{"date":"2026-03-05","schema_version":1,"captured_at":"2026-08-25T23:30:46.365Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":12},"attention":[{"title":"2026 Iran war","views":601818,"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":"James Talarico","views":415376,"rank":5,"extract":"James Dell Talarico is an American politician and educator who has served since 2018 as a member of the Texas House of Representatives. 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dual challenge of utility and privacy. Too much anonymity renders the images useless, but too little privacy does not protect sensitive data. We propose a novel design for privacy preservation, where the imagery is stored in quantum states. In the future, this will be enabled by quantum imaging cameras, and, currently, storing very low resolution imagery in quantum states i","authors":["Kirkland, Hannah","Koppal, Sanjeev J."],"url":"https://arxiv.org/abs/2303.07510","arxiv_id":"2303.07510","published":"2026-03-05"},{"title":"Recurrent Action Transformer with Memory","abstract":"Transformers have become increasingly popular in offline reinforcement learning (RL) due to their ability to treat agent trajectories as sequences, reframing policy learning as a sequence modeling task. However, in partially observable environments (POMDPs), effective decision-making depends on retaining information about past events -- something that standard transformers struggle with due to the quadratic complexit","authors":["Cherepanov, Egor","Staroverov, Alexey","Kovalev, Alexey K.","Panov, Aleksandr I."],"url":"https://arxiv.org/abs/2306.09459","arxiv_id":"2306.09459","published":"2026-03-05"},{"title":"Crystal-GFN: sampling crystals with desirable properties and constraints","abstract":"The discovery of novel solid-state materials, such as electrocatalysts, super-ionic conductors, or photovoltaic materials, plays a critical role in addressing various global challenges. It has, for instance, the potential to significantly improve the efficiency of renewable energy production and storage, thereby making substantial contributions to climate crisis mitigation strategies. In this paper, we introduce Crys","authors":["AI4Science, Mila",":","Hernandez-Garcia, Alex","Duval, Alexandre","Volokhova, Alexandra","Bengio, Yoshua"],"url":"https://arxiv.org/abs/2310.04925","arxiv_id":"2310.04925","published":"2026-03-05"},{"title":"GeoTop: Advancing Image Classification with Geometric-Topological Analysis","abstract":"A fundamental challenge in diagnostic imaging is the phenomenon of topological equivalence, where benign and malignant structures share global topology but differ in critical geometric detail, leading to diagnostic errors in both conventional and deep learning models. We introduce GeoTop, a mathematically principled framework that unifies Topological Data Analysis (TDA) and Lipschitz-Killing Curvatures (LKCs) to reso","authors":["Abaach, Mariem","Morilla, Ian"],"url":"https://arxiv.org/abs/2311.16157","arxiv_id":"2311.16157","published":"2026-03-05"},{"title":"Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity","abstract":"We study the problem of hypothesis selection under the constraint of local differential privacy. Given a class $\\mathcal{F}$ of $k$ distributions and a set of i.i.d. samples from an unknown distribution $h$, the goal of hypothesis selection is to pick a distribution $\\hat{f}$ whose total variation distance to $h$ is comparable with the best distribution in $\\mathcal{F}$ (with high probability). We devise an $\\varepsi","authors":["Pour, Alireza F.","Ashtiani, Hassan","Asoodeh, Shahab"],"url":"https://arxiv.org/abs/2312.05645","arxiv_id":"2312.05645","published":"2026-03-05"},{"title":"Catch Me If You Can Describe Me: Open-Vocabulary Camouflaged Instance Segmentation with Diffusion","abstract":"Text-to-image diffusion techniques have shown exceptional capabilities in producing high-quality, dense visual predictions from open-vocabulary text. This indicates a strong correlation between visual and textual domains in open concepts and that diffusion-based text-to-image models can capture rich and diverse information for computer vision tasks. However, we found that those advantages do not hold for learning of ","authors":["Vu, Tuan-Anh","Nguyen, Duc Thanh","Guo, Qing","Chung, Nhat","Hua, Binh-Son","Tsang, Ivor W."],"url":"https://arxiv.org/abs/2312.17505","arxiv_id":"2312.17505","published":"2026-03-05"},{"title":"Graph Neural Networks in EEG-based Emotion Recognition: A Survey","abstract":"Compared to other modalities, EEG-based emotion recognition can intuitively respond to the emotional patterns in the human brain and, therefore, has become one of the most concerning tasks in the brain-computer interfaces field. Since dependencies within brain regions are closely related to emotion, a significant trend is to develop Graph Neural Networks (GNNs) for EEG-based emotion recognition. However, brain region","authors":["Liu, Chenyu","Deng, Yuqiu","Wu, Yihao","Yang, Ruizhi","Wang, Zhongruo","Zhang, Liangwei"],"url":"https://arxiv.org/abs/2402.01138","arxiv_id":"2402.01138","published":"2026-03-05"},{"title":"Learning to Generate Conditional Tri-plane for 3D-aware Expression Controllable Portrait Animation","abstract":"In this paper, we present Export3D, a one-shot 3D-aware portrait animation method that is able to control the facial expression and camera view of a given portrait image. To achieve this, we introduce a tri-plane generator with an effective expression conditioning method, which directly generates a tri-plane of 3D prior by transferring the expression parameter of 3DMM into the source image. The tri-plane is then deco","authors":["Ki, Taekyung","Min, Dongchan","Chae, Gyeongsu"],"url":"https://arxiv.org/abs/2404.00636","arxiv_id":"2404.00636","published":"2026-03-05"},{"title":"FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Matching","abstract":"The paper proposes FireANTs, a multi-scale Adaptive Riemannian Optimization algorithm for dense diffeomorphic image matching. Existing state-of-the-art methods for diffeomorphic image matching are slow due to inefficient implementations and slow convergence due to the ill-conditioned nature of the optimization problem. Deep learning methods offer fast inference but require extensive training time, substantial inferen","authors":["Jena, Rohit","Chaudhari, Pratik","Gee, James C."],"url":"https://arxiv.org/abs/2404.01249","arxiv_id":"2404.01249","published":"2026-03-05"},{"title":"A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving","abstract":"Reinforcement learning has emerged as an important approach for autonomous driving. A reward function is used in reinforcement learning to establish the learned skill objectives and guide the agent toward the optimal policy. Since autonomous driving is a complex domain with partly conflicting objectives with varying degrees of priority, developing a suitable reward function represents a fundamental challenge. This pa","authors":["Abouelazm, Ahmed","Michel, Jonas","Zoellner, J. Marius"],"url":"https://arxiv.org/abs/2405.01440","arxiv_id":"2405.01440","published":"2026-03-05"},{"title":"RAEE: A Robust Retrieval-Augmented Early Exit Framework for Efficient Inference","abstract":"Deploying large language model inference remains challenging due to their high computational overhead. Early exit optimizes model inference by adaptively reducing the number of inference layers. Current methods typically train internal classifiers or use heuristic methods to determine the exit layer. However, those methods either introduce significant training overheads or lead to performance degradation. To address ","authors":["Huang, Lianming","Wu, Shangyu","Cui, Yufei","Xiong, Ying","Hu, Haibo","Liu, Xue"],"url":"https://arxiv.org/abs/2405.15198","arxiv_id":"2405.15198","published":"2026-03-05"},{"title":"Leveraging Large Language Models for Semantic Query Processing in a Scholarly Knowledge Graph","abstract":"The proposed research aims to develop an innovative semantic query processing system that enables users to obtain comprehensive information about research works produced by Computer Science (CS) researchers at the Australian National University (ANU). The system integrates Large Language Models (LLMs) with the ANU Scholarly Knowledge Graph (ASKG), a structured repository of all research-related artifacts produced at ","authors":["Jia, Runsong","Zhang, Bowen","Méndez, Sergio J. Rodríguez","Omran, Pouya G."],"url":"https://arxiv.org/abs/2405.15374","arxiv_id":"2405.15374","published":"2026-03-05"},{"title":"Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset","abstract":"The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previous state-of-the-art approaches for automated analysis leverage vision-language models (VLMs) that jointly model images and radiology reports. However, current medical VLMs are generally limited to 2D images and short reports. Here to overcom","authors":["Blankemeier, Louis","Kumar, Ashwin","Cohen, Joseph Paul","Liu, Jiaming","Liu, Longchao","Van Veen, Dave"],"url":"https://arxiv.org/abs/2406.06512","arxiv_id":"2406.06512","published":"2026-03-05"},{"title":"Tracking solutions of time-varying variational inequalities","abstract":"Tracking the solution of time-varying variational inequalities is an important problem with applications in game theory, optimization, and machine learning. Existing work considers time-varying games or time-varying optimization problems. For strongly convex optimization problems or strongly monotone games, these results provide tracking guarantees under the assumption that the variation of the time-varying problem i","authors":["Hadiji, Hédi","Sachs, Sarah","Guzmán, Cristóbal"],"url":"https://arxiv.org/abs/2406.14059","arxiv_id":"2406.14059","published":"2026-03-05"},{"title":"Manipulating language models' training data to study syntactic constraint learning: the case of English passivization","abstract":"Grammatical rules in natural languages are often characterized by exceptions. How do language learners learn these exceptions to otherwise general patterns? Here, we study this question through the case study of English passivization. While passivization is in general quite productive, there are cases where it cannot apply (cf. the following sentence is ungrammatical: *One hour was lasted by the meeting). Using neura","authors":["Leong, Cara Su-Yi","Linzen, Tal"],"url":"https://arxiv.org/abs/2407.04593","arxiv_id":"2407.04593","published":"2026-03-05"},{"title":"Black Box Meta-Learning Intrinsic Rewards","abstract":"The broader application of reinforcement learning (RL) is limited by challenges including data efficiency, generalization capability, and ability to learn in sparse-reward environments. Meta-learning has emerged as a promising approach to address these issues by optimizing components of the learning algorithm to meet desired characteristics. Additionally, a different line of work has extensively studied the use of in","authors":["Pappalardo, Octavio","Ramele, Rodrigo","Santos, Juan Miguel"],"url":"https://arxiv.org/abs/2407.21546","arxiv_id":"2407.21546","published":"2026-03-05"},{"title":"AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithm","abstract":"We present AuToMATo, a novel clustering algorithm based on persistent homology. While AuToMATo is not parameter-free per se, we provide default choices for its parameters that make it into an out-of-the-box clustering algorithm that performs well across the board. AuToMATo combines the existing ToMATo clustering algorithm with a bootstrapping procedure in order to separate significant peaks of an estimated density fu","authors":["Huber, Marius","Kalisnik, Sara","Schnider, Patrick"],"url":"https://arxiv.org/abs/2408.06958","arxiv_id":"2408.06958","published":"2026-03-05"},{"title":"A computational transition for detecting correlated stochastic block models by low-degree polynomials","abstract":"Detection of correlation in a pair of random graphs is a fundamental statistical and computational problem that has been extensively studied in recent years. In this work, we consider a pair of correlated (sparse) stochastic block models $\\mathcal{S}(n,\\tfracλ{n};k,ε;s)$ that are subsampled from a common parent stochastic block model $\\mathcal S(n,\\tfracλ{n};k,ε)$ with $k=O(1)$ symmetric communities, average degree $","authors":["Chen, Guanyi","Ding, Jian","Gong, Shuyang","Li, Zhangsong"],"url":"https://arxiv.org/abs/2409.00966","arxiv_id":"2409.00966","published":"2026-03-05"},{"title":"A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning","abstract":"As humans can explore and understand the world through active touch, similar capability is desired for robots. In this paper, we address the problem of active tactile object recognition, pose estimation and shape transfer learning, where a customized particle filter (PF) and Gaussian process implicit surface (GPIS) is combined in a unified Bayesian framework. Upon new tactile input, the customized PF updates the join","authors":["Zheng, Haodong","Jalba, Andrei","Cuijpers, Raymond H.","IJsselsteijn, Wijnand","Schoenmakers, Sanne"],"url":"https://arxiv.org/abs/2409.06912","arxiv_id":"2409.06912","published":"2026-03-05"},{"title":"FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models","abstract":"The training of diffusion models is computationally intensive, making effective pre-training essential. However, real-world deployments often demand models of variable sizes due to diverse memory and computational constraints, posing challenges when corresponding pre-trained versions are unavailable. To address this, we propose FINE, a novel pre-training method whose resulting model can flexibly factorize its knowled","authors":["Xie, Yucheng","Feng, Fu","Shi, Ruixiao","Shen, Jianlu","Wang, Jing","Rui, Yong"],"url":"https://arxiv.org/abs/2409.19289","arxiv_id":"2409.19289","published":"2026-03-05"},{"title":"Diffusion & Adversarial Schrödinger Bridges via Iterative Proportional Markovian Fitting","abstract":"The Iterative Markovian Fitting (IMF) procedure, which iteratively projects onto the space of Markov processes and the reciprocal class, successfully solves the Schrödinger Bridge (SB) problem. However, an efficient practical implementation requires a heuristic modification -- alternating between fitting forward and backward time diffusion at each iteration. This modification is crucial for stabilizing training and a","authors":["Kholkin, Sergei","Ksenofontov, Grigoriy","Li, David","Kornilov, Nikita","Gushchin, Nikita","Suvorikova, Alexandra"],"url":"https://arxiv.org/abs/2410.02601","arxiv_id":"2410.02601","published":"2026-03-05"},{"title":"Scaling Laws For Diffusion Transformers","abstract":"Diffusion transformers (DiT) have already achieved appealing synthesis and scaling properties in content recreation, e.g., image and video generation. However, scaling laws of DiT are less explored, which usually offer precise predictions regarding optimal model size and data requirements given a specific compute budget. Therefore, experiments across a broad range of compute budgets, from 1e17 to 6e18 FLOPs are condu","authors":["Liang, Zhengyang","He, Hao","Yang, Ceyuan","Dai, Bo"],"url":"https://arxiv.org/abs/2410.08184","arxiv_id":"2410.08184","published":"2026-03-05"},{"title":"TextMaster: A Unified Framework for Realistic Text Editing via Glyph-Style Dual-Control","abstract":"In image editing tasks, high-quality text editing capabilities can significantly reduce both human and material resource costs. Existing methods, however, face significant limitations in terms of stroke accuracy for complex text and controllability of generated text styles. To address these challenges, we propose TextMaster, a solution capable of accurately editing text across various scenarios and image regions, whi","authors":["Yan, Zhenyu","Wang, Jian","Wang, Aoqiang","Li, Yuhan","Shang, Wenxiang","Lin, Ran"],"url":"https://arxiv.org/abs/2410.09879","arxiv_id":"2410.09879","published":"2026-03-05"},{"title":"Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration","abstract":"Offline-to-Online Reinforcement Learning has emerged as a powerful paradigm, leveraging offline data for initialization and online fine-tuning to enhance both sample efficiency and performance. However, most existing research has focused on single-agent settings, with limited exploration of the multi-agent extension, i.e., Offline-to-Online Multi-Agent Reinforcement Learning (O2O MARL). In O2O MARL, two critical chal","authors":["Zhong, Hai","Wang, Xun","Li, Zhuoran","Huang, Longbo"],"url":"https://arxiv.org/abs/2410.19450","arxiv_id":"2410.19450","published":"2026-03-05"},{"title":"Toward Reasoning on the Boundary: A Mixup-based Approach for Graph Anomaly Detection","abstract":"While GNN-based detection methods excel at identifying overt outliers, they often struggle with boundary anomalies -- subtly camouflaged nodes that are difficult to distinguish from normal instances. This limitation highlights a fundamental gap in the reasoning capabilities of existing methods. We attribute this issue to the reliance of standard Graph Contrastive Learning (GCL) on easy negatives, which fosters the le","authors":["Kim, Hwan","Kim, Junghoon","Lim, Sungsu"],"url":"https://arxiv.org/abs/2410.20310","arxiv_id":"2410.20310","published":"2026-03-05"}],"history":{"events":[{"year":1981,"text":"The ZX81, a pioneering British home computer, was launched by Sinclair Research, and went on to sell more than 1.5 million units around the world.","url":"https://en.wikipedia.org/wiki/ZX81"},{"year":1966,"text":"BOAC Flight 911 disintegrated and crashed near Mount Fuji shortly after departure from Tokyo International Airport, killing all 113 passengers and 11 crew members on board.","url":"https://en.wikipedia.org/wiki/BOAC_Flight_911"},{"year":1963,"text":"American country-music performers Patsy Cline, Cowboy Copas, and Hawkshaw Hawkins were killed when their PA-24 crashed shortly after takeoff in Camden, Tennessee.","url":"https://en.wikipedia.org/wiki/Country_music"},{"year":1960,"text":"Cuban photographer Alberto Korda took his iconic photograph Guerrillero Heroico of Marxist revolutionary Che Guevara.","url":"https://en.wikipedia.org/wiki/Alberto_Korda"},{"year":1943,"text":"World War II: The Gloster Meteor, the Allies' only operational jet aircraft, made its maiden flight.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1936,"text":"The prototype (pictured) of the Supermarine Spitfire flew for the first time.","url":"https://en.wikipedia.org/wiki/Supermarine_Spitfire_prototype_K5054"},{"year":1825,"text":"Roberto Cofresí, one of the last Caribbean pirates, was apprehended after his flagship sloop Anne was captured by authorities.","url":"https://en.wikipedia.org/wiki/Roberto_Cofres%C3%AD"},{"year":1824,"text":"The First Anglo-Burmese War began.","url":"https://en.wikipedia.org/wiki/First_Anglo-Burmese_War"},{"year":1811,"text":"Peninsular War: At the Battle of Barrosa, Anglo-Iberian forces trying to lift the Siege of Cádiz defeated a French attack but could not break the siege itself.","url":"https://en.wikipedia.org/wiki/Peninsular_War"},{"year":1770,"text":"American Revolution: British soldiers fired into a crowd in Boston, killing five people.","url":"https://en.wikipedia.org/wiki/American_Revolution"},{"year":1279,"text":"The Livonian branch of the Teutonic Order suffered a great loss when 71 knights died in the Battle of Aizkraukle.","url":"https://en.wikipedia.org/wiki/Livonian_Order"},{"year":363,"text":"Roman–Persian Wars: Roman emperor Julian and his army set out from Antioch to attack the Sasanian Empire.","url":"https://en.wikipedia.org/wiki/Roman%E2%80%93Persian_wars"}],"births":[{"year":2007,"text":"Roman Griffin Davis, English actor","url":"https://en.wikipedia.org/wiki/Roman_Griffin_Davis"},{"year":2000,"text":"Doug Edert, American basketball player","url":"https://en.wikipedia.org/wiki/Doug_Edert"},{"year":1999,"text":"Madison Beer, American singer-songwriter","url":"https://en.wikipedia.org/wiki/Madison_Beer"},{"year":1999,"text":"Justin Fields, American football player","url":"https://en.wikipedia.org/wiki/Justin_Fields"},{"year":1999,"text":"Yeri, South Korean singer and actress","url":"https://en.wikipedia.org/wiki/Yeri_(singer)"},{"year":1998,"text":"Bo Bichette, American baseball player","url":"https://en.wikipedia.org/wiki/Bo_Bichette"},{"year":1997,"text":"Milena Venega, Cuban rower","url":"https://en.wikipedia.org/wiki/Milena_Venega"},{"year":1996,"text":"Taylor Hill, American model","url":"https://en.wikipedia.org/wiki/Taylor_Hill_(model)"}],"deaths":[{"year":2017,"text":"Kurt Moll, German opera singer (born 1938)","url":"https://en.wikipedia.org/wiki/Kurt_Moll"},{"year":2016,"text":"Hassan Al-Turabi, Sudanese activist and politician (born 1932)","url":"https://en.wikipedia.org/wiki/Hassan_al-Turabi"},{"year":2016,"text":"Ray Tomlinson, American computer programmer and engineer (born 1941)","url":"https://en.wikipedia.org/wiki/Ray_Tomlinson"},{"year":2016,"text":"Al Wistert, American football player and coach (born 1920)","url":"https://en.wikipedia.org/wiki/Al_Wistert"},{"year":2015,"text":"Vlada Divljan, Serbian singer-songwriter and guitarist (born 1958)","url":"https://en.wikipedia.org/wiki/Vlada_Divljan"},{"year":2015,"text":"Edward Egan, American cardinal and former Archbishop of New York (born 1932)","url":"https://en.wikipedia.org/wiki/Edward_Egan"},{"year":2014,"text":"Geoff Edwards, American actor and game show host (born 1931)","url":"https://en.wikipedia.org/wiki/Geoff_Edwards"},{"year":2014,"text":"Ailsa McKay, Scottish economist and academic (born 1963)","url":"https://en.wikipedia.org/wiki/Ailsa_McKay"}]},"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 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Every item links to its source."}