{"date":"2026-05-05","schema_version":1,"captured_at":"2026-08-25T23:07:24.480Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"2026 West Bengal Legislative Assembly election","views":1275727,"rank":2,"extract":"Legislative Assembly elections were held in West Bengal to elect all 294 members of the West Bengal Legislative Assembly in two phases on 23 and 29 April 2026, with the votes counted and results for 293 seats released on 4 May 2026. 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insurance","points":257,"comments":192,"url":"https://www.anthropic.com/news/finance-agents","discussion":"https://news.ycombinator.com/item?id=48023533"},{"title":"AI Product Graveyard","points":255,"comments":88,"url":"https://tooldirectory.ai/ai-graveyard","discussion":"https://news.ycombinator.com/item?id=48021968"}],"research":[{"title":"Neural Network Training with Approximate Logarithmic Computations","abstract":"The high computational complexity associated with training deep neural networks limits online and real-time training on edge devices. This paper proposed an end-to-end training and inference scheme that eliminates multiplications by approximate operations in the log-domain which has the potential to significantly reduce implementation complexity. We implement the entire training procedure in the log-domain, with fixe","authors":["Sanyal, Arnab","Beerel, Peter A.","Chugg, Keith M."],"url":"https://arxiv.org/abs/1910.09876","arxiv_id":"1910.09876","published":"2026-05-05"},{"title":"Efficient Parameter Estimation of Truncated Boolean Product Distributions","abstract":"We study the problem of estimating the parameters of a Boolean product distribution in $d$ dimensions, when the samples are truncated by a set $S \\subset \\{0, 1\\}^d$ accessible through a membership oracle. This is the first time that the computational and statistical complexity of learning from truncated samples is considered in a discrete setting. We introduce a natural notion of fatness of the truncation set $S$, u","authors":["Fotakis, Dimitris","Kalavasis, Alkis","Tzamos, Christos"],"url":"https://arxiv.org/abs/2007.02392","arxiv_id":"2007.02392","published":"2026-05-05"},{"title":"Online Graph Topology Learning from Matrix-valued Time Series","abstract":"The focus is on the statistical analysis of matrix-valued time series, where data is collected over a network of sensors, typically at spatial locations, over time. Each sensor records a vector of features at each time point, creating a vectorial time series for each sensor. The goal is to identify the dependency structure among these sensors and represent it with a graph. When only one feature per sensor is observed","authors":["Jiang, Yiye","Bigot, Jérémie","Maabout, Sofian"],"url":"https://arxiv.org/abs/2107.08020","arxiv_id":"2107.08020","published":"2026-05-05"},{"title":"Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation","abstract":"Robust and efficient interpretation of QSAR methods is quite useful to validate AI prediction rationales with subjective opinion (chemist or biologist expertise), understand sophisticated chemical or biological process mechanisms, and provide heuristic ideas for structure optimization in pharmaceutical industry. For this purpose, we construct a multi-layer self-attention based Graph Neural Network framework, namely L","authors":["Guo, Jinjiang","Liu, Qi","Guo, Han","Lu, Xi"],"url":"https://arxiv.org/abs/2202.10873","arxiv_id":"2202.10873","published":"2026-05-05"},{"title":"Wasserstein multivariate auto-regressive models for modeling distributional time series","abstract":"This paper is focused on the statistical analysis of data consisting of a collection of multiple series of probability measures that are indexed by distinct time instants and supported over a bounded interval of the real line. By modeling these time-dependent probability measures as random objects in the Wasserstein space, we propose a new auto-regressive model for the statistical analysis of multivariate distributio","authors":["Jiang, Yiye","Bigot, Jérémie"],"url":"https://arxiv.org/abs/2207.05442","arxiv_id":"2207.05442","published":"2026-05-05"},{"title":"Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis","abstract":"Imitation learning learns a policy from expert trajectories. While the expert data is believed to be crucial for imitation quality, it was found that a kind of imitation learning approach, adversarial imitation learning (AIL), can have exceptional performance. With as little as only one expert trajectory, AIL can match the expert performance even in a long horizon, on tasks such as locomotion control. There are two m","authors":["Xu, Tian","Li, Ziniu","Yu, Yang","Luo, Zhi-Quan"],"url":"https://arxiv.org/abs/2208.01899","arxiv_id":"2208.01899","published":"2026-05-05"},{"title":"The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts","abstract":"Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trials are computational studies that seek to ascertain the performance of a medical device by collecting this information entirely via computer simulations. The benefits of in silico trials for evaluating new technology include significant reso","authors":["Badano, A","Lago, M","Sizikova, E","Delfino, JG","Guan, S","Anastasio, MA"],"url":"https://arxiv.org/abs/2301.08719","arxiv_id":"2301.08719","published":"2026-05-05"},{"title":"Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matching","abstract":"The matching of 3D shapes has been extensively studied for shapes represented as surface meshes, as well as for shapes represented as point clouds. While point clouds are a common representation of raw real-world 3D data (e.g. from laser scanners), meshes encode rich and expressive topological information, but their creation typically requires some form of (often manual) curation. In turn, methods that purely rely on","authors":["Cao, Dongliang","Bernard, Florian"],"url":"https://arxiv.org/abs/2303.10971","arxiv_id":"2303.10971","published":"2026-05-05"},{"title":"Unsupervised Learning of Robust Spectral Shape Matching","abstract":"We propose a novel learning-based approach for robust 3D shape matching. Our method builds upon deep functional maps and can be trained in a fully unsupervised manner. Previous deep functional map methods mainly focus on predicting optimised functional maps alone, and then rely on off-the-shelf post-processing to obtain accurate point-wise maps during inference. However, this two-stage procedure for obtaining point-w","authors":["Cao, Dongliang","Roetzer, Paul","Bernard, Florian"],"url":"https://arxiv.org/abs/2304.14419","arxiv_id":"2304.14419","published":"2026-05-05"},{"title":"Near-Optimal Privacy-Preserving Learning for Max-Min Fair Multi-Agent Bandits","abstract":"We study fair multi-agent multi-armed bandit learning under collision-only coordination. Agents cannot communicate explicitly during learning and observe only their own rewards and whether collisions occur when several agents access the same arm. The goal is to learn a max-min fair allocation while keeping each agent's reward samples and empirical reward estimates local. We propose a fully distributed algorithm f","authors":["Leshem, Amir"],"url":"https://arxiv.org/abs/2306.04498","arxiv_id":"2306.04498","published":"2026-05-05"},{"title":"Unreflected Acceptance -- Investigating the Negative Consequences of ChatGPT-Assisted Problem Solving in Physics Education","abstract":"Large language models (LLMs) have recently gained popularity. However, the impact of their general availability through ChatGPT on sensitive areas of everyday life, such as education, remains unclear. Nevertheless, the societal impact on established educational methods is already being experienced by both students and educators. Our work focuses on higher physics education and examines problem solving strategies. In ","authors":["Krupp, Lars","Steinert, Steffen","Kiefer-Emmanouilidis, Maximilian","Avila, Karina E.","Lukowicz, Paul","Kuhn, Jochen"],"url":"https://arxiv.org/abs/2309.03087","arxiv_id":"2309.03087","published":"2026-05-05"},{"title":"Boosting Multimodal Remote Sensing Image Classification with Transformer-based Heterogeneously Salient Graph Representation","abstract":"Data collected by different modalities can provide a wealth of complementary information, such as hyperspectral image (HSI) to offer rich spectral-spatial properties, synthetic aperture radar (SAR) to provide structural information about the Earth's surface, and light detection and ranging (LiDAR) to cover altitude information about ground elevation. Therefore, a natural idea is to combine multimodal images for r","authors":["Yang, Jiaqi","Du, Bo","Liu, Rong","Mao, Zhu","Zhang, Liangpei"],"url":"https://arxiv.org/abs/2311.10320","arxiv_id":"2311.10320","published":"2026-05-05"},{"title":"Using Analytics on Student Created Data to Content Validate Pedagogical Tools","abstract":"Conceptual and simulation models can function as useful pedagogical tools, however it is important to categorize different outcomes when evaluating them in order to more meaningfully interpret results. VERA is a ecology-based conceptual modeling software that enables users to simulate interactions between biotics and abiotics in an ecosystem, allowing users to form and then verify hypothesis through observing a time ","authors":["Kos, John","Eaton, Kenneth","Zhang, Sareen","Dass, Rahul","Buckley, Stephen","An, Sungeun"],"url":"https://arxiv.org/abs/2312.06871","arxiv_id":"2312.06871","published":"2026-05-05"},{"title":"CCNETS: A Modular Causal Learning Framework for Pattern Recognition in Imbalanced Datasets","abstract":"Handling class imbalance remains a central challenge in machine learning, particularly in pattern recognition tasks where identifying rare but critical anomalies is of paramount importance. Traditional generative models often decouple data synthesis from classification,leading to a distribution mismatch that limits their practical benefit. To address these shortcomings, we introduce Causal Cooperative Networks (CCNET","authors":["Park, Hanbeot","Cho, Yunjeong","Kim, Hunhee"],"url":"https://arxiv.org/abs/2401.04139","arxiv_id":"2401.04139","published":"2026-05-05"},{"title":"Assessing the Efficacy of Deep Learning Approaches for Facial Expression Recognition in Individuals with Intellectual Disabilities","abstract":"Facial expression recognition has gained significance as a means of imparting social robots with the capacity to discern the emotional states of users. The use of social robotics includes a variety of settings, including homes, nursing homes or daycare centers, serving to a wide range of users. Remarkable performance has been achieved by deep learning approaches, however, its direct use for recognizing facial express","authors":["Gaya-Morey, F. Xavier","Ramis, Silvia","Buades-Rubio, Jose M.","Manresa-Yee, Cristina"],"url":"https://arxiv.org/abs/2401.11877","arxiv_id":"2401.11877","published":"2026-05-05"},{"title":"BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion","abstract":"Witnessing the evolution of text-to-image diffusion models, significant strides have been made in text-to-3D generation. Currently, two primary paradigms dominate the field of text-to-3D: the feed-forward generation solutions, capable of swiftly producing 3D assets but often yielding coarse results, and the Score Distillation Sampling (SDS) based solutions, known for generating high-fidelity 3D assets albeit at a slo","authors":["Yu, Yonghao","Zhu, Shunan","Qin, Huai","Li, Haorui"],"url":"https://arxiv.org/abs/2401.16764","arxiv_id":"2401.16764","published":"2026-05-05"},{"title":"Analyzing Adversarial Inputs in Deep Reinforcement Learning","abstract":"In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-world and complex systems. However, even the state-of-the-art DRL models have been shown to suffer from reliability concerns -- for example, their susceptibility to adversarial inputs, i.e., small and abundant input perturbations that can fool the models into making unpredict","authors":["Corsi, Davide","Amir, Guy","Katz, Guy","Farinelli, Alessandro"],"url":"https://arxiv.org/abs/2402.05284","arxiv_id":"2402.05284","published":"2026-05-05"},{"title":"Proof-of-Learning with Incentive Security","abstract":"Most concurrent blockchain systems rely heavily on the Proof-of-Work (PoW) or Proof-of-Stake (PoS) mechanisms for decentralized consensus and security assurance. However, the substantial energy expenditure stemming from computationally intensive yet meaningless tasks has raised considerable concerns surrounding traditional PoW approaches, The PoS mechanism, while free of energy consumption, is subject to security and","authors":["Zhao, Zishuo","Fang, Zhixuan","Wang, Xuechao","Chen, Xi","Su, Hongxu","Xiao, Haibo"],"url":"https://arxiv.org/abs/2404.09005","arxiv_id":"2404.09005","published":"2026-05-05"},{"title":"PipeFusion: Patch-level Pipeline Parallelism for Diffusion Transformers Inference","abstract":"This paper presents PipeFusion, an innovative parallel methodology to tackle the high latency issues associated with generating high-resolution images using diffusion transformers (DiTs) models. PipeFusion partitions images into patches and the model layers across multiple GPUs. It employs a patch-level pipeline parallel strategy to orchestrate communication and computation efficiently. By capitalizing on the high si","authors":["Fang, Jiarui","Pan, Jinzhe","Li, Aoyu","Sun, Xibo","Wang, Jiannan"],"url":"https://arxiv.org/abs/2405.14430","arxiv_id":"2405.14430","published":"2026-05-05"},{"title":"Deep Time Series Models: A Comprehensive Survey and Benchmark","abstract":"Time series, characterized by a sequence of data points organized in a discrete-time order, are ubiquitous in real-world scenarios. Unlike other data modalities, time series present unique challenges in learning and modeling due to their intricate and dynamic nature, including the entanglement of nonlinear patterns and time-variant trends. Recent years have witnessed remarkable breakthroughs in time series analysis, ","authors":["Wang, Yuxuan","Wu, Haixu","Dong, Jiaxiang","Liu, Yong","Wang, Chen","Long, Mingsheng"],"url":"https://arxiv.org/abs/2407.13278","arxiv_id":"2407.13278","published":"2026-05-05"},{"title":"xAI-Drop: Don't Use What You Cannot Explain","abstract":"Graph Neural Networks (GNNs) have emerged as the predominant paradigm for learning from graph-structured data, offering a wide range of applications from social network analysis to bioinformatics. Despite their versatility, GNNs face challenges such as lack of generalization and poor interpretability, which hinder their wider adoption and reliability in critical applications. Dropping has emerged as an effective para","authors":["De Luca, Vincenzo Marco","Longa, Antonio","Liò, Pietro","Passerini, Andrea"],"url":"https://arxiv.org/abs/2407.20067","arxiv_id":"2407.20067","published":"2026-05-05"},{"title":"Towards Agentic Runtime Healing","abstract":"Self-healing systems have long been a focus of research, aiming to enable software to recover from unexpected runtime errors without human intervention. Traditional approaches rely on predefined heuristic rules, such as reusing error handlers or rolling back to checkpoints, but these methods struggle to adapt to the diverse range of runtime errors. The emergence of Large Language Models offers a new opportunity to ad","authors":["Sun, Zhensu","Zhu, Haotian","Xu, Bowen","Du, Xiaoning","Li, Li","Lo, David"],"url":"https://arxiv.org/abs/2408.01055","arxiv_id":"2408.01055","published":"2026-05-05"},{"title":"Partial-differential-algebraic equations of nonlinear dynamics by Physics-Informed Neural-Network: (I) Operator splitting and framework assessment","abstract":"Several forms for constructing novel physics-informed neural-networks (PINN) for the solution of partial-differential-algebraic equations based on derivative operator splitting are proposed, using the nonlinear Kirchhoff rod as a prototype for demonstration. The open-source DeepXDE is likely the most well documented framework with many examples. Yet, we encountered some pathological problems and proposed novel method","authors":["Vu-Quoc, Loc","Humer, Alexander"],"url":"https://arxiv.org/abs/2408.01914","arxiv_id":"2408.01914","published":"2026-05-05"},{"title":"Code-switching in text and speech challenges information-theoretic speaker design","abstract":"In this work, we use language modeling to investigate the factors that influence insertional code-switching. Code-switching occurs when a speaker alternates between one language variety (the primary language) and another (the secondary language), and is widely observed in multilingual contexts. Recent work has shown that code-switching is often correlated with areas of low predictability in the primary language, but ","authors":["Bhattacharya, Debasmita","van Schijndel, Marten"],"url":"https://arxiv.org/abs/2408.04596","arxiv_id":"2408.04596","published":"2026-05-05"},{"title":"GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data","abstract":"Although data that can be naturally represented as graphs is widespread in real-world applications across diverse industries, popular graph ML benchmarks for node property prediction only cover a surprisingly narrow set of data domains, and graph neural networks (GNNs) are often evaluated on just a few academic citation networks. This issue is particularly pressing in light of the recent growing interest in designing","authors":["Bazhenov, Gleb","Platonov, Oleg","Prokhorenkova, Liudmila"],"url":"https://arxiv.org/abs/2409.14500","arxiv_id":"2409.14500","published":"2026-05-05"}],"history":{"events":[{"year":2023,"text":"The World Health Organization declared the end of COVID-19 as a global health emergency.","url":"https://en.wikipedia.org/wiki/World_Health_Organization"},{"year":2020,"text":"Philippine broadcast network ABS-CBN was forced to go off-air by the National Telecommunications Commission after Congress failed to renew its franchise granted in 1995.","url":"https://en.wikipedia.org/wiki/ABS-CBN"},{"year":2007,"text":"Kenya Airways Flight 507 crashed immediately after takeoff from Douala International Airport in Cameroon, resulting in the deaths of all 114 people aboard.","url":"https://en.wikipedia.org/wiki/Kenya_Airways_Flight_507"},{"year":1994,"text":"American teenager Michael P. Fay was caned in Singapore for theft and vandalism, a punishment that the United States deemed to be excessive for a teenager committing a non-violent crime.","url":"https://en.wikipedia.org/wiki/Caning_of_Michael_Fay"},{"year":1992,"text":"The Twenty-seventh Amendment to the United States Constitution was ratified, 202 years after it was first proposed.","url":"https://en.wikipedia.org/wiki/Twenty-seventh_Amendment_to_the_United_States_Constitution"},{"year":1991,"text":"Riots broke out in Washington, D.C., after a rookie police officer shot a Salvadoran man in the chest.","url":"https://en.wikipedia.org/wiki/1991_Washington%2C_D.C.%2C_riot"},{"year":1981,"text":"After a 66-day hunger strike, Irish republican Bobby Sands died of starvation in HM Prison Maze.","url":"https://en.wikipedia.org/wiki/1981_Irish_hunger_strike"},{"year":1980,"text":"The British Special Air Service recaptured the Iranian embassy in London following a six-day siege after Iranian Arab separatists had seized it.","url":"https://en.wikipedia.org/wiki/Special_Air_Service"},{"year":1961,"text":"Project Mercury: American astronaut Alan Shepard made a sub-orbital spaceflight on board Freedom 7, becoming the second person to travel into outer space.","url":"https://en.wikipedia.org/wiki/Project_Mercury"},{"year":1960,"text":"The Netherlands Carillon was inaugurated in Arlington, Virginia, on the 15th anniversary of Dutch Liberation Day.","url":"https://en.wikipedia.org/wiki/Netherlands_Carillon"},{"year":1945,"text":"World War II: American and German troops fought side-by-side at the Battle for Castle Itter, defending a prison against Waffen-SS forces.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1945,"text":"World War II: The citizens of Prague spontaneously rose up against the city's German occupiers.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1945,"text":"The Mauthausen and Gusen concentration camps were liberated by the United States Army.","url":"https://en.wikipedia.org/wiki/Mauthausen_concentration_camp"},{"year":1940,"text":"World War II: A squad of 250 Norwegian volunteers defending Hegra Fortress surrendered to a superior Nazi force after a 25-day siege.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1936,"text":"Second Italo-Ethiopian War: Italian troops (pictured) captured the Ethiopian capital Addis Ababa unopposed.","url":"https://en.wikipedia.org/wiki/Second_Italo-Ethiopian_War"},{"year":1904,"text":"Pitching for the Boston Americans, Cy Young threw the first perfect game in modern professional baseball.","url":"https://en.wikipedia.org/wiki/Pitcher"},{"year":1891,"text":"Carnegie Hall (interior pictured) in New York City, built by the philanthropist Andrew Carnegie, officially opened with a concert conducted by Pyotr Ilyich Tchaikovsky.","url":"https://en.wikipedia.org/wiki/Carnegie_Hall"},{"year":1864,"text":"American Civil War: Union Lt. Gen. Ulysses S. Grant's Overland Campaign in Virginia began with the inconclusive Battle of the Wilderness in Spotsylvania County.","url":"https://en.wikipedia.org/wiki/American_Civil_War"}],"births":[{"year":2004,"text":"Jenna Davis, American actress and YouTuber","url":"https://en.wikipedia.org/wiki/Jenna_Davis"},{"year":2004,"text":"Kirsty Muir, Scottish freestyle skier","url":"https://en.wikipedia.org/wiki/Kirsty_Muir"},{"year":2003,"text":"Carlos Alcaraz, Spanish tennis player","url":"https://en.wikipedia.org/wiki/Carlos_Alcaraz"},{"year":1999,"text":"Nathan Chen, American figure skater","url":"https://en.wikipedia.org/wiki/Nathan_Chen"},{"year":1999,"text":"Justin Kluivert, Dutch footballer","url":"https://en.wikipedia.org/wiki/Justin_Kluivert"},{"year":1998,"text":"Aryna Sabalenka, Belarusian tennis player","url":"https://en.wikipedia.org/wiki/Aryna_Sabalenka"},{"year":1997,"text":"Logan Gilbert, American baseball player","url":"https://en.wikipedia.org/wiki/Logan_Gilbert"},{"year":1997,"text":"Mitch Marner, Canadian hockey player","url":"https://en.wikipedia.org/wiki/Mitch_Marner"}],"deaths":[{"year":2026,"text":"Chris Phelan, Irish-Australian rugby league player (born 1955)","url":"https://en.wikipedia.org/wiki/Chris_Phelan"},{"year":2024,"text":"Jeannie Epper, American stuntwoman and actress (born 1941)","url":"https://en.wikipedia.org/wiki/Jeannie_Epper"},{"year":2024,"text":"Bernard Hill, English actor (born 1944)","url":"https://en.wikipedia.org/wiki/Bernard_Hill"},{"year":2024,"text":"César Luis Menotti, Argentine footballer and manager (born 1938)","url":"https://en.wikipedia.org/wiki/C%C3%A9sar_Luis_Menotti"},{"year":2020,"text":"Millie Small, Jamaican singer-songwriter (born 1947)","url":"https://en.wikipedia.org/wiki/Millie_Small"},{"year":2017,"text":"Binyamin Elon, Israeli Orthodox rabbi and politician (born 1954)","url":"https://en.wikipedia.org/wiki/Binyamin_Elon"},{"year":2017,"text":"Ely Ould Mohamed Vall, Mauritanian politician (born 1953)","url":"https://en.wikipedia.org/wiki/Ely_Ould_Mohamed_Vall"},{"year":2015,"text":"Jobst Brandt, American cyclist, engineer, and author (born 1935)","url":"https://en.wikipedia.org/wiki/Jobst_Brandt"}]},"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."}