{"date":"2026-02-09","schema_version":1,"captured_at":"2026-08-25T23:28:08.902Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Lindsey Vonn","views":954212,"rank":2,"extract":"Lindsey Caroline Vonn is an American alpine ski racer. She won four World Cup overall championships with titles in 2008, 2009, 2010, and 2012. Vonn won the gold medal in downhill at the 2010 Winter Olympics, the first one for an American woman. 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two-stage paradigm. These methods typically comprise two steps: 1) Utilize a region proposal network to propose a handful of high-quality proposals in a bottom-up fashion. 2) Resize and pool the semantic features from the proposed regions to summarize RoI-wise representations for further refinement. Note that these RoI-wise representations in step 2) are","authors":["Qian, Rui","Lai, Xin","Li, Xirong"],"url":"https://arxiv.org/abs/2104.10330","arxiv_id":"2104.10330","published":"2026-02-09"},{"title":"When Fair Ranking Meets Uncertain Inference","abstract":"Existing fair ranking systems, especially those designed to be demographically fair, assume that accurate demographic information about individuals is available to the ranking algorithm. In practice, however, this assumption may not hold -- in real-world contexts like ranking job applicants or credit seekers, social and legal barriers may prevent algorithm operators from collecting peoples' demographic informatio","authors":["Ghosh, Avijit","Dutt, Ritam","Wilson, Christo"],"url":"https://arxiv.org/abs/2105.02091","arxiv_id":"2105.02091","published":"2026-02-09"},{"title":"3D Object Detection for Autonomous Driving: A Survey","abstract":"Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of perception stack especially for the sake of path planning, motion prediction, and collision avoidance etc. Taking a quick glance at the progress we have made, we attribute challenges to visual appearance recovery in the absence of depth informati","authors":["Qian, Rui","Lai, Xin","Li, Xirong"],"url":"https://arxiv.org/abs/2106.10823","arxiv_id":"2106.10823","published":"2026-02-09"},{"title":"Bayesian Matrix Decomposition and Applications","abstract":"The sole aim of this book is to give a self-contained introduction to concepts and mathematical tools in Bayesian matrix decomposition in order to seamlessly introduce matrix decomposition techniques and their applications in subsequent sections. However, we clearly realize our inability to cover all the useful and interesting results concerning Bayesian matrix decomposition and given the paucity of scope to present ","authors":["Lu, Jun"],"url":"https://arxiv.org/abs/2302.11337","arxiv_id":"2302.11337","published":"2026-02-09"},{"title":"A computational framework for human values","abstract":"In the diverse array of work investigating the nature of human values from psychology, philosophy and social sciences, there is a clear consensus that values guide behaviour. More recently, a recognition that values provide a means to engineer ethical AI has emerged. Indeed, Stuart Russell proposed shifting AI's focus away from simply ``intelligence'' towards intelligence ``provably aligned with human val","authors":["Osman, Nardine","d'Inverno, Mark"],"url":"https://arxiv.org/abs/2305.02748","arxiv_id":"2305.02748","published":"2026-02-09"},{"title":"On CNF formulas irredundant with respect to unit clause propagation","abstract":"Two CNF formulas are called ucp-equivalent, if they behave in the same way with respect to the unit clause propagation (UCP). A formula is called ucp-irredundant, if removing any clause leads to a formula which is not ucp-equivalent to the original one. As a consequence of known results, the ratio of the size of a ucp-irredundant formula and the size of a smallest ucp-equivalent formula is at most $n^2$, where $n$ is","authors":["Savický, Petr"],"url":"https://arxiv.org/abs/2309.01750","arxiv_id":"2309.01750","published":"2026-02-09"},{"title":"A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning","abstract":"Most federated learning (FL) methods use a client-server scheme, where clients communicate only with a central server. However, this scheme is prone to bandwidth bottlenecks at the server and has a single point of failure. In contrast, in a (fully) decentralized approach, clients communicate directly with each other, dispensing with the server and mitigating these issues. Yet, as the client network grows larger and s","authors":["Valdeira, Pedro","Chi, Yuejie","Soares, Cláudia","Xavier, João"],"url":"https://arxiv.org/abs/2309.09977","arxiv_id":"2309.09977","published":"2026-02-09"},{"title":"Nonparametric Evaluation of Noisy ICA Solutions","abstract":"Independent Component Analysis (ICA) was introduced in the 1980's as a model for Blind Source Separation (BSS), which refers to the process of recovering the sources underlying a mixture of signals, with little knowledge about the source signals or the mixing process. While there are many sophisticated algorithms for estimation, different methods have different shortcomings. In this paper, we develop a nonparamet","authors":["Kumar, Syamantak","Sarkar, Purnamrita","Bickel, Peter","Bean, Derek"],"url":"https://arxiv.org/abs/2401.08468","arxiv_id":"2401.08468","published":"2026-02-09"},{"title":"STAG: Structural Test-time Alignment of Gradients for Online Adaptation","abstract":"Test-Time Adaptation (TTA) adapts pre-trained models using only unlabeled test streams, requiring real-time inference and update without access to source data. We propose StructuralTest-time Alignment of Gradients (STAG), a lightweight plug-in enhancer that exploits an always-available structural signal: the classifier's intrinsic geometry. STAG derives class-wise structural anchors from classifier weights via se","authors":["Shin, Juhyeon","Oh, Yujin","Lee, Jonghyun","Lee, Saehyung","Park, Minjun","Lee, Dongjun"],"url":"https://arxiv.org/abs/2402.09004","arxiv_id":"2402.09004","published":"2026-02-09"},{"title":"Extracting Manifold Information from Point Clouds","abstract":"A kernel based method is proposed for the construction of signature (defining) functions of subsets of $\\mathbb{R}^d$. The subsets can range from full dimensional manifolds (open subsets) to point clouds (a finite number of points) and include bounded (closed) smooth manifolds of any codimension. The interpolation and analysis of point clouds are the main application. Two extreme cases in terms of regularity are cons","authors":["Guidotti, Patrick"],"url":"https://arxiv.org/abs/2404.00427","arxiv_id":"2404.00427","published":"2026-02-09"},{"title":"Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows","abstract":"Alongside optimization-based planners, sampling-based approaches are often used in trajectory planning for autonomous driving due to their simplicity. Model predictive path integral control is a framework that builds upon optimization principles while incorporating stochastic sampling of input trajectories. This paper investigates several sampling approaches for trajectory generation. In this context, normalizing flo","authors":["Rabenstein, Georg","Ullrich, Lars","Graichen, Knut"],"url":"https://arxiv.org/abs/2404.09657","arxiv_id":"2404.09657","published":"2026-02-09"},{"title":"Generalizing the SINDy approach with nested neural networks","abstract":"Symbolic Regression (SR) is a widely studied field of research that aims to infer symbolic expressions from data. A popular approach for SR is the Sparse Identification of Nonlinear Dynamical Systems (SINDy) framework, which uses sparse regression to identify governing equations from data. This study introduces an enhanced method, Nested SINDy, that aims to increase the expressivity of the SINDy approach thanks to a ","authors":["Fiorini, Camilla","Flint, Clément","Fostier, Louis","Franck, Emmanuel","Hashemi, Reyhaneh","Michel-Dansac, Victor"],"url":"https://arxiv.org/abs/2404.15742","arxiv_id":"2404.15742","published":"2026-02-09"},{"title":"EEG-MACS: Manifold Attention and Confidence Stratification for EEG-based Cross-Center Brain Disease Diagnosis under Unreliable Annotations","abstract":"Cross-center data heterogeneity and annotation unreliability significantly challenge the intelligent diagnosis of diseases using brain signals. A notable example is the EEG-based diagnosis of neurodegenerative diseases, which features subtler abnormal neural dynamics typically observed in small-group settings. To advance this area, in this work, we introduce a transferable framework employing Manifold Attention and C","authors":["Song, Zhenxi","Qin, Ruihan","Ren, Huixia","Liang, Zhen","Guo, Yi","Zhang, Min"],"url":"https://arxiv.org/abs/2405.00734","arxiv_id":"2405.00734","published":"2026-02-09"},{"title":"ProDAG: Projected Variational Inference for Directed Acyclic Graphs","abstract":"Directed acyclic graph (DAG) learning is a central task in structure discovery and causal inference. Although the field has witnessed remarkable advances over the past few years, it remains statistically and computationally challenging to learn a single (point estimate) DAG from data, let alone provide uncertainty quantification. We address the difficult task of quantifying graph uncertainty by developing a Bayesian ","authors":["Thompson, Ryan","Bonilla, Edwin V.","Kohn, Robert"],"url":"https://arxiv.org/abs/2405.15167","arxiv_id":"2405.15167","published":"2026-02-09"},{"title":"Training-Conditional Coverage Bounds under Covariate Shift","abstract":"Conformal prediction methodology has recently been extended to the covariate shift setting, where the distribution of covariates differs between training and test data. While existing results ensure that the prediction sets from these methods achieve marginal coverage above a nominal level, their coverage rate conditional on the training dataset (referred to as training-conditional coverage) remains unexplored. In th","authors":["Pournaderi, Mehrdad","Xiang, Yu"],"url":"https://arxiv.org/abs/2405.16594","arxiv_id":"2405.16594","published":"2026-02-09"},{"title":"Anonymization Prompt Learning for Facial Privacy-Preserving Text-to-Image Generation","abstract":"Text-to-image diffusion models, such as Stable Diffusion, generate highly realistic images from text descriptions. However, the generation of certain content at such high quality raises concerns. A prominent issue is the accurate depiction of identifiable facial images, which could lead to malicious deepfake generation and privacy violations. In this paper, we propose Anonymization Prompt Learning (APL) to address th","authors":["Shi, Liang","Zhang, Jie","Shan, Shiguang"],"url":"https://arxiv.org/abs/2405.16895","arxiv_id":"2405.16895","published":"2026-02-09"},{"title":"Predicting the fatigue life of asphalt concrete using neural networks","abstract":"Asphalt concrete's (AC) durability and maintenance demands are strongly influenced by its fatigue life. Traditional methods for determining this characteristic are both resource-intensive and time-consuming. This study employs artificial neural networks (ANNs) to predict AC fatigue life, focusing on the impact of strain level, binder content, and air-void content. Leveraging a substantial dataset, we tailored our","authors":["Houlík, Jakub","Valentin, Jan","Nežerka, Václav"],"url":"https://arxiv.org/abs/2406.01523","arxiv_id":"2406.01523","published":"2026-02-09"},{"title":"Science-Informed Design of Deep Learning With Applications to Wireless Systems: A Tutorial","abstract":"Recent advances in computational infrastructure and large-scale data processing have accelerated the adoption of data-driven inference methods, particularly deep learning (DL), to solve problems in many scientific and engineering domains. In wireless systems, DL has been applied to problems where analytical modeling or optimization is difficult to formulate, relies on oversimplified assumptions, or becomes computatio","authors":["Termehchi, Atefeh","Hossain, Ekram","Vera-Rivera, Angelo","Ibrahim, Muhammad","Woungang, Isaac"],"url":"https://arxiv.org/abs/2407.07742","arxiv_id":"2407.07742","published":"2026-02-09"},{"title":"Self-Supervised Video Representation Learning in a Heuristic Decoupled Perspective","abstract":"Video contrastive learning (V-CL) has emerged as a popular framework for unsupervised video representation learning, demonstrating strong results in tasks such as action classification and detection. Yet, to harness these benefits, it is critical for the learned representations to fully capture both static and dynamic semantics. However, our experiments show that existing V-CL methods fail to effectively learn either","authors":["Song, Zeen","Qiang, Wenwen","Zheng, Changwen","Xiong, Hui","Hua, Gang"],"url":"https://arxiv.org/abs/2407.14069","arxiv_id":"2407.14069","published":"2026-02-09"},{"title":"IsUMap: Manifold Learning and Data Visualization leveraging Vietoris-Rips filtrations","abstract":"This work introduces IsUMap, a novel manifold learning technique that enhances data representation by integrating aspects of UMAP and Isomap with Vietoris-Rips filtrations. We present a systematic and detailed construction of a metric representation for locally distorted metric spaces that captures complex data structures more accurately than the previous schemes. Our approach addresses limitations in existing method","authors":["Barth, Lukas Silvester","Fatemeh","Fahimi","Joharinad, Parvaneh","Jost, Jürgen","Keck, Janis"],"url":"https://arxiv.org/abs/2407.17835","arxiv_id":"2407.17835","published":"2026-02-09"},{"title":"High-Precision Edge Detection via Task-Adaptive Texture Handling and Ideal-Prior Guidance","abstract":"Image edge detection (ED) requires specialized architectures, reliable supervision, and rigorous evaluation criteria to ensure accurate localization. In this work, we present a framework for high-precision ED that jointly addresses architectural design, data supervision, and evaluation consistency. We propose SDPED, a compact ED model built upon Cascaded Skipping Density Blocks (CSDB), motivated by a task-adaptive ar","authors":["Shu, Hao"],"url":"https://arxiv.org/abs/2407.19992","arxiv_id":"2407.19992","published":"2026-02-09"},{"title":"Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches","abstract":"3D Content Generation is at the heart of many computer graphics applications, including video gaming, film-making, virtual and augmented reality, etc. This paper proposes a novel deep-learning based approach for automatically generating interactive and playable 3D game scenes, all from the user's casual prompts such as a hand-drawn sketch. Sketch-based input offers a natural, and convenient way to convey the user","authors":["Xu, Yongzhi","Ng, Yonhon","Wang, Yifu","Sa, Inkyu","Duan, Yunfei","Sun, Zhenhong"],"url":"https://arxiv.org/abs/2408.04567","arxiv_id":"2408.04567","published":"2026-02-09"},{"title":"Downscaling Neural Network for Coastal Simulations","abstract":"Learning the fine-scale details of a coastal ocean simulation from a coarse representation is a challenging task. For real-world applications, high-resolution simulations are necessary to advance understanding of many coastal processes, specifically, to predict flooding resulting from tsunamis and storm surges. We propose a Downscaling Neural Network for Coastal Simulation (DNNCS) for spatiotemporal enhancement to le","authors":["Liu, Zhi-Song","Büttner, Markus","Scarborough, Matthew","Valseth, Eirik","Aizinger, Vadym","Kainz, Bernhard"],"url":"https://arxiv.org/abs/2408.16553","arxiv_id":"2408.16553","published":"2026-02-09"},{"title":"Hyperbolic Fine-Tuning for Large Language Models","abstract":"Large language models (LLMs) have demonstrated remarkable performance across various tasks. However, it remains an open question whether the default Euclidean space is the most suitable choice for LLMs. In this study, we investigate the geometric characteristics of LLMs, focusing specifically on tokens and their embeddings. Our findings reveal that token frequency follows a power-law distribution, where high-frequenc","authors":["Yang, Menglin","B, Ram Samarth B","Feng, Aosong","Xiong, Bo","Liu, Jihong","King, Irwin"],"url":"https://arxiv.org/abs/2410.04010","arxiv_id":"2410.04010","published":"2026-02-09"},{"title":"Enhancing Hyperspectral Image Prediction with Contrastive Learning in Low-Label Regime","abstract":"Self-supervised contrastive learning is an effective approach for addressing the challenge of limited labelled data. This study builds upon the previously established two-stage patch-level, multi-label classification method for hyperspectral remote sensing imagery. We evaluate the method's performance for both the single-label and multi-label classification tasks, particularly under scenarios of limited training ","authors":["Haidar, Salma","Oramas, José"],"url":"https://arxiv.org/abs/2410.07790","arxiv_id":"2410.07790","published":"2026-02-09"}],"history":{"events":[{"year":2020,"text":"Japanese figure skater Yuzuru Hanyu won the Four Continents Championships to become the only man to complete a Super Slam.","url":"https://en.wikipedia.org/wiki/Figure_skating"},{"year":2016,"text":"Two commuter trains collided head-on at Bad Aibling in southeastern Germany, killing 12 people and injuring 85.","url":"https://en.wikipedia.org/wiki/Bad_Aibling_rail_accident"},{"year":2001,"text":"The American submarine USS Greeneville collided with the Ehime Maru, a Japanese training vessel operated by a high school, sinking the latter ship and killing nine people on board.","url":"https://en.wikipedia.org/wiki/USS_Greeneville"},{"year":1996,"text":"Researchers at the GSI Helmholtz Centre for Heavy Ion Research in Darmstadt, Germany, first created the chemical element copernicium.","url":"https://en.wikipedia.org/wiki/GSI_Helmholtz_Centre_for_Heavy_Ion_Research"},{"year":1996,"text":"Breaking a seventeen-month ceasefire, the Provisional Irish Republican Army detonated a powerful truck bomb in the London Docklands, killing two people and injuring more than a hundred others.","url":"https://en.wikipedia.org/wiki/Provisional_Irish_Republican_Army"},{"year":1976,"text":"The Australian Defence Force (ensign pictured) was formed by the integration of the Australian Army, the Royal Australian Navy, and the Royal Australian Air Force.","url":"https://en.wikipedia.org/wiki/Australian_Defence_Force"},{"year":1975,"text":"The spacecraft and crew of the Soviet Soyuz 17 mission returned to earth after 29 days in orbit at the Salyut 4 station.","url":"https://en.wikipedia.org/wiki/Soyuz_17"},{"year":1971,"text":"An earthquake registering 6.6 Mw struck the northern San Fernando Valley near the Los Angeles district of Sylmar, killing 65 people.","url":"https://en.wikipedia.org/wiki/1971_San_Fernando_earthquake"},{"year":1964,"text":"As Beatlemania swept the United States, the Beatles (pictured) made their first appearance on The Ed Sullivan Show before a record-breaking audience, beginning a musical phenomenon known as the British Invasion.","url":"https://en.wikipedia.org/wiki/Beatlemania"},{"year":1945,"text":"World War II: Allied aircraft unsuccessfully attacked a German destroyer in Førde Fjord, Norway.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1943,"text":"World War II: Allied forces declared Guadalcanal secure, ending the Guadalcanal campaign as a significant strategic victory for Allied forces fighting Japan in the Pacific War.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1942,"text":"The Imperial Japanese Army began the Battle of Kranji as part of their campaign to capture Singapore.","url":"https://en.wikipedia.org/wiki/Imperial_Japanese_Army"},{"year":1923,"text":"Stanley Bruce became prime minister of Australia as leader of the country's first Coalition government.","url":"https://en.wikipedia.org/wiki/Stanley_Bruce"},{"year":1920,"text":"The Svalbard Treaty was signed in Paris, recognizing Norwegian sovereignty over the Arctic archipelago of Svalbard.","url":"https://en.wikipedia.org/wiki/Svalbard_Treaty"},{"year":1913,"text":"A meteor procession was observed along a great circle arc from Canada to Brazil, leading astronomers to conclude that its source was a small, short-lived natural satellite of the Earth.","url":"https://en.wikipedia.org/wiki/Meteor_procession"},{"year":1907,"text":"More than 3,000 women in London participated in the Mud March (pictured), the first large procession organised by the National Union of Women's Suffrage Societies.","url":"https://en.wikipedia.org/wiki/Mud_March_(suffragists)"},{"year":1861,"text":"Jefferson Davis was named the provisional president of the Confederate States of America.","url":"https://en.wikipedia.org/wiki/Jefferson_Davis"},{"year":1855,"text":"A series of hoof-like marks in the snow continuing through the countryside for some 40 to 100 miles (60 to 160 km) were discovered in Devon, England.","url":"https://en.wikipedia.org/wiki/Devil's_Footprints"}],"births":[{"year":2007,"text":"Ryan Coleman-Williams, American football player","url":"https://en.wikipedia.org/wiki/Ryan_Coleman-Williams"},{"year":2003,"text":"Cooper DeJean, American football player","url":"https://en.wikipedia.org/wiki/Cooper_DeJean"},{"year":2002,"text":"Jalen Green, American basketball player","url":"https://en.wikipedia.org/wiki/Jalen_Green"},{"year":2001,"text":"Dylan Cozens, Canadian ice hockey player","url":"https://en.wikipedia.org/wiki/Dylan_Cozens"},{"year":1998,"text":"Cem Bölükbaşı, Turkish racing driver and former sim racer","url":"https://en.wikipedia.org/wiki/Cem_B%C3%B6l%C3%BCkba%C5%9F%C4%B1"},{"year":1998,"text":"Isabella Gomez, Colombian-American actress","url":"https://en.wikipedia.org/wiki/Isabella_Gomez"},{"year":1997,"text":"Jaire Alexander, American football player","url":"https://en.wikipedia.org/wiki/Jaire_Alexander"},{"year":1997,"text":"Saquon Barkley, American football player","url":"https://en.wikipedia.org/wiki/Saquon_Barkley"}],"deaths":[{"year":2025,"text":"Tom Robbins, American writer (born 1932)","url":"https://en.wikipedia.org/wiki/Tom_Robbins"},{"year":2022,"text":"Johnny Raper, Australian rugby league player and coach (born 1939)","url":"https://en.wikipedia.org/wiki/Johnny_Raper"},{"year":2021,"text":"Chick Corea, American jazz composer (born 1941)","url":"https://en.wikipedia.org/wiki/Chick_Corea"},{"year":2018,"text":"Reg E. Cathey, American actor of stage, film, and television (born 1958)","url":"https://en.wikipedia.org/wiki/Reg_E._Cathey"},{"year":2018,"text":"Jóhann Jóhannsson, Icelandic composer (born 1969)","url":"https://en.wikipedia.org/wiki/J%C3%B3hann_J%C3%B3hannsson"},{"year":2018,"text":"John Gavin, American actor and United States ambassador to Mexico (born 1931)","url":"https://en.wikipedia.org/wiki/John_Gavin"},{"year":2017,"text":"André Salvat, French Army colonel (born 1920)","url":"https://en.wikipedia.org/wiki/Andr%C3%A9_Salvat"},{"year":2016,"text":"Sushil Koirala, Nepalese politician, 37th Prime Minister of Nepal (born 1939)","url":"https://en.wikipedia.org/wiki/Sushil_Koirala"}]},"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."}