Author: Aria Cook

Authors: Gregory Kell、Angus Roberts、Serge Umansky、Yuti Khare、Najma Ahmed、Nikhil Patel、Chloe Simela、Jack Coumbe、Julian Rozario、Ryan-Rhys Griffiths、Iain J. Marshall Paper: https://arxiv.org/abs/2408.08624 RealMedQA: A Pilot Biomedical Question Answering Dataset Containing Realistic Clinical Questions Introduction Clinical question answering (QA) systems have the potential to revolutionize the way clinicians access information during consultations. Despite significant advancements, the adoption of these systems in clinical settings remains limited. One major barrier is the lack of QA datasets that reflect the real-world needs of healthcare professionals. Existing datasets often derive questions from research article titles or are designed to test comprehension of biomedical corpora, which may not align with the practical…

Authors: Duong Nguyen、Ana Ulianovici、Sami Achour、Soline Aubry、Nicolas Chesneau Paper: https://arxiv.org/abs/2408.08637 Magazine Supply Optimization: A Case Study Introduction The print magazine industry has faced significant challenges due to the rise of digital media. This shift has led to a decline in print magazine sales, making it difficult for publishers to maintain profitability and relevance. Additionally, the industry is under pressure to adopt sustainable practices due to environmental concerns and rising production costs. Efficient supply optimization is crucial in this context to minimize losses from out-of-stock situations and reduce the costs associated with excess supply. However, optimizing magazine supply is complex due to…

Authors: Xixi Wang、Zitian Wang、Jingtao Jiang、Lan Chen、Xiao Wang、Bo Jiang Paper: https://arxiv.org/abs/2408.08078 Introduction Remote sensing image change detection is a critical task that involves identifying variable pixel-level regions between two images taken at different times. This task has numerous applications, including damage assessment, urban studies, ecosystem monitoring, agricultural surveying, and resource management. Despite the progress made in this field, challenges remain, especially in extreme scenarios. Existing methods often rely on Convolutional Neural Networks (CNN) and Transformers to build their backbones for remote sensing image change detection. However, these methods typically focus on extracting multi-scale spatial features and often overlook the importance of…

Authors: Zibo Liu、Zhe Jiang、Shigang Chen Paper: https://arxiv.org/abs/2408.06445 Introduction Long-term traffic flow forecasting is essential for intelligent transportation systems, enabling traffic managers to make informed decisions well in advance. However, accurately predicting traffic flow over extended periods is challenging due to the complex spatio-temporal correlations and dynamic patterns present in continuous-time stream data. Traditional Neural Differential Equations (NDEs) have shown promise in modeling continuous-time traffic dynamics but fall short in long-term forecasting due to their inability to capture delayed traffic patterns, dynamic spatial dependencies, and abrupt trend shifts. To address these limitations, the authors propose a novel architecture called Multi-View Neural…

Authors: Yu Liu、Baoxiong Jia、Yixin Chen、Siyuan Huang Paper: https://arxiv.org/abs/2408.06697 Introduction The paper “SlotLifter: Slot-guided Feature Lifting for Learning Object-centric Radiance Fields” introduces a novel approach to learning object-centric representations in 3D scenes. This method, named SlotLifter, aims to address the challenges of scene reconstruction and decomposition by leveraging slot-guided feature lifting. The approach integrates object-centric learning representations with image-based rendering methods, achieving state-of-the-art performance in scene decomposition and novel-view synthesis on both synthetic and real-world datasets. Background Object-centric Learning Object-centric learning focuses on disentangling visual scenes into object-like entities for reasoning and manipulation. Traditional methods have primarily focused on 2D images,…

Authors: Firas Bayram、Bestoun S. Ahmed、Erik Hallin Paper: https://arxiv.org/abs/2408.06724 Introduction In the modern industrial landscape, data has become a critical asset, driving the success of artificial intelligence (AI) and machine learning (ML) solutions. Ensuring the quality of this data is paramount for reliable decision-making. This paper introduces the Adaptive Data Quality Scoring Operations Framework, a novel approach designed to address the dynamic nature of data quality in industrial applications. By integrating a dynamic change detector mechanism, this framework ensures that data quality scores remain relevant and accurate over time. Conceptual Background Continuous Monitoring and Drift Detection Continuous monitoring of data streams…