{"date":"2025-12-29","schema_version":1,"captured_at":"2026-08-25T23:06:37.635Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":13},"attention":[{"title":"Brigitte Bardot","views":1184177,"rank":2,"extract":"Brigitte Anne-Marie Bardot, often referred to by her initials B.B., was a French actress, singer, model, and animal rights activist. 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Our presentation is divided into two parts. In the first part, we present the definition and fundamental properties of the signature of a path. The signature is a sequence of numbers associated with a path that captures many of its important analytic and geometric properties. As a sequence of ","authors":["Chevyrev, Ilya","Kormilitzin, Andrey"],"url":"https://arxiv.org/abs/1603.03788","arxiv_id":"1603.03788","published":"2025-12-29"},{"title":"Learning pairwise Markov network structures using correlation neighborhoods","abstract":"Markov networks are widely studied and used throughout multivariate statistics and computer science. In particular, the problem of learning the structure of Markov networks from data without invoking chordality assumptions in order to retain expressiveness of the model class has been given a considerable attention in the recent literature, where numerous constraint-based or score-based methods have been introduced. H","authors":["Kuronen, Juri","Corander, Jukka","Pensar, Johan"],"url":"https://arxiv.org/abs/1910.13832","arxiv_id":"1910.13832","published":"2025-12-29"},{"title":"Co-Teaching for Unsupervised Domain Adaptation and Expansion","abstract":"Unsupervised Domain Adaptation (UDA) essentially trades a model's performance on a source domain for improving its performance on a target domain. To overcome this, Unsupervised Domain Expansion (UDE) has been introduced, which adapts the model to the target domain while preserving its performance in the source domain. In both UDA and UDE, a model tailored to a given domain is assumed to well handle samples from ","authors":["Lin, Hailan","Wei, Qijie","Tian, Kaibin","Zhao, Ruixiang","Li, Xirong"],"url":"https://arxiv.org/abs/2204.01210","arxiv_id":"2204.01210","published":"2025-12-29"},{"title":"Pre-training Vision Transformers with Formula-driven Supervised Learning","abstract":"In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k and can approach that of the JFT-300M dataset without the use of real images, human supervision, or self-supervision during the pre-training of vision transformers (ViTs). For example, ViT-Base pre-trained on ImageNet-21k and JFT-300M showed 83.0 and 84.1% top-1 accuracy when fi","authors":["Kataoka, Hirokatsu","Takashima, Sora","Hayamizu, Ryo","Yamada, Ryosuke","Nakashima, Kodai","Zhang, Xinyu"],"url":"https://arxiv.org/abs/2206.09132","arxiv_id":"2206.09132","published":"2025-12-29"},{"title":"Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models","abstract":"Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-ta","authors":["Tian, Ye","Weng, Haolei","Xia, Lucy","Feng, Yang"],"url":"https://arxiv.org/abs/2209.15224","arxiv_id":"2209.15224","published":"2025-12-29"},{"title":"The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning","abstract":"Populist rhetoric employed on online media is characterized as deeply impassioned and often imbued with strong emotions. The aim of this paper is to empirically investigate the differences in affective nonverbal communication of political leaders. We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries, analyze their facial expressions of emotion and ","authors":["Major, Sara","Tomašević, Aleksandar"],"url":"https://arxiv.org/abs/2304.09914","arxiv_id":"2304.09914","published":"2025-12-29"},{"title":"Creative Agents: Empowering Agents with Imagination for Creative Tasks","abstract":"We study building embodied agents for open-ended creative tasks. While existing methods build instruction-following agents that can perform diverse open-ended tasks, none of them demonstrates creativity -- the ability to give novel and diverse solutions implicit in the language instructions. This limitation comes from their inability to convert abstract language instructions into concrete goals and perform long-horiz","authors":["Cai, Penglin","Zhang, Chi","Fu, Yuhui","Yuan, Haoqi","Lu, Zongqing"],"url":"https://arxiv.org/abs/2312.02519","arxiv_id":"2312.02519","published":"2025-12-29"},{"title":"SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustments","abstract":"Split Federated Learning (SFL) is a distributed machine learning framework which strategically divides the learning process between a server and clients and collaboratively trains a shared model by aggregating local models updated based on data from distributed clients. However, data heterogeneity and partial client participation result in label distribution skew, which severely degrades the learning performance. To ","authors":["Yang, Jiarong","Liu, Yuan"],"url":"https://arxiv.org/abs/2405.04875","arxiv_id":"2405.04875","published":"2025-12-29"},{"title":"MAD: Multi-Alignment MEG-to-Text Decoding","abstract":"Deciphering language from brain activity is a crucial task in brain-computer interface (BCI) research. Non-invasive cerebral signaling techniques including electroencephalography (EEG) and magnetoencephalography (MEG) are becoming increasingly popular due to their safety and practicality, avoiding invasive electrode implantation. However, current works under-investigated three points: 1) a predominant focus on EEG wi","authors":["Yang, Yiqian","Jo, Hyejeong","Duan, Yiqun","Zhang, Qiang","Zhou, Jinni","Hu, Xuming"],"url":"https://arxiv.org/abs/2406.01512","arxiv_id":"2406.01512","published":"2025-12-29"},{"title":"Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond","abstract":"Self-supervised learning (SSL), which aims to learn meaningful prior representations from unlabeled data, has been proven effective for skeleton-based action understanding. Different from the image domain, skeleton data possesses sparser spatial structures and diverse representation forms, with the absence of background clues and the additional temporal dimension, presenting new challenges for spatial-temporal motion","authors":["Zhang, Jiahang","Lin, Lilang","Yang, Shuai","Liu, Jiaying"],"url":"https://arxiv.org/abs/2406.02978","arxiv_id":"2406.02978","published":"2025-12-29"},{"title":"Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge","abstract":"Recent advances in Hierarchical Multi-label Classification (HMC), particularly neurosymbolic-based approaches, have demonstrated improved consistency and accuracy by enforcing constraints on a neural model during training. However, such work assumes the existence of such constraints a-priori. In this paper, we relax this strong assumption and present an approach based on Error Detection Rules (EDR) that allow for lea","authors":["Kricheli, Joshua Shay","Vo, Khoa","Datta, Aniruddha","Ozgur, Spencer","Shakarian, Paulo"],"url":"https://arxiv.org/abs/2407.15192","arxiv_id":"2407.15192","published":"2025-12-29"},{"title":"Revisiting Bi-Encoder Neural Search: An Encoding--Searching Separation Perspective","abstract":"This paper reviews, analyzes, and proposes a new perspective on the bi-encoder architecture for neural search. While the bi-encoder architecture is widely used due to its simplicity and scalability at test time, it has some notable issues such as low performance on seen datasets and weak zero-shot performance on new datasets. In this paper, we analyze these issues and summarize two main critiques: the encoding inform","authors":["Tran, Hung-Nghiep","Aizawa, Akiko","Takasu, Atsuhiro"],"url":"https://arxiv.org/abs/2408.01094","arxiv_id":"2408.01094","published":"2025-12-29"},{"title":"Making Robust Generalizers Less Rigid with Loss Concentration","abstract":"While the traditional formulation of machine learning tasks is in terms of performance on average, in practice we are often interested in how well a trained model performs on rare or difficult data points at test time. To achieve more robust and balanced generalization, methods applying sharpness-aware minimization to a subset of worst-case examples have proven successful for image classification tasks, but only usin","authors":["Holland, Matthew J.","Hamada, Toma"],"url":"https://arxiv.org/abs/2408.03619","arxiv_id":"2408.03619","published":"2025-12-29"},{"title":"HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive Modelling","abstract":"Machine learning in computational pathology (CPath) often aggregates patch-level predictions from multi-gigapixel Whole Slide Images (WSIs) to generate WSI-level prediction scores for crucial tasks such as survival prediction and drug effect prediction. However, current methods do not explicitly characterize distributional differences between patch sets within WSIs. We introduce HistoKernel, a novel Maximum Mean Disc","authors":["Keller, Piotr","Dawood, Muhammad","Chohan, Brinder Singh","Minhas, Fayyaz ul Amir Afsar"],"url":"https://arxiv.org/abs/2408.05195","arxiv_id":"2408.05195","published":"2025-12-29"},{"title":"GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussion","abstract":"In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse NLP tasks. Extensive research has explored how to enhance the logical reasoning abilities such as Chain-of-Thought, Chain-of-Thought with Self-Consistency, Tree-Of-Thoughts, and multi-agent debates. In the context of multi-agent debates, significant performance improvements can be achieved with an increasing number ","authors":["Liu, Tongxuan","Wang, Xingyu","Huang, Weizhe","Xu, Wenjiang","Zeng, Yuting","Jiang, Lei"],"url":"https://arxiv.org/abs/2409.14051","arxiv_id":"2409.14051","published":"2025-12-29"},{"title":"Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rank","abstract":"State-of-the-art Extreme Multi-Label Text Classification models rely on multi-label attention to focus on key tokens in input text, but learning good attention weights is challenging. We introduce PLANT - Pretrained and Leveraged Attention - a plug-and-play strategy for initializing attention. PLANT works by planting label-specific attention using a pretrained Learning-to-Rank model guided by mutual information gain.","authors":["Roy, Debjyoti Saha","Wallace, Byron C.","Aslam, Javed A."],"url":"https://arxiv.org/abs/2410.23066","arxiv_id":"2410.23066","published":"2025-12-29"},{"title":"Online Convex Optimization with Memory and Limited Predictions","abstract":"This paper addresses an online convex optimization problem where the cost function at each step depends on a history of past decisions (i.e., memory), and the decision maker has access to limited predictions of future cost values within a finite window. The goal is to design an algorithm that minimizes the dynamic regret against the optimal sequence of decisions in hindsight. To this end, we propose a novel predictiv","authors":["Wang, Zhengmiao","Liu, Zhi-Wei","Chi, Ming","Wang, Xiaoling","Su, Housheng","Ye, Lintao"],"url":"https://arxiv.org/abs/2410.23574","arxiv_id":"2410.23574","published":"2025-12-29"},{"title":"An Exploration of Higher Education Course Evaluation by Large Language Models","abstract":"Course evaluation plays a critical role in ensuring instructional quality and guiding curriculum development in higher education. However, traditional evaluation methods, such as student surveys, classroom observations, and expert reviews, are often constrained by subjectivity, high labor costs, and limited scalability. With recent advancements in large language models (LLMs), new opportunities have emerged for gener","authors":["Yuan, Bo","Hu, Jiazi"],"url":"https://arxiv.org/abs/2411.02455","arxiv_id":"2411.02455","published":"2025-12-29"},{"title":"New advances in universal approximation with neural networks of minimal width","abstract":"We prove several universal approximation results at minimal or near-minimal width for approximation of $L^p(\\mathbb{R}^{d_x}, \\mathbb{R}^{d_y})$ and $C^0(\\mathbb{R}^{d_x}, \\mathbb{R}^{d_y})$ on compact sets. Our approach uses a unified coding scheme that yields explicit constructions relying only on standard analytic tools. We show that feedforward neural networks with two leaky ReLU activations $σ_α$, $σ_{-α}$ achie","authors":["Rochau, Dennis","Chan, Robin","Gottschalk, Hanno"],"url":"https://arxiv.org/abs/2411.08735","arxiv_id":"2411.08735","published":"2025-12-29"},{"title":"Impact of Data Snooping on Deep Learning Models for Locating Vulnerabilities in Lifted Code","abstract":"This study examines the impact of data snooping on neural networks used to detect vulnerabilities in lifted code, and builds on previous research that used word2vec and unidirectional and bidirectional transformer-based embeddings. The research specifically focuses on how model performance is affected when embedding models are trained with datasets, which include samples used for neural network training and validatio","authors":["McCully, Gary A.","Hastings, John D.","Xu, Shengjie"],"url":"https://arxiv.org/abs/2412.02048","arxiv_id":"2412.02048","published":"2025-12-29"},{"title":"CEKER: A Generalizable LLM Framework for Literature Analysis with a Case Study in Unikernel Security","abstract":"Literature reviews are a critical component of formulating and justifying new research, but are a manual and often time-consuming process. This research introduces a novel, generalizable approach to literature analysis called CEKER which uses a three-step process to streamline the collection of literature, the extraction of key insights, and the summarized analysis of key trends and gaps. Leveraging Large Language Mo","authors":["Wollman, Alex","Hastings, John"],"url":"https://arxiv.org/abs/2412.10904","arxiv_id":"2412.10904","published":"2025-12-29"},{"title":"PIMphony: Overcoming Bandwidth and Capacity Inefficiency in PIM-based Long-Context LLM Inference System","abstract":"The expansion of long-context Large Language Models (LLMs) creates significant memory system challenges. While Processing-in-Memory (PIM) is a promising accelerator, we identify that it suffers from critical inefficiencies when scaled to long contexts: severe channel underutilization, performance-limiting I/O bottlenecks, and massive memory waste from static KV cache management. In this work, we propose PIMphony, a P","authors":["Kwon, Hyucksung","Koo, Kyungmo","Kim, Janghyeon","Lee, Woongkyu","Lee, Minjae","Jung, Gyeonggeun"],"url":"https://arxiv.org/abs/2412.20166","arxiv_id":"2412.20166","published":"2025-12-29"},{"title":"VTAO-BiManip: Masked Visual-Tactile-Action Pre-training with Object Understanding for Bimanual Dexterous Manipulation","abstract":"Bimanual dexterous manipulation remains significant challenges in robotics due to the high DoFs of each hand and their coordination. Existing single-hand manipulation techniques often leverage human demonstrations to guide RL methods but fail to generalize to complex bimanual tasks involving multiple sub-skills. In this paper, we introduce VTAO-BiManip, a novel framework that combines visual-tactile-action pretrainin","authors":["Sun, Zhengnan","Shi, Zhaotai","Chen, Jiayin","Liu, Qingtao","Cui, Yu","Ye, Qi"],"url":"https://arxiv.org/abs/2501.03606","arxiv_id":"2501.03606","published":"2025-12-29"},{"title":"Transforming Indoor Localization: Advanced Transformer Architecture for NLOS Dominated Wireless Environments with Distributed Sensors","abstract":"Indoor localization in challenging non-line-of-sight (NLOS) environments often leads to poor accuracy with traditional approaches. Deep learning (DL) has been applied to tackle these challenges; however, many DL approaches overlook computational complexity, especially for floating-point operations (FLOPs), making them unsuitable for resource-limited devices. Transformer-based models have achieved remarkable success i","authors":["Masrur, Saad","Jung-Fu","Cheng","Khamesi, Atieh R.","Guvenc, Ismail"],"url":"https://arxiv.org/abs/2501.07774","arxiv_id":"2501.07774","published":"2025-12-29"},{"title":"Computerized Assessment of Motor Imitation for Distinguishing Autism in Video (CAMI-2DNet)","abstract":"Motor imitation impairments are commonly reported in individuals with autism spectrum conditions (ASCs), suggesting that motor imitation could be used as a phenotype for addressing autism heterogeneity. Traditional methods for assessing motor imitation are subjective, labor-intensive, and require extensive human training. Modern Computerized Assessment of Motor Imitation (CAMI) methods, such as CAMI-3D for motion cap","authors":["Kinfu, Kaleab A.","Pacheco, Carolina","Sperry, Alice D.","Crocetti, Deana","Tunçgenç, Bahar","Mostofsky, Stewart H."],"url":"https://arxiv.org/abs/2501.08609","arxiv_id":"2501.08609","published":"2025-12-29"}],"history":{"events":[{"year":2024,"text":"Jeju Air Flight 2216 crashed at Muan International Airport while attempting to perform a belly-landing after losing engine thrust to a flock of Baikal teal.","url":"https://en.wikipedia.org/wiki/Jeju_Air_Flight_2216"},{"year":1994,"text":"Turkish Airlines Flight 278 crashed on approach to Van Ferit Melen Airport in Van, Turkey, killing 57 of the 76 people on board.","url":"https://en.wikipedia.org/wiki/Turkish_Airlines_Flight_278"},{"year":1975,"text":"Planted by unknown perpetrators, a bomb exploded (aftermath pictured) at LaGuardia Airport in New York City, killing 11 people and seriously injuring 74 others.","url":"https://en.wikipedia.org/wiki/1975_LaGuardia_Airport_bombing"},{"year":1959,"text":"American physicist Richard Feynman gave a speech entitled \"There's Plenty of Room at the Bottom\" at Caltech, anticipating the field of nanotechnology.","url":"https://en.wikipedia.org/wiki/Richard_Feynman"},{"year":1940,"text":"Second World War: The Luftwaffe began a major night bombing raid on the British capital as part of the Blitz, beginning what was later called the \"Second Great Fire of London\".","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1928,"text":"The Northern Expedition, a military campaign by the National Revolutionary Army of the Kuomintang, ended with the complete control of the Republic of China.","url":"https://en.wikipedia.org/wiki/Northern_Expedition"},{"year":1915,"text":"First World War: The French parliament passed a law granting the land occupied by British war graves as \"the free gift of the French people\".","url":"https://en.wikipedia.org/wiki/World_War_I"},{"year":1913,"text":"Cecil B. DeMille started filming Hollywood's first feature film, The Squaw Man (featured).","url":"https://en.wikipedia.org/wiki/Cecil_B._DeMille"},{"year":1890,"text":"Sioux Wars: The United States Army killed 250 to 300 Lakota men, women and children at the Wounded Knee Massacre, beginning the Ghost Dance War.","url":"https://en.wikipedia.org/wiki/Sioux_Wars"},{"year":1876,"text":"A railway bridge collapsed over the Ashtabula River in Ohio, killing 92 people and injuring 64 others on a Lake Shore and Michigan Southern Railway train.","url":"https://en.wikipedia.org/wiki/Ashtabula_River_railroad_disaster"},{"year":1860,"text":"To counter the French Navy's Gloire, the world's first ironclad warship, the Royal Navy launched HMS Warrior, the world's first iron-hulled armoured warship.","url":"https://en.wikipedia.org/wiki/French_Navy"},{"year":1845,"text":"The Republic of Texas was annexed by the United States, becoming the 28th state to be admitted to the Union.","url":"https://en.wikipedia.org/wiki/Republic_of_Texas"},{"year":1812,"text":"War of 1812: In a three-hour single-ship action, HMS Java (drawing shown) was captured by USS Constitution off the coast of Brazil.","url":"https://en.wikipedia.org/wiki/War_of_1812"}],"births":[{"year":2006,"text":"Ethan Mbappé, French footballer","url":"https://en.wikipedia.org/wiki/Ethan_Mbapp%C3%A9"},{"year":2005,"text":"Davide Bartesaghi, Italian footballer","url":"https://en.wikipedia.org/wiki/Davide_Bartesaghi"},{"year":2000,"text":"Orkun Kökçü, Dutch-Turkish footballer","url":"https://en.wikipedia.org/wiki/Orkun_K%C3%B6k%C3%A7%C3%BC"},{"year":2000,"text":"Julio Rodríguez, Dominican baseball player","url":"https://en.wikipedia.org/wiki/Julio_Rodr%C3%ADguez"},{"year":2000,"text":"Eliot Vassamillet, Belgian singer","url":"https://en.wikipedia.org/wiki/Eliot_Vassamillet"},{"year":1999,"text":"Andreas Skov Olsen, Danish footballer","url":"https://en.wikipedia.org/wiki/Andreas_Skov_Olsen"},{"year":1999,"text":"Francisco Trincão, Portuguese footballer","url":"https://en.wikipedia.org/wiki/Francisco_Trinc%C3%A3o"},{"year":1998,"text":"Seamus Davey-Fitzpatrick, American actor","url":"https://en.wikipedia.org/wiki/Seamus_Davey-Fitzpatrick"}],"deaths":[{"year":2024,"text":"Aaron Brown, American journalist and academic (born 1948)","url":"https://en.wikipedia.org/wiki/Aaron_Brown_(journalist)"},{"year":2024,"text":"Jimmy Carter, American politician, 39th President of the United States (born 1924)","url":"https://en.wikipedia.org/wiki/Jimmy_Carter"},{"year":2024,"text":"Linda Lavin, American actress and singer (born 1937)","url":"https://en.wikipedia.org/wiki/Linda_Lavin"},{"year":2024,"text":"Tomiko Itooka, Japanese supercentenarian (born 1908)","url":"https://en.wikipedia.org/wiki/Tomiko_Itooka"},{"year":2023,"text":"Gil de Ferran, French-born Brazilian racing driver, CART champion (2000, 2001), 2003 Indianapolis 500 winner (born 1967)","url":"https://en.wikipedia.org/wiki/Gil_de_Ferran"},{"year":2022,"text":"Pelé, Brazilian footballer (born 1940)","url":"https://en.wikipedia.org/wiki/Pel%C3%A9"},{"year":2022,"text":"Edgar Savisaar, Estonian politician, Estonian Minister of the Interior (born 1950)","url":"https://en.wikipedia.org/wiki/Edgar_Savisaar"},{"year":2022,"text":"Vivienne Westwood, English fashion designer (born 1941)","url":"https://en.wikipedia.org/wiki/Vivienne_Westwood"}]},"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."}