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Although natural language processing techniques, such as recurrent neural networks, and self-attention, have been adapted for learning medical representations from hierarchical, time-stamped EHR data, they often struggle when either general or task-specific data are limited. Recent efforts have attem","authors":["Peng, Xueping","Long, Guodong","Shen, Tao","Wang, Sen","Zhang, Chengqi","Clarke, Allison"],"url":"https://arxiv.org/abs/2107.09288","arxiv_id":"2107.09288","published":"2026-01-12"},{"title":"SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images","abstract":"Deep convolutional neural networks (Deep CNN) have achieved hopeful performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose an approach called SDT-DCSCN that jointly performs super-resolution and deblurring of low-resolution blurry text images based o","authors":["Neji, Hala","Halima, Mohamed Ben","Nogueras-Iso, Javier","Hamdani, Tarek. M.","Qahtani, Abdulrahman M.","Almutiry, Omar"],"url":"https://arxiv.org/abs/2201.05865","arxiv_id":"2201.05865","published":"2026-01-12"},{"title":"Precise Asymptotics for Spectral Methods in Mixed Generalized Linear Models","abstract":"In a mixed generalized linear model, the goal is to learn multiple signals from unlabeled observations: each sample comes from exactly one signal, but it is not known which one. We consider the prototypical problem of estimating two statistically independent signals in a mixed generalized linear model with Gaussian covariates. Spectral methods are a popular class of estimators which output the top two eigenvectors of","authors":["Zhang, Yihan","Mondelli, Marco","Venkataramanan, Ramji"],"url":"https://arxiv.org/abs/2211.11368","arxiv_id":"2211.11368","published":"2026-01-12"},{"title":"Utilising physics-guided deep learning to overcome data scarcity","abstract":"Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, well-annotated datasets can be challenging or even impossible in many real-world applications, such as structural risk estimation and medical diagnosis. This presents a significant barrier to the practical implementation of DL in these fields. Physics-guided deep learning (PGDL","authors":["Bai, Jinshuai","Alzubaidi, Laith","Wang, Qingxia","Kuhl, Ellen","Bennamoun, Mohammed","Gu, Yuantong"],"url":"https://arxiv.org/abs/2211.15664","arxiv_id":"2211.15664","published":"2026-01-12"},{"title":"PASS: Peer-Agreement based Sample Selection for training with Noisy Labels","abstract":"The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. This has, therefore, motivated the emergence of learning with noisy-label (LNL) techniques that focus on separating noisy- and clean-label samples to apply different learning strategies to each group of samples. Current methodologies often rely on the small-loss hypothesis or feature-based selection to ","authors":["Garg, Arpit","Nguyen, Cuong","Felix, Rafael","Do, Thanh-Toan","Carneiro, Gustavo"],"url":"https://arxiv.org/abs/2303.10802","arxiv_id":"2303.10802","published":"2026-01-12"},{"title":"An Evaluation on Large Language Model Outputs: Discourse and Memorization","abstract":"We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs). Our analysis is done with off-the-shelf, readily-available tools. We find a correlation between percentage of memorized text, percentage of unique text, and overall output quality, when measured with respect to output pathologies such as counterfactual and logically-flawed statements, and","authors":["de Wynter, Adrian","Wang, Xun","Sokolov, Alex","Gu, Qilong","Chen, Si-Qing"],"url":"https://arxiv.org/abs/2304.08637","arxiv_id":"2304.08637","published":"2026-01-12"},{"title":"Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation","abstract":"Deep learning faces a formidable challenge when handling noisy labels, as models tend to overfit samples affected by label noise. This challenge is further compounded by the presence of instance-dependent noise (IDN), a realistic form of label noise arising from ambiguous sample information. To address IDN, Label Noise Learning (LNL) incorporates a sample selection stage to differentiate clean and noisy-label samples","authors":["Garg, Arpit","Nguyen, Cuong","Felix, Rafael","Do, Thanh-Toan","Carneiro, Gustavo"],"url":"https://arxiv.org/abs/2305.19486","arxiv_id":"2305.19486","published":"2026-01-12"},{"title":"DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning","abstract":"Recent advances in natural language processing, primarily propelled by Large Language Models (LLMs), have showcased their remarkable capabilities grounded in in-context learning. A promising avenue for guiding LLMs in intricate reasoning tasks involves the utilization of intermediate reasoning steps within the Chain-of-Thought (CoT) paradigm. Nevertheless, the central challenge lies in the effective selection of exem","authors":["Xiong, Jing","Li, Zixuan","Zheng, Chuanyang","Guo, Zhijiang","Yin, Yichun","Xie, Enze"],"url":"https://arxiv.org/abs/2310.02954","arxiv_id":"2310.02954","published":"2026-01-12"},{"title":"Simple Mechanisms for Representing, Indexing and Manipulating Concepts","abstract":"Supervised and unsupervised learning using deep neural networks typically aims to exploit the underlying structure in the training data; this structure is often explained using a latent generative process that produces the data, and the generative process is often hierarchical, involving latent concepts. Despite the significant work on understanding the learning of the latent structure and underlying concepts using t","authors":["Li, Yuanzhi","Meka, Raghu","Panigrahy, Rina","Shah, Kulin"],"url":"https://arxiv.org/abs/2310.12143","arxiv_id":"2310.12143","published":"2026-01-12"},{"title":"Expression Syntax Information Bottleneck for Math Word Problems","abstract":"Math Word Problems (MWP) aims to automatically solve mathematical questions given in texts. Previous studies tend to design complex models to capture additional information in the original text so as to enable the model to gain more comprehensive features. In this paper, we turn our attention in the opposite direction, and work on how to discard redundant features containing spurious correlations for MWP. To this end","authors":["Xiong, Jing","Li, Chengming","Yang, Min","Hu, Xiping","Hu, Bin"],"url":"https://arxiv.org/abs/2310.15664","arxiv_id":"2310.15664","published":"2026-01-12"},{"title":"Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting","abstract":"Accurate spatial-temporal traffic flow forecasting is crucial in aiding traffic managers in implementing control measures and assisting drivers in selecting optimal travel routes. Traditional deep-learning based methods for traffic flow forecasting typically rely on historical data to train their models, which are then used to make predictions on future data. However, the performance of the trained model usually degr","authors":["Guo, Pengxin","Jin, Pengrong","Li, Ziyue","Bai, Lei","Zhang, Yu"],"url":"https://arxiv.org/abs/2401.04148","arxiv_id":"2401.04148","published":"2026-01-12"},{"title":"Simulating Multi-Stakeholder Decision-Making with Generative Agents in Urban Planning","abstract":"Reaching consensus in urban planning is a complex process often hindered by prolonged negotiations, trade-offs, power dynamics, and competing stakeholder interests, resulting in inefficiencies and inequities. Advances in large language models (LLMs), with their increasing capabilities in knowledge transfer, reasoning, and planning, have enabled the development of multi-generative agent systems, offering a promising a","authors":["Gao, Jin","Xu, Hanyong","Dao, Luc"],"url":"https://arxiv.org/abs/2402.11314","arxiv_id":"2402.11314","published":"2026-01-12"},{"title":"Dynamic and Adaptive Feature Generation with LLM","abstract":"The representation of feature space is a crucial environment where data points get vectorized and embedded for subsequent modeling. Thus the efficacy of machine learning (ML) algorithms is closely related to the quality of feature engineering. As one of the most important techniques, feature generation transforms raw data into an optimized feature space conducive to model training and further refines the space. Despi","authors":["Zhang, Xinhao","Zhang, Jinghan","Rekabdar, Banafsheh","Zhou, Yuanchun","Wang, Pengfei","Liu, Kunpeng"],"url":"https://arxiv.org/abs/2406.03505","arxiv_id":"2406.03505","published":"2026-01-12"},{"title":"Competency-Aware Planning for Probabilistically Safe Navigation Under Perception Uncertainty","abstract":"Perception-based navigation systems are useful for unmanned ground vehicle (UGV) navigation in complex terrains, where traditional depth-based navigation schemes are insufficient. However, these data-driven methods are highly dependent on their training data and can fail in surprising and dramatic ways with little warning. To ensure the safety of the vehicle and the surrounding environment, it is imperative that the ","authors":["Pohland, Sara","Tomlin, Claire"],"url":"https://arxiv.org/abs/2409.06111","arxiv_id":"2409.06111","published":"2026-01-12"},{"title":"KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistry","abstract":"Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building an AI-powered chemical brain, which frames chemical intelligence around four core capabilities: information extraction, semantic parsing, knowledge-based QA, and reasoning & planning. We argue that domain knowledge and logic are essentia","authors":["Dai, Weichen","Chen, Yezeng","Dai, Zijie","Liu, Yubo","Huang, Zhijie","Pan, Yixuan"],"url":"https://arxiv.org/abs/2409.18695","arxiv_id":"2409.18695","published":"2026-01-12"},{"title":"Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap","abstract":"The rise of AI-assisted software engineering (SE 2.0), powered by Foundation Models (FMs) and FM-powered coding assistants, has shown promise in improving developer productivity. However, it has also exposed inherent limitations, such as cognitive overload on developers and inefficiencies. We propose a shift towards Software Engineering 3.0 (SE 3.0), an AI-native approach characterized by intent-centric, conversation","authors":["Hassan, Ahmed E.","Oliva, Gustavo A.","Lin, Dayi","Chen, Boyuan","Ming, Zhen","Jiang"],"url":"https://arxiv.org/abs/2410.06107","arxiv_id":"2410.06107","published":"2026-01-12"},{"title":"Controlled Automatic Task-Specific Synthetic Data Generation for Hallucination Detection","abstract":"We present a novel approach to automatically generate non-trivial task-specific synthetic datasets for hallucination detection. Our approach features a two-step generation-selection pipeline, using hallucination pattern guidance and a language style alignment during generation. Hallucination pattern guidance leverages the most important task-specific hallucination patterns while language style alignment aligns the st","authors":["Xie, Yong","Aggarwal, Karan","Ahmad, Aitzaz","Lau, Stephen"],"url":"https://arxiv.org/abs/2410.12278","arxiv_id":"2410.12278","published":"2026-01-12"},{"title":"Water quality polluted by total suspended solids classified within an Artificial Neural Network approach","abstract":"This study investigates the application of an artificial neural network framework for analysing water pollution caused by solids. Water pollution by suspended solids poses significant environmental and health risks. Traditional methods for assessing and predicting pollution levels are often time-consuming and resource-intensive. To address these challenges, we developed a model that leverages a comprehensive dataset ","authors":["Soto, I. Luviano","Sánchez, Y. Concha","Raya, A."],"url":"https://arxiv.org/abs/2410.14929","arxiv_id":"2410.14929","published":"2026-01-12"},{"title":"Assessing Superposition-Targeted Coverage Criteria for Quantum Neural Networks","abstract":"Quantum Neural Networks (QNNs) have achieved initial success in various tasks by integrating quantum computing and neural networks. However, growing concerns about their reliability and robustness highlight the need for systematic testing. Unfortunately, current testing methods for QNNs remain underdeveloped, with limited practical utility and insufficient empirical evaluation. As an initial effort, we design a set o","authors":["Shao, Minqi","Zhao, Jianjun"],"url":"https://arxiv.org/abs/2411.02450","arxiv_id":"2411.02450","published":"2026-01-12"},{"title":"AtomThink: Multimodal Slow Thinking with Atomic Step Reasoning","abstract":"In this paper, we address the challenging task of multimodal reasoning by incorporating the notion of ``slow thinking'' into multimodal large language models (MLLMs). Our core idea is that models can learn to adaptively use different levels of reasoning to tackle questions of varying complexity. We propose a novel paradigm of Self-structured Chain of Thought (SCoT), which consists of minimal semantic atomic s","authors":["Xiang, Kun","Liu, Zhili","Zhang, Terry Jingchen","Huang, Yinya","Nie, Yunshuang","Cai, Kaixin"],"url":"https://arxiv.org/abs/2411.11930","arxiv_id":"2411.11930","published":"2026-01-12"},{"title":"Infrared-Assisted Single-Stage Framework for Joint Restoration and Fusion of Visible and Infrared Images under Hazy Conditions","abstract":"Infrared and visible (IR-VIS) image fusion has gained significant attention for its broad application value. However, existing methods often neglect the complementary role of infrared image in restoring visible image features under hazy conditions. To address this, we propose a joint learning framework that utilizes infrared image for the restoration and fusion of hazy IR-VIS images. To mitigate the adverse effects o","authors":["Li, Huafeng","Fang, Jiaqi","Zhang, Yafei","Liu, Yu"],"url":"https://arxiv.org/abs/2411.12586","arxiv_id":"2411.12586","published":"2026-01-12"},{"title":"RobustFormer: Noise-Robust Pre-training for images and videos","abstract":"While deep learning-based models like transformers, have revolutionized time-series and vision tasks, they remain highly susceptible to noise and often overfit on noisy patterns rather than robust features. This issue is exacerbated in vision transformers, which rely on pixel-level details that can easily be corrupt. To address this, we leverage the discrete wavelet transform (DWT) for its ability to decompose into m","authors":["Bastola, Ashish","Luitel, Nishant","Wang, Hao","Paudel, Danda Pani","Poudel, Roshani","Razi, Abolfazl"],"url":"https://arxiv.org/abs/2411.13040","arxiv_id":"2411.13040","published":"2026-01-12"},{"title":"PaRCE: Probabilistic and Reconstruction-based Competency Estimation for CNN-based Image Classification","abstract":"Convolutional neural networks (CNNs) are extremely popular and effective for image classification tasks but tend to be overly confident in their predictions. Various works have sought to quantify uncertainty associated with these models, detect out-of-distribution (OOD) inputs, or identify anomalous regions in an image, but limited work has sought to develop a holistic approach that can accurately estimate perception","authors":["Pohland, Sara","Tomlin, Claire"],"url":"https://arxiv.org/abs/2411.16715","arxiv_id":"2411.16715","published":"2026-01-12"},{"title":"3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes","abstract":"Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike diffusion models, AR models enable more efficient and controllable generation with faster inference times, making them especially suitable for data-intensive domains. Traditional 3D generative models using AR approaches often rely on ``next","authors":["Medi, Tejaswini","Rampini, Arianna","Reddy, Pradyumna","Jayaraman, Pradeep Kumar","Keuper, Margret"],"url":"https://arxiv.org/abs/2411.19037","arxiv_id":"2411.19037","published":"2026-01-12"},{"title":"DYRECT Computed Tomography: DYnamic Reconstruction of Events on a Continuous Timescale","abstract":"Time-resolved high-resolution X-ray Computed Tomography (4D $μ$CT) is an imaging technique that offers insight into the evolution of dynamic processes inside materials that are opaque to visible light. Conventional tomographic reconstruction techniques are based on recording a sequence of 3D images that represent the sample state at different moments in time. This frame-based approach limits the temporal resolution c","authors":["Goethals, Wannes","Bultreys, Tom","Berg, Steffen","Boone, Matthieu N.","Aelterman, Jan"],"url":"https://arxiv.org/abs/2412.00065","arxiv_id":"2412.00065","published":"2026-01-12"}],"history":{"events":[{"year":2010,"text":"Iranian physicist Massoud Ali-Mohammadi was assassinated while leaving his home for the University of Tehran, where he was a professor.","url":"https://en.wikipedia.org/wiki/Massoud_Ali-Mohammadi"},{"year":2010,"text":"An earthquake registering 7.0 Mw struck Haiti, killing more than 100,000 people.","url":"https://en.wikipedia.org/wiki/2010_Haiti_earthquake"},{"year":2007,"text":"Comet McNaught (pictured) reached perihelion, becoming the brightest comet in over 40 years, with an apparent magnitude of −5.5.","url":"https://en.wikipedia.org/wiki/Comet_McNaught"},{"year":1967,"text":"Seventy-three-year-old psychology professor James Bedford became the first person to be cryonically preserved with intent of future resuscitation.","url":"https://en.wikipedia.org/wiki/James_Bedford"},{"year":1964,"text":"Sultan Jamshid bin Abdullah was overthrown by rebels led by John Okello, ending 200 years of Arab dominance in Zanzibar.","url":"https://en.wikipedia.org/wiki/Jamshid_bin_Abdullah"},{"year":1918,"text":"An underground explosion at a coal mine in Staffordshire, England, killed 155 men and boys.","url":"https://en.wikipedia.org/wiki/Minnie_Pit_Disaster"},{"year":1916,"text":"Oswald Boelcke and Max Immelmann became the first German aviators to be awarded the Pour le Mérite, Germany's highest military honour.","url":"https://en.wikipedia.org/wiki/Oswald_Boelcke"},{"year":1899,"text":"During a storm, the crew of Lynmouth Lifeboat Station transported their 10-ton lifeboat 15 mi (24 km) overland in order to rescue a damaged schooner.","url":"https://en.wikipedia.org/wiki/Lynmouth_Lifeboat_Station"},{"year":1895,"text":"The National Trust, a conservation organisation in England, Wales and Northern Ireland, was founded.","url":"https://en.wikipedia.org/wiki/National_Trust"},{"year":1879,"text":"Anglo-Zulu War: Natal Native Contingent and British troops defeated Zulu forces in the Action at Sihayo's Kraal.","url":"https://en.wikipedia.org/wiki/Anglo-Zulu_War"},{"year":1865,"text":"The foundation stone of Saint Fin Barre's Cathedral in Cork was laid.","url":"https://en.wikipedia.org/wiki/Saint_Fin_Barre's_Cathedral"},{"year":1808,"text":"John Rennie's scheme to defend St Mary's Church (pictured) in Reculver from coastal erosion was abandoned in favour of demolition, despite the church being an exemplar of Anglo-Saxon architecture.","url":"https://en.wikipedia.org/wiki/John_Rennie_the_Elder"},{"year":1777,"text":"Mission Santa Clara de Asís (pictured), a Spanish mission in California that formed the basis of both the city of Santa Clara and Santa Clara University, was established by the Franciscans.","url":"https://en.wikipedia.org/wiki/Mission_Santa_Clara_de_As%C3%ADs"},{"year":1659,"text":"The fort at Allahabad was surrendered to the forces of Mughal emperor Aurangzeb.","url":"https://en.wikipedia.org/wiki/Allahabad_Fort"},{"year":1554,"text":"Bayinnaung, who later assembled what was probably the largest empire in the history of mainland Southeast Asia, was crowned as the king of the Burmese Toungoo dynasty.","url":"https://en.wikipedia.org/wiki/Bayinnaung"},{"year":475,"text":"Basiliscus became Byzantine emperor after Zeno was forced to flee Constantinople.","url":"https://en.wikipedia.org/wiki/Basiliscus"}],"births":[{"year":2012,"text":"Artem Kotenko, Ukrainian singer","url":"https://en.wikipedia.org/wiki/Artem_Kotenko"},{"year":2005,"text":"Yuika, Japanese singer-songwriter","url":"https://en.wikipedia.org/wiki/Yuika_(singer)"},{"year":2002,"text":"Eva Lys, German tennis player","url":"https://en.wikipedia.org/wiki/Eva_Lys"},{"year":2001,"text":"Sam LaPorta, American football player","url":"https://en.wikipedia.org/wiki/Sam_LaPorta"},{"year":2000,"text":"Sven Botman, Dutch footballer","url":"https://en.wikipedia.org/wiki/Sven_Botman"},{"year":1999,"text":"Xavier Tillman, American basketball player","url":"https://en.wikipedia.org/wiki/Xavier_Tillman"},{"year":1999,"text":"Tyler Roberts, Welsh footballer","url":"https://en.wikipedia.org/wiki/Tyler_Roberts"},{"year":1998,"text":"Juan Foyth, Argentinian footballer","url":"https://en.wikipedia.org/wiki/Juan_Foyth"}],"deaths":[{"year":2026,"text":"Rick Garcia (activist), American LGBTQ rights activist (born 1956)","url":"https://en.wikipedia.org/wiki/Rick_Garcia_(activist)"},{"year":2025,"text":"Leslie Charleson, American actress (born 1945)","url":"https://en.wikipedia.org/wiki/Leslie_Charleson"},{"year":2025,"text":"Claude Jarman Jr., American actor and producer (born 1934)","url":"https://en.wikipedia.org/wiki/Claude_Jarman_Jr."},{"year":2023,"text":"Lisa Marie Presley, American singer-songwriter (born 1968)","url":"https://en.wikipedia.org/wiki/Lisa_Marie_Presley"},{"year":2023,"text":"Sharad Yadav, Indian politician, 30th Minister of Civil Aviation, 29th Labour Minister (born 1947)","url":"https://en.wikipedia.org/wiki/Sharad_Yadav"},{"year":2022,"text":"Ronnie Spector, American singer (born 1943)","url":"https://en.wikipedia.org/wiki/Ronnie_Spector"},{"year":2020,"text":"Sir Roger Scruton, English philosopher and writer (born 1944)","url":"https://en.wikipedia.org/wiki/Roger_Scruton"},{"year":2018,"text":"Keith Jackson, American sports commentator and journalist (born 1928)","url":"https://en.wikipedia.org/wiki/Keith_Jackson"}]},"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 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Every item links to its source."}