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The extremely sharp transition between molecular and ionized gas in the Horsehead nebula

, , , , , , , , , , , , , , and . (2023)cite arxiv:2307.09540Comment: 15 pages, 1 table, 9 figures; Accepted for publication in A&A.

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The extremely sharp transition between molecular and ionized gas in the Horsehead nebula, , , , , , , , , and 5 other author(s). (2023)cite arxiv:2307.09540Comment: 15 pages, 1 table, 9 figures; Accepted for publication in A&A.A Discriminative Event Based Model for Alzheimer's Disease Progression Modeling., , , and . IPMI, volume 10265 of Lecture Notes in Computer Science, page 121-133. Springer, (2017)An Interpretable Machine Learning Model with Deep Learning-Based Imaging Biomarkers for Diagnosis of Alzheimer's Disease., , , , , , , , , and 2 other author(s). ISIC/Care-AI/MedAGI/DeCaF@MICCAI, volume 14393 of Lecture Notes in Computer Science, page 69-78. Springer, (2023)A Hybrid Deep Learning Framework for Integrated Segmentation and Registration: Evaluation on Longitudinal White Matter Tract Changes., , , , , , and . MICCAI (3), volume 11766 of Lecture Notes in Computer Science, page 645-653. Springer, (2019)Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI., , , , , and . Medical Imaging: Image Processing, volume 11596 of SPIE Proceedings, SPIE, (2021)Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations., , , , , , , and . NeuroImage, (2021)Projection-Wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis., , , , , and . MICCAI (5), volume 12905 of Lecture Notes in Computer Science, page 814-823. Springer, (2021)Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset., , , , , and . MLMI@MICCAI, volume 11046 of Lecture Notes in Computer Science, page 205-213. Springer, (2018)Deep learning denoising by dimension reduction: Application to the ORION-B line cubes, , , , , , , , , and 22 other author(s). (2023)cite arxiv:2307.13009.Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration., , , , , , and . CoRR, (2020)