{"date":"2026-01-01","schema_version":1,"captured_at":"2026-08-25T23:21:36.646Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Tatiana Schlossberg","views":406028,"rank":3,"extract":"Tatiana Celia Kennedy Schlossberg was an American environmental journalist and author. 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Our goal is to identify the optimal approximation-smoothness tradeoffs for different measures of approximation and smoothness. This leads to novel soft-max functions, each of which is optimal for a differ","authors":["Epasto, Alessandro","Mahdian, Mohammad","Mirrokni, Vahab","Zampetakis, Manolis"],"url":"https://arxiv.org/abs/2010.11450","arxiv_id":"2010.11450","published":"2026-01-01"},{"title":"Efficient Active Learning with Abstention","abstract":"The goal of active learning is to achieve the same accuracy achievable by passive learning, while using much fewer labels. Exponential savings in terms of label complexity have been proved in very special cases, but fundamental lower bounds show that such improvements are impossible in general. This suggests a need to explore alternative goals for active learning. Learning with abstention is one such alternative. In ","authors":["Zhu, Yinglun","Nowak, Robert"],"url":"https://arxiv.org/abs/2204.00043","arxiv_id":"2204.00043","published":"2026-01-01"},{"title":"Effective Online Exam Proctoring by Combining Lightweight Face Detection and Deep Recognition","abstract":"Online exams conducted via video conferencing platforms such as Zoom have become widespread, yet ensuring exam integrity remains challenging due to the difficulty of monitoring multiple video feeds in real time. We present iExam, an online exam proctoring and analysis system that combines lightweight real-time face detection with deep face recognition for postexam analysis. iExam assists invigilators by monitoring st","authors":["Yang, Xu","Zhong, Juantao","Wu, Daoyuan","Yi, Xiao","Lee, Jimmy H. M.","Lee, Tan"],"url":"https://arxiv.org/abs/2206.13356","arxiv_id":"2206.13356","published":"2026-01-01"},{"title":"Active Learning with Neural Networks: Insights from Nonparametric Statistics","abstract":"Deep neural networks have great representation power, but typically require large numbers of training examples. This motivates deep active learning methods that can significantly reduce the amount of labeled training data. Empirical successes of deep active learning have been recently reported in the literature, however, rigorous label complexity guarantees of deep active learning have remained elusive. This constitu","authors":["Zhu, Yinglun","Nowak, Robert"],"url":"https://arxiv.org/abs/2210.08367","arxiv_id":"2210.08367","published":"2026-01-01"},{"title":"The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing","abstract":"We propose $\\textsf{ScaledGD($λ$)}$, a preconditioned gradient descent method to tackle the low-rank matrix sensing problem when the true rank is unknown, and when the matrix is possibly ill-conditioned. Using overparametrized factor representations, $\\textsf{ScaledGD($λ$)}$ starts from a small random initialization, and proceeds by gradient descent with a specific form of damped preconditioning to combat bad curvatu","authors":["Xu, Xingyu","Shen, Yandi","Chi, Yuejie","Ma, Cong"],"url":"https://arxiv.org/abs/2302.01186","arxiv_id":"2302.01186","published":"2026-01-01"},{"title":"CascadeNS: Confidence-Cascaded Neurosymbolic Model for Sarcasm Detection","abstract":"Sarcasm detection in product reviews requires balancing domain-specific symbolic pattern recognition with deep semantic understanding. Symbolic representations capture explicit linguistic phenomena that are often decisive for sarcasm detection. Existing work either favors interpretable symbolic representation or semantic neural modeling, but rarely achieves both effectively. Prior hybrid methods typically combine the","authors":["Mane, Swapnil","Khatavkar, Vaibhav"],"url":"https://arxiv.org/abs/2304.01424","arxiv_id":"2304.01424","published":"2026-01-01"},{"title":"Pulse shape discrimination based on the Tempotron: a powerful classifier on GPU","abstract":"This study utilized the Tempotron, a robust classifier based on a third-generation neural network model, for pulse shape discrimination. By eliminating the need for manual feature extraction, the Tempotron model can process pulse signals directly, generating discrimination results based on prior knowledge. The study performed experiments using GPU acceleration, resulting in over 500 times faster compared to the CPU-b","authors":["Liu, Haoran","Li, Peng","Liu, Ming-Zhe","Wang, Kai-Ming","Zuo, Zhuo","Liu, Bing-Qi"],"url":"https://arxiv.org/abs/2305.18205","arxiv_id":"2305.18205","published":"2026-01-01"},{"title":"HiGen: Hierarchical Graph Generative Networks","abstract":"Most real-world graphs exhibit a hierarchical structure, which is often overlooked by existing graph generation methods. To address this limitation, we propose a novel graph generative network that captures the hierarchical nature of graphs and successively generates the graph sub-structures in a coarse-to-fine fashion. At each level of hierarchy, this model generates communities in parallel, followed by the predicti","authors":["Karami, Mahdi"],"url":"https://arxiv.org/abs/2305.19337","arxiv_id":"2305.19337","published":"2026-01-01"},{"title":"HIDFlowNet: A Flow-Based Deep Network for Hyperspectral Image Denoising","abstract":"Hyperspectral image (HSI) denoising is essentially ill-posed since a noisy HSI can be degraded from multiple clean HSIs. However, existing deep learning (DL)-based approaches only restore one clean HSI from the given noisy HSI with a deterministic mapping, thus ignoring the ill-posed issue and always resulting in an over-smoothing problem. Additionally, these DL-based methods often neglect that noise is part of the h","authors":["Wang, Qizhou","Pang, Li","Cao, Xiangyong","Tian, Zhiqiang","Meng, Deyu"],"url":"https://arxiv.org/abs/2306.17797","arxiv_id":"2306.17797","published":"2026-01-01"},{"title":"Generative Modelling of Lévy Area for High Order SDE Simulation","abstract":"It is well understood that, when numerically simulating SDEs with general noise, achieving a strong convergence rate better than $O(\\sqrt{h})$ (where h is the step size) requires the use of certain iterated integrals of Brownian motion, commonly referred to as its "Lévy areas". However, these stochastic integrals are difficult to simulate due to their non-Gaussian nature and for a $d$-dimensional Brownian mot","authors":["Jelinčič, Andraž","Tao, Jiajie","Turner, William F.","Cass, Thomas","Foster, James","Ni, Hao"],"url":"https://arxiv.org/abs/2308.02452","arxiv_id":"2308.02452","published":"2026-01-01"},{"title":"Content-based Recommendation Engine for Video Streaming Platform","abstract":"Recommendation engines suggest content, products, or services to the user by using machine learning algorithms. This paper proposes a content-based recommendation engine that provides personalized video suggestions based on users' previous interactions and preferences. The engine uses TF-IDF (Term Frequency-Inverse Document Frequency) text vectorization technique to evaluate the relevance of words in video descri","authors":["Khadka, Puskal","Lamichhane, Prabhav"],"url":"https://arxiv.org/abs/2308.08406","arxiv_id":"2308.08406","published":"2026-01-01"},{"title":"Spectrum-Aware Debiasing: A Modern Inference Framework with Applications to Principal Components Regression","abstract":"Debiasing is a fundamental concept in high-dimensional statistics. While degrees-of-freedom adjustment is the state-of-the-art technique in high-dimensional linear regression, it is limited to i.i.d. samples and sub-Gaussian covariates. These constraints hinder its broader practical use. Here, we introduce Spectrum-Aware Debiasing--a novel method for high-dimensional regression. Our approach applies to problems with ","authors":["Li, Yufan","Sur, Pragya"],"url":"https://arxiv.org/abs/2309.07810","arxiv_id":"2309.07810","published":"2026-01-01"},{"title":"Multi-fidelity Bayesian Optimization: A Review","abstract":"Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorporating physical and mathematical understandings of the problems, saving resources, addressing exploitation-exploration trade-off, considering uncertainty, and processing parallel computing. The increas","authors":["Do, Bach","Zhang, Ruda"],"url":"https://arxiv.org/abs/2311.13050","arxiv_id":"2311.13050","published":"2026-01-01"},{"title":"Are Ensembles Getting Better all the Time?","abstract":"Ensemble methods combine the predictions of several base models. We study whether or not including more models always improves their average performance. This question depends on the kind of ensemble considered, as well as the predictive metric chosen. We focus on situations where all members of the ensemble are a priori expected to perform equally well, which is the case of several popular methods such as random for","authors":["Mattei, Pierre-Alexandre","Garreau, Damien"],"url":"https://arxiv.org/abs/2311.17885","arxiv_id":"2311.17885","published":"2026-01-01"},{"title":"Distribution-Dependent Rates for Multi-Distribution Learning","abstract":"To address the needs of modeling uncertainty in sensitive machine learning applications, the setup of distributionally robust optimization (DRO) seeks good performance uniformly across a variety of tasks. The recent multi-distribution learning (MDL) framework tackles this objective in a dynamic interaction with the environment, where the learner has sampling access to each target distribution. Drawing inspiration fro","authors":["Hanashiro, Rafael","Jaillet, Patrick"],"url":"https://arxiv.org/abs/2312.13130","arxiv_id":"2312.13130","published":"2026-01-01"},{"title":"Stochastic Gradient Descent for Nonparametric Additive Regression","abstract":"This paper introduces an iterative algorithm for training nonparametric additive models that enjoys favorable memory storage and computational requirements. The algorithm can be viewed as the functional counterpart of stochastic gradient descent, applied to the coefficients of a truncated basis expansion of the component functions. We show that the resulting estimator satisfies an oracle inequality that allows for mo","authors":["Chen, Xin","Klusowski, Jason M."],"url":"https://arxiv.org/abs/2401.00691","arxiv_id":"2401.00691","published":"2026-01-01"},{"title":"Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling","abstract":"In the rapidly evolving field of deep learning, the demand for models that are both expressive and computationally efficient has never been more critical. This paper introduces Orchid, a novel architecture designed to address the quadratic complexity of traditional attention mechanisms without compromising the ability to capture long-range dependencies and in-context learning. At the core of this architecture lies a ","authors":["Karami, Mahdi","Ghodsi, Ali"],"url":"https://arxiv.org/abs/2402.18508","arxiv_id":"2402.18508","published":"2026-01-01"},{"title":"Structuring Concept Space with the Musical Circle of Fifths by Utilizing Music Grammar Based Activations","abstract":"We propose a neural coding framework harmonic toroidal codes in which abstract cognitive operations are implemented through dynamical activity on manifolds derived from music theoretic structures.","authors":["Moyo, Tofara","Chiurunge, Panashe"],"url":"https://arxiv.org/abs/2403.00790","arxiv_id":"2403.00790","published":"2026-01-01"},{"title":"Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons","abstract":"When training neural networks, dying neurons -- units becoming inactive or saturated -- are traditionally seen as harmful. This paper sheds new light on this phenomenon. By exploring the impact of various hyperparameter configurations on dying neurons during training, we gather insights on how to improve upon sparse training approaches to pruning. We introduce Demon Pruning (DemP), a method that controls the prolifer","authors":["Dufort-Labbé, Simon","D'Oro, Pierluca","Nikishin, Evgenii","Pascanu, Razvan","Bacon, Pierre-Luc","Baratin, Aristide"],"url":"https://arxiv.org/abs/2403.07688","arxiv_id":"2403.07688","published":"2026-01-01"},{"title":"Matching Semantically Similar Non-Identical Objects","abstract":"Not identical but similar objects are ubiquitous in our world, ranging from four-legged animals such as dogs and cats to cars of different models and flowers of various colors. This study addresses a novel task of matching such non-identical objects at the pixel level. We propose a weighting scheme of descriptors, Semantic Enhancement Weighting (SEW), that incorporates semantic information from object detectors into ","authors":["Marumo, Yusuke","Kawamoto, Kazuhiko","Tanaka, Satomi","Hirano, Shigenobu","Kera, Hiroshi"],"url":"https://arxiv.org/abs/2403.08227","arxiv_id":"2403.08227","published":"2026-01-01"},{"title":"Random-coupled Neural Network","abstract":"Improving the efficiency of current neural networks and modeling them in biological neural systems have become popular research directions in recent years. Pulse-coupled neural network (PCNN) is a well applicated model for imitating the computation characteristics of the human brain in computer vision and neural network fields. However, differences between the PCNN and biological neural systems remain: limited neural","authors":["Liu, Haoran","Liu, Mingzhe","Li, Peng","Wu, Jiahui","Jiang, Xin","Zuo, Zhuo"],"url":"https://arxiv.org/abs/2403.17512","arxiv_id":"2403.17512","published":"2026-01-01"},{"title":"Reconstructing Hand-Held Objects in 3D from Images and Videos","abstract":"Objects manipulated by the hand (i.e., manipulanda) are particularly challenging to reconstruct from Internet videos. Not only does the hand occlude much of the object, but also the object is often only visible in a small number of image pixels. At the same time, two strong anchors emerge in this setting: (1) estimated 3D hands help disambiguate the location and scale of the object, and (2) the set of manipulanda is ","authors":["Wu, Jane","Pavlakos, Georgios","Gkioxari, Georgia","Malik, Jitendra"],"url":"https://arxiv.org/abs/2404.06507","arxiv_id":"2404.06507","published":"2026-01-01"},{"title":"FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol","abstract":"The NOSTR is a communication protocol for the social web, based on the w3c websockets standard. Although it is still in its infancy, it is well known as a social media protocol, with thousands of trusted users and multiple user interfaces, offering a unique experience and enormous capabilities. To name a few, the NOSTR applications include but are not limited to direct messaging, file sharing, audio/video streaming, ","authors":["Nikolakakis, Konstantinos E.","Chantzialexiou, George","Kalogerias, Dionysis"],"url":"https://arxiv.org/abs/2404.15834","arxiv_id":"2404.15834","published":"2026-01-01"},{"title":"ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning","abstract":"We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the","authors":["Pak, Peter Myung-Won","Ogoke, Francis","Polonsky, Andrew","Garland, Anthony","Bolintineanu, Dan S.","Moser, Dan R."],"url":"https://arxiv.org/abs/2404.16882","arxiv_id":"2404.16882","published":"2026-01-01"},{"title":"Symmetric Linear Bandits with Hidden Symmetry","abstract":"High-dimensional linear bandits with low-dimensional structure have received considerable attention in recent studies due to their practical significance. The most common structure in the literature is sparsity. However, it may not be available in practice. Symmetry, where the reward is invariant under certain groups of transformations on the set of arms, is another important inductive bias in the high-dimensional ca","authors":["Tran, Nam Phuong","Ta, The Anh","Mandal, Debmalya","Tran-Thanh, Long"],"url":"https://arxiv.org/abs/2405.13899","arxiv_id":"2405.13899","published":"2026-01-01"}],"history":{"events":[{"year":2019,"text":"The NASA space probe New Horizons flew by the trans-Neptunian object Arrokoth, making it the farthest object visited by a spacecraft.","url":"https://en.wikipedia.org/wiki/NASA"},{"year":2011,"text":"A suicide bombing took place outside a Coptic Orthodox Church in Alexandria, Egypt, following a New Year service, killing 23 people.","url":"https://en.wikipedia.org/wiki/2011_Alexandria_bombing"},{"year":2010,"text":"A suicide bomber killed 105 spectators at a volleyball game in the Lakki Marwat District of Pakistan.","url":"https://en.wikipedia.org/wiki/2010_Lakki_Marwat_suicide_bombing"},{"year":2007,"text":"Adam Air Flight 574 crashed into the sea off Polewali, Indonesia, killing all 102 people on board, when the pilots inadvertently disconnected the autopilot.","url":"https://en.wikipedia.org/wiki/Adam_Air_Flight_574"},{"year":2005,"text":"Jeanna Giese, the first known rabies survivor who did not receive the vaccine, was discharged from Children's Wisconsin.","url":"https://en.wikipedia.org/wiki/Jeanna_Giese"},{"year":1998,"text":"Argentinian physicist Juan Maldacena published a landmark paper initiating the study of AdS/CFT correspondence, which links string theory and quantum gravity.","url":"https://en.wikipedia.org/wiki/Juan_Maldacena"},{"year":1995,"text":"The World Trade Organization, the international organization designed to supervise and liberalize international trade, came into being, replacing the General Agreement on Tariffs and Trade.","url":"https://en.wikipedia.org/wiki/World_Trade_Organization"},{"year":1994,"text":"The revolutionary leftist Zapatista Army of National Liberation initiated twelve days of armed conflict in the Mexican state of Chiapas.","url":"https://en.wikipedia.org/wiki/Far-left_politics"},{"year":1978,"text":"Air India Flight 855 (aircraft pictured) crashed into the Arabian Sea seconds after take-off, killing all 213 people.","url":"https://en.wikipedia.org/wiki/Air_India_Flight_855"},{"year":1965,"text":"The People's Democratic Party of Afghanistan, which later helped the country become a republic, was founded.","url":"https://en.wikipedia.org/wiki/People's_Democratic_Party_of_Afghanistan"},{"year":1960,"text":"Three men were killed and two wounded in a mass shooting at a public house in Sheffield, England.","url":"https://en.wikipedia.org/wiki/East_House_mass_shooting"},{"year":1957,"text":"The revised Thai criminal code came into force, strengthening the law on lèse-majesté in Thailand to include insult and treating it as a crime against national security.","url":"https://en.wikipedia.org/wiki/L%C3%A8se-majest%C3%A9_in_Thailand"},{"year":1948,"text":"British Railways came into existence when the \"Big Four\" railway companies were nationalised.","url":"https://en.wikipedia.org/wiki/British_Rail"},{"year":1945,"text":"World War II: In retaliation for the massacre of captured Americans by Waffen SS soldiers, U.S. Army personnel killed an estimated 80 Wehrmacht prisoners near Chenogne, Belgium.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1945,"text":"Second World War: The Luftwaffe launched Operation Bodenplatte in an attempt to cripple Allied air forces in the Low Countries.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1928,"text":"Joseph Stalin's personal secretary, Boris Bazhanov, crossed the Iranian border and defected from the Soviet Union.","url":"https://en.wikipedia.org/wiki/Joseph_Stalin"},{"year":1926,"text":"Ireland's first broadcaster, 2RN, began broadcasting.","url":"https://en.wikipedia.org/wiki/2RN"},{"year":1915,"text":"Russia withdraws from its occupation of northwestern Iran, enabling a subsequent Ottoman invasion and massacres of Assyrians and Armenians.","url":"https://en.wikipedia.org/wiki/Sayfo"}],"births":[{"year":2007,"text":"Ian Subiabre, Argentine footballer","url":"https://en.wikipedia.org/wiki/Ian_Subiabre"},{"year":2004,"text":"Lamine Camara, Senegalese footballer","url":"https://en.wikipedia.org/wiki/Lamine_Camara"},{"year":2003,"text":"Daria Trubnikova, Russian rhythmic gymnast","url":"https://en.wikipedia.org/wiki/Daria_Trubnikova"},{"year":2002,"text":"Simon Adingra, Ivorian footballer","url":"https://en.wikipedia.org/wiki/Simon_Adingra"},{"year":2001,"text":"Angourie Rice, Australian actress","url":"https://en.wikipedia.org/wiki/Angourie_Rice"},{"year":2001,"text":"Winter, South Korean singer","url":"https://en.wikipedia.org/wiki/Winter_(singer)"},{"year":2000,"text":"Nicolas Kühn, German footballer","url":"https://en.wikipedia.org/wiki/Nicolas_K%C3%BChn"},{"year":2000,"text":"Ice Spice, American rapper","url":"https://en.wikipedia.org/wiki/Ice_Spice"}],"deaths":[{"year":2025,"text":"David Lodge, English author and critic (born 1935)","url":"https://en.wikipedia.org/wiki/David_Lodge_(author)"},{"year":2025,"text":"Chad Morgan, Australian musician (born 1933)","url":"https://en.wikipedia.org/wiki/Chad_Morgan"},{"year":2025,"text":"Wayne Osmond, American singer-songwriter and actor (born 1951)","url":"https://en.wikipedia.org/wiki/Wayne_Osmond"},{"year":2024,"text":"Lynja, American celebrity chef and YouTuber (born 1956)","url":"https://en.wikipedia.org/wiki/Lynja"},{"year":2023,"text":"Fred White, American musician and songwriter (born 1955)","url":"https://en.wikipedia.org/wiki/Earth%2C_Wind_%26_Fire"},{"year":2022,"text":"Gary Burgess, British broadcaster and journalist (born 1975)","url":"https://en.wikipedia.org/wiki/Gary_Burgess"},{"year":2022,"text":"Dan Reeves, American football player and coach (born 1944)","url":"https://en.wikipedia.org/wiki/Dan_Reeves"},{"year":2021,"text":"Carlos do Carmo, Portuguese fado singer (born 1939)","url":"https://en.wikipedia.org/wiki/Carlos_do_Carmo"}]},"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."}