{"date":"2026-05-25","schema_version":1,"captured_at":"2026-08-25T23:09:47.944Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Obsession (2025 film)","views":231743,"rank":5,"extract":"Obsession is a 2025 American supernatural horror film written, directed, and edited by Curry Barker. 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Scripts produced with MASC include (1) English descriptions of sub-events that comprise a larger, complex event; (2) event types for each of those events; (3) a record of entities expected to participate in multiple sub-events; and (4) temporal sequencing between the sub-events. MASC automates portions of the s","authors":["Ciosici, Manuel R.","Cummings, Joseph","DeHaven, Mitchell","Hedges, Alex","Kankanampati, Yash","Lee, Dong-Ho"],"url":"https://arxiv.org/abs/2101.05400","arxiv_id":"2101.05400","published":"2026-05-25"},{"title":"Thermal transmittance prediction based on the application of artificial neural networks on heat flux method results","abstract":"Deep energy renovation of building stock came more into focus in the European Union due to energy efficiency related directives. Many buildings that must undergo deep energy renovation are old and may lack design/renovation documentation, or possible degradation of materials might have occurred in building elements over time. Thermal transmittance (i.e. U-value) is one of the most important parameters for determining","authors":["Gumbarević, Sanjin","Milovanović, Bojan","Gaši, Mergim","Bagarić, Marina"],"url":"https://arxiv.org/abs/2103.14995","arxiv_id":"2103.14995","published":"2026-05-25"},{"title":"Perhaps PTLMs Should Go to School -- A Task to Assess Open Book and Closed Book QA","abstract":"Our goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e.g., an introductory college textbook or a manual. PTLMs have shown great success in many question-answering tasks, given significant supervised training, but much less so in zero-shot settings. We propose a new task that includes tw","authors":["Ciosici, Manuel R.","Cecil, Joe","Hedges, Alex","Lee, Dong-Ho","Freedman, Marjorie","Weischedel, Ralph"],"url":"https://arxiv.org/abs/2110.01552","arxiv_id":"2110.01552","published":"2026-05-25"},{"title":"Remember what you did so you know what to do next","abstract":"We explore using a moderately sized large language model (GPT-J 6B parameters) to create a plan for a simulated robot to achieve 30 classes of goals in ScienceWorld, a text game simulator for elementary science experiments. Previously published empirical work claimed that large language models (LLMs) are a poor fit (Wang et al., 2022) compared to reinforcement learning. Using the Markov assumption (a single previous ","authors":["Ciosici, Manuel R.","Hedges, Alex","Kankanampati, Yash","Martin, Justin","Freedman, Marjorie","Weischedel, Ralph"],"url":"https://arxiv.org/abs/2311.01468","arxiv_id":"2311.01468","published":"2026-05-25"},{"title":"Moonwalk: Inverse-Forward Differentiation","abstract":"Backpropagation's main limitation is its need to store intermediate activations (residuals) during the forward pass, which restricts the depth of trainable networks. This raises a fundamental question: can we avoid storing these activations? We address this by revisiting the structure of gradient computation. Backpropagation computes gradients through a sequence of vector-Jacobian products, an operation that is g","authors":["Krylov, Dmitrii","Karamzade, Armin","Fox, Roy"],"url":"https://arxiv.org/abs/2402.14212","arxiv_id":"2402.14212","published":"2026-05-25"},{"title":"ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection","abstract":"Post-hoc out-of-distribution (OOD) detection has garnered intensive attention in reliable machine learning. Many efforts have been dedicated to deriving score functions based on logits, distances, or rigorous data distribution assumptions to identify low-scoring OOD samples. Nevertheless, these estimate scores may fail to accurately reflect the true data density or impose impractical constraints. To provide a unified","authors":["Peng, Bo","Luo, Yadan","Zhang, Yonggang","Li, Yixuan","Fang, Zhen"],"url":"https://arxiv.org/abs/2402.17888","arxiv_id":"2402.17888","published":"2026-05-25"},{"title":"RT-NeRV: Rethinking Hybrid Neural Representations for Video via Residual Tokenization","abstract":"Neural Representations for Videos(NeRV) have emerged as a promising paradigm for video compression by representing videos as compact neural networks with efficient decoding. Hybrid NeRV methods further improve reconstruction quality through content adaptive embeddings, but still struggle to preserve fine details at low bitrates. A key limitation is that shallow residual support in formation, although highly beneficia","authors":["Xu, Yunjie","Feng, Xiang","Wang, Chengkai","Liew, Alan Wee-Chung","Yin, Xuefei","Zhu, Yanming"],"url":"https://arxiv.org/abs/2403.12401","arxiv_id":"2403.12401","published":"2026-05-25"},{"title":"Efficient Gradient Estimation for Parameterized Quantum Systems with Lie Algebraic Symmetries","abstract":"Gradient estimation is a central challenge in training parameterized quantum circuits (PQCs) for hybrid quantum-classical optimization and learning problems. This difficulty arises from several factors, including the exponential dimensionality of the Hilbert spaces and the information loss in quantum measurements. Existing estimators, such as finite difference and the parameter shift rule, often fail to adequately ad","authors":["Heidari, Mohsen","Mozakka, Masih","Szpankowski, Wojciech"],"url":"https://arxiv.org/abs/2404.05108","arxiv_id":"2404.05108","published":"2026-05-25"},{"title":"Nonlinear Transformations Against Unlearnable Datasets","abstract":"Automated scraping stands out as a common method for collecting data in deep learning models without the authorization of data owners. Recent studies have begun to tackle the privacy concerns associated with this data collection method. Notable approaches include Deepconfuse, error-minimizing, error-maximizing (also known as adversarial poisoning), Neural Tangent Generalization Attack, synthetic, autoregressive, One-","authors":["Hapuarachchi, Thushari","Lin, Jing","Xiong, Kaiqi","Rahouti, Mohamed","Ost, Gitte"],"url":"https://arxiv.org/abs/2406.02883","arxiv_id":"2406.02883","published":"2026-05-25"},{"title":"BVI-RLV: A Fully Registered Dataset for Low-Light Video Enhancement","abstract":"Low-light videos often exhibit spatiotemporally incoherent noise, compromising visibility and degrading performance in computer vision applications. A major challenge for enhancing such content using deep learning lies in the scarcity of pixel-aligned, high-quality training data. We introduce BVI-RLV, a fully registered low-light video dataset comprising over 30k paired frames from 40 diverse scenes under two low-lig","authors":["Lin, Ruirui","Huang, Guoxi","Lin, Joanne","Sun, Qi","Malyugina, Alexandra","Bull, David R"],"url":"https://arxiv.org/abs/2407.03535","arxiv_id":"2407.03535","published":"2026-05-25"},{"title":"Vector Retrieval with Similarity and Diversity: How Hard Is It?","abstract":"Dense vector retrieval is an important building block of modern machine learning systems, underlying applications ranging from semantic search to retrieval-augmented generation and knowledge-intensive reasoning. Beyond retrieving items that are individually similar to a query, many applications require a set of results that is also diverse, complementary, and collectively informative. Balancing similarity and diversi","authors":["Gao, Hang","Deng, Dong","Zhang, Yongfeng"],"url":"https://arxiv.org/abs/2407.04573","arxiv_id":"2407.04573","published":"2026-05-25"},{"title":"Universal Matrix Multiplication on Quantum Computer","abstract":"As the most central and computationally intensive component of deep neural networks, the execution efficiency of matrix multiplication directly determines the training and inference performance of models. Harnessing the parallel processing capabilities afforded by quantum superposition and entanglement to reshape matrix multiplication implementations has become a promising entry point for optimising underlying quantu","authors":["Yao, Jiaqi","Huang, Tianjian","Cai, Zipeng","Liu, Ding"],"url":"https://arxiv.org/abs/2408.03085","arxiv_id":"2408.03085","published":"2026-05-25"},{"title":"Heterogeneous Sheaf Neural Networks","abstract":"Heterogeneous graphs, whose nodes and edges can belong to different types and feature spaces, arise in many real-world domains, including biology, recommendation, social networks, and computer systems. Existing heterogeneous graph neural networks typically handle this heterogeneity at the architectural level through relation-specific modules, meta-path machinery or type-aware attention, which often leads to increasin","authors":["Braithwaite, Luke","Borgi, Alessio","Onorato, Gabriele","Tarantelli, Kristjan","Restuccia, Francesco","Silvestri, Fabrizio"],"url":"https://arxiv.org/abs/2409.08036","arxiv_id":"2409.08036","published":"2026-05-25"},{"title":"A comprehensive evaluation of pretraining strategies for channel-agnostic contrastive self-supervision of biosignals","abstract":"Contrastive learning yields impressive results for self-supervision in computer vision. The approach relies on the creation of positive pairs, something which is often achieved through augmentations. However, for multivariate time series effective augmentations can be difficult to design. Additionally, the number of input channels for biosignal datasets often varies from application to application, limiting the usefu","authors":["Brüsch, Thea","Schmidt, Mikkel N.","Alstrøm, Tommy S."],"url":"https://arxiv.org/abs/2410.19842","arxiv_id":"2410.19842","published":"2026-05-25"},{"title":"CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks","abstract":"We introduce the CRONOS algorithm for convex optimization of two-layer neural networks. CRONOS is the first algorithm capable of scaling to high-dimensional datasets such as ImageNet, which are ubiquitous in modern deep learning. This significantly improves upon prior work, which has been restricted to downsampled versions of MNIST and CIFAR-10. Taking CRONOS as a primitive, we then develop a new algorithm called CRO","authors":["Feng, Miria","Frangella, Zachary","Pilanci, Mert"],"url":"https://arxiv.org/abs/2411.01088","arxiv_id":"2411.01088","published":"2026-05-25"},{"title":"A Tale of Two Cities: Pessimism and Opportunism in Offline Dynamic Pricing","abstract":"We study offline dynamic pricing when historical data provide incomplete coverage of the price space such that some candidate prices, including the optimal one, may be entirely unobserved. This setting is common in practice and is especially difficult in dynamic environments. Existing offline reinforcement learning methods typically rely on full or partial coverage and can therefore perform poorly in such settings. W","authors":["Bian, Zeyu","Wang, Lan","Qi, Zhengling"],"url":"https://arxiv.org/abs/2411.08126","arxiv_id":"2411.08126","published":"2026-05-25"},{"title":"SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks","abstract":"Deep reinforcement learning (DRL) has achieved remarkable success in various research domains. However, its reliance on neural networks results in a lack of transparency, which limits its practical applications. To achieve explainability, decision trees have emerged as a popular and promising alternative to neural networks. Nonetheless, due to their limited expressiveness, traditional decision trees struggle with hig","authors":["Wen, Yongyan","Li, Siyuan","Zuo, Rongchang","Yuan, Lei","Mao, Hangyu","Liu, Peng"],"url":"https://arxiv.org/abs/2411.12173","arxiv_id":"2411.12173","published":"2026-05-25"},{"title":"SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation","abstract":"Model merging combines independently trained models into a single multi-task model. However, most existing approaches focus primarily on avoiding task interference. We argue that its greater potential lies in enabling task synergy, where tasks actively improve one another. We identify cross-task performance, defined by compatibility between encoders and predictors across tasks, as a key indicator of merge quality. We","authors":["Jung, Aecheon","Lee, Seunghwan","Han, Dongyoon","Hong, Sungeun"],"url":"https://arxiv.org/abs/2412.19098","arxiv_id":"2412.19098","published":"2026-05-25"},{"title":"XAttnMark: Learning Robust Audio Watermarking with Cross-Attention","abstract":"The rapid proliferation of generative audio synthesis and editing technologies has raised serious concerns about copyright infringement, data provenance, and the spread of misinformation via deepfake audio. Watermarking offers a proactive solution by embedding imperceptible yet identifiable and traceable signals into audio content. While recent neural network-based watermarking methods like WavMark and AudioSeal have","authors":["Liu, Yixin","Lu, Lie","Jin, Jihui","Sun, Lichao","Fanelli, Andrea"],"url":"https://arxiv.org/abs/2502.04230","arxiv_id":"2502.04230","published":"2026-05-25"},{"title":"TerraQ: Spatiotemporal Question-Answering on Satellite Image Archives","abstract":"TerraQ is a spatiotemporal question-answering engine for satellite image archives. It is a natural language processing system that is built to process requests for satellite images satisfying certain criteria. The requests can refer to image metadata and entities from a specialized knowledge base (e.g., the Emilia-Romagna region). With it, users can make requests like "Give me a hundred images of rivers near port","authors":["Kefalidis, Sergios-Anestis","Plas, Konstantinos","Koubarakis, Manolis"],"url":"https://arxiv.org/abs/2502.04415","arxiv_id":"2502.04415","published":"2026-05-25"},{"title":"Targeted Regularization for Causal Effect Estimation with Exponential Dispersion Family Outcomes","abstract":"Neural Networks (NNs) for causal effect estimation have shown strong empirical performance, yet endowing them with desirable semiparametric properties -- doubly robustness and fast convergence rates -- remains challenging. A common approach to address this is targeted regularization, which modifies the objective function of NNs. However, existing work on neural causal effect estimation is largely limited to continuou","authors":["Li, Jiahong","Yang, Zeqin","Xu, Jixing","Hua, Enzheng","Zou, Zhichao","Zhen, Peng"],"url":"https://arxiv.org/abs/2502.07295","arxiv_id":"2502.07295","published":"2026-05-25"},{"title":"Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs","abstract":"State-of-the-art methods for forecasting irregularly sampled time series with missing values predominantly rely on just four datasets and a few small toy examples for evaluation. While ordinary differential equations (ODE) are the prevalent models in science and engineering, a baseline model that forecasts a constant value outperforms ODE-based models from the last five years on three of these existing datasets. This","authors":["Klötergens, Christian","Yalavarthi, Vijaya Krishna","Scholz, Randolf","Stubbemann, Maximilian","Born, Stefan","Schmidt-Thieme, Lars"],"url":"https://arxiv.org/abs/2502.07489","arxiv_id":"2502.07489","published":"2026-05-25"},{"title":"Causal Additive Models with Unobserved Causal Paths and Backdoor Paths","abstract":"Causal additive models provide a tractable yet expressive framework for causal discovery in the presence of hidden variables. When unobserved backdoor or causal paths exist between two variables, their causal relationship is often unidentifiable under existing theories. We establish sufficient conditions under which causal directions can be identified in many such cases. These conditions rely on new characterizations","authors":["Pham, Thong","Maeda, Takashi Nicholas","Shimizu, Shohei"],"url":"https://arxiv.org/abs/2502.07646","arxiv_id":"2502.07646","published":"2026-05-25"},{"title":"Robust Counterfactual Inference in Markov Decision Processes","abstract":"This paper addresses a key limitation in existing counterfactual inference methods for Markov Decision Processes (MDPs). Current approaches assume a specific causal model to make counterfactuals identifiable. However, there are usually many causal models that align with the observational and interventional distributions of an MDP, each yielding different counterfactual distributions, so fixing a particular causal mod","authors":["Lally, Jessica","Kazemi, Milad","Paoletti, Nicola"],"url":"https://arxiv.org/abs/2502.13731","arxiv_id":"2502.13731","published":"2026-05-25"},{"title":"Diffusion and Flow Matching Models for Tabular Data: A Survey","abstract":"Deep generative models have made rapid progress in image, text, audio, and video generation, and are increasingly being applied to structured records. For tabular data, however, generative modeling remains difficult: a dataset may contain numerical and categorical attributes, missing values, sensitive fields, imbalanced categories, complex feature dependencies, and domain constraints. Earlier tabular data modeling me","authors":["Li, Zhong","Huang, Qi","Yang, Lincen","Shi, Jiayang","Yang, Zhao","van Stein, Niki"],"url":"https://arxiv.org/abs/2502.17119","arxiv_id":"2502.17119","published":"2026-05-25"}],"history":{"events":[{"year":2020,"text":"George Floyd, a black American man, was murdered (memorial pictured) during an arrest by a white police officer in Minneapolis, sparking protests in the U.S. and other countries.","url":"https://en.wikipedia.org/wiki/George_Floyd"},{"year":2013,"text":"Naxalite insurgents of the Communist Party of India (Maoist) attacked a convoy of Indian National Congress leaders in the state of Chhattisgarh, causing at least 27 deaths.","url":"https://en.wikipedia.org/wiki/Naxalite%E2%80%93Maoist_insurgency"},{"year":2012,"text":"In a test flight, SpaceX's Dragon became the first commercial spacecraft to rendezvous and berth with the International Space Station.","url":"https://en.wikipedia.org/wiki/SpaceX_COTS_Demo_Flight_2"},{"year":2011,"text":"The final episode of The Oprah Winfrey Show, the highest-rated daytime talk show in U.S. television history, hosted by Oprah Winfrey (pictured), was broadcast.","url":"https://en.wikipedia.org/wiki/The_Oprah_Winfrey_Show"},{"year":2009,"text":"North Korea conducted a nuclear test and several other missile tests that were widely condemned internationally and led to sanctions from the United Nations Security Council.","url":"https://en.wikipedia.org/wiki/2009_North_Korean_nuclear_test"},{"year":1979,"text":"Six-year-old Etan Patz disappeared on his way to school in New York City, and later became one of the first missing children to have his picture featured on milk cartons.","url":"https://en.wikipedia.org/wiki/Disappearance_of_Etan_Patz"},{"year":1979,"text":"During takeoff from O'Hare International Airport in Chicago, an engine detached from American Airlines Flight 191, causing a crash that killed 273 people in the deadliest aviation accident in United States history.","url":"https://en.wikipedia.org/wiki/O'Hare_International_Airport"},{"year":1971,"text":"Joetha Collier, a recent high school graduate, was killed in Drew, Mississippi, in a case that attracted widespread attention from civil rights activists.","url":"https://en.wikipedia.org/wiki/Killing_of_Joetha_Collier"},{"year":1967,"text":"Having purged a group of rivals, Supreme Leader of North Korea Kim Il Sung delivered the \"May 25 teaching\", entrenching his son Kim Jong Il as his designated successor.","url":"https://en.wikipedia.org/wiki/Kapsan_faction_incident"},{"year":1962,"text":"The Baltimore Steam Packet Company, the last overnight steamboat service in the United States, went out of business.","url":"https://en.wikipedia.org/wiki/Baltimore_Steam_Packet_Company"},{"year":1961,"text":"In an address to Congress, U.S. president John F. Kennedy announced his support for the Apollo program, with \"the goal, before this decade is out, of landing a man on the Moon and returning him safely to the Earth\".","url":"https://en.wikipedia.org/wiki/Joint_session_of_the_United_States_Congress"},{"year":1961,"text":"A fire broke out at a squatter settlement in Bukit Ho Swee, Singapore, rendering approximately 16,000 people homeless.","url":"https://en.wikipedia.org/wiki/Bukit_Ho_Swee_fire"},{"year":1955,"text":"Joe Brown and George Band, members of the British Kangchenjunga expedition, made the first ascent of the world's third-highest mountain but deliberately did not set foot on the summit.","url":"https://en.wikipedia.org/wiki/Joe_Brown_(climber)"},{"year":1944,"text":"The Wehrmacht and their collaborationist allies launched Operation Rösselsprung, a failed attempt to assassinate the Yugoslav Partisan leader Josip Broz Tito.","url":"https://en.wikipedia.org/wiki/Wehrmacht"},{"year":1940,"text":"Second World War: German troops captured Boulogne-sur-Mer, France, forcing British forces to evacuate via Dunkirk.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1936,"text":"Employees of the Remington Rand company began an 11-month strike action, during which time the company executives developed the notorious \"Mohawk Valley formula\" to intimidate the strikers.","url":"https://en.wikipedia.org/wiki/Remington_Rand"},{"year":1895,"text":"Oscar Wilde was sentenced by Alfred Wills to two years of hard labor for gross indecency.","url":"https://en.wikipedia.org/wiki/Oscar_Wilde"},{"year":1878,"text":"Gilbert and Sullivan's comic opera H.M.S. Pinafore (poster featured) premiered at the Opera Comique in London.","url":"https://en.wikipedia.org/wiki/Gilbert_and_Sullivan"}],"births":[{"year":2002,"text":"Cam Ward, American football player","url":"https://en.wikipedia.org/wiki/Cam_Ward_(American_football)"},{"year":2001,"text":"Chloé Lukasiak, American actress and dancer","url":"https://en.wikipedia.org/wiki/Chlo%C3%A9_Lukasiak"},{"year":2000,"text":"Claire Liu, American tennis player","url":"https://en.wikipedia.org/wiki/Claire_Liu"},{"year":1999,"text":"Brec Bassinger, American actress","url":"https://en.wikipedia.org/wiki/Brec_Bassinger"},{"year":1999,"text":"Ibrahima Konaté, French footballer","url":"https://en.wikipedia.org/wiki/Ibrahima_Konat%C3%A9"},{"year":1996,"text":"David Pastrňák, Czech ice hockey player","url":"https://en.wikipedia.org/wiki/David_Pastr%C5%88%C3%A1k"},{"year":1995,"text":"Kagiso Rabada, South African cricketer","url":"https://en.wikipedia.org/wiki/Kagiso_Rabada"},{"year":1994,"text":"Matt Murray, Canadian ice hockey player","url":"https://en.wikipedia.org/wiki/Matt_Murray_(ice_hockey%2C_born_1994)"}],"deaths":[{"year":2026,"text":"Sonny Rollins, American jazz musician (born 1930)","url":"https://en.wikipedia.org/wiki/Sonny_Rollins"},{"year":2024,"text":"Grayson Murray, American professional golfer (born 1993)","url":"https://en.wikipedia.org/wiki/Grayson_Murray"},{"year":2024,"text":"Albert S. Ruddy, Canadian film producer (born 1930)","url":"https://en.wikipedia.org/wiki/Albert_S._Ruddy"},{"year":2024,"text":"Richard M. Sherman, American songwriter (born 1928)","url":"https://en.wikipedia.org/wiki/Richard_M._Sherman"},{"year":2024,"text":"Johnny Wactor, American actor (born 1986)","url":"https://en.wikipedia.org/wiki/Johnny_Wactor"},{"year":2023,"text":"Joy McKean, Australian country music singer-songwriter (born 1930)","url":"https://en.wikipedia.org/wiki/Joy_McKean"},{"year":2022,"text":"Morton L. Janklow, American literary agent (born 1930)","url":"https://en.wikipedia.org/wiki/Morton_L._Janklow"},{"year":2021,"text":"John Warner, American attorney and politician (born 1927)","url":"https://en.wikipedia.org/wiki/John_Warner"}]},"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."}