{"date":"2026-01-16","schema_version":1,"captured_at":"2026-08-25T23:23:52.067Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":16},"attention":[{"title":"Charlie Kirk","views":223263,"rank":4,"extract":"Charles James Kirk was an American right-wing political activist, entrepreneur, and media personality. He co‑founded the conservative student organization Turning Point USA (TPUSA) in 2012 and served as its executive director until his assassination in 2025. 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theory","points":161,"comments":182,"url":"https://dl.acm.org/doi/10.1145/3780063.3780066","discussion":"https://news.ycombinator.com/item?id=46646226"},{"title":"My Gripes with Prolog","points":155,"comments":102,"url":"https://buttondown.com/hillelwayne/archive/my-gripes-with-prolog/","discussion":"https://news.ycombinator.com/item?id=46641348"}],"research":[{"title":"Spatial As Deep: Spatial CNN for Traffic Scene Understanding","abstract":"Convolutional neural networks (CNNs) are usually built by stacking convolutional operations layer-by-layer. Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored. These relationships are important to learn semantic objects with strong shape priors but weak appearance coherences, s","authors":["Pan, Xingang","Zhan, Xiaohang","Shi, Jianping","Luo, Ping","Wang, Xiaogang","Tang, Xiaoou"],"url":"https://arxiv.org/abs/1712.06080","arxiv_id":"1712.06080","published":"2026-01-16"},{"title":"Data-Driven Feature Tracking for Event Cameras With and Without Frames","abstract":"Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios. Existing feature tracking methods for event cameras are either handcrafted or derived from first principles but require extensive parameter tuning, are sensitive to noise, and do not generali","authors":["Messikommer, Nico","Fang, Carter","Gehrig, Mathias","Cioffi, Giovanni","Scaramuzza, Davide"],"url":"https://arxiv.org/abs/2211.12826","arxiv_id":"2211.12826","published":"2026-01-16"},{"title":"Genetic Algorithm Based Combinatorial Optimization for the Optimal Design of Water Distribution Network of Gurudeniya Service Zone, Sri Lanka","abstract":"This paper brings an in detail Genetic Algorithm (GA) based combinatorial optimization method used for the optimal design of the water distribution network (WDN) of Gurudeniya Service Zone, Sri Lanka. Genetic Algorithm (GA) mimics the survival of the fittest principle of nature to develop a search process. Methodology employs fuzzy combinations of pipe diameters to check their suitability to be considered as the cost","authors":["Senavirathna, K. H. M. R. N.","Walgampaya, C. K."],"url":"https://arxiv.org/abs/2304.09720","arxiv_id":"2304.09720","published":"2026-01-16"},{"title":"Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoising","abstract":"Recently, the mainstream practice for training low-light raw image denoising methods has shifted towards employing synthetic data. Noise modeling, which focuses on characterizing the noise distribution of real-world sensors, profoundly influences the effectiveness and practicality of synthetic data. Currently, physics-based noise modeling struggles to characterize the entire real noise distribution, while learning-ba","authors":["Feng, Hansen","Wang, Lizhi","Huang, Yiqi","Wang, Yuzhi","Zhu, Lin","Huang, Hua"],"url":"https://arxiv.org/abs/2310.09126","arxiv_id":"2310.09126","published":"2026-01-16"},{"title":"MiLe Loss: a New Entropy-Weighed Loss for Mitigating the Bias of Learning Difficulties in Large Language Models","abstract":"Generative language models are usually pretrained on large text corpus via predicting the next token (i.e., sub-word/word/phrase) given the previous ones. Recent works have demonstrated the impressive performance of large generative language models on downstream tasks. However, existing generative language models generally neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequ","authors":["Su, Zhenpeng","Wu, Xing","Bai, Xue","Lin, Zijia","Chen, Hui","Ding, Guiguang"],"url":"https://arxiv.org/abs/2310.19531","arxiv_id":"2310.19531","published":"2026-01-16"},{"title":"A Kolmogorov metric embedding for live cell microscopy signaling patterns","abstract":"We present a metric embedding that captures spatiotemporal patterns of cell signaling dynamics in 5-D $(x,y,z,channel,time)$ live cell microscopy movies. The embedding uses a metric distance called the normalized information distance (NID) based on Kolmogorov complexity theory, an absolute measure of information content between digital objects. The NID uses statistics of lossless compression to compute a theoreticall","authors":["Aho, Layton","Winter, Mark","DeCarlo, Marc","Frismantiene, Agne","Blum, Yannick","Gagliardi, Paolo Armando"],"url":"https://arxiv.org/abs/2401.02501","arxiv_id":"2401.02501","published":"2026-01-16"},{"title":"Tuning-Free Adaptive Style Incorporation for Structure-Consistent Text-Driven Style Transfer","abstract":"In this work, we target the task of text-driven style transfer in the context of text-to-image (T2I) diffusion models. The main challenge is consistent structure preservation while enabling effective style transfer effects. The past approaches in this field directly concatenate the content and style prompts for a prompt-level style injection, leading to unavoidable structure distortions. In this work, we propose a no","authors":["Ge, Yanqi","Liu, Jiaqi","Fan, Qingnan","Jiang, Xi","Huang, Ye","Qin, Shuai"],"url":"https://arxiv.org/abs/2404.06835","arxiv_id":"2404.06835","published":"2026-01-16"},{"title":"SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning","abstract":"In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are trained and communicated each ro","authors":["Ohib, Riyasat","Thapaliya, Bishal","Dziugaite, Gintare Karolina","Liu, Jingyu","Calhoun, Vince","Plis, Sergey"],"url":"https://arxiv.org/abs/2405.09037","arxiv_id":"2405.09037","published":"2026-01-16"},{"title":"Jump-teaching: Combating Sample Selection Bias via Temporal Disagreement","abstract":"Sample selection is a straightforward technique to combat noisy labels, aiming to prevent mislabeled samples from degrading the robustness of neural networks. However, existing methods mitigate compounding selection bias either by leveraging dual-network disagreement or additional forward propagations, leading to multiplied training overhead. To address this challenge, we introduce $\\textit{Jump-teaching}$, an effici","authors":["Ji, Kangye","Cheng, Fei","Wang, Zeqing","Zhang, Qichang","Huang, Bohu"],"url":"https://arxiv.org/abs/2405.17137","arxiv_id":"2405.17137","published":"2026-01-16"},{"title":"Leveraging Open-Source Large Language Models for encoding Social Determinants of Health using an Intelligent Router","abstract":"Social Determinants of Health (SDOH), also known as Health-Related Social Needs (HSRN), play a significant role in patient health outcomes. The Centers for Disease Control and Prevention (CDC) introduced a subset of ICD-10 codes called Z-codes to recognize and measure SDOH. However, Z-codes are infrequently coded in a patient's Electronic Health Record (EHR), and instead, in many cases, need to be inferred from c","authors":["Goel, Akul","Hari, Surya Narayanan","Waltman, Belinda","Thomson, Matt"],"url":"https://arxiv.org/abs/2405.19631","arxiv_id":"2405.19631","published":"2026-01-16"},{"title":"Instance-level quantitative saliency in multiple sclerosis lesion segmentation","abstract":"Explainable artificial intelligence (XAI) methods have been proposed to interpret model decisions in classification and, more recently, in semantic segmentation. However, instance-level XAI for semantic segmentation, namely explanations focused on a single object among multiple instances of the same class, remains largely unexplored. Such explanations are particularly important in multi-lesional diseases to understan","authors":["Spagnolo, Federico","Molchanova, Nataliia","Cuadra, Meritxell Bach","Pineda, Mario Ocampo","Melie-Garcia, Lester","Granziera, Cristina"],"url":"https://arxiv.org/abs/2406.09335","arxiv_id":"2406.09335","published":"2026-01-16"},{"title":"AITTI: Learning Adaptive Inclusive Token for Text-to-Image Generation","abstract":"Despite the high-quality results of text-to-image generation, stereotypical biases have been spotted in their generated contents, compromising the fairness of generative models. In this work, we propose to learn adaptive inclusive tokens to shift the attribute distribution of the final generative outputs. Unlike existing de-biasing approaches, our method requires neither explicit attribute specification nor prior kno","authors":["Hou, Xinyu","Li, Xiaoming","Loy, Chen Change"],"url":"https://arxiv.org/abs/2406.12805","arxiv_id":"2406.12805","published":"2026-01-16"},{"title":"Machine Unlearning Fails to Remove Data Poisoning Attacks","abstract":"We revisit the efficacy of several practical methods for approximate machine unlearning developed for large-scale deep learning. In addition to complying with data deletion requests, one often-cited potential application for unlearning methods is to remove the effects of poisoned data. We experimentally demonstrate that, while existing unlearning methods have been demonstrated to be effective in a number of settings,","authors":["Pawelczyk, Martin","Di, Jimmy Z.","Lu, Yiwei","Kamath, Gautam","Sekhari, Ayush","Neel, Seth"],"url":"https://arxiv.org/abs/2406.17216","arxiv_id":"2406.17216","published":"2026-01-16"},{"title":"Mathematical theory of deep learning","abstract":"This book provides an introduction to the mathematical analysis of deep learning. It covers fundamental results in approximation theory, optimization theory, and statistical learning theory, which are the three main pillars of deep neural network theory. Serving as a guide for students and researchers in mathematics and related fields, the book aims to equip readers with foundational knowledge on the topic. It priori","authors":["Petersen, Philipp","Zech, Jakob"],"url":"https://arxiv.org/abs/2407.18384","arxiv_id":"2407.18384","published":"2026-01-16"},{"title":"Fairness Definitions in Language Models Explained","abstract":"Language Models (LMs) have demonstrated exceptional performance across various Natural Language Processing (NLP) tasks. Despite these advancements, LMs can inherit and amplify societal biases related to sensitive attributes such as gender and race, limiting their adoption in real-world applications. Therefore, fairness has been extensively explored in LMs, leading to the proposal of various fairness notions. However,","authors":["Yin, Zhipeng","Wang, Zichong","Palikhe, Avash","Zhang, Wenbin"],"url":"https://arxiv.org/abs/2407.18454","arxiv_id":"2407.18454","published":"2026-01-16"},{"title":"Persistent Homology via Ellipsoids","abstract":"Persistent homology is one of the most popular methods in topological data analysis. An initial step in its use involves constructing a nested sequence of simplicial complexes. There is an abundance of different complexes to choose from, with Čech, Rips, alpha, and witness complexes being popular choices. In this manuscript, we build a novel type of geometrically informed simplicial complex, called a Rips-type ellips","authors":["Canova, Niklas","Kališnik, Sara","Moser, Aaron","Rieck, Bastian","Žegarac, Ana"],"url":"https://arxiv.org/abs/2408.11450","arxiv_id":"2408.11450","published":"2026-01-16"},{"title":"Deep learning-based ecological analysis of camera trap images is impacted by training data quality and quantity","abstract":"Large image collections generated from camera traps offer valuable insights into species richness, occupancy, and activity patterns, significantly aiding biodiversity monitoring. However, the manual processing of these datasets is time-consuming, hindering analytical processes. To address this, deep neural networks have been widely adopted to automate image labelling, but the impact of classification error on key eco","authors":["Bevan, Peggy A.","Pantazis, Omiros","Pringle, Holly","Ferreira, Guilherme Braga","Ingram, Daniel J.","Madsen, Emily"],"url":"https://arxiv.org/abs/2408.14348","arxiv_id":"2408.14348","published":"2026-01-16"},{"title":"Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors","abstract":"Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective therapies by tailoring treatment based on biomarkers, beyond cancer type. Unstructured data that requires manual curation hinders efficient use of biomarker profiling for th","authors":["Lammert, Jacqueline","Pfarr, Nicole","Kuligin, Leonid","Mathes, Sonja","Dreyer, Tobias","Modersohn, Luise"],"url":"https://arxiv.org/abs/2409.00544","arxiv_id":"2409.00544","published":"2026-01-16"},{"title":"Machine Learning and Theory Ladenness -- A Phenomenological Account","abstract":"We provide an analysis of theory ladenness in machine learning in science, where "theory", that we call "domain theory", refers to the domain knowledge of the scientific discipline where ML is used. By constructing an account of ML models based on a comparison with phenomenological models, we show, against recent trends in philosophy of science, that ML model-building is mostly indifferent to domain t","authors":["Termine, Alberto","Ratti, Emanuele","Facchini, Alessandro"],"url":"https://arxiv.org/abs/2409.11277","arxiv_id":"2409.11277","published":"2026-01-16"},{"title":"Debiased Orthogonal Boundary-Driven Efficient Noise Mitigation","abstract":"Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical, as obtaining entirely clean or human-annotated samples for large-scale pre-training tasks is often impractical. Nonetheless, existing noise mitigation methods often encounter limitations in practical applications due to their task-specific design, model dependency, and significant computational overhead. In th","authors":["Li, Hao","Gu, Jiayang","Song, Jingkuan","Zhang, An","Gao, Lianli"],"url":"https://arxiv.org/abs/2410.01944","arxiv_id":"2410.01944","published":"2026-01-16"},{"title":"Permissive Information-Flow Analysis for Large Language Models","abstract":"Large Language Models (LLMs) are rapidly becoming commodity components of larger software systems. This poses natural security and privacy problems: poisoned data retrieved from one component can change the model's behavior and compromise the entire system, including coercing the model to spread confidential data to untrusted components. One promising approach is to tackle this problem at the system level via dyn","authors":["Siddiqui, Shoaib Ahmed","Gaonkar, Radhika","Köpf, Boris","Krueger, David","Paverd, Andrew","Salem, Ahmed"],"url":"https://arxiv.org/abs/2410.03055","arxiv_id":"2410.03055","published":"2026-01-16"},{"title":"CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning","abstract":"Mathematical reasoning remains a significant challenge for large language models (LLMs), despite progress in prompting techniques such as Chain-of-Thought (CoT). We present **Chain of Mathematically Annotated Thought (CoMAT)**, which enhances reasoning through two stages: *Symbolic Conversion* (converting natural language queries into symbolic form) and *Reasoning Execution* (deriving answers from symbolic representa","authors":["Leang, Joshua Ong Jun","Gema, Aryo Pradipta","Cohen, Shay B."],"url":"https://arxiv.org/abs/2410.10336","arxiv_id":"2410.10336","published":"2026-01-16"},{"title":"Topology meets Machine Learning: An Introduction using the Euler Characteristic Transform","abstract":"This overview article makes the case for how topological concepts can enrich research in machine learning. Using the Euler Characteristic Transform (ECT), a geometrical-topological invariant, as a running example, I present different use cases that result in more efficient models for analyzing point clouds, graphs, and meshes. Moreover, I outline a vision for how topological concepts could be used in the future, comp","authors":["Rieck, Bastian"],"url":"https://arxiv.org/abs/2410.17760","arxiv_id":"2410.17760","published":"2026-01-16"},{"title":"The Hatching-Box: A Novel System for Automated Monitoring and Quantification of Drosophila melanogaster Developmental Behavior","abstract":"In this paper we propose the Hatching-Box, a novel imaging and analysis system to automatically monitor and quantify the developmental behavior of Drosophila in standard rearing vials and during regular rearing routines, rendering explicit experiments obsolete. This is achieved by combining custom tailored imaging hardware with dedicated detection and tracking algorithms, enabling the quantification of larvae, filled","authors":["Bigge, Julian","Ogueta, Maite","Garcia, Luis","Risse, Benjamin"],"url":"https://arxiv.org/abs/2411.15390","arxiv_id":"2411.15390","published":"2026-01-16"},{"title":"VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction","abstract":"In-Context Operator Networks (ICONs) have demonstrated the ability to learn operators across diverse partial differential equations using few-shot, in-context learning. However, existing ICONs process each spatial point as an individual token, severely limiting computational efficiency when handling dense data in higher spatial dimensions. We propose Vision In-Context Operator Networks (VICON), which integrates visio","authors":["Cao, Yadi","Liu, Yuxuan","Yang, Liu","Yu, Rose","Schaeffer, Hayden","Osher, Stanley"],"url":"https://arxiv.org/abs/2411.16063","arxiv_id":"2411.16063","published":"2026-01-16"}],"history":{"events":[{"year":2018,"text":"In Mrauk U, Myanmar, police fired into a crowd protesting the ban of an event to mark the anniversary of the end of the Kingdom of Mrauk U, resulting in seven deaths and twelve injuries.","url":"https://en.wikipedia.org/wiki/Mrauk_U"},{"year":2017,"text":"Turkish Airlines Flight 6491 crashed in Manas International Airport (aftermath pictured) after the flight overflew the entire runway, which resulted in 39 deaths.","url":"https://en.wikipedia.org/wiki/Turkish_Airlines_Flight_6491"},{"year":2016,"text":"After gunmen took hostages the previous night at a restaurant in Ouagadougou, Burkina Faso, government commandos stormed the premises to bring the situation to an end.","url":"https://en.wikipedia.org/wiki/Ouagadougou"},{"year":1964,"text":"The musical Hello, Dolly! opened at the St. James Theatre on Broadway, and went on to win ten Tony Awards, a record that stood for 37 years.","url":"https://en.wikipedia.org/wiki/Hello%2C_Dolly!_(musical)"},{"year":1945,"text":"World War II: Adolf Hitler and his staff moved into the Führerbunker in Berlin, where he would eventually commit suicide.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1942,"text":"TWA Flight 3 crashed into Potosi Mountain in Nevada, killing actress Carole Lombard and all of the other 21 people on board.","url":"https://en.wikipedia.org/wiki/TWA_Flight_3"},{"year":1942,"text":"World War II: During the Battle of Bataan, U.S. Army sergeant Jose Calugas (pictured) organized a squad of volunteers to man an artillery position under heavy fire, an action that later earned him the Medal of Honor.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1920,"text":"The League of Nations, the first worldwide intergovernmental organization with a focus on peace and security, held its first council meeting in Paris.","url":"https://en.wikipedia.org/wiki/League_of_Nations"},{"year":1905,"text":"Despite being blind in one eye, ice hockey player Frank McGee (pictured) set the record for most goals in a Stanley Cup game when he scored 14 against the Dawson City Nuggets.","url":"https://en.wikipedia.org/wiki/Ice_hockey"},{"year":1883,"text":"The Pendleton Civil Service Reform Act, establishing the United States Civil Service, was enacted by the U.S. Congress.","url":"https://en.wikipedia.org/wiki/Pendleton_Civil_Service_Reform_Act"},{"year":1862,"text":"A pumping engine at a colliery in New Hartley, England, broke and fell down the shaft, trapping miners below and resulting in 204 deaths.","url":"https://en.wikipedia.org/wiki/Coal_mining"},{"year":1809,"text":"Peninsular War: French forces under Jean-de-Dieu Soult attacked the British amphibious evacuation under Sir John Moore at Corunna in Galicia, Spain.","url":"https://en.wikipedia.org/wiki/Peninsular_War"},{"year":1780,"text":"American Revolutionary War: The Royal Navy gained their first major naval victory over their European enemies in the war when they defeated a Spanish squadron in the Battle of Cape St. Vincent.","url":"https://en.wikipedia.org/wiki/American_Revolutionary_War"},{"year":1537,"text":"Sir Francis Bigod began an armed rebellion against King Henry VIII and the English Parliament.","url":"https://en.wikipedia.org/wiki/Francis_Bigod"},{"year":1275,"text":"Eleanor of Provence received permission from her son King Edward I of England to expel Jews from the towns of Worcester, Marlborough, Cambridge and Gloucester.","url":"https://en.wikipedia.org/wiki/Eleanor_of_Provence"},{"year":-27,"text":"Gaius Octavianus was given the titles Augustus and Princeps by the Roman Senate when he became the first Roman emperor.","url":"https://en.wikipedia.org/wiki/Augustus"}],"births":[{"year":2000,"text":"Andrew Nembhard, Canadian basketball player","url":"https://en.wikipedia.org/wiki/Andrew_Nembhard"},{"year":1998,"text":"Boo Seung-kwan, South Korean singer","url":"https://en.wikipedia.org/wiki/Seungkwan"},{"year":1996,"text":"Jennie, South Korean singer","url":"https://en.wikipedia.org/wiki/Jennie_(singer)"},{"year":1996,"text":"Zhou Qi, Chinese basketball player","url":"https://en.wikipedia.org/wiki/Zhou_Qi"},{"year":1995,"text":"Jonathan Allen, American football player","url":"https://en.wikipedia.org/wiki/Jonathan_Allen"},{"year":1995,"text":"Mikaela Turik, Australian-Canadian cricketer","url":"https://en.wikipedia.org/wiki/Mikaela_Turik"},{"year":1995,"text":"Tre'Davious White, American football player","url":"https://en.wikipedia.org/wiki/Tre'Davious_White"},{"year":1994,"text":"Mikko Lehtonen, Finnish ice hockey player","url":"https://en.wikipedia.org/wiki/Mikko_Lehtonen_(ice_hockey%2C_born_1994)"}],"deaths":[{"year":2025,"text":"David Lynch, American filmmaker and actor (born 1946)","url":"https://en.wikipedia.org/wiki/David_Lynch"},{"year":2025,"text":"Dame Joan Plowright, English actress (born 1929)","url":"https://en.wikipedia.org/wiki/Joan_Plowright"},{"year":2025,"text":"Bob Uecker, American professional baseball catcher and sportscaster (born 1934)","url":"https://en.wikipedia.org/wiki/Bob_Uecker"},{"year":2022,"text":"Ibrahim Boubacar Keita, Former Malian President (born 1945)","url":"https://en.wikipedia.org/wiki/Ibrahim_Boubacar_Ke%C3%AFta"},{"year":2021,"text":"Pedro Trebbau, German-born Venezuelan zoologist (born 1929)","url":"https://en.wikipedia.org/wiki/Pedro_Trebbau"},{"year":2021,"text":"Chris Cramer, British journalist (born 1948)","url":"https://en.wikipedia.org/wiki/Chris_Cramer"},{"year":2021,"text":"Phil Spector, American record producer, songwriter (born 1939)","url":"https://en.wikipedia.org/wiki/Phil_Spector"},{"year":2020,"text":"Christopher Tolkien, British academic and editor (born 1924)","url":"https://en.wikipedia.org/wiki/Christopher_Tolkien"}]},"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."}