{"date":"2026-03-20","schema_version":1,"captured_at":"2026-08-25T23:32:38.403Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":17},"attention":[{"title":"Dhurandhar: The Revenge","views":727683,"rank":3,"extract":"Dhurandhar: The Revenge is a 2026 Indian Hindi-language spy action-thriller film written and directed by Aditya Dhar. It is produced by Dhar, Lokesh Dhar, and Jyoti Deshpande under Jio Studios and B62 Studios. It is a sequel to the 2025 film Dhurandhar and the","url":"https://en.wikipedia.org/wiki/Dhurandhar%3A_The_Revenge","thumb":"https://upload.wikimedia.org/wikipedia/en/c/c8/Dhurandhar-_The_Revenge_poster.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail_unscaled"},{"title":"Cesar Chavez","views":378399,"rank":4,"extract":"Cesario Estrada \"Cesar\" Chavez was an American labor unionist and political activist. 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This work provides a comprehensive review of methods, datasets, and evaluation metrics across four such domains: aesthetics, personali","authors":["Khalid, Laila","Gong, Wei"],"url":"https://arxiv.org/abs/2101.08301","arxiv_id":"2101.08301","published":"2026-03-20"},{"title":"Improved Learning Rates for Stochastic Optimization","abstract":"Stochastic optimization is a cornerstone of modern machine learning. This paper studies the generalization performance of two classical stochastic optimization algorithms: stochastic gradient descent (SGD) and Nesterov's accelerated gradient (NAG). We establish new learning rates for both algorithms, with improved guarantees in some settings or comparable rates under weaker assumptions in others. We also provide ","authors":["Li, Shaojie","Tang, Pengwei","Liu, Yong"],"url":"https://arxiv.org/abs/2107.08686","arxiv_id":"2107.08686","published":"2026-03-20"},{"title":"Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings","abstract":"Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces. For contextualized embeddings, alignment becomes more complex as we additionally take context into consideration. In this work, we propose using Optimal Transport (OT) as an alignment objective during fine-tuning to further i","authors":["Alqahtani, Sawsan","Lalwani, Garima","Zhang, Yi","Romeo, Salvatore","Mansour, Saab"],"url":"https://arxiv.org/abs/2110.02887","arxiv_id":"2110.02887","published":"2026-03-20"},{"title":"&#34;Calibeating&#34;: Beating Forecasters at Their Own Game","abstract":"In order to identify expertise, forecasters should not be tested by their calibration score, which can always be made arbitrarily small, but rather by their Brier score. The Brier score is the sum of the calibration score and the refinement score; the latter measures how good the sorting into bins with the same forecast is, and thus attests to &#34;expertise.&#34; This raises the question of whether one can gain cali","authors":["Foster, Dean P.","Hart, Sergiu"],"url":"https://arxiv.org/abs/2209.04892","arxiv_id":"2209.04892","published":"2026-03-20"},{"title":"Inverse classification with logistic and softmax classifiers: efficient optimization","abstract":"In recent years, a certain type of problems have become of interest where one wants to query a trained classifier. Specifically, one wants to find the closest instance to a given input instance such that the classifier's predicted label is changed in a desired way. Examples of these &#34;inverse classification&#34; problems are counterfactual explanations, adversarial examples and model inversion. All of them are","authors":["Carreira-Perpiñán, Miguel Á.","Hada, Suryabhan Singh"],"url":"https://arxiv.org/abs/2309.08945","arxiv_id":"2309.08945","published":"2026-03-20"},{"title":"Algebras of actions in an agent's representations of the world","abstract":"In this paper, we propose a framework to extract the algebra of the transformations of worlds from the perspective of an agent. As a starting point, we use our framework to reproduce the symmetry-based representations from the symmetry-based disentangled representation learning (SBDRL) formalism proposed by [1]; only the algebra of transformations of worlds that form groups can be described using symmetry-based repre","authors":["Dean, Alexander","Alonso, Eduardo","Mondragon, Esther"],"url":"https://arxiv.org/abs/2310.01536","arxiv_id":"2310.01536","published":"2026-03-20"},{"title":"BarcodeBERT: Transformers for Biodiversity Analysis","abstract":"In the global challenge of understanding and characterizing biodiversity, short species-specific genomic sequences known as DNA barcodes play a critical role, enabling fine-grained comparisons among organisms within the same kingdom of life. Although machine learning algorithms specifically designed for the analysis of DNA barcodes are becoming more popular, most existing methodologies rely on generic supervised trai","authors":["Arias, Pablo Millan","Sadjadi, Niousha","Safari, Monireh","Gong, ZeMing","Wang, Austin T.","Haurum, Joakim Bruslund"],"url":"https://arxiv.org/abs/2311.02401","arxiv_id":"2311.02401","published":"2026-03-20"},{"title":"Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Dataset","abstract":"We present PD-REAL, a novel large-scale dataset for unsupervised anomaly detection (AD) in the 3D domain. It is motivated by the fact that 2D-only representations in the AD task may fail to capture the geometric structures of anomalies due to uncertainty in lighting conditions or shooting angles. PD-REAL consists entirely of Play-Doh models for 15 object categories and focuses on the analysis of potential benefits fr","authors":["Qin, Jianjian","Zhang, Chao","Gu, Chunzhi","Wang, Zi","Yu, Jun","Wei, Yijin"],"url":"https://arxiv.org/abs/2311.04095","arxiv_id":"2311.04095","published":"2026-03-20"},{"title":"Hidden yet quantifiable: A lower bound for confounding strength using randomized trials","abstract":"In the era of fast-paced precision medicine, observational studies play a major role in properly evaluating new treatments in clinical practice. Yet, unobserved confounding can significantly compromise causal conclusions drawn from non-randomized data. We propose a novel strategy that leverages randomized trials to quantify unobserved confounding. First, we design a statistical test to detect unobserved confounding w","authors":["De Bartolomeis, Piersilvio","Abad, Javier","Donhauser, Konstantin","Yang, Fanny"],"url":"https://arxiv.org/abs/2312.03871","arxiv_id":"2312.03871","published":"2026-03-20"},{"title":"Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods","abstract":"In the past several years, the last-iterate convergence of the Stochastic Gradient Descent (SGD) algorithm has triggered people's interest due to its good performance in practice but lack of theoretical understanding. For Lipschitz convex functions, different works have established the optimal $O(\\log(1/δ)\\log T/\\sqrt{T})$ or $O(\\sqrt{\\log(1/δ)/T})$ high-probability convergence rates for the final iterate, where ","authors":["Liu, Zijian","Zhou, Zhengyuan"],"url":"https://arxiv.org/abs/2312.08531","arxiv_id":"2312.08531","published":"2026-03-20"},{"title":"Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts","abstract":"We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts, such as the conditional average treatment effect and conditional relative risk. The EP-learning framework enjoys the same oracle efficiency as Neyman-orthogonal learning strategies, such as DR-learning and R-learning, while addressing some of their primary drawbacks: (i) their practical applicability ","authors":["van der Laan, Lars","Carone, Marco","Luedtke, Alex"],"url":"https://arxiv.org/abs/2402.01972","arxiv_id":"2402.01972","published":"2026-03-20"},{"title":"On Minimal Depth in Neural Networks","abstract":"Understanding the relationship between the depth of a neural network and its representational capacity is a central problem in deep learning theory. In this work, we develop a geometric framework to analyze the expressivity of ReLU networks with the notion of depth complexity for convex polytopes. The depth of a polytope recursively quantifies the number of alternating convex hull and Minkowski sum operations require","authors":["Valerdi, Juan L."],"url":"https://arxiv.org/abs/2402.15315","arxiv_id":"2402.15315","published":"2026-03-20"},{"title":"Heuristic Multiobjective Discrete Optimization using Restricted Decision Diagrams","abstract":"Decision diagrams (DDs) have emerged as a state-of-the-art method for exact multiobjective integer linear programming. When the DD is too large to fit into memory or the decision-maker prefers a fast approximation to the Pareto frontier, the complete DD must be restricted to a subset of its states (or nodes). We introduce new node-selection heuristics for constructing restricted DDs that produce a high-quality approx","authors":["Patel, Rahul","Khalil, Elias B.","Bergman, David"],"url":"https://arxiv.org/abs/2403.02482","arxiv_id":"2403.02482","published":"2026-03-20"},{"title":"SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Models","abstract":"Large Language Models (LLMs) have emerged as a powerful tool in advancing the Text-to-SQL task, significantly outperforming traditional methods.Nevertheless, as a nascent research field, there is still no consensus on the optimal prompt templates and design frameworks. Additionally, existing benchmarks inadequately explore the performance of LLMs across the various sub-tasks of the Text-to-SQL process, which hinders ","authors":["Zhang, Bin","Ye, Yuxiao","Du, Guoqing","Hu, Xiaoru","Li, Zhishuai","Liu, Chi Harold"],"url":"https://arxiv.org/abs/2403.02951","arxiv_id":"2403.02951","published":"2026-03-20"},{"title":"A multiscale cavity method for sublinear-rank symmetric matrix factorization","abstract":"We consider a statistical model for symmetric matrix factorization with additive Gaussian noise in the high-dimensional regime, where the rank of the signal matrix to infer $M$ scales with its size $N$ as $M=\\mathrm{o}(\\sqrt{\\ln N})$. Allowing for an $N$-dependent rank offers new challenges and requires new methods. Working in the Bayes-optimal setting, we show that whenever the signal has i.i.d. entries, the limitin","authors":["Barbier, Jean","Ko, Justin","Rahman, Anas A."],"url":"https://arxiv.org/abs/2403.07189","arxiv_id":"2403.07189","published":"2026-03-20"},{"title":"CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions","abstract":"Examining Drug-Drug Interactions (DDIs) is a pivotal element in the process of drug development. DDIs occur when one drug's properties are affected by the inclusion of other drugs. Detecting favorable DDIs has the potential to pave the way for creating and advancing innovative medications applicable in practical settings. However, existing DDI prediction models continue to face challenges related to generalizatio","authors":["Wasi, Azmine Toushik","Rafi, Taki Hasan","Islam, Raima","Karlo, Serbetar","Chae, Dong-Kyu"],"url":"https://arxiv.org/abs/2403.17210","arxiv_id":"2403.17210","published":"2026-03-20"},{"title":"Improved Convex Decomposition with Ensembling and Negative Primitives","abstract":"Describing a scene in terms of primitives -- geometrically simple shapes that offer a parsimonious but accurate abstraction of structure -- is an established and difficult fitting problem. Different scenes require different numbers of primitives, and these primitives interact strongly. Existing methods are evaluated by comparing predicted depth, normals, and segmentation against ground truth. The state of the art met","authors":["Vavilala, Vaibhav","Kluger, Florian","Jain, Seemandhar","Rosenhahn, Bodo","Bhattad, Anand","Forsyth, David"],"url":"https://arxiv.org/abs/2405.19569","arxiv_id":"2405.19569","published":"2026-03-20"},{"title":"$μ$LO: Compute-Efficient Meta-Generalization of Learned Optimizers","abstract":"Learned optimizers (LOs) have the potential to significantly reduce the wall-clock training time of neural networks. However, they can struggle to optimize unseen tasks (meta-generalize), especially when training networks wider than those seen during meta-training. To address this, we derive the Maximal Update Parametrization ($μ$P) for two state-of-the-art learned optimizer architectures and propose a simple meta-tr","authors":["Thérien, Benjamin","Joseph, Charles-Étienne","Knyazev, Boris","Oyallon, Edouard","Rish, Irina","Belilovsky, Eugene"],"url":"https://arxiv.org/abs/2406.00153","arxiv_id":"2406.00153","published":"2026-03-20"},{"title":"PLM-Net: Perception Latency Mitigation Network for Vision-Based Lateral Control of Autonomous Vehicles","abstract":"This study introduces the Perception Latency Mitigation Network (PLM-Net), a modular deep learning framework designed to mitigate perception latency in vision-based imitation-learning lane-keeping systems. Perception latency, defined as the delay between visual sensing and steering actuation, can degrade lateral tracking performance and steering stability. While delay compensation has been extensively studied in clas","authors":["Khalil, Aws","Kwon, Jaerock"],"url":"https://arxiv.org/abs/2407.16740","arxiv_id":"2407.16740","published":"2026-03-20"},{"title":"Automated Explanation Selection for Scientific Discovery","abstract":"Automated reasoning is a key technology in the young but rapidly growing field of Explainable Artificial Intelligence (XAI). Explanability helps build trust in artificial intelligence systems beyond their mere predictive accuracy and robustness. In this paper, we propose a cycle of scientific discovery that combines machine learning with automated reasoning for the generation and the selection of explanations. We pre","authors":["Iser, Ashlin"],"url":"https://arxiv.org/abs/2407.17454","arxiv_id":"2407.17454","published":"2026-03-20"},{"title":"Modeling Inverse Ellipsometry Problem via Flow Matching with a Large-Scale Dataset","abstract":"Inverse ellipsometry, i.e., reconstructing optical constants and film thickness from the measured phase difference $Δ$ and amplitude ratio $Ψ$, is a fundamentally ill-posed problem. Traditional solutions rely on slow, expert-driven iterative fitting, while the development of machine learning approaches has been severely limited by the lack of large-scale, physically consistent datasets. To address this gap, we introd","authors":["Ma, Yiming","Teng, Jianzhi","Li, Xinjie","Sun, Xin","Wang, Zhiyong","Song, Yuzhou"],"url":"https://arxiv.org/abs/2407.17869","arxiv_id":"2407.17869","published":"2026-03-20"},{"title":"A Review of Pseudo-Labeling for Computer Vision","abstract":"Deep neural models have achieved state of the art performance on a wide range of problems in computer science, especially in computer vision. However, deep neural networks often require large datasets of labeled samples to generalize effectively, and an important area of active research is semi-supervised learning, which attempts to instead utilize large quantities of (easily acquired) unlabeled samples. One family o","authors":["Kage, Patrick","Rothenberger, Jay C.","Andreadis, Pavlos","Diochnos, Dimitrios I."],"url":"https://arxiv.org/abs/2408.07221","arxiv_id":"2408.07221","published":"2026-03-20"},{"title":"Latent Causal Modeling for 3D Brain MRI Counterfactuals","abstract":"The number of samples in structural brain MRI studies is often too small to properly train deep learning models. Generative models show promise in addressing this issue by effectively learning the data distribution and generating high-fidelity MRI. However, they struggle to produce diverse, high-quality data outside the distribution defined by the training data. One way to address this issue is to use causal models d","authors":["Peng, Wei","Xia, Tian","Ribeiro, Fabio De Sousa","Bosschieter, Tomas","Adeli, Ehsan","Zhao, Qingyu"],"url":"https://arxiv.org/abs/2409.05585","arxiv_id":"2409.05585","published":"2026-03-20"},{"title":"ODE-Constrained Generative Modeling of Cardiac Dynamics for 12-Lead ECG Synthesis","abstract":"Generating realistic training data for supervised learning remains a significant challenge in artificial intelligence, particularly in domains where large, expert-labeled datasets are scarce or costly to obtain. This is especially true for electrocardiograms (ECGs), where privacy constraints, class imbalance, and the need for physician annotation limit the availability of labeled 12-lead recordings, motivating the de","authors":["Yehuda, Yakir","Radinsky, Kira"],"url":"https://arxiv.org/abs/2409.17833","arxiv_id":"2409.17833","published":"2026-03-20"},{"title":"Biased AI can Influence Political Decision-Making","abstract":"As modern large language models (LLMs) become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. While bias in models are well-documented, less is known about how these biases influence human decisions. This paper presents two interactive experiments investigating the effects of partisan bias in LLMs on political opinions and decision-mak","authors":["Fisher, Jillian","Feng, Shangbin","Aron, Robert","Richardson, Thomas","Choi, Yejin","Fisher, Daniel W."],"url":"https://arxiv.org/abs/2410.06415","arxiv_id":"2410.06415","published":"2026-03-20"}],"history":{"events":[{"year":2014,"text":"Taliban militants killed nine civilians in a mass shooting at the Kabul Serena Hotel in Afghanistan.","url":"https://en.wikipedia.org/wiki/Taliban"},{"year":2010,"text":"Eyjafjallajökull (pictured) in Iceland began a series of volcanic events, later disrupting air travel across Europe.","url":"https://en.wikipedia.org/wiki/Eyjafjallaj%C3%B6kull"},{"year":1993,"text":"The Troubles: Two children were killed by the second of two bomb attacks by the Provisional Irish Republican Army in Warrington, England.","url":"https://en.wikipedia.org/wiki/The_Troubles"},{"year":1987,"text":"The antiretroviral drug zidovudine became the first treatment approved by the U.S. Food and Drug Administration for HIV/AIDS.","url":"https://en.wikipedia.org/wiki/Antiviral_drug"},{"year":1944,"text":"World War II: U.S. Marines made a landing on Emirau Island in the Bismarck Archipelago to develop an airbase as part of Operation Cartwheel.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1942,"text":"World War II: After being forced to flee the Philippines for Australia, US Army general Douglas MacArthur announced: \"I came through and I shall return.\"","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1939,"text":"Germany issued an ultimatum to Lithuania, demanding the return of the Klaipėda Region under threat of invasion.","url":"https://en.wikipedia.org/wiki/1939_German_ultimatum_to_Lithuania"},{"year":1926,"text":"Chiang Kai-shek initiated a purge of communist elements within the National Revolutionary Army in Guangzhou, China.","url":"https://en.wikipedia.org/wiki/Chiang_Kai-shek"},{"year":1923,"text":"The Arts Club of Chicago hosted the opening of Pablo Picasso's first solo United States showing, entitled Original Drawings by Pablo Picasso.","url":"https://en.wikipedia.org/wiki/Arts_Club_of_Chicago"},{"year":1922,"text":"The United States Navy commissioned its first aircraft carrier, USS Langley.","url":"https://en.wikipedia.org/wiki/United_States_Navy"},{"year":1890,"text":"Emperor Wilhelm II dismissed Otto von Bismarck as the chancellor of the German Empire.","url":"https://en.wikipedia.org/wiki/Wilhelm_II"},{"year":1861,"text":"An earthquake occurred in the Argentine province of Mendoza, causing at least 6,000 deaths and destroying most of the buildings in the province's capital city.","url":"https://en.wikipedia.org/wiki/1861_Mendoza_earthquake"},{"year":1854,"text":"The Republican Party was founded in Ripon, Wisconsin, by politicians opposing the Kansas–Nebraska Act and the expansion of slavery in the United States.","url":"https://en.wikipedia.org/wiki/Republican_Party_(United_States)"},{"year":1852,"text":"Harriet Beecher Stowe's novel Uncle Tom's Cabin, which had a profound effect on attitudes toward African Americans and slavery in the United States, was published.","url":"https://en.wikipedia.org/wiki/Harriet_Beecher_Stowe"},{"year":1760,"text":"A fire of unknown cause broke out in Boston, Massachusetts, destroying 349 buildings.","url":"https://en.wikipedia.org/wiki/Great_Boston_Fire_of_1760"},{"year":1724,"text":"Following the death of Pope Innocent XIII, a papal conclave convened in Rome to elect a new pope.","url":"https://en.wikipedia.org/wiki/Pope_Innocent_XIII"},{"year":1602,"text":"The Dutch East India Company, the first company to issue stock, was established.","url":"https://en.wikipedia.org/wiki/Dutch_East_India_Company"}],"births":[{"year":2004,"text":"Belle, American singer","url":"https://en.wikipedia.org/wiki/Belle_(singer)"},{"year":2003,"text":"Cooper Hoffman, American actor","url":"https://en.wikipedia.org/wiki/Cooper_Hoffman"},{"year":2002,"text":"Jahmyr Gibbs, American football player","url":"https://en.wikipedia.org/wiki/Jahmyr_Gibbs"},{"year":2001,"text":"Trevor Zegras, American ice hockey player","url":"https://en.wikipedia.org/wiki/Trevor_Zegras"},{"year":2000,"text":"Hyunjin, South Korean rapper","url":"https://en.wikipedia.org/wiki/Hyunjin_(Stray_Kids_singer)"},{"year":1995,"text":"Lindsay Allen, American basketball player","url":"https://en.wikipedia.org/wiki/Lindsay_Allen"},{"year":1995,"text":"Kei, South Korean singer","url":"https://en.wikipedia.org/wiki/Kei_(singer)"},{"year":1995,"text":"Nick Paul, Canadian ice hockey player","url":"https://en.wikipedia.org/wiki/Nick_Paul"}],"deaths":[{"year":2026,"text":"Nicholas Brendon, American actor (born 1971)","url":"https://en.wikipedia.org/wiki/Nicholas_Brendon"},{"year":2026,"text":"Filaret Denysenko, Ukrainian religious leader, Patriarch of the Ukrainian Orthodox Church – Kyiv Patriarchate (born 1929)","url":"https://en.wikipedia.org/wiki/Filaret_Denysenko"},{"year":2026,"text":"Robert Mueller, American attorney, Director of the Federal Bureau of Investigation (born 1944)","url":"https://en.wikipedia.org/wiki/Robert_Mueller"},{"year":2025,"text":"Eddie Jordan, Irish businessman, television personality and motorsport team owner (born 1948)","url":"https://en.wikipedia.org/wiki/Eddie_Jordan"},{"year":2023,"text":"John Sattler, Australian rugby league player (born 1942)","url":"https://en.wikipedia.org/wiki/John_Sattler"},{"year":2023,"text":"Kyle White, Australian rugby league player (born 1970)","url":"https://en.wikipedia.org/wiki/Kyle_White_(rugby_league)"},{"year":2020,"text":"Amadeo Carrizo, Argentine footballer (born 1926)","url":"https://en.wikipedia.org/wiki/Amadeo_Carrizo"},{"year":2020,"text":"Kenny Rogers, American singer (born 1938)","url":"https://en.wikipedia.org/wiki/Kenny_Rogers"}]},"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."}