{"date":"2026-04-17","schema_version":1,"captured_at":"2026-08-25T23:37:13.821Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":17},"attention":[{"title":"Justin Fairfax","views":639167,"rank":3,"extract":"Justin Edward Fairfax was an American lawyer and politician who served as the 41st lieutenant governor of Virginia from 2018 to 2022. A member of the Democratic Party, he was the second African American to be elected to statewide office in Virginia, after Doug","url":"https://en.wikipedia.org/wiki/Justin_Fairfax","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/5/5d/Justin_Fairfax_20.jpg/330px-Justin_Fairfax_20.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Alex Manninger","views":268502,"rank":5,"extract":"Alexander Manninger was an Austrian footballer who played as a goalkeeper. 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Network Beneath This Cemetery","points":183,"comments":31,"url":"https://www.discovermagazine.com/up-to-8-million-bees-are-living-in-an-underground-network-beneath-this-cemetery-48977","discussion":"https://news.ycombinator.com/item?id=47810101"}],"research":[{"title":"Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements","abstract":"We present a novel semantic framework for modeling linguistic expressions of generalization---generic, habitual, and episodic statements---as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of type-level and token","authors":["Govindarajan, Venkata Subrahmanyan","Van Durme, Benjamin","White, Aaron Steven"],"url":"https://arxiv.org/abs/1901.11429","arxiv_id":"1901.11429","published":"2026-04-17"},{"title":"Help! Need Advice on Identifying Advice","abstract":"Humans use language to accomplish a wide variety of tasks - asking for and giving advice being one of them. In online advice forums, advice is mixed in with non-advice, like emotional support, and is sometimes stated explicitly, sometimes implicitly. Understanding the language of advice would equip systems with a better grasp of language pragmatics; practically, the ability to identify advice would drastically increa","authors":["Govindarajan, Venkata Subrahmanyan","Chen, Benjamin T","Warholic, Rebecca","Erk, Katrin","Li, Junyi Jessy"],"url":"https://arxiv.org/abs/2010.02494","arxiv_id":"2010.02494","published":"2026-04-17"},{"title":"How people talk about each other: Modeling Generalized Intergroup Bias and Emotion","abstract":"Current studies of bias in NLP rely mainly on identifying (unwanted or negative) bias towards a specific demographic group. While this has led to progress recognizing and mitigating negative bias, and having a clear notion of the targeted group is necessary, it is not always practical. In this work we extrapolate to a broader notion of bias, rooted in social science and psychology literature. We move towards predicti","authors":["Govindarajan, Venkata S","Atwell, Katherine","Sinno, Barea","Alikhani, Malihe","Beaver, David I.","Li, Junyi Jessy"],"url":"https://arxiv.org/abs/2209.06687","arxiv_id":"2209.06687","published":"2026-04-17"},{"title":"RECALL: Rehearsal-free Continual Learning for Object Classification","abstract":"Convolutional neural networks show remarkable results in classification but struggle with learning new things on the fly. We present a novel rehearsal-free approach, where a deep neural network is continually learning new unseen object categories without saving any data of prior sequences. Our approach is called RECALL, as the network recalls categories by calculating logits for old categories before training new one","authors":["Knauer, Markus","Denninger, Maximilian","Triebel, Rudolph"],"url":"https://arxiv.org/abs/2209.14774","arxiv_id":"2209.14774","published":"2026-04-17"},{"title":"Universal hidden monotonic trend estimation with contrastive learning","abstract":"In this paper, we describe a universal method for extracting the underlying monotonic trend factor from time series data. We propose an approach related to the Mann-Kendall test, a standard monotonic trend detection method and call it contrastive trend estimation (CTE). We show that the CTE method identifies any hidden trend underlying temporal data while avoiding the standard assumptions used for monotonic trend ide","authors":["Pineau, Edouard","Razakarivony, Sébastien"],"url":"https://arxiv.org/abs/2210.09817","arxiv_id":"2210.09817","published":"2026-04-17"},{"title":"An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning","abstract":"In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because previous samples have limited replay value when learning a new task. Prior research has typically relied on either a single adaptive centroid or multiple fixed centroids to represent each class in the latent space. However, such methods struggle when class data streams are inheren","authors":["Tran, Quyen","Nguyen, Hai","Phan, Hoang","Dao, Quan","Ngo, Linh","Than, Khoat"],"url":"https://arxiv.org/abs/2211.16780","arxiv_id":"2211.16780","published":"2026-04-17"},{"title":"Competitive plasticity to reduce the energetic costs of learning","abstract":"The brain is not only constrained by energy needed to fuel computation, but it is also constrained by energy needed to form memories. Experiments have shown that learning simple conditioning tasks already carries a significant metabolic cost. Yet, learning a task like MNIST to 95% accuracy appears to require at least 10^{8} synaptic updates. Therefore the brain has likely evolved to be able to learn using as little e","authors":["van Rossum, Mark CW"],"url":"https://arxiv.org/abs/2304.02594","arxiv_id":"2304.02594","published":"2026-04-17"},{"title":"Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias","abstract":"While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social context, and its effects on language behavior. In this paper, we investigate if two pragmatic features (specificity and affect) systematically vary in different intergroup contexts -- thus connecting this new framing of bias to language out","authors":["Govindarajan, Venkata S","Mahowald, Kyle","Beaver, David I.","Li, Junyi Jessy"],"url":"https://arxiv.org/abs/2305.16409","arxiv_id":"2305.16409","published":"2026-04-17"},{"title":"Using deep learning to construct stochastic local search SAT solvers with performance bounds","abstract":"The Boolean Satisfiability problem (SAT), as the prototypical $\\mathsf{NP}$-complete problem, is crucial in both theoretical computer science and practical applications. To address this problem, stochastic local search (SLS) algorithms, which iteratively and randomly update candidate assignments, present an important and theoretically well-studied class of solvers. Recent theoretical advancements have identified cond","authors":["Kramer, Maximilian J.","Boes, Paul","Eisert, Jens"],"url":"https://arxiv.org/abs/2309.11452","arxiv_id":"2309.11452","published":"2026-04-17"},{"title":"Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways","abstract":"We present Lil-Bevo, our submission to the BabyLM Challenge. We pretrained our masked language models with three ingredients: an initial pretraining with music data, training on shorter sequences before training on longer ones, and masking specific tokens to target some of the BLiMP subtasks. Overall, our baseline models performed above chance, but far below the performance levels of larger LLMs trained on more data.","authors":["Govindarajan, Venkata S","Rodriguez, Juan Diego","Bostrom, Kaj","Mahowald, Kyle"],"url":"https://arxiv.org/abs/2310.17591","arxiv_id":"2310.17591","published":"2026-04-17"},{"title":"Towards Adaptive, Learning-Based Security in Decentralized Applications","abstract":"Web3 systems expose a fundamentally different security landscape from centralized platforms, characterized by composability, pseudonymous identities, decentralized governance, and rapidly evolving attack strategies that span social, application, and protocol layers. Existing security mechanisms, such as static smart contract analysis, blacklist-based phishing detection, and network-level mitigation, operate in isolat","authors":["Behfar, Stefan Kambiz","Crowcroft, Jon"],"url":"https://arxiv.org/abs/2311.01956","arxiv_id":"2311.01956","published":"2026-04-17"},{"title":"DA-Cramming: Enhancing Cost-Effective Language Model Pretraining with Dependency Agreement Integration","abstract":"Pretraining language models is still a challenge for many researchers due to its substantial computational costs. As such, there is growing interest in developing more affordable pretraining methods. One notable advancement in this area is the Cramming technique (Geiping and Goldstein, 2022), which enables the pretraining of BERT-style language models using just one GPU in a single day. Building on this innovative ap","authors":["Kuo, Martin","Zhang, Jianyi","Li, Dongting","Chen, Yiran"],"url":"https://arxiv.org/abs/2311.04799","arxiv_id":"2311.04799","published":"2026-04-17"},{"title":"High Probability Guarantees for Random Reshuffling","abstract":"We consider the stochastic gradient method with random reshuffling ($\\mathsf{RR}$) for tackling smooth nonconvex optimization problems. $\\mathsf{RR}$ finds broad applications in practice, notably in training neural networks. In this work, we provide high probability complexity guarantees for this method. First, we establish a high probability ergodic sample complexity result (without taking expectation) for finding a","authors":["Yu, Hengxu","Li, Xiao"],"url":"https://arxiv.org/abs/2311.11841","arxiv_id":"2311.11841","published":"2026-04-17"},{"title":"Enhanced Deep Q-Learning for 2D Self-Driving Cars: Implementation and Evaluation on a Custom Track Environment","abstract":"This research project presents the implementation of a Deep Q-Learning Network (DQN) for a self-driving car on a 2-dimensional (2D) custom track, with the objective of enhancing the DQN network's performance. It encompasses the development of a custom driving environment using Pygame on a track surrounding the University of Memphis map, as well as the design and implementation of the DQN model. The algorithm util","authors":["Pathak, Sagar","Shrestha, Bidhya"],"url":"https://arxiv.org/abs/2402.08780","arxiv_id":"2402.08780","published":"2026-04-17"},{"title":"Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN","abstract":"Hand Gesture Recognition (HGR) enables intuitive human-computer interactions in various real-world contexts. However, existing frameworks often struggle to meet the real-time requirements essential for practical HGR applications. This study introduces a robust, skeleton-based framework for dynamic HGR that simplifies the recognition of dynamic hand gestures into a static image classification task, effectively reducin","authors":["Yusuf, Oluwaleke","Habib, Maki","Moustafa, Mohamed"],"url":"https://arxiv.org/abs/2406.15003","arxiv_id":"2406.15003","published":"2026-04-17"},{"title":"Improving Clean Accuracy via a Tangent-Space Perspective on Adversarial Training","abstract":"Adversarial training has proven effective in improving the robustness of deep neural networks against adversarial attacks. However, this enhanced robustness often comes at the cost of a substantial drop in accuracy on clean data. In this paper, we address this limitation by introducing Tangent Direction Guided Adversarial Training (TART), a novel method that enhances clean accuracy by exploiting the geometry of the d","authors":["Yi, Bongsoo","Lai, Rongjie","Li, Yao"],"url":"https://arxiv.org/abs/2408.14728","arxiv_id":"2408.14728","published":"2026-04-17"},{"title":"Edge-preserving noise for diffusion models","abstract":"Classical diffusion models typically rely on isotropic Gaussian noise, treating all regions uniformly and overlooking structural information important for high-quality generation. We introduce an edge-preserving diffusion process that generalizes isotropic models via a hybrid noise scheme with an edge-aware scheduler that smoothly transitions from edge-preserving to isotropic noise. This enables the model to capture ","authors":["Vandersanden, Jente","Holl, Sascha","Huang, Xingchang","Singh, Gurprit"],"url":"https://arxiv.org/abs/2410.01540","arxiv_id":"2410.01540","published":"2026-04-17"},{"title":"Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers","abstract":"Respiratory motion complicates accurate irradiation of thoraco-abdominal tumors during radiotherapy, as treatment-system latency entails target-location uncertainties. This work addresses frame forecasting in chest and liver cine MRI to compensate for such delays. We investigate RNNs trained with online learning algorithms, enabling adaptation to changing respiratory patterns via on-the-fly parameter updates, and tra","authors":["Pohl, Michel","Uesaka, Mitsuru","Takahashi, Hiroyuki","Demachi, Kazuyuki","Chhatkuli, Ritu Bhusal"],"url":"https://arxiv.org/abs/2410.05882","arxiv_id":"2410.05882","published":"2026-04-17"},{"title":"Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging","abstract":"Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth's subsurface, acoustic imaging and non-destructive testing in material science, and ultrasound computed tomography in medicine. Current approaches for solving CWI problems can be divided into two catego","authors":["Lin, Youzuo","Feng, Shihang","Theiler, James","Chen, Yinpeng","Villa, Umberto","Rao, Jing"],"url":"https://arxiv.org/abs/2410.08329","arxiv_id":"2410.08329","published":"2026-04-17"},{"title":"Sampling Transferable Graph Neural Networks with Limited Graph Information","abstract":"Graph neural networks (GNNs) achieve strong performance on graph learning tasks, but training on large-scale networks remains computationally challenging. Transferability results show that GNNs with fixed weights can generalize from smaller graphs to larger ones drawn from the same family, motivating the use of sampled subgraphs to boost training efficiency. Yet most existing sampling strategies rely on reliable acce","authors":["Wang, Haoyu","Ma, Renyuan","Mateos, Gonzalo","Ruiz, Luana"],"url":"https://arxiv.org/abs/2410.16593","arxiv_id":"2410.16593","published":"2026-04-17"},{"title":"In Context Learning and Reasoning for Symbolic Regression with Large Language Models","abstract":"Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not explicitly trained. Here, we explore the potential of LLMs to perform symbolic regression -- a machine-learning method for finding simple and accurate equations from datasets. We prompt GPT-4 and GPT-4o models to suggest expressions from data, which are then optimized and ","authors":["Sharlin, Samiha","Josephson, Tyler R."],"url":"https://arxiv.org/abs/2410.17448","arxiv_id":"2410.17448","published":"2026-04-17"},{"title":"Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach","abstract":"Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However, HRL methods face two fundamental challenges: (i) non-stationarity caused by the evolving lower-level policy during training, which destabilizes higher-level learning, and (ii) the generation of infeasible subgoals that lower-level policies cannot achieve. To address the","authors":["Singh, Utsav","Chakraborty, Souradip","Suttle, Wesley A.","Sadler, Brian M.","Asher, Derrik E.","Sahu, Anit Kumar"],"url":"https://arxiv.org/abs/2411.00361","arxiv_id":"2411.00361","published":"2026-04-17"},{"title":"Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation","abstract":"The paradigm shift toward structure-driven molecule generation has been propelled by advances in deep generative models, such as variational auto-encoders and diffusion models. However, these generative models for molecular design remain constrained by exposure bias, error accumulation, and suboptimal handling of activity cliffs. Here, we introduce DiffGap, a diffusion-based framework that integrates adaptive samplin","authors":["Liu, Peidong","Zhang, Wenbo","Ju, Wei","Lv, Jiancheng","Liu, Xianggen"],"url":"https://arxiv.org/abs/2411.05472","arxiv_id":"2411.05472","published":"2026-04-17"},{"title":"JoyVASA: Portrait and Animal Image Animation with Diffusion-Based Audio-Driven Facial Dynamics and Head Motion Generation","abstract":"Audio-driven portrait animation has made significant advances with diffusion-based models, improving video quality and lipsync accuracy. However, the increasing complexity of these models has led to inefficiencies in training and inference, as well as constraints on video length and inter-frame continuity. In this paper, we propose JoyVASA, a diffusion-based method for generating facial dynamics and head motion in au","authors":["Cao, Xuyang","Wang, Guoxin","Shi, Sheng","Zhao, Jun","Yao, Yang","Fei, Jintao"],"url":"https://arxiv.org/abs/2411.09209","arxiv_id":"2411.09209","published":"2026-04-17"},{"title":"Query pipeline optimization for cancer patient question answering systems","abstract":"Retrieval-augmented generation (RAG) mitigates hallucination in Large Language Models (LLMs) by using query pipelines to retrieve relevant external information and grounding responses in retrieved knowledge. However, query pipeline optimization for cancer patient question-answering (CPQA) systems requires separately optimizing multiple components with domain-specific considerations. We propose a novel three-aspect op","authors":["He, Maolin","Gao, Rena","Conway, Mike","Chapman, Brian E."],"url":"https://arxiv.org/abs/2412.14751","arxiv_id":"2412.14751","published":"2026-04-17"}],"history":{"events":[{"year":2014,"text":"NASA announced the discovery of Kepler-186f, the first exoplanet with a radius similar to Earth's discovered in the habitable zone of another star.","url":"https://en.wikipedia.org/wiki/NASA"},{"year":2013,"text":"An ammonium nitrate explosion occurred at a fertilizer company facility in West, Texas, U.S., killing 15 people and injuring 160 others.","url":"https://en.wikipedia.org/wiki/West_Fertilizer_Company_explosion"},{"year":2006,"text":"A Palestinian suicide bomber detonated explosives in a restaurant in Tel Aviv, Israel, killing 11 people and injuring 70 others.","url":"https://en.wikipedia.org/wiki/Palestinian_suicide_attacks"},{"year":1986,"text":"The Netherlands and the Isles of Scilly declared peace, having hypothetically been at war for 335 years without a single shot being fired and no casualties incurred.","url":"https://en.wikipedia.org/wiki/Isles_of_Scilly"},{"year":1984,"text":"Metropolitan Police officer Yvonne Fletcher was shot and killed while on duty during a protest outside the Libyan embassy in London, resulting in an 11-day police siege of the building and a breakdown of Libya–United Kingdom relations.","url":"https://en.wikipedia.org/wiki/Metropolitan_Police"},{"year":1975,"text":"The Khmer Rouge captured Phnom Penh, the capital of the Khmer Republic, ending the Cambodian Civil War and establishing the socialist state of Democratic Kampuchea.","url":"https://en.wikipedia.org/wiki/Khmer_Rouge"},{"year":1973,"text":"George Lucas began writing a 13-page film treatment that later formed the basis of Star Wars.","url":"https://en.wikipedia.org/wiki/George_Lucas"},{"year":1969,"text":"Czechoslovak Communist Party chairman Alexander Dubček was deposed.","url":"https://en.wikipedia.org/wiki/Communist_Party_of_Czechoslovakia"},{"year":1969,"text":"Sirhan Sirhan was convicted of the assassination of U.S. senator Robert F. Kennedy.","url":"https://en.wikipedia.org/wiki/Sirhan_Sirhan"},{"year":1951,"text":"The Peak District was designated the first national park in the United Kingdom.","url":"https://en.wikipedia.org/wiki/Peak_District"},{"year":1942,"text":"World War II: Captured French general Henri Giraud escaped from German captivity in the Königstein Castle.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1907,"text":"Minas Geraes (pictured), the first of three Brazilian dreadnought battleships, was laid down, sparking a vastly expensive South American naval arms race with Argentina and Chile.","url":"https://en.wikipedia.org/wiki/Brazilian_battleship_Minas_Geraes"},{"year":1809,"text":"Napoleonic Wars: After a three-day chase, the French ship D'Hautpoul was captured off Puerto Rico by a British squadron under Alexander Cochrane.","url":"https://en.wikipedia.org/wiki/Napoleonic_Wars"},{"year":1797,"text":"French Revolutionary Wars: British forces commanded by Lieutenant-General Ralph Abercromby invaded the Spanish colonial port of San Juan, Puerto Rico.","url":"https://en.wikipedia.org/wiki/French_Revolutionary_Wars"},{"year":1783,"text":"The Mechanical Turk (pictured), a fraudulent chess-playing \"machine\" by Wolfgang von Kempelen that was secretly controlled by a hidden human, began a tour of Europe.","url":"https://en.wikipedia.org/wiki/Mechanical_Turk"},{"year":1362,"text":"Lithuanian Crusade: After a month-long siege, the Teutonic Order captured and destroyed Kaunas Castle, which was defended by troops of the Grand Duchy of Lithuania.","url":"https://en.wikipedia.org/wiki/Lithuanian_Crusade"},{"year":1080,"text":"Canute IV became King of Denmark upon the death of his brother Harald III.","url":"https://en.wikipedia.org/wiki/Canute_IV_of_Denmark"}],"births":[{"year":2005,"text":"Antonio Nusa, Norwegian footballer","url":"https://en.wikipedia.org/wiki/Antonio_Nusa"},{"year":2001,"text":"Shin Ryujin, South Korean rapper, singer and dancer","url":"https://en.wikipedia.org/wiki/Ryujin_(rapper)"},{"year":2001,"text":"Violette Dorange, French Professional Round the World Sailor","url":"https://en.wikipedia.org/wiki/Violette_Dorange"},{"year":1998,"text":"Anna Odine Strøm, Norwegian ski jumper and two-time Olympic champion","url":"https://en.wikipedia.org/wiki/Anna_Odine_Str%C3%B8m"},{"year":1998,"text":"Suppapong Udomkaewkanjana (Saint), Thai actor and singer","url":"https://en.wikipedia.org/wiki/Suppapong_Udomkaewkanjana"},{"year":1996,"text":"Lorna Fitzgerald, British actress","url":"https://en.wikipedia.org/wiki/Lorna_Fitzgerald"},{"year":1996,"text":"Caitlin Parker, Australian boxer","url":"https://en.wikipedia.org/wiki/Caitlin_Parker"},{"year":1996,"text":"Helene Spilling, Norwegian dancer","url":"https://en.wikipedia.org/wiki/Helene_Spilling_%C3%98degaard"}],"deaths":[{"year":2022,"text":"Radu Lupu, Romanian pianist (born 1945)","url":"https://en.wikipedia.org/wiki/Radu_Lupu"},{"year":2019,"text":"Alan García, Peruvian lawyer and politician, twice President of Peru (born 1949)","url":"https://en.wikipedia.org/wiki/Alan_Garc%C3%ADa"},{"year":2019,"text":"Gwen Marston, American quilter and writer (born 1936)","url":"https://en.wikipedia.org/wiki/Gwen_Marston"},{"year":2018,"text":"Barbara Bush, American political matriarch and literacy advocate, First Lady of the United States (1989–1993), and Second Lady of the United States (1981–1989) (born 1925)","url":"https://en.wikipedia.org/wiki/Barbara_Bush"},{"year":2018,"text":"Carl Kasell, American radio personality (born 1934)","url":"https://en.wikipedia.org/wiki/Carl_Kasell"},{"year":2016,"text":"Chyna, American wrestler (born 1969)","url":"https://en.wikipedia.org/wiki/Chyna"},{"year":2016,"text":"Doris Roberts, American actress (born 1925)","url":"https://en.wikipedia.org/wiki/Doris_Roberts"},{"year":2015,"text":"Robert P. Griffin, American soldier, lawyer, and politician (born 1923)","url":"https://en.wikipedia.org/wiki/Robert_P._Griffin"}]},"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."}