{"date":"2026-04-01","schema_version":1,"captured_at":"2026-08-25T23:34:36.257Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Dhurandhar: The Revenge","views":359053,"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. 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The tournament began on June 11, 2026, and concluded on July 19 with Spain winning ","url":"https://en.wikipedia.org/wiki/2026_FIFA_World_Cup","thumb":"https://upload.wikimedia.org/wikipedia/en/thumb/1/17/2026_FIFA_World_Cup_emblem.svg/330px-2026_FIFA_World_Cup_emblem.svg.png?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Jaden Ivey","views":161580,"rank":8,"extract":"Jaden Edward Dhananjay Ivey is an American professional basketball player who last played for the Chicago Bulls of the National Basketball Association (NBA). He played college basketball for the Purdue Boilermakers. 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SR of a signal is given by superposition of very few columns of a matrix called Dictionary, implicitly reducing dimensionality. Training dictionaries such that they represent each class of signals with minimal loss is called Dictionary Learning (DL). Dictionary learning methods like Method of Op","authors":["Madhuri, G.","Negi, Atul"],"url":"https://arxiv.org/abs/2111.09027","arxiv_id":"2111.09027","published":"2026-04-01"},{"title":"What do CNNs Learn in the First Layer and Why? A Linear Systems Perspective","abstract":"It has previously been reported that the representation that is learned in the first layer of deep Convolutional Neural Networks (CNNs) is highly consistent across initializations and architectures. In this work, we quantify this consistency by considering the first layer as a filter bank and measuring its energy distribution. We find that the energy distribution is very different from that of the initial weights and","authors":["Chowers, Rhea","Weiss, Yair"],"url":"https://arxiv.org/abs/2206.02454","arxiv_id":"2206.02454","published":"2026-04-01"},{"title":"Early Exiting Predictive Coding Neural Networks for Edge AI","abstract":"The Internet of Things is transforming various fields, with sensors increasingly embedded in wearables, smart buildings, and connected equipment. While deep learning enables valuable insights from IoT data, conventional models are too computationally demanding for resource-limited edge devices. Moreover, privacy concerns and real-time processing needs make local computation a necessity over cloud-based solutions. Ins","authors":["Zniber, Alaa","Ghogho, Mounir","Karrakchou, Ouassim","Zakroum, Mehdi"],"url":"https://arxiv.org/abs/2309.02022","arxiv_id":"2309.02022","published":"2026-04-01"},{"title":"Generative AI Enables Structural Brain Network Construction from fMRI via Symmetric Diffusion Learning","abstract":"Mapping from functional connectivity (FC) to structural connectivity (SC) can facilitate multimodal brain network fusion and discover potential biomarkers for clinical implications. However, it is challenging to directly bridge the reliable non-linear mapping relations between SC and functional magnetic resonance imaging (fMRI). In this paper, a novel symmetric diffusive generative adversarial network-based fMRI-to-S","authors":["Zuo, Qiankun","Lei, Bangjun","Qiu, Wanyu","Jing, Changhong","Hong, Jin","Wang, Shuqiang"],"url":"https://arxiv.org/abs/2309.16205","arxiv_id":"2309.16205","published":"2026-04-01"},{"title":"Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent","abstract":"Stochastic gradient descent (SGD) or stochastic approximation has been widely used in model training and stochastic optimization. While there is a huge literature on analyzing its convergence, inference on the obtained solutions from SGD has only been recently studied, yet it is important due to the growing need for uncertainty quantification. We investigate two computationally cheap resampling-based methods to const","authors":["Lam, Henry","Wang, Zitong"],"url":"https://arxiv.org/abs/2310.11065","arxiv_id":"2310.11065","published":"2026-04-01"},{"title":"Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion","abstract":"Existing methods for synthesizing 3D human gestures from speech have shown promising results, but they do not explicitly model the impact of emotions on the generated gestures. Instead, these methods directly output animations from speech without control over the expressed emotion. To address this limitation, we present AMUSE, an emotional speech-driven body animation model based on latent diffusion. Our observation ","authors":["Chhatre, Kiran","Daněček, Radek","Athanasiou, Nikos","Becherini, Giorgio","Peters, Christopher","Black, Michael J."],"url":"https://arxiv.org/abs/2312.04466","arxiv_id":"2312.04466","published":"2026-04-01"},{"title":"Exploring Prime Number Classification: Achieving High Recall Rate and Rapid Convergence with Sparse Encoding","abstract":"This paper presents a novel approach at the intersection of machine learning and number theory, focusing on the classification of prime and non-prime numbers. At the core of our research is the development of a highly sparse encoding method, integrated with conventional neural network architectures. This combination has shown promising results, achieving a recall of over 99\\% in identifying prime numbers and 79\\% for","authors":["Lee, Serin","Kim, S."],"url":"https://arxiv.org/abs/2402.03363","arxiv_id":"2402.03363","published":"2026-04-01"},{"title":"GenOL: Generating Diverse Examples for Name-only Online Learning","abstract":"Online learning methods often rely on supervised data. However, under data distribution shifts, such as in continual learning (CL), where continuously arriving online data streams incorporate new concepts (e.g., classes), real-time manual annotation is impractical due to its costs and latency, which hinder real-time adaptation. To alleviate this, 'name-only' setup has been proposed, requiring only the name of","authors":["Seo, Minhyuk","Cho, Seongwon","Lee, Minjae","Misra, Diganta","Choi, Hyeonbeom","Kim, Seon Joo"],"url":"https://arxiv.org/abs/2403.10853","arxiv_id":"2403.10853","published":"2026-04-01"},{"title":"Measuring the Predictability of Recommender Systems using Structural Complexity Metrics","abstract":"Recommender Systems (RS) shape the filtering and curation of online content, yet we have limited understanding of how predictable their recommendation outputs are. We propose data-driven metrics that quantify the predictability of recommendation datasets by measuring the structural complexity of the user-item interaction matrix. High complexity indicates intricate interaction patterns that are harder to predict; low ","authors":["Abeliuk, Andrés","Valderrama, Alfonso","Campos, Simón","Mendoza, Marcelo"],"url":"https://arxiv.org/abs/2404.08829","arxiv_id":"2404.08829","published":"2026-04-01"},{"title":"Image-Specific Adaptation of Transformer Encoders for Compute-Efficient Segmentation","abstract":"Vision transformer based models bring significant improvements for image segmentation tasks. Although these architectures offer powerful capabilities irrespective of specific segmentation tasks, their use of computational resources can be taxing on deployed devices. One way to overcome this challenge is by adapting the computation level to the specific needs of the input image rather than the current one-size-fits-al","authors":["Yao, Manyi","Aich, Abhishek","Suh, Yumin","Roy-Chowdhury, Amit","Shelton, Christian","Chandraker, Manmohan"],"url":"https://arxiv.org/abs/2404.15244","arxiv_id":"2404.15244","published":"2026-04-01"},{"title":"Improving Plan Execution Flexibility using Block-Substitution","abstract":"Partial-order plans in AI planning facilitate execution flexibility due to their less-constrained nature. Maximizing plan flexibility has been studied through the notions of plan deordering, and plan reordering. Plan deordering removes unnecessary action orderings within a plan, while plan reordering modifies them arbitrarily to minimize action orderings. This study, in contrast with traditional plan deordering and r","authors":["Noor, Sabah Binte","Siddiqui, Fazlul Hasan"],"url":"https://arxiv.org/abs/2406.03091","arxiv_id":"2406.03091","published":"2026-04-01"},{"title":"A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms","abstract":"We devise a control-theoretic reinforcement learning approach to support direct learning of the optimal policy. We establish various theoretical properties of our approach, such as convergence and optimality of our analog of the Bellman operator and Q-learning, a new control-policy-variable gradient theorem, and a specific gradient ascent algorithm based on this theorem within the context of a specific control-theore","authors":["Chen, Weiqin","Squillante, Mark S.","Wu, Chai Wah","Paternain, Santiago"],"url":"https://arxiv.org/abs/2406.14753","arxiv_id":"2406.14753","published":"2026-04-01"},{"title":"Improving Execution Concurrency in Partial-Order Plans via Block-Substitution","abstract":"Partial-order plans in AI planning facilitate execution flexibility and several other tasks, such as plan reuse, modification, and decomposition, due to their less constrained nature. A \\acrfull*{pop} specifies partial-order over actions, providing the flexibility of executing unordered actions in different sequences. This flexibility can be further extended by enabling parallel execution of actions in the POP to red","authors":["Noor, Sabah Binte","Siddiqui, Fazlul Hasan"],"url":"https://arxiv.org/abs/2406.18615","arxiv_id":"2406.18615","published":"2026-04-01"},{"title":"SemioLLM: Evaluating Large Language Models for Diagnostic Reasoning from Unstructured Clinical Narratives in Epilepsy","abstract":"Large Language Models (LLMs) have been shown to encode clinical knowledge. Many evaluations, however, rely on structured question-answer benchmarks, overlooking critical challenges of interpreting and reasoning about unstructured clinical narratives in real-world settings. In this study we task eight Large Language models including two medical models (GPT-3.5, GPT-4, Mixtral-8x7B, Qwen-72B, LlaMa2, LlaMa3, OpenBioLLM","authors":["Dani, Meghal","Prakash, Muthu Jeyanthi","Rosa, Filip","Akata, Zeynep","Liebe, Stefanie"],"url":"https://arxiv.org/abs/2407.03004","arxiv_id":"2407.03004","published":"2026-04-01"},{"title":"EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context","abstract":"Large language models (LLMs) present an enormous evolution in the strategic potential of conversational recommender systems (CRS). Yet to date, research has predominantly focused upon technical frameworks to implement LLM-driven CRS, rather than end-user evaluations or strategic implications for firms, particularly from the perspective of a small to medium enterprises (SME) that makeup the bedrock of the global econo","authors":["Kunstmann, Hannes","Ollier, Joseph","Persson, Joel","von Wangenheim, Florian"],"url":"https://arxiv.org/abs/2407.04472","arxiv_id":"2407.04472","published":"2026-04-01"},{"title":"Codebook LLMs: Evaluating LLMs as Measurement Tools for Political Science Concepts","abstract":"Codebooks -- documents that operationalize concepts and outline annotation procedures -- are used almost universally by social scientists when coding political texts. To code these texts automatically, researchers are increasing turning to generative large language models (LLMs). However, there is limited empirical evidence on whether "off-the-shelf" LLMs faithfully follow real-world codebook operationalizati","authors":["Halterman, Andrew","Keith, Katherine A."],"url":"https://arxiv.org/abs/2407.10747","arxiv_id":"2407.10747","published":"2026-04-01"},{"title":"Image Segmentation via Divisive Normalization: dealing with environmental diversity","abstract":"Autonomous driving is a challenging scenario for image segmentation due to the presence of uncontrolled environmental conditions and the eventually catastrophic consequences of failures. Previous work suggested that a biologically motivated computation, the so-called Divisive Normalization, could be useful to deal with image variability, but its effects have not been systematically studied over different data sources","authors":["Hernández-Cámara, Pablo","Vila-Tomás, Jorge","Dauden-Oliver, Paula","Alabau-Bosque, Nuria","Laparra, Valero","Malo, Jesús"],"url":"https://arxiv.org/abs/2407.17829","arxiv_id":"2407.17829","published":"2026-04-01"},{"title":"Towards Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models","abstract":"Evolutionary multi-task optimization (EMTO) is an advanced optimization paradigm that improves search efficiency by enabling knowledge transfer across multiple tasks solved in parallel. Accordingly, a broad range of knowledge transfer methods (KTMs) have been developed as integral components of EMTO algorithms, most of which are tailored to specific problem settings. However, the design of effective KTMs typically re","authors":["Lyu, Xuebin","Huang, Yuxiao","Chen, XueFeng","Tang, Jing","Feng, Liang","Tan, Kay Chen"],"url":"https://arxiv.org/abs/2409.04270","arxiv_id":"2409.04270","published":"2026-04-01"},{"title":"JKO for Landau: a variational particle method for homogeneous Landau equation","abstract":"Inspired by the gradient flow viewpoint of the Landau equation and the corresponding dynamic formulation of the Landau metric in [arXiv:2007.08591], we develop a novel implicit particle method for the Landau equation in the framework of the JKO scheme. We first reformulate the Landau metric in a computationally friendly form, and then translate it into the Lagrangian viewpoint using the flow map. A key observation is","authors":["Huang, Yan","Wang, Li"],"url":"https://arxiv.org/abs/2409.12296","arxiv_id":"2409.12296","published":"2026-04-01"},{"title":"An Information-Theoretic Approach to Understanding Transformers' In-Context Learning of Variable-Order Markov Chains","abstract":"We study transformers' in-context learning of variable-length Markov chains (VOMCs), focusing on the finite-sample accuracy as the number of in-context examples increases. Compared to fixed-order Markov chains (FOMCs), learning VOMCs is substantially more challenging due to the additional structural learning component. The problem is naturally suited to a Bayesian formulation, where the context-tree weighting (CT","authors":["Zhou, Ruida","Tian, Chao","Diggavi, Suhas"],"url":"https://arxiv.org/abs/2410.05493","arxiv_id":"2410.05493","published":"2026-04-01"},{"title":"Tightening convex relaxations of trained neural networks: a unified approach for convex and S-shaped activations","abstract":"The non-convex nature of trained neural networks has created significant obstacles in their incorporation into optimization models. In this context, Anderson et al. (2020) provided a framework to obtain the convex hull of the graph of a piecewise linear convex activation function composed with an affine function; this effectively convexifies activations such as the ReLU together with the affine transformation that pr","authors":["Carrasco, Pablo","Muñoz, Gonzalo"],"url":"https://arxiv.org/abs/2410.23362","arxiv_id":"2410.23362","published":"2026-04-01"},{"title":"Cycle-Constrained Adversarial Denoising Convolutional Network for PET Image Denoising: Multi-Dimensional Validation on Large Datasets with Reader Study and Real Low-Dose Data","abstract":"Positron emission tomography (PET) is a critical tool for diagnosing tumors and neurological disorders but poses radiation risks to patients, particularly to sensitive populations. While reducing injected radiation dose mitigates this risk, it often compromises image quality. To reconstruct full-dose-quality images from low-dose scans, we propose a Cycle-constrained Adversarial Denoising Convolutional Network (Cycle-","authors":["Hou, Yucun","Zhan, Fenglin","Cheng, Xin","Li, Chenxi","Yuan, Ziquan","Liao, Runze"],"url":"https://arxiv.org/abs/2410.23628","arxiv_id":"2410.23628","published":"2026-04-01"},{"title":"An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains","abstract":"Named Entity Recognition (NER) is a machine learning task that traditionally relies on supervised learning and annotated data. Acquiring such data is often a challenge, particularly in specialized fields like medical, legal, and financial sectors. Those are commonly referred to as low-resource domains, which comprise long-tail entities, due to the scarcity of available data. To address this, data augmentation techniq","authors":["Torres, Arthur Elwing","de Moura, Edleno Silva","da Silva, Altigran Soares","Nascimento, Mario A.","Mesquita, Filipe"],"url":"https://arxiv.org/abs/2411.14551","arxiv_id":"2411.14551","published":"2026-04-01"},{"title":"Real-Time Operator Takeover for Visuomotor Diffusion Policy Training","abstract":"We present a Real-Time Operator Takeover (RTOT) paradigm that enables operators to seamlessly take control of a live visuomotor diffusion policy, guiding the system back to desirable states or providing targeted corrective demonstrations. Within this framework, the operator can intervene to correct the robot's motion, after which control is smoothly returned to the policy until further intervention is needed. We ","authors":["Moletta, Marco","Welle, Michael C.","Ingelhag, Nils","Munkeby, Jesper","Kragic, Danica"],"url":"https://arxiv.org/abs/2502.02308","arxiv_id":"2502.02308","published":"2026-04-01"},{"title":"Value Gradient Sampler: Learning Invariant Value Functions for Equivariant Diffusion Sampling","abstract":"We propose the Value Gradient Sampler (VGS), a diffusion sampler parameterized by value functions. VGS generates samples from an unnormalized target density (i.e., energy) by evolving randomly initialized particles along the gradient of the value function. In many sampling problems where the target density exhibits invariant symmetries, value functions provide a novel approach to leveraging invariant networks for sam","authors":["Hwang, Himchan","Jeong, Hyeokju","Shin, Dong Kyu","Park, Che-Sang","Kweon, Sehee","Yoon, Sangwoong"],"url":"https://arxiv.org/abs/2502.13280","arxiv_id":"2502.13280","published":"2026-04-01"}],"history":{"events":[{"year":2001,"text":"Same-sex marriage in the Netherlands was legalized, making it the first country to do so.","url":"https://en.wikipedia.org/wiki/Same-sex_marriage_in_the_Netherlands"},{"year":2001,"text":"An American Lockheed EP-3 and a Chinese Shenyang J-8 collided in mid-air off Hainan, resulting in an international dispute between the two countries.","url":"https://en.wikipedia.org/wiki/Lockheed_EP-3"},{"year":1990,"text":"The longest prison riot in British penal history began at Strangeways Prison in Manchester, lasting 25 days.","url":"https://en.wikipedia.org/wiki/Strangeways_Prison_riot"},{"year":1976,"text":"Steve Jobs and Steve Wozniak founded Apple Computer, Inc. (first logo pictured).","url":"https://en.wikipedia.org/wiki/Steve_Jobs"},{"year":1974,"text":"A man ignited tires in the crater of Mount Edgecumbe, prompting a false eruption scare later revealed as an April Fools' hoax.","url":"https://en.wikipedia.org/wiki/Mount_Edgecumbe_(Alaska)"},{"year":1970,"text":"U.S. president Richard Nixon signed the Public Health Cigarette Smoking Act into law, requiring that a prominent warning by the surgeon general be placed on cigarette packages.","url":"https://en.wikipedia.org/wiki/Richard_Nixon"},{"year":1970,"text":"The American Motors Corporation introduced its Gremlin, promoted as America's first subcompact car.","url":"https://en.wikipedia.org/wiki/American_Motors_Corporation"},{"year":1969,"text":"The Hawker Siddeley Harrier, the first operational fighter aircraft with V/STOL capabilities, entered service with the Royal Air Force.","url":"https://en.wikipedia.org/wiki/Hawker_Siddeley_Harrier"},{"year":1963,"text":"On Her Majesty's Secret Service, the tenth novel of Ian Fleming's James Bond series, was released in the United Kingdom.","url":"https://en.wikipedia.org/wiki/On_Her_Majesty's_Secret_Service_(novel)"},{"year":1955,"text":"The British Army's Mobile Defence Corps, intended to assist with civil defence after a nuclear attack, was established by royal warrant.","url":"https://en.wikipedia.org/wiki/Mobile_Defence_Corps"},{"year":1952,"text":"Israel enacted a citizenship law, prior to which the country technically had no citizens.","url":"https://en.wikipedia.org/wiki/Israeli_citizenship_law"},{"year":1941,"text":"Soviet border guards opened fire on civilians attempting to cross the border from the Soviet Union to Romania near Fântâna Albă, killing between 44 and 3,000 people.","url":"https://en.wikipedia.org/wiki/F%C3%A2nt%C3%A2na_Alb%C4%83_massacre"},{"year":1933,"text":"English cricketer Wally Hammond set a record for the highest individual score in Test cricket of 336 not out during a match against New Zealand.","url":"https://en.wikipedia.org/wiki/Wally_Hammond"},{"year":1922,"text":"Under the South Seas Mandate, Japan set up a government in Koror, precipitating large-scale Japanese settlement in Palau.","url":"https://en.wikipedia.org/wiki/South_Seas_Mandate"},{"year":1918,"text":"The United Kingdom established the Royal Air Force, near the end of the First World War.","url":"https://en.wikipedia.org/wiki/Royal_Air_Force"},{"year":1879,"text":"The Inland Customs Line, established by the British for the collection of the salt tax in India, was abandoned.","url":"https://en.wikipedia.org/wiki/Inland_Customs_Line"},{"year":1871,"text":"The Duke of Buckingham opened the first section of the Brill Tramway, a short railway line to transport goods between his lands and the national rail network.","url":"https://en.wikipedia.org/wiki/Richard_Temple-Nugent-Brydges-Chandos-Grenville%2C_3rd_Duke_of_Buckingham_and_Chandos"},{"year":1865,"text":"American Civil War: The Union Army under Major General Philip Sheridan (pictured) inflicted more than 2,900 casualties on the Confederates at the Battle of Five Forks.","url":"https://en.wikipedia.org/wiki/American_Civil_War"}],"births":[{"year":2000,"text":"Rhian Brewster, English footballer","url":"https://en.wikipedia.org/wiki/Rhian_Brewster"},{"year":1999,"text":"Gabe Davis, American football player","url":"https://en.wikipedia.org/wiki/Gabe_Davis"},{"year":1998,"text":"King Combs, American rapper","url":"https://en.wikipedia.org/wiki/King_Combs"},{"year":1998,"text":"Mitchell Robinson, American basketball player","url":"https://en.wikipedia.org/wiki/Mitchell_Robinson"},{"year":1997,"text":"Asa Butterfield, English actor","url":"https://en.wikipedia.org/wiki/Asa_Butterfield"},{"year":1997,"text":"Álex Palou, Spanish racing driver","url":"https://en.wikipedia.org/wiki/%C3%81lex_Palou"},{"year":1996,"text":"Sophia Hutchins, American socialite","url":"https://en.wikipedia.org/wiki/Sophia_Hutchins"},{"year":1995,"text":"Jofra Archer, Barbadian-English cricketer","url":"https://en.wikipedia.org/wiki/Jofra_Archer"}],"deaths":[{"year":2025,"text":"Val Kilmer, American actor (born 1959)","url":"https://en.wikipedia.org/wiki/Val_Kilmer"},{"year":2025,"text":"Johnny Tillotson, American singer-songwriter (born 1938)","url":"https://en.wikipedia.org/wiki/Johnny_Tillotson"},{"year":2024,"text":"Lou Conter, American naval commander (born 1921)","url":"https://en.wikipedia.org/wiki/Lou_Conter"},{"year":2024,"text":"Vontae Davis, American football player (born 1988)","url":"https://en.wikipedia.org/wiki/Vontae_Davis"},{"year":2024,"text":"Joe Flaherty, American actor, writer, and comedian (born 1941)","url":"https://en.wikipedia.org/wiki/Joe_Flaherty"},{"year":2024,"text":"Sami Michael, Iraqi-born Israeli writer and human rights activist (born 1926)","url":"https://en.wikipedia.org/wiki/Sami_Michael"},{"year":2024,"text":"Ed Piskor, American comic book artist (born 1982)","url":"https://en.wikipedia.org/wiki/Ed_Piskor"},{"year":2024,"text":"Mohammad Reza Zahedi, Iranian senior military officer (born 1960)","url":"https://en.wikipedia.org/wiki/Mohammad_Reza_Zahedi"}]},"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."}