{"date":"2026-03-13","schema_version":1,"captured_at":"2026-08-25T23:31:52.641Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"2026 Iran war","views":304183,"rank":4,"extract":"Since 28 February 2026, the United States and Israel have been at war with Iran and its regional allies. Hostilities broke out after US–Israeli airstrikes killed several Iranian officials, including Supreme Leader Ali Khamenei. The strikes were launched amid o","url":"https://en.wikipedia.org/wiki/2026_Iran_war","thumb":null},{"title":"Mojtaba Khamenei","views":135225,"rank":7,"extract":"Mojtaba Hosseini Khamenei is an Iranian Shia cleric and politician who has served as the third supreme leader of Iran since 2026. 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This task is multimodal by its very nature and, recently, baseline results achieved on the publicly available AIforCOVID dataset","authors":["Guarrasi, Valerio","Soda, Paolo"],"url":"https://arxiv.org/abs/2204.03772","arxiv_id":"2204.03772","published":"2026-03-13"},{"title":"Multimodal Explainability via Latent Shift applied to COVID-19 stratification","abstract":"We are witnessing a widespread adoption of artificial intelligence in healthcare. However, most of the advancements in deep learning in this area consider only unimodal data, neglecting other modalities. Their multimodal interpretation necessary for supporting diagnosis, prognosis and treatment decisions. In this work we present a deep architecture, which jointly learns modality reconstructions and sample classificat","authors":["Guarrasi, Valerio","Tronchin, Lorenzo","Albano, Domenico","Faiella, Eliodoro","Fazzini, Deborah","Santucci, Domiziana"],"url":"https://arxiv.org/abs/2212.14084","arxiv_id":"2212.14084","published":"2026-03-13"},{"title":"Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural network","abstract":"Group-equivariant convolutional neural networks (G-CNN) heavily rely on parameter sharing to increase CNN's data efficiency and performance. However, the parameter-sharing strategy greatly increases the computational burden for each added parameter, which hampers its application to deep neural network models. In this paper, we address these problems by proposing a non-parameter-sharing approach for group equivari","authors":["Zhao, Wenzhao","Wichtmann, Barbara D.","Albert, Steffen","Maurer, Angelika","Zöllner, Frank G.","Hesser, Jürgen"],"url":"https://arxiv.org/abs/2305.10110","arxiv_id":"2305.10110","published":"2026-03-13"},{"title":"A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Values","abstract":"In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical domain, our primary objective is to develop an AI model capable of dynamically handling this missing data. Additionally, we aim to leverage all accessible data, effectively analyzing both uncensored p","authors":["Caruso, Camillo Maria","Guarrasi, Valerio","Ramella, Sara","Soda, Paolo"],"url":"https://arxiv.org/abs/2307.11465","arxiv_id":"2307.11465","published":"2026-03-13"},{"title":"A Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography","abstract":"Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first needs intravenously administration of an iodinated contrast medium; then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image. The two scans are then combined to get a recombined image showing contrast enhancement. Despite CESM diagnostic advantages for breast cancer di","authors":["Rofena, Aurora","Guarrasi, Valerio","Sarli, Marina","Piccolo, Claudia Lucia","Sammarra, Matteo","Zobel, Bruno Beomonte"],"url":"https://arxiv.org/abs/2308.00471","arxiv_id":"2308.00471","published":"2026-03-13"},{"title":"Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation","abstract":"State-of-the-art methods for conditional average treatment effect (CATE) estimation make widespread use of representation learning. Here, the idea is to reduce the variance of the low-sample CATE estimation by a (potentially constrained) low-dimensional representation. However, low-dimensional representations can lose information about the observed confounders and thus lead to bias, because of which the validity of r","authors":["Melnychuk, Valentyn","Frauen, Dennis","Feuerriegel, Stefan"],"url":"https://arxiv.org/abs/2311.11321","arxiv_id":"2311.11321","published":"2026-03-13"},{"title":"Onflow: a model free, online portfolio allocation algorithm robust to transaction fees","abstract":"We introduce Onflow, a reinforcement learning method for optimizing portfolio allocation via gradient flows. Our approach dynamically adjusts portfolio allocations to maximize expected log returns while accounting for transaction costs. Using a softmax parameterization, Onflow updates allocations through an ordinary differential equation derived from gradient flow methods. This algorithm belongs to the large class of","authors":["Turinici, Gabriel","Brugiere, Pierre"],"url":"https://arxiv.org/abs/2312.05169","arxiv_id":"2312.05169","published":"2026-03-13"},{"title":"A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1","abstract":"Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings, resulting in clinical dataset with highly unbalanced therapy representation. To tackle this issue, we introduce ","authors":["Di Teodoro, Giulia","Siciliano, Federico","Guarrasi, Valerio","Vandamme, Anne-Mieke","Ghisetti, Valeria","Sönnerborg, Anders"],"url":"https://arxiv.org/abs/2312.17506","arxiv_id":"2312.17506","published":"2026-03-13"},{"title":"Domain-Independent Dynamic Programming","abstract":"For combinatorial optimization problems, model-based paradigms such as mixed-integer programming (MIP) and constraint programming (CP) aim to decouple modeling and solving a problem: the `holy grail' of declarative problem solving. We propose domain-independent dynamic programming (DIDP), a novel model-based paradigm based on dynamic programming (DP). While DP is not new, it has typically been implemented as a pr","authors":["Kuroiwa, Ryo","Beck, J. Christopher"],"url":"https://arxiv.org/abs/2401.13883","arxiv_id":"2401.13883","published":"2026-03-13"},{"title":"Estimating Canopy Height at Scale","abstract":"We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the re","authors":["Pauls, Jan","Zimmer, Max","Kelly, Una M.","Schwartz, Martin","Saatchi, Sassan","Ciais, Philippe"],"url":"https://arxiv.org/abs/2406.01076","arxiv_id":"2406.01076","published":"2026-03-13"},{"title":"Preserving Full Degradation Details for Blind Image Super-Resolution","abstract":"The performance of image super-resolution relies heavily on the accuracy of degradation information, especially under blind settings. Due to the absence of true degradation models in real-world scenarios, previous methods learn distinct representations by distinguishing different degradations in a batch. However, the most significant degradation differences may provide shortcuts for the learning of representations su","authors":["Liu, Hongda","Wang, Longguang","Zhang, Ye","Xue, Kaiwen","Zhou, Shunbo","Guo, Yulan"],"url":"https://arxiv.org/abs/2407.01299","arxiv_id":"2407.01299","published":"2026-03-13"},{"title":"Not Another Imputation Method: A Transformer-based Model for Missing Values in Tabular Datasets","abstract":"Handling missing values in tabular datasets presents a significant challenge in training and testing artificial intelligence models, an issue usually addressed using imputation techniques. Here we introduce &#34;Not Another Imputation Method&#34; (NAIM), a novel transformer-based model specifically designed to address this issue without the need for traditional imputation techniques. NAIM's ability to avoid the n","authors":["Caruso, Camillo Maria","Soda, Paolo","Guarrasi, Valerio"],"url":"https://arxiv.org/abs/2407.11540","arxiv_id":"2407.11540","published":"2026-03-13"},{"title":"A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications","abstract":"Deep learning has revolutionized biomedical research by providing sophisticated methods to handle complex, high-dimensional data. Multimodal deep learning (MDL) further enhances this capability by integrating diverse data types such as imaging, textual data, and genetic information, leading to more robust and accurate predictive models. In MDL, differently from early and late fusion methods, intermediate fusion stand","authors":["Guarrasi, Valerio","Aksu, Fatih","Caruso, Camillo Maria","Di Feola, Francesco","Rofena, Aurora","Ruffini, Filippo"],"url":"https://arxiv.org/abs/2408.02686","arxiv_id":"2408.02686","published":"2026-03-13"},{"title":"Enhancing Heterogeneous Multi-Agent Cooperation in Decentralized MARL via GNN-driven Intrinsic Rewards","abstract":"Multi-agent Reinforcement Learning (MARL) is emerging as a key framework for various sequential decision-making and control tasks. Unlike their single-agent counterparts, multi-agent systems necessitate successful cooperation among the agents. The deployment of these systems in real-world scenarios often requires decentralized training, a diverse set of agents, and learning from infrequent environmental reward signal","authors":["Monon, Jahir Sadik","Barua, Deeparghya Dutta","Khan, Md. Mosaddek"],"url":"https://arxiv.org/abs/2408.06503","arxiv_id":"2408.06503","published":"2026-03-13"},{"title":"Geometry of Singular Foliations and Learning Manifolds in ReLU Networks via the Data Information Matrix","abstract":"Understanding how real data is distributed in high dimensional spaces is the key to many tasks in machine learning. We want to provide a natural geometric structure on the space of data employing a ReLU neural network trained as a classifier. Through the Data Information Matrix (DIM), a variation of the Fisher information matrix, the model will discern a singular foliation structure on the space of data. We show that","authors":["Tron, Eliot","Fioresi, Rita"],"url":"https://arxiv.org/abs/2409.07412","arxiv_id":"2409.07412","published":"2026-03-13"},{"title":"Llettuce: An Open Source Natural Language Processing Tool for the Translation of Medical Terms into Uniform Clinical Encoding","abstract":"This paper introduces Llettuce, an open-source tool designed to address the complexities of converting medical terms into OMOP standard concepts. Unlike existing solutions such as the Athena database search and Usagi, which struggle with semantic nuances and require substantial manual input, Llettuce leverages advanced natural language processing, including large language models and fuzzy matching, to automate and en","authors":["Mitchell-White, James","Omdivar, Reza","Partridge, Benjamin","Urwin, Esmond","Sivakumar, Karthikeyan","Li, Ruizhe"],"url":"https://arxiv.org/abs/2410.09076","arxiv_id":"2410.09076","published":"2026-03-13"},{"title":"Stein Variational Evolution Strategies","abstract":"Stein Variational Gradient Descent (SVGD) is a highly efficient method to sample from an unnormalized probability distribution. However, the SVGD update relies on gradients of the log-density, which may not always be available. Existing gradient-free versions of SVGD make use of simple Monte Carlo approximations or gradients from surrogate distributions, both with limitations. To improve gradient-free Stein variation","authors":["Braun, Cornelius V.","Lange, Robert T.","Toussaint, Marc"],"url":"https://arxiv.org/abs/2410.10390","arxiv_id":"2410.10390","published":"2026-03-13"},{"title":"CARROT: A Learned Cost-Constrained Retrieval Optimization System for RAG","abstract":"Large Language Models (LLMs) have demonstrated impressive ability in generation and reasoning tasks but struggle with handling up-to-date knowledge, leading to inaccuracies or hallucinations. Retrieval-Augmented Generation (RAG) mitigates this by retrieving and incorporating external knowledge into input prompts. In particular, due to LLMs' context window limitations and long-context hallucinations, only the most","authors":["Wang, Ziting","Yuan, Haitao","Dong, Wei","Cong, Gao","Li, Feifei"],"url":"https://arxiv.org/abs/2411.00744","arxiv_id":"2411.00744","published":"2026-03-13"},{"title":"Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner","abstract":"Estimating causal quantities from observational data is crucial for understanding the safety and effectiveness of medical treatments. However, to make reliable inferences, medical practitioners require not only estimating averaged causal quantities, such as the conditional average treatment effect, but also understanding the randomness of the treatment effect as a random variable. This randomness is referred to as al","authors":["Melnychuk, Valentyn","Feuerriegel, Stefan","van der Schaar, Mihaela"],"url":"https://arxiv.org/abs/2411.03387","arxiv_id":"2411.03387","published":"2026-03-13"},{"title":"Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems","abstract":"In this work, we address unconstrained finite-sum optimization problems, with particular focus on instances originating in large scale deep learning scenarios. Our main interest lies in the exploration of the relationship between recent line search approaches for stochastic optimization in the overparametrized regime and momentum directions. First, we point out that combining these two elements with computational ben","authors":["Lapucci, Matteo","Pucci, Davide"],"url":"https://arxiv.org/abs/2411.07102","arxiv_id":"2411.07102","published":"2026-03-13"},{"title":"Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models","abstract":"We address the issue of the testability of instrumental variables derived from observational data. Most existing testable implications are centered on scenarios where the treatment is a discrete variable, e.g., instrumental inequality (Pearl, 1995), or where the effect is assumed to be constant, e.g., instrumental variables condition based on the principle of independent mechanisms (Burauel, 2023). However, treatment","authors":["Guo, Xichen","Li, Zheng","Huang, Biwei","Zeng, Yan","Geng, Zhi","Xie, Feng"],"url":"https://arxiv.org/abs/2411.12184","arxiv_id":"2411.12184","published":"2026-03-13"},{"title":"Finance-Informed Neural Network: Learning the Geometry of Option Pricing","abstract":"We propose a Finance-Informed Neural Network (FINN) for option pricing and hedging that integrates financial theory directly into machine learning. Instead of training on observed option prices, FINN is learned through a self-supervised replication objective based on dynamic hedging, ensuring economic consistency by construction. We show theoretically that minimizing replication error recovers the arbitrage-free pric","authors":["Aboussalah, Amine M.","Li, Xuanze","Chi, Cheng","Patel, Raj"],"url":"https://arxiv.org/abs/2412.12213","arxiv_id":"2412.12213","published":"2026-03-13"},{"title":"MARIA: a Multimodal Transformer Model for Incomplete Healthcare Data","abstract":"In healthcare, the integration of multimodal data is pivotal for developing comprehensive diagnostic and predictive models. However, managing missing data remains a significant challenge in real-world applications. We introduce MARIA (Multimodal Attention Resilient to Incomplete datA), a novel transformer-based deep learning model designed to address these challenges through an intermediate fusion strategy. Unlike co","authors":["Caruso, Camillo Maria","Soda, Paolo","Guarrasi, Valerio"],"url":"https://arxiv.org/abs/2412.14810","arxiv_id":"2412.14810","published":"2026-03-13"},{"title":"RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning","abstract":"Network simulation is pivotal in network modeling, assisting with tasks ranging from capacity planning to performance estimation. Traditional approaches such as Discrete Event Simulation (DES) face limitations in terms of computational cost and accuracy. This paper introduces RouteNet-Gauss, a novel integration of a testbed network with a Machine Learning (ML) model to address these challenges. By using the testbed a","authors":["Güemes-Palau, Carlos","Ferriol-Galmés, Miquel","Paillisse-Vilanova, Jordi","López-Brescó, Albert","Barlet-Ros, Pere","Cabellos-Aparicio, Albert"],"url":"https://arxiv.org/abs/2501.08848","arxiv_id":"2501.08848","published":"2026-03-13"},{"title":"Enhancing accuracy of uncertainty estimation in appearance-based gaze tracking with probabilistic evaluation and calibration","abstract":"Accurately knowing uncertainties in appearance-based gaze tracking is critical for ensuring reliable downstream applications. Due to the lack of individual uncertainty labels, current uncertainty-aware approaches adopt probabilistic models to acquire uncertainties by following distributions in the training dataset. Without regulations, this approach lets the uncertainty model build biases and overfits the training da","authors":["Zheng, Qiaojie","Zhang, Jiucai","Zhang, Xiaoli"],"url":"https://arxiv.org/abs/2501.14894","arxiv_id":"2501.14894","published":"2026-03-13"}],"history":{"events":[{"year":2013,"text":"Cardinal Jorge Mario Bergoglio was elected as Pope Francis, making him the first Jesuit pope, the first from the Americas, and the first from the Southern Hemisphere.","url":"https://en.wikipedia.org/wiki/2013_conclave"},{"year":2004,"text":"The Filipino singer Rachelle Ann Go won the reality talent show Search for a Star, performing a cover of Mariah Carey's \"Through the Rain\" in the grand final.","url":"https://en.wikipedia.org/wiki/Rachelle_Ann_Go"},{"year":2002,"text":"The Sutton Hoo visitor centre, featuring Rick Kirby's sculpture Sutton Hoo Helmet (pictured in present location) was unveiled by Nobel laureate Seamus Heaney.","url":"https://en.wikipedia.org/wiki/Sutton_Hoo"},{"year":1996,"text":"A mass shooting at a primary school occurred in Dunblane, Scotland, killing 16 children and a teacher and prompting tighter gun control in the United Kingdom.","url":"https://en.wikipedia.org/wiki/Dunblane_massacre"},{"year":1988,"text":"The 53.85 km (33.46 mi) Seikan Tunnel opened between the cities of Hakodate and Aomori, Japan.","url":"https://en.wikipedia.org/wiki/Seikan_Tunnel"},{"year":1986,"text":"Claiming the right of innocent passage, the American warships Yorktown and Caron entered Soviet territorial waters in the Black Sea.","url":"https://en.wikipedia.org/wiki/Innocent_passage"},{"year":1985,"text":"One of England's worst incidents of football hooliganism occurred when supporters of Luton Town and Millwall rioted before a match at Kenilworth Road stadium.","url":"https://en.wikipedia.org/wiki/Football_hooliganism"},{"year":1964,"text":"Kitty Genovese was murdered in New York City, prompting research into the bystander effect due to the false story that neighbors witnessed the killing and did nothing to help her.","url":"https://en.wikipedia.org/wiki/Murder_of_Kitty_Genovese"},{"year":1943,"text":"The Holocaust: Nazi troops began the final liquidation of the Kraków Ghetto in German-occupied Poland, sending about 2,000 Jews to the Płaszów labor camp (deportation pictured), with the remaining 5,000 either killed or sent to Auschwitz.","url":"https://en.wikipedia.org/wiki/The_Holocaust"},{"year":1920,"text":"The Kapp Putsch (participants pictured), an attempted coup aiming to undo the German Revolution of 1918–1919, briefly ousted the government of the Weimar Republic.","url":"https://en.wikipedia.org/wiki/Kapp_Putsch"},{"year":1888,"text":"The eruption of Ritter Island triggered tsunamis that killed up to 3,000 people on nearby islands.","url":"https://en.wikipedia.org/wiki/1888_Ritter_Island_eruption_and_tsunami"},{"year":1848,"text":"Klemens von Metternich was forced to resign as the foreign minister of Austria following student demonstrations in Vienna.","url":"https://en.wikipedia.org/wiki/Klemens_von_Metternich"},{"year":1845,"text":"German composer Felix Mendelssohn's Violin Concerto was premiered in Leipzig with Ferdinand David as the soloist.","url":"https://en.wikipedia.org/wiki/Felix_Mendelssohn"},{"year":1811,"text":"Napoleonic Wars: A British frigate squadron defeated a much larger squadron of French and Italian frigates and smaller vessels in the Battle of Lissa (depicted) in the Adriatic Sea.","url":"https://en.wikipedia.org/wiki/Napoleonic_Wars"},{"year":1781,"text":"William Herschel discovered the planet Uranus from the garden of his house in Bath, England, initially considering it to be a comet.","url":"https://en.wikipedia.org/wiki/William_Herschel"},{"year":1741,"text":"War of Jenkins' Ear: The British began an assault against Spanish forts in the Caribbean in the Battle of Cartagena de Indias.","url":"https://en.wikipedia.org/wiki/War_of_Jenkins'_Ear"},{"year":1697,"text":"Nojpetén, capital of the Itza Maya kingdom, fell to Spanish conquistadors, the final step in the Spanish conquest of Guatemala.","url":"https://en.wikipedia.org/wiki/Nojpet%C3%A9n"},{"year":1567,"text":"A Spanish mercenary army surprised a band of rebels at the Battle of Oosterweel in the Habsburg Netherlands, beginning the Eighty Years' War.","url":"https://en.wikipedia.org/wiki/Battle_of_Oosterweel"}],"births":[{"year":2004,"text":"Coco Gauff, American tennis player","url":"https://en.wikipedia.org/wiki/Coco_Gauff"},{"year":2002,"text":"Frank Gore Jr., American football player","url":"https://en.wikipedia.org/wiki/Frank_Gore_Jr."},{"year":2001,"text":"Beomgyu, South Korean singer-songwriter","url":"https://en.wikipedia.org/wiki/Beomgyu"},{"year":2001,"text":"Thomas Dearden, Australian rugby league player","url":"https://en.wikipedia.org/wiki/Tom_Dearden"},{"year":1998,"text":"Jay-Roy Grot, Dutch footballer","url":"https://en.wikipedia.org/wiki/Jay-Roy_Grot"},{"year":1998,"text":"Jack Harlow, American rapper, singer-songwriter, and actor","url":"https://en.wikipedia.org/wiki/Jack_Harlow"},{"year":1997,"text":"Pyper America, American model, actress, and musician","url":"https://en.wikipedia.org/wiki/Pyper_America"},{"year":1997,"text":"Landry Shamet, American basketball player","url":"https://en.wikipedia.org/wiki/Landry_Shamet"}],"deaths":[{"year":2025,"text":"John Feinstein, American sportswriter and commentator (born 1956)","url":"https://en.wikipedia.org/wiki/John_Feinstein"},{"year":2025,"text":"Raúl Grijalva, United States representative from Arizona (born 1948)","url":"https://en.wikipedia.org/wiki/Ra%C3%BAl_Grijalva"},{"year":2025,"text":"Sofia Gubaidulina, Russian-German pianist and composer (born 1931)","url":"https://en.wikipedia.org/wiki/Sofia_Gubaidulina"},{"year":2024,"text":"Philippe de Gaulle, French admiral (born 1921)","url":"https://en.wikipedia.org/wiki/Philippe_de_Gaulle"},{"year":2022,"text":"William Hurt, American actor (born 1950)","url":"https://en.wikipedia.org/wiki/William_Hurt"},{"year":2021,"text":"Marvelous Marvin Hagler, American professional boxer (born 1954)","url":"https://en.wikipedia.org/wiki/Marvelous_Marvin_Hagler"},{"year":2021,"text":"Murray Walker, English motorsport commentator and journalist (born 1923)","url":"https://en.wikipedia.org/wiki/Murray_Walker"},{"year":2018,"text":"Emily Nasrallah, Lebanese writer and women's rights activist. (born 1931)","url":"https://en.wikipedia.org/wiki/Emily_Nasrallah"}]},"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."}