{"date":"2026-04-08","schema_version":1,"captured_at":"2026-08-25T23:36:15.461Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Artemis II","views":288498,"rank":4,"extract":"Artemis II was a crewed flyby of the Moon. It is currently the only crewed flight beyond low Earth orbit since Apollo 17 in 1972. 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This unidirectional property makes them can only leverage past context but not future context. Though refinement-based models can exploit both past and future context by generating a new caption in the second stage based on pre-retrieved or pre-generated captions in the first stage, the decoder of these models generally consists of t","authors":["Song, Zijie","Zhou, Yuanen","Hu, Zhenzhen","Liu, Daqing","Ben, Huixia","Hong, Richang"],"url":"https://arxiv.org/abs/2201.01984","arxiv_id":"2201.01984","published":"2026-04-08"},{"title":"Reachability In Simple Neural Networks","abstract":"We investigate the complexity of the reachability problem for (deep) neural networks: does it compute valid output given some valid input? It was recently claimed that the problem is NP-complete for general neural networks and specifications over the input/output dimension given by conjunctions of linear inequalities. We recapitulate the proof and repair some flaws in the original upper and lower bound proofs. Motiva","authors":["Sälzer, Marco","Lange, Martin"],"url":"https://arxiv.org/abs/2203.07941","arxiv_id":"2203.07941","published":"2026-04-08"},{"title":"Edgeworth Accountant: An Analytical Approach to Differential Privacy Composition","abstract":"In privacy-preserving data analysis, many procedures and algorithms are structured as compositions of multiple private building blocks. As such, an important question is how to efficiently compute the overall privacy loss under composition. This paper introduces the Edgeworth Accountant, an analytical approach to composing differential privacy guarantees for private algorithms. Leveraging the $f$-differential privacy","authors":["Wang, Hua","Gao, Sheng","Zhang, Huanyu","Shen, Milan","Su, Weijie J.","Wu, Jiayuan"],"url":"https://arxiv.org/abs/2206.04236","arxiv_id":"2206.04236","published":"2026-04-08"},{"title":"Solving Quantified Modal Logic Problems by Translation to Classical Logics","abstract":"This article describes an evaluation of Automated Theorem Proving (ATP) systems on problems taken from the QMLTP library of first-order modal logic problems. Principally, the problems are translated to both typed first-order and higher-order logic in the TPTP language using an embedding approach, and solved using first-order resp. higher-order logic ATP systems and model finders. Additionally, the results from native","authors":["Steen, Alexander","Sutcliffe, Geoff","Benzmüller, Christoph"],"url":"https://arxiv.org/abs/2212.09570","arxiv_id":"2212.09570","published":"2026-04-08"},{"title":"On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains","abstract":"In this paper, we provide a strategy to determine the eigenvalue decay rate (EDR) of a large class of kernel functions defined on a general domain rather than $\\mathbb S^{d}$. This class of kernel functions include but are not limited to the neural tangent kernel associated with neural networks with different depths and various activation functions. After proving that the dynamics of training the wide neural networks","authors":["Li, Yicheng","Yu, Zixiong","Chen, Guhan","Lin, Qian"],"url":"https://arxiv.org/abs/2305.02657","arxiv_id":"2305.02657","published":"2026-04-08"},{"title":"Variational Sequential Optimal Experimental Design using Reinforcement Learning","abstract":"We present variational sequential optimal experimental design (vsOED), a novel method for optimally designing a finite sequence of experiments within a Bayesian framework with information-theoretic criteria. vsOED employs a one-point reward formulation with variational posterior approximations, providing a provable lower bound to the expected information gain. Numerical methods are developed following an actor-critic","authors":["Shen, Wanggang","Dong, Jiayuan","Huan, Xun"],"url":"https://arxiv.org/abs/2306.10430","arxiv_id":"2306.10430","published":"2026-04-08"},{"title":"Understanding Uncertainty Sampling via Equivalent Loss","abstract":"Uncertainty sampling is a prevalent active learning algorithm that queries sequentially the annotations of data samples which the current prediction model is uncertain about. However, the usage of uncertainty sampling has been largely heuristic: There is no consensus on the proper definition of ``uncertainty'' for a specific task under a specific loss, nor a theoretical guarantee that prescribes a standard pr","authors":["Liu, Shang","Li, Xiaocheng"],"url":"https://arxiv.org/abs/2307.02719","arxiv_id":"2307.02719","published":"2026-04-08"},{"title":"Single-sample versus case-control sampling scheme for Positive Unlabeled data: the story of two scenarios","abstract":"In the paper we argue that performance of the classifiers based on Empirical Risk Minimization (ERM) for positive unlabeled data, which are designed for case-control sampling scheme may significantly deteriorate when applied to a single-sample scenario. We reveal why their behavior depends, in all but very specific cases, on the scenario. Also, we introduce a single-sample case analogue of the popular non-negative ri","authors":["Mielniczuk, Jan","Wawrzeńczyk, Adam"],"url":"https://arxiv.org/abs/2312.02095","arxiv_id":"2312.02095","published":"2026-04-08"},{"title":"An Encoding of Abstract Dialectical Frameworks into Higher-Order Logic","abstract":"An approach for encoding abstract dialectical frameworks and their semantics into classical higher-order logic is presented. Important properties and semantic relationships are formally encoded and proven using the proof assistant Isabelle/HOL. This approach allows for the computer-assisted analysis of abstract dialectical frameworks using automated and interactive reasoning tools within a uniform logic environment. ","authors":["Martina, Antoine","Steen, Alexander"],"url":"https://arxiv.org/abs/2312.08375","arxiv_id":"2312.08375","published":"2026-04-08"},{"title":"CodeMind: Evaluating Large Language Models for Code Reasoning","abstract":"Large Language Models (LLMs) have been widely used to automate programming tasks. Their capabilities have been evaluated by assessing the quality of generated code through tests or proofs. The extent to which they can reason about code is a critical question revealing important insights about their true capabilities. This paper introduces CodeMind, a framework designed to gauge the code reasoning abilities of LLMs th","authors":["Liu, Changshu","Chen, Yang","Jabbarvand, Reyhaneh"],"url":"https://arxiv.org/abs/2402.09664","arxiv_id":"2402.09664","published":"2026-04-08"},{"title":"The Umeyama algorithm for matching correlated Gaussian geometric models in the low-dimensional regime","abstract":"Motivated by the problem of matching two correlated random geometric graphs, we study the problem of matching two Gaussian geometric models correlated through a latent node permutation. Specifically, given an unknown permutation $π^*$ on $\\{1,\\ldots,n\\}$ and given $n$ i.i.d. pairs of correlated Gaussian vectors $\\{X_{π^*(i)},Y_i\\}$ in $\\mathbb{R}^d$ with noise parameter $σ$, we consider two types of (correlated) weig","authors":["Gong, Shuyang","Li, Zhangsong"],"url":"https://arxiv.org/abs/2402.15095","arxiv_id":"2402.15095","published":"2026-04-08"},{"title":"Towards Better Statistical Understanding of Watermarking LLMs","abstract":"In this paper, we study the problem of watermarking large language models (LLMs). We consider the trade-off between model distortion and detection ability and formulate it as a constrained optimization problem based on the red-green list watermarking algorithm. We show that the optimal solution to the optimization problem enjoys a nice analytical property which provides a better understanding and inspires the algorit","authors":["Cai, Zhongze","Liu, Shang","Wang, Hanzhao","Zhong, Huaiyang","Li, Xiaocheng"],"url":"https://arxiv.org/abs/2403.13027","arxiv_id":"2403.13027","published":"2026-04-08"},{"title":"Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo","abstract":"Optimal experimental design (OED) provides a systematic approach to quantify and maximize the value of experimental data. Under a Bayesian approach, conventional OED maximizes the expected information gain (EIG) on model parameters. However, we are often interested in not the parameters themselves, but predictive quantities of interest (QoIs) that depend on the parameters in a nonlinear manner. We present a computati","authors":["Zhong, Shijie","Shen, Wanggang","Catanach, Tommie","Huan, Xun"],"url":"https://arxiv.org/abs/2403.18072","arxiv_id":"2403.18072","published":"2026-04-08"},{"title":"Controllable Image Generation with Composed Parallel Token Prediction","abstract":"Conditional discrete generative models struggle to faithfully compose multiple input conditions. To address this, we derive a theoretically-grounded formulation for composing discrete probabilistic generative processes, with masked generation (absorbing diffusion) as a special case. Our formulation enables precise specification of novel combinations and numbers of input conditions that lie outside the training data, ","authors":["Stirling, Jamie","Al-Moubayed, Noura","Willcocks, Chris G.","Shum, Hubert P. H."],"url":"https://arxiv.org/abs/2405.06535","arxiv_id":"2405.06535","published":"2026-04-08"},{"title":"Solving a Stackelberg Game on Transportation Networks in a Dynamic Crime Scenario: A Mixed Approach on Multi-Layer Networks","abstract":"Interdicting a criminal with limited police resources is a challenging task as the criminal changes location over time. The size of the large transportation network further adds to the difficulty of this scenario. To tackle this issue, we consider the concept of a layered graph. At each time stamp, we create a copy of the entire transportation network to track the possible movements of both players, the attacker and ","authors":["Samanta, Sukanya","Kimura, Kei","Yokoo, Makoto","Dey, Palash"],"url":"https://arxiv.org/abs/2406.14514","arxiv_id":"2406.14514","published":"2026-04-08"},{"title":"Sim-CLIP: Unsupervised Siamese Adversarial Fine-Tuning for Robust and Semantically-Rich Vision-Language Models","abstract":"Vision-Language Models (VLMs) rely heavily on pretrained vision encoders to support downstream tasks such as image captioning, visual question answering, and zero-shot classification. Despite their strong performance, these encoders remain highly vulnerable to imperceptible adversarial perturbations, which can severely degrade both robustness and semantic quality in multimodal reasoning. In this work, we introduce Si","authors":["Hossain, Md Zarif","Imteaj, Ahmed"],"url":"https://arxiv.org/abs/2407.14971","arxiv_id":"2407.14971","published":"2026-04-08"},{"title":"The DeepSpeak Dataset","abstract":"Deepfakes represent a growing concern across domains such as disinformation, fraud, and non-consensual media. In particular, the rise of video conference and identity-driven attacks in high-stakes scenarios--such as impostor hiring--demands new forensic resources. Despite significant efforts to develop robust detection classifiers to distinguish the real from the fake, commonly used training datasets remain inadequat","authors":["Barrington, Sarah","Bohacek, Maty","Farid, Hany"],"url":"https://arxiv.org/abs/2408.05366","arxiv_id":"2408.05366","published":"2026-04-08"},{"title":"Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout","abstract":"Sleep stages play an important role in identifying sleep patterns and diagnosing sleep disorders. In this study, we present an automated sleep stage classifier called the Attentive Dilated Convolutional Neural Network (AttDiCNN), which uses deep learning methodologies to address challenges related to data heterogeneity, computational complexity, and reliable and automatic sleep staging. We employed a force-directed l","authors":["Jobayer, Md","Shawon, Md Mehedi Hasan","Mahmud, Tasfin","Antor, Md. Borhan Uddin","Chowdhury, Arshad M."],"url":"https://arxiv.org/abs/2409.01962","arxiv_id":"2409.01962","published":"2026-04-08"},{"title":"Recent Advances in Multimodal Affective Computing: An NLP Perspective","abstract":"Multimodal affective computing has gained increasing attention due to its broad applications in understanding human behavior and intentions, particularly in text-centric multimodal scenarios. Existing research spans diverse tasks, modalities, and modeling paradigms, yet lacks a unified perspective. In this survey, we systematically review recent advances from an NLP perspective, focusing on four representative tasks:","authors":["Hu, Guimin","Lyu, Weimin","Sun, Chang","Zhu, Zhihong","Gui, Lin","Cai, Ruichu"],"url":"https://arxiv.org/abs/2409.07388","arxiv_id":"2409.07388","published":"2026-04-08"},{"title":"TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment","abstract":"Code translation transforms code between programming languages while preserving functionality, which is critical in software development and maintenance. While traditional learning-based code translation methods have limited effectiveness due to the lack of sufficient parallel training data, Large Language Models (LLMs) have recently advanced this field with their strong code generation and comprehension capabilities","authors":["Yuan, Zhiqiang","Chen, Weitong","Wang, Hanlin","Peng, Xin","Chen, Zhenpeng","Lou, Yiling"],"url":"https://arxiv.org/abs/2409.19894","arxiv_id":"2409.19894","published":"2026-04-08"},{"title":"ForgeryGPT: A Multimodal LLM for Interpretable Image Forgery Detection and Localization","abstract":"Multimodal Large Language Models (MLLMs), such as GPT4o, have shown strong capabilities in visual reasoning and explanation generation. However, despite these strengths, they face significant challenges in the increasingly critical task of Image Forgery Detection and Localization (IFDL). Moreover, existing IFDL methods are typically limited to the learning of low-level semantic-agnostic clues and merely provide a sin","authors":["Zhang, Fanrui","Liu, Jiawei","Zhu, Jiaying","Sun, Esther","Li, Dong","Zhang, Qiang"],"url":"https://arxiv.org/abs/2410.10238","arxiv_id":"2410.10238","published":"2026-04-08"},{"title":"Interpreting Temporal Graph Neural Networks with Koopman Theory","abstract":"Spatiotemporal graph neural networks (STGNNs) have shown promising results in many domains, from forecasting to epidemiology. However, understanding the dynamics learned by these models and explaining their behaviour is significantly more difficult than for models that deal with static data. Inspired by Koopman theory, which allows a simple description of intricate, nonlinear dynamical systems, we introduce new expla","authors":["Guerra, Michele","Scardapane, Simone","Bianchi, Filippo Maria"],"url":"https://arxiv.org/abs/2410.13469","arxiv_id":"2410.13469","published":"2026-04-08"},{"title":"Cobblestone: A Divide-and-Conquer Approach for Automating Formal Verification","abstract":"Formal verification using proof assistants, such as Coq, is an effective way of improving software quality, but requires significant effort and expertise. Machine learning can automatically synthesize proofs, but such tools are able to prove only a fraction of desired software properties. We introduce Cobblestone, a divide-and-conquer approach for proof synthesis. Cobblestone uses a large language model (LLM) to gene","authors":["Kasibatla, Saketh Ram","Agarwal, Arpan","Brun, Yuriy","Lerner, Sorin","Ringer, Talia","First, Emily"],"url":"https://arxiv.org/abs/2410.19940","arxiv_id":"2410.19940","published":"2026-04-08"},{"title":"From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap","abstract":"The rapid expansion of foundation models (FMs), such as large language models (LLMs), has given rise to FMware, software systems that integrate FM(s) as core components. While building demonstration-level FMware is relatively straightforward, transitioning to production-ready systems presents numerous challenges, including reliability, high implementation costs, scalability, and compliance with privacy regulations. O","authors":["Rajbahadur, Gopi Krishnan","Oliva, Gustavo A.","Lin, Dayi","Shin, Jiho","Hassan, Ahmed E."],"url":"https://arxiv.org/abs/2410.20791","arxiv_id":"2410.20791","published":"2026-04-08"},{"title":"Why CNN Features Are not Gaussian: A Statistical Anatomy of Deep Representations","abstract":"Deep convolutional neural networks (CNNs) are commonly analyzed through geometric and linear-algebraic perspectives, yet the statistical distribution of their internal feature activations remains poorly understood. In many applications, deep features are implicitly treated as Gaussian when modeling densities. In this work, we empirically examine this assumption and show that it does not accurately describe the distri","authors":["Chapman, David","Farvardin, Parniyan"],"url":"https://arxiv.org/abs/2411.05183","arxiv_id":"2411.05183","published":"2026-04-08"}],"history":{"events":[{"year":2008,"text":"On board Soyuz TMA-12, Yi So-yeon became the first Korean astronaut.","url":"https://en.wikipedia.org/wiki/Soyuz_TMA-12"},{"year":1973,"text":"The Progress Party was founded in a movie theater in Oslo, Norway.","url":"https://en.wikipedia.org/wiki/Progress_Party_(Norway)"},{"year":1968,"text":"BOAC Flight 712 experienced an engine fire shortly after take-off from London Heathrow, leading to the deaths of five people on board, including flight attendant Jane Harrison, who was posthumously awarded a George Cross for heroism.","url":"https://en.wikipedia.org/wiki/BOAC_Flight_712"},{"year":1961,"text":"A large explosion on board the MV Dara in the Persian Gulf killed 238 people.","url":"https://en.wikipedia.org/wiki/MV_Dara"},{"year":1959,"text":"A team of computer scientists and others met to discuss the creation of a common business-oriented programming language, which became COBOL.","url":"https://en.wikipedia.org/wiki/Computer_science"},{"year":1943,"text":"Otto and Elise Hampel were executed in Berlin for performing acts of resistance against Nazism.","url":"https://en.wikipedia.org/wiki/Otto_and_Elise_Hampel"},{"year":1942,"text":"World War II: The United States Army Air Forces flew its first mission from India over the Hump (the eastern end of the Himalayas) to deliver materiel to China.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1933,"text":"The Australian state of Western Australia voted to secede from the federation, but efforts to implement the result proved to be unsuccessful.","url":"https://en.wikipedia.org/wiki/Western_Australia"},{"year":1917,"text":"World War I: German naval officer Wilhelm Werner murdered 19 captured crew members of the British cargo ship Torrington by submerging U-55 when they were on deck.","url":"https://en.wikipedia.org/wiki/World_War_I"},{"year":1911,"text":"American cartoonist Winsor McCay released the silent short film Little Nemo (featured), one of the earliest animated films.","url":"https://en.wikipedia.org/wiki/Winsor_McCay"},{"year":1904,"text":"Longacre Square in Midtown Manhattan was renamed Times Square after the New York Times building.","url":"https://en.wikipedia.org/wiki/Midtown_Manhattan"},{"year":1904,"text":"France and the United Kingdom signed the Entente Cordiale, agreeing to a peaceful coexistence after centuries of intermittent conflict.","url":"https://en.wikipedia.org/wiki/Entente_Cordiale"},{"year":1864,"text":"American Civil War: Late-arriving reinforcements helped Confederate forces rout the Union Army at the Battle of Mansfield.","url":"https://en.wikipedia.org/wiki/American_Civil_War"},{"year":1820,"text":"The Venus de Milo (pictured), an ancient Greek statue of a woman with its arms missing, was discovered on the island of Milos.","url":"https://en.wikipedia.org/wiki/Venus_de_Milo"},{"year":1630,"text":"Kiliaen van Rensselaer purchased land near present-day Albany, New York, to establish Rensselaerswyck, which became the most successful patroonship under the Dutch West India Company.","url":"https://en.wikipedia.org/wiki/Kiliaen_van_Rensselaer_(merchant)"},{"year":1271,"text":"Crusades: The Knights Hospitaller surrendered the Krak des Chevaliers, a castle in present-day Syria, to the army of the Mamluk sultan Baybars.","url":"https://en.wikipedia.org/wiki/Crusades"},{"year":876,"text":"Abbasid forces decisively defeated the Saffarid emir Ya'qub ibn Laith, forcing the latter to halt his advance into Iraq.","url":"https://en.wikipedia.org/wiki/Abbasid_Caliphate"},{"year":217,"text":"Roman emperor Caracalla (pictured) was assassinated near Harran and succeeded by his Praetorian Guard prefect Macrinus.","url":"https://en.wikipedia.org/wiki/Caracalla"}],"births":[{"year":2013,"text":"Big Justice, American social media personality","url":"https://en.wikipedia.org/wiki/A.J._%26_Big_Justice"},{"year":2005,"text":"Zaccharie Risacher, French basketball player","url":"https://en.wikipedia.org/wiki/Zaccharie_Risacher"},{"year":2002,"text":"Jamie Drysdale, Canadian ice hockey player","url":"https://en.wikipedia.org/wiki/Jamie_Drysdale"},{"year":2002,"text":"Viktória Forster, Slovak track and field athlete","url":"https://en.wikipedia.org/wiki/Vikt%C3%B3ria_Forster"},{"year":2002,"text":"Skai Jackson, American actress","url":"https://en.wikipedia.org/wiki/Skai_Jackson"},{"year":1999,"text":"CeeDee Lamb, American football player","url":"https://en.wikipedia.org/wiki/CeeDee_Lamb"},{"year":1998,"text":"Lavinia Valbonesi, Ecuadorian nutritionist, businesswoman and First Lady of Ecuador","url":"https://en.wikipedia.org/wiki/Lavinia_Valbonesi"},{"year":1997,"text":"Kim Woo-jin, South Korean singer","url":"https://en.wikipedia.org/wiki/Kim_Woojin"}],"deaths":[{"year":2025,"text":"Nelsy Cruz, Dominican politician, governor of Monte Cristi (born 1982)","url":"https://en.wikipedia.org/wiki/Nelsy_Cruz"},{"year":2024,"text":"Keith Barnes, Welsh-Australian rugby league player and coach (born 1934)","url":"https://en.wikipedia.org/wiki/Keith_Barnes"},{"year":2024,"text":"Peter Higgs, British physicist, Nobel Prize laureate (born 1929)","url":"https://en.wikipedia.org/wiki/Peter_Higgs"},{"year":2024,"text":"Ralph Puckett, American Army officer, Medal of Honor recipient (born 1926)","url":"https://en.wikipedia.org/wiki/Ralph_Puckett"},{"year":2022,"text":"Mimi Reinhardt, Jewish Austrian secretary (born 1915)","url":"https://en.wikipedia.org/wiki/Mimi_Reinhardt"},{"year":2020,"text":"Rick May, American-Canadian voice actor (born 1940)","url":"https://en.wikipedia.org/wiki/Rick_May"},{"year":2020,"text":"Abdul Momin Imambari, Bangladeshi Islamic scholar (born 1930)","url":"https://en.wikipedia.org/wiki/Abdul_Momin_Imambari"},{"year":2019,"text":"Josine Ianco-Starrels, Romanian-born American art curator (born 1926)","url":"https://en.wikipedia.org/wiki/Josine_Ianco-Starrels"}]},"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."}