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Evidence for feedback in action from the molecular gas content in the z~1.6 outflowing QSO XID2028

, , , , , , , , , , , , , , , , , , and . (2015)cite arxiv:1503.01783Comment: 8 pages, 4 figures. Astronomy and Astrophysics, in press.

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The SINS/zC-SINF survey of z~2 galaxy kinematics: Evidence for powerful AGN-driven nuclear outflows in massive star-forming galaxies, , , , , , , , , and 22 other author(s). (2013)cite arxiv:1311.2596Comment: 17 pages, 5 figures, submitted to Astrophysical Journal.Evidence for Wide-Spread AGN Driven Outflows in the Most Massive z~1-2 Star Forming Galaxies, , , , , , , , , and 28 other author(s). (2014)cite arxiv:1406.0183Comment: Submitted to Astrophysical Journal.Evidence for feedback in action from the molecular gas content in the z~1.6 outflowing QSO XID2028, , , , , , , , , and 9 other author(s). (2015)cite arxiv:1503.01783Comment: 8 pages, 4 figures. Astronomy and Astrophysics, in press.The KMOS^3D Survey: design, first results, and the evolution of galaxy kinematics from 0.7<z<2.7, , , , , , , , , and 17 other author(s). (2014)cite arxiv:1409.6791Comment: 20 pages, 11 figures, submitted to ApJ.ALMA and Herschel reveal that AGN and main-sequence galaxies have different star formation rate distributions, , , , , , , , , and 8 other author(s). (2015)cite arxiv:1506.05459Comment: 5 pages, 3 figures, 1 table; Submitted to MNRAS.A nuclear molecular outflow in the Seyfert galaxy NGC3227, , , , , , , , , and 17 other author(s). (2019)cite arxiv:1906.06889Comment: Accepted for publication in Astronomy and Astrophysics, 17 pages, 12 figures.The Mean Star-Forming Properties of QSO Host Galaxies, , , , , , , , , and 15 other author(s). (2013)cite arxiv:1310.1922Comment: 10 figures, 1 table; accepted for publication in Astronomy & Astrophysics.ALMA observations of a z~3.1 Protocluster: Star Formation from Active Galactic Nuclei and Lyman-Alpha Blobs in an Overdense Environment, , , , , , , , , and 7 other author(s). (2016)cite arxiv:1601.00682Comment: MNRAS, submitted: 9 pages, 3 figures, 2 tables.Classification of Breast Cancer Histopathological Images using Convolutional Neural Networks with Hierarchical Loss and Global Pooling., , , , and . ICIAR, volume 10882 of Lecture Notes in Computer Science, page 745-753. Springer, (2018)Ellipse Detection of Optic Disc-and-Cup Boundary in Fundus Images., , , , , and . ISBI, page 601-604. IEEE, (2019)