{"date":"2026-06-17","schema_version":1,"captured_at":"2026-08-25T23:12:42.930Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"2026 FIFA World Cup","views":561800,"rank":3,"extract":"The 2026 FIFA World Cup was the 23rd FIFA World Cup, the quadrennial international men's soccer championship contested by the national teams of the member associations of FIFA. 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":"Oliver Tree","views":473753,"rank":5,"extract":"Oliver Tree Nickell was an American singer-songwriter, rapper, and record producer. 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As a part of this research, hundreds of hours of daylong recordings from preterm infants' audio environments were collected from two hospitals in Finland and Estonia in the context of so-called APPLE study. In order to analyze the emotional content of speech in such a massive dataset","authors":["Vaaras, Einari","Ahlqvist-Björkroth, Sari","Drossos, Konstantinos","Räsänen, Okko"],"url":"https://arxiv.org/abs/2106.09539","arxiv_id":"2106.09539","published":"2026-06-17"},{"title":"Neural Computed Tomography","abstract":"Motion during acquisition of a set of projections can lead to significant motion artifacts in computed tomography reconstructions despite fast acquisition of individual views. In cases such as cardiac imaging, motion may be unavoidable and evaluating motion may be of clinical interest. Reconstructing images with reduced motion artifacts has typically been achieved by developing systems with faster gantry rotation or ","authors":["Gupta, Kunal","Colvert, Brendan","Contijoch, Francisco"],"url":"https://arxiv.org/abs/2201.06574","arxiv_id":"2201.06574","published":"2026-06-17"},{"title":"Automated Evaluation of Standardized Dementia Screening Tests","abstract":"For dementia screening and monitoring, standardized tests play a key role in clinical routine since they aim at minimizing subjectivity by measuring performance on a variety of cognitive tasks. In this paper, we report on a study that consists of a semi-standardized history taking followed by two standardized neuropsychological tests, namely the SKT and the CERAD-NB. The tests include basic tasks such as naming objec","authors":["Braun, Franziska","Förstel, Markus","Oppermann, Bastian","Erzigkeit, Andreas","Hillemacher, Thomas","Lehfeld, Hartmut"],"url":"https://arxiv.org/abs/2206.06208","arxiv_id":"2206.06208","published":"2026-06-17"},{"title":"Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition","abstract":"When domain experts are needed to perform data annotation for complex machine-learning tasks, reducing annotation effort is crucial in order to cut down time and expenses. For cases when there are no annotations available, one approach is to utilize the structure of the feature space for clustering-based active learning (AL) methods. However, these methods are heavily dependent on how the samples are organized in the","authors":["Vaaras, Einari","Airaksinen, Manu","Räsänen, Okko"],"url":"https://arxiv.org/abs/2206.10188","arxiv_id":"2206.10188","published":"2026-06-17"},{"title":"Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razor","abstract":"The current trend of applying transfer learning from convolutional neural networks (CNNs) trained on large datasets can be an overkill when the target application is a custom and delimited problem, with enough data to train a network from scratch. On the other hand, the training of custom and lighter CNNs requires expertise, in the from-scratch case, and or high-end resources, as in the case of hardware-aware neural ","authors":["Garavagno, Andrea Mattia","Leonardis, Daniele","Frisoli, Antonio"],"url":"https://arxiv.org/abs/2212.07700","arxiv_id":"2212.07700","published":"2026-06-17"},{"title":"Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors","abstract":"The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings. This information could be used for developmental research and to support clinical decision-making, such as detection of developmental problems and guiding of their therapeutic interventions. MAIJU-based analyses rely fully on the classi","authors":["Vaaras, Einari","Airaksinen, Manu","Vanhatalo, Sampsa","Räsänen, Okko"],"url":"https://arxiv.org/abs/2305.09366","arxiv_id":"2305.09366","published":"2026-06-17"},{"title":"SSIL: Self-Supervised Imitation Learning for End-to-End Driving","abstract":"In autonomous driving, the end-to-end (E2E) driving approach that predicts vehicle control signals directly from sensor data is rapidly gaining attention. To learn a safe E2E driving system, one needs an extensive amount of driving data and human intervention. Vehicle control data is constructed by many hours of human driving, and it is challenging to construct large vehicle control datasets. Often, publicly availabl","authors":["Park, Jin Bok","Lee, Jinkyu","Back, Muhyun","Han, Hyun Min","Ma, Tianwei","Won, Sang Min"],"url":"https://arxiv.org/abs/2308.14329","arxiv_id":"2308.14329","published":"2026-06-17"},{"title":"ARC-Fi: Exploiting Antenna Spatial Diversity for Label-Efficient Domain Generalization in Wi-Fi Sensing","abstract":"Wi-Fi sensing systems are severely hindered by domain shifts when deployed in unseen real-world environments. While existing methods attempt to tackle this through Unsupervised Domain Adaptation (UDA) or Domain Generalization (DG), they critically rely on either inaccessible target data or prohibitively expensive, massive labeled source datasets. In practice, collecting abundant unlabeled Channel State Information (C","authors":["Xu, Ke","Zheng, Zhiyong","Zhu, Hongyuan","Wang, Lei","Wang, Jiangtao"],"url":"https://arxiv.org/abs/2310.06328","arxiv_id":"2310.06328","published":"2026-06-17"},{"title":"Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs","abstract":"We propose two graph neural network layers for graphs with features in a Riemannian manifold. First, based on a manifold-valued graph diffusion equation, we construct a diffusion layer that can be applied to an arbitrary number of nodes and graph connectivity patterns. Second, we model a tangent multilayer perceptron by transferring ideas from the vector neuron framework to our general setting. Both layers are equiva","authors":["Hanik, Martin","Steidl, Gabriele","von Tycowicz, Christoph"],"url":"https://arxiv.org/abs/2401.14381","arxiv_id":"2401.14381","published":"2026-06-17"},{"title":"Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models","abstract":"Online user generated content games (UGCGs) are increasingly popular among children and adolescents for social interaction and more creative online entertainment. However, they pose a heightened risk of exposure to explicit content, raising growing concerns for the online safety of children and adolescents. Despite these concerns, few studies have addressed the issue of illicit image-based promotions of unsafe UGCGs ","authors":["Guo, Keyan","Utkarsh, Ayush","Ding, Wenbo","Ondracek, Isabelle","Zhao, Ziming","Freeman, Guo"],"url":"https://arxiv.org/abs/2403.18957","arxiv_id":"2403.18957","published":"2026-06-17"},{"title":"Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks","abstract":"Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose environmental and resource challenges. Deep shift neural networks (DSNNs) offer a solution by leveraging shift operations to reduce computational complexity at inference. Following the insights from standard DNNs, we are interested in leveraging the full potential of ","authors":["Hennig, Leona","Tornede, Tanja","Lindauer, Marius"],"url":"https://arxiv.org/abs/2404.01965","arxiv_id":"2404.01965","published":"2026-06-17"},{"title":"NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results","abstract":"This paper reviews the NTIRE 2024 challenge on image super-resolution ($\\times$4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs using prior information. The LR images originate from bicubic downsampling degradation. The aim of the challenge is to obtain designs/","authors":["Chen, Zheng","Wu, Zongwei","Zamfir, Eduard","Zhang, Kai","Zhang, Yulun","Timofte, Radu"],"url":"https://arxiv.org/abs/2404.09790","arxiv_id":"2404.09790","published":"2026-06-17"},{"title":"Randomized Midpoint Method for Log-Concave Sampling under Constraints","abstract":"In this paper, we study the problem of sampling from log-concave distributions supported on convex and compact sets, with a particular focus on the randomized midpoint discretization of both overdamped and kinetic Langevin diffusions in constrained domains. We revisit the proximal framework for handling constraints through projection operators and develop a more general formulation that encompasses Euclidean, Bregman","authors":["Yu, Yifeng","Zhang, Shijie","Yu, Lu"],"url":"https://arxiv.org/abs/2405.15379","arxiv_id":"2405.15379","published":"2026-06-17"},{"title":"Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration","abstract":"Image restoration networks are usually comprised of an encoder and a decoder, responsible for aggregating image content from noisy, distorted data and to restore clean, undistorted images, respectively. Data aggregation as well as high-resolution image generation both usually come at the risk of involving aliases, i.e.~standard architectures put their ability to reconstruct the model input in jeopardy to reach high P","authors":["Agnihotri, Shashank","Grabinski, Julia","Keuper, Janis","Keuper, Margret"],"url":"https://arxiv.org/abs/2406.07435","arxiv_id":"2406.07435","published":"2026-06-17"},{"title":"E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book Systems","abstract":"Digital textbook (e-book) systems record student interactions with textbooks as a sequence of events called EventStream data. In the past, researchers extracted meaningful features from EventStream, and utilized them as inputs for downstream tasks such as grade prediction and modeling of student behavior. Previous research evaluated models that mainly used statistical-based features derived from EventStream logs, suc","authors":["Miyazaki, Yuma","Švábenský, Valdemar","Taniguchi, Yuta","Okubo, Fumiya","Minematsu, Tsubasa","Shimada, Atsushi"],"url":"https://arxiv.org/abs/2407.13053","arxiv_id":"2407.13053","published":"2026-06-17"},{"title":"Query-Efficient Video Adversarial Attack with Stylized Logo on Service Computing","abstract":"In service computing, video classification has become fundamental to many intelligent applications. While Deep Neural Networks (DNNs) have demonstrated excellent performance in recognizing video content, recent studies have shown that DNNs are highly vulnerable to adversarial examples. Thus, understanding adversarial attacks can better respond to emergency situations. In order to improve attack performance, many styl","authors":["Tang, Duoxun","Cao, Yuxin","Xiao, Xi","Wang, Derui","Wen, Sheng","Zhu, Tianqing"],"url":"https://arxiv.org/abs/2408.12099","arxiv_id":"2408.12099","published":"2026-06-17"},{"title":"Infusing Acoustic Pause Context into Text-Based Dementia Assessment","abstract":"Speech pauses, alongside content and structure, offer a valuable and non-invasive biomarker for detecting dementia. This work investigates the use of pause-enriched transcripts in transformer-based language models to differentiate the cognitive states of subjects with no cognitive impairment, mild cognitive impairment, and Alzheimer's dementia based on their speech from a clinical assessment. We address three bin","authors":["Braun, Franziska","Bayerl, Sebastian P.","Hönig, Florian","Lehfeld, Hartmut","Hillemacher, Thomas","Bocklet, Tobias"],"url":"https://arxiv.org/abs/2408.15188","arxiv_id":"2408.15188","published":"2026-06-17"},{"title":"Broadcast Product: Redefining Shape-aligned Element-wise Multiplication and Beyond","abstract":"Broadcast operations are widely used in scientific computing libraries, yet their mathematical formulation is often implicit and inconsistently represented in machine learning literature. This problem frequently leads to invalid equations when element-wise products are written despite mismatched tensor shapes. In this paper, we formalize such operations by introducing the broadcast product $\\boxdot$, which explicitly","authors":["Matsui, Yusuke","Yokota, Tatsuya"],"url":"https://arxiv.org/abs/2409.17502","arxiv_id":"2409.17502","published":"2026-06-17"},{"title":"Adaptable Method for Crystal Design across Diverse Constraints and Objectives with Pretrained Property Predictors","abstract":"Advanced crystal design can accelerate materials discovery across applications from photovoltaics to spintronics. Practical design must satisfy multiple properties and physical constraints, yet existing machine-learning-based approaches to such design often depend on large datasets, retraining, or task-specific generators. Here, we show that direct predictor-guided gradient optimization enables data-efficient, constr","authors":["Fujii, Akihiro","Ushiku, Yoshitaka","Shimizu, Koji","Lu, Anh Khoa Augustin","Watanabe, Satoshi"],"url":"https://arxiv.org/abs/2410.08562","arxiv_id":"2410.08562","published":"2026-06-17"},{"title":"Variational autoencoders with latent high-dimensional steady geometric flows for dynamics","abstract":"We develop Riemannian approaches to variational autoencoders (VAEs) for PDE-type ambient data with regularizing geometric latent dynamics, which we refer to as VAE-DLM, or VAEs with dynamical latent manifolds. We redevelop the VAE framework such that manifold geometries, subject to our geometric flow, embedded in Euclidean space are learned in the intermediary latent space developed by encoders and decoders. By tailo","authors":["Gracyk, Andrew"],"url":"https://arxiv.org/abs/2410.10137","arxiv_id":"2410.10137","published":"2026-06-17"},{"title":"Conditional Local Importance by Quantile Expectations","abstract":"Global variable importance measures are commonly used to interpret the results of machine learning models. Local variable importance techniques assess how variables contribute to individual observations. Current, popular methods, including LIME and SHAP, provide useful measures of feature contribution in the prediction space, while leaving opportunities for improved characterization of local structure in the model lo","authors":["Bladen, Kelvyn K.","Cutler, Adele","Cutler, D. Richard","Moon, Kevin R."],"url":"https://arxiv.org/abs/2411.08821","arxiv_id":"2411.08821","published":"2026-06-17"},{"title":"TACOMORE: Exploring a replicable prompting protocol for LLM-assisted corpus analysis","abstract":"As corpus linguistics continues to scale, researchers are facing a growing methodological bottleneck: while computational tools can easily count billions of words, the qualitative interpretation of these data remains a slow and labor-intensive human task. Large Language Models (LLMs) offer a promising way to automate this process, yet their integration into the field is often hindered by concerns over black-box unpre","authors":["Li, Bingru","Wang, Han","Groom, Nicholas"],"url":"https://arxiv.org/abs/2412.10139","arxiv_id":"2412.10139","published":"2026-06-17"},{"title":"LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management","abstract":"Cryptocurrency portfolio management requires the fusion of heterogeneous multi-modal signals, including structured price and on-chain time series, unstructured news text, and technical indicators, under high-volatility and real-time constraints. While deep learning approaches show predictive capability, their opacity limits practical adoption, and single large language model (LLM) agents struggle to process the bread","authors":["Luo, Yichen","Feng, Yebo","Xu, Jiahua","Tasca, Paolo","Liu, Yang"],"url":"https://arxiv.org/abs/2501.00826","arxiv_id":"2501.00826","published":"2026-06-17"},{"title":"Geometry-Preserving Encoder/Decoder in Latent Generative Models","abstract":"Generative modeling aims to generate new data samples that resemble a given dataset. When using diffusion models for this task, one of the main challenges is solving the problem in the input space, which tends to be very high-dimensional. To address this, recent approaches solve diffusion models in the latent space through an encoder that maps from the data space to a lower-dimensional latent space, improving trainin","authors":["Lee, Wonjun","O'Neill, Riley C. W.","Zou, Dongmian","Calder, Jeff","Lerman, Gilad"],"url":"https://arxiv.org/abs/2501.09876","arxiv_id":"2501.09876","published":"2026-06-17"},{"title":"Robust Local Polynomial Regression with Similarity Kernels","abstract":"Local Polynomial Regression (LPR) is a widely used nonparametric method for modeling complex relationships due to its flexibility and simplicity. It estimates a regression function by fitting low-degree polynomials to localized subsets of the data, weighted by proximity. However, traditional LPR is sensitive to outliers and high-leverage points, which can significantly affect estimation accuracy. This paper revisits ","authors":["Shulman, Yaniv"],"url":"https://arxiv.org/abs/2501.10729","arxiv_id":"2501.10729","published":"2026-06-17"}],"history":{"events":[{"year":2017,"text":"Wildfires erupted across central Portugal, eventually causing the deaths of 66 people.","url":"https://en.wikipedia.org/wiki/June_2017_Portugal_wildfires"},{"year":2015,"text":"A white supremacist committed a mass shooting at the Emanuel African Methodist Episcopal Church in Charleston, South Carolina, killing nine people during a prayer service.","url":"https://en.wikipedia.org/wiki/White_supremacy"},{"year":2009,"text":"Following a petition, the liberal progressive Czech Pirate Party was officially registered as a political party.","url":"https://en.wikipedia.org/wiki/Czech_Pirate_Party"},{"year":1972,"text":"Five men were arrested for attempted burglary on the offices of the Democratic National Committee, igniting the Watergate scandal that ultimately led to the resignation of US president Richard Nixon more than two years later.","url":"https://en.wikipedia.org/wiki/Watergate_complex"},{"year":1963,"text":"Riots broke out in Saigon one day after the signing of the Joint Communiqué, an attempt to resolve the Buddhist crisis in South Vietnam.","url":"https://en.wikipedia.org/wiki/Ho_Chi_Minh_City"},{"year":1952,"text":"Guatemalan Revolution: The Guatemalan Congress passed Decree 900, redistributing unused land greater than 224 acres (0.91 km2) in area to local peasants.","url":"https://en.wikipedia.org/wiki/Guatemalan_Revolution"},{"year":1940,"text":"Second World War: RMS Lancastria was sunk by German aircraft near Saint-Nazaire, France, causing thousands of fatalities in Britain's worst maritime disaster.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1919,"text":"Hundreds of Canadian soldiers rioted in Epsom, England, leading to the death of a British police officer.","url":"https://en.wikipedia.org/wiki/Epsom_riot"},{"year":1913,"text":"In Detroit, autoworkers for car manufacturer Studebaker went on strike in the American auto industry's first major strike action.","url":"https://en.wikipedia.org/wiki/Detroit"},{"year":1900,"text":"Boxer Rebellion: Allied naval forces captured the Taku Forts from Qing China after a brief but bloody battle.","url":"https://en.wikipedia.org/wiki/Boxer_Rebellion"},{"year":1861,"text":"American Civil War: The Battle of Vienna, Virginia, took place, which involved one of the world's first military movements of troops by train.","url":"https://en.wikipedia.org/wiki/American_Civil_War"},{"year":1795,"text":"French Revolutionary Wars: Off the coast of Brittany, a Royal Navy squadron commanded by William Cornwallis (pictured) fended off a numerically superior French Navy fleet.","url":"https://en.wikipedia.org/wiki/French_Revolutionary_Wars"},{"year":1775,"text":"American Revolutionary War: British forces took Bunker Hill outside of Boston, Massachusetts.","url":"https://en.wikipedia.org/wiki/American_Revolutionary_War"},{"year":1631,"text":"Mumtaz Mahal (pictured), wife of the Mughal emperor Shah Jahan, died in childbirth; Jahan spent the next seventeen years constructing her mausoleum, the Taj Mahal.","url":"https://en.wikipedia.org/wiki/Mumtaz_Mahal"},{"year":1579,"text":"Explorer Francis Drake landed in a region of present-day California, naming it New Albion and claiming it for England.","url":"https://en.wikipedia.org/wiki/Francis_Drake"},{"year":1462,"text":"Hungarian–Ottoman Wars: Wallachian forces led by Vlad Dracula attacked an Ottoman camp at night in an unsuccessful attempt to assassinate Sultan Mehmed II.","url":"https://en.wikipedia.org/wiki/Hungarian%E2%80%93Ottoman_Wars"},{"year":1397,"text":"The kingdoms of Denmark, Sweden, and Norway formed the Kalmar Union, a personal union under Eric of Pomerania.","url":"https://en.wikipedia.org/wiki/Kalmar_Union"},{"year":653,"text":"Pope Martin I (pictured) was arrested in the Lateran Palace, Rome, and taken to Constantinople.","url":"https://en.wikipedia.org/wiki/Pope_Martin_I"}],"births":[{"year":2003,"text":"Rizki Juniansyah, Indonesian weightlifter","url":"https://en.wikipedia.org/wiki/Rizki_Juniansyah"},{"year":2000,"text":"Odessa A'zion, American actress","url":"https://en.wikipedia.org/wiki/Odessa_A'zion"},{"year":1999,"text":"Henri Jokiharju, Finnish ice hockey player","url":"https://en.wikipedia.org/wiki/Henri_Jokiharju"},{"year":1999,"text":"Elena Rybakina, Russian-born Kazakhstani tennis player","url":"https://en.wikipedia.org/wiki/Elena_Rybakina"},{"year":1997,"text":"KJ Apa, New Zealand actor","url":"https://en.wikipedia.org/wiki/KJ_Apa"},{"year":1997,"text":"Raluca Șerban, Romanian-Cypriot tennis player","url":"https://en.wikipedia.org/wiki/Raluca_%C8%98erban"},{"year":1997,"text":"Alexander Bublik, Russian-born Kazakhstani tennis player","url":"https://en.wikipedia.org/wiki/Alexander_Bublik"},{"year":1995,"text":"Clément Lenglet, French footballer","url":"https://en.wikipedia.org/wiki/Cl%C3%A9ment_Lenglet"}],"deaths":[{"year":2021,"text":"Kenneth Kaunda, Zambian educator and politician, first president of Zambia (born 1924)","url":"https://en.wikipedia.org/wiki/Kenneth_Kaunda"},{"year":2020,"text":"Jean Kennedy Smith, American activist, humanitarian, author and diplomat (United States Ambassador to Ireland, 1993–1998) (born 1928)","url":"https://en.wikipedia.org/wiki/Jean_Kennedy_Smith"},{"year":2019,"text":"Gloria Vanderbilt, American artist, author actress, fashion designer, heiress and socialite (born 1924)","url":"https://en.wikipedia.org/wiki/Gloria_Vanderbilt"},{"year":2019,"text":"Mohamed Morsi, Egyptian professor and politician, first elected president of Egypt after Egyptian revolution (born 1951)","url":"https://en.wikipedia.org/wiki/Mohamed_Morsi"},{"year":2017,"text":"Baldwin Lonsdale, president of Vanuatu (born 1948)","url":"https://en.wikipedia.org/wiki/Baldwin_Lonsdale"},{"year":2015,"text":"Ron Clarke, Australian runner and politician, Mayor of the Gold Coast (born 1937)","url":"https://en.wikipedia.org/wiki/Ron_Clarke"},{"year":2015,"text":"John David Crow, American football player and coach (born 1935)","url":"https://en.wikipedia.org/wiki/John_David_Crow"},{"year":2015,"text":"Süleyman Demirel, Turkish engineer and politician, 9th President of Turkey (born 1924)","url":"https://en.wikipedia.org/wiki/S%C3%BCleyman_Demirel"}]},"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."}