederate veterans of the American Civil War founded a social club they named the Ku Klux Klan, which later became a white supremacist group.","url":"https://en.wikipedia.org/wiki/Confederate_States_Army"},{"year":1846,"text":"The Sultanate of Brunei ceded the island of Labuan to the British Empire.","url":"https://en.wikipedia.org/wiki/Sultanate_of_Brunei_(1368%E2%80%931888)"},{"year":1818,"text":"The Christmas carol \"Silent Night\" (audio featured) by Joseph Mohr and Franz Gruber was first performed in a church in Oberndorf bei Salzburg, Austria.","url":"https://en.wikipedia.org/wiki/Christmas_carol"},{"year":1814,"text":"The United Kingdom and the United States signed a peace treaty in Ghent, present-day Belgium, ending the War of 1812.","url":"https://en.wikipedia.org/wiki/Treaty_of_Ghent"}],"births":[{"year":2002,"text":"Joshua Primo, Canadian basketball player","url":"https://en.wikipedia.org/wiki/Joshua_Primo"},{"year":2002,"text":"Jeremiah Trotter Jr., American football player","url":"https://en.wikipedia.org/wiki/Jeremiah_Trotter_Jr."},{"year":2001,"text":"Choi Sung-beom, South Korean football player","url":"https://en.wikipedia.org/wiki/Choi_Sung-beom"},{"year":1998,"text":"Alexis Mac Allister, Argentine footballer","url":"https://en.wikipedia.org/wiki/Alexis_Mac_Allister"},{"year":1997,"text":"Neeraj Chopra, Indian javelin thrower","url":"https://en.wikipedia.org/wiki/Neeraj_Chopra"},{"year":1997,"text":"William Contreras, Venezuelan baseball player","url":"https://en.wikipedia.org/wiki/William_Contreras"},{"year":1995,"text":"Anett Kontaveit, Estonian tennis player","url":"https://en.wikipedia.org/wiki/Anett_Kontaveit"},{"year":1994,"text":"Han Seung-woo, South Korean singer","url":"https://en.wikipedia.org/wiki/Han_Seung-woo"}],"deaths":[{"year":2024,"text":"Hudson Meek, American actor (born 2008)","url":"https://en.wikipedia.org/wiki/Hudson_Meek"},{"year":2024,"text":"Richard Perry, American record producer (born 1942)","url":"https://en.wikipedia.org/wiki/Richard_Perry"},{"year":2023,"text":"Cheri Barry, American politician and mayor of Meridian, Mississippi (born 1955)","url":"https://en.wikipedia.org/wiki/Cheri_Barry"},{"year":2023,"text":"Richard Bowes, American science fiction author (born 1944)","url":"https://en.wikipedia.org/wiki/Richard_Bowes"},{"year":2023,"text":"Troy Dargan, Cook Islands rugby league footballer (born 1997)","url":"https://en.wikipedia.org/wiki/Troy_Dargan"},{"year":2018,"text":"Martha Érika Alonso, Governor of Puebla (born 1973)","url":"https://en.wikipedia.org/wiki/Martha_%C3%89rika_Alonso"},{"year":2018,"text":"Rafael Moreno Valle Rosas, former governor of Puebla (born 1968)","url":"https://en.wikipedia.org/wiki/Rafael_Moreno_Valle_Rosas"},{"year":2017,"text":"Jerry Kindall, American baseball player and coach (born 1935)","url":"https://en.wikipedia.org/wiki/Jerry_Kindall"}]},"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."}