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We present the cold neutral content (H i and C i gas) of 13 quasar 2175Å dust absorbers (2DAs) at z = 1.6 -2.5 to investigate the correlation between the presence of the UV extinction bump with other physical characteristics. These 2DAs were initially selected from the Sloan Digital Sky Surveys I -III and followed up with the Keck-II telescope and the Multiple Mirror Telescope as detailed in our Paper I. We perform a correlation analysis between metallicity, redshift, depletion level, velocity width, and explore relationships between 2DAs and other absorption line systems. The 2DAs on average have higher metallicity, higher depletion levels, and larger velocity widths than Damped Lyman-α absorbers (DLAs) or subDLAs. The correlation between [Zn/H] and [Fe/Zn] or [Zn/H] and log∆V 90 can be used as alternative stellar mass estimators based on the well-established mass-metallicity relation. The estimated stellar masses of the 2DAs in this sample are in the range of ∼ 10 9 to ∼2 × 10 11 M ⊙ with a median value of ∼2 × 10 10 M ⊙ . The relationship with other quasar absorption line systems can be described as (1) 2DAs are a subset of Mg ii and Fe ii absorbers, (2) 2DAs are preferentially metal-strong DLAs/subDLAs, (3) More importantly, all of the 2DAs show C i detections with logN(C i) > 14.0 cm −2 , (4) 2DAs can be used as molecular gas tracers. Their host galaxies are likely to be chemically enriched, evolved, massive (more massive than typical DLA/subDLA galaxies), and presumably star-forming galaxies.

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Many people search for foreground objects to use when editing images. While existing methods can retrieve candidates to aid in this, they are constrained to returning objects that belong to a pre-specified semantic class. We instead propose a novel problem of unconstrained foreground object (UFO) search and introduce a solution that supports efficient search by encoding the background image in the same latent space as the candidate foreground objects. A key contribution of our work is a cost-free, scalable approach for creating a large-scale training dataset with a variety of foreground objects of differing semantic categories per image location. Quantitative and human-perception experiments with two diverse datasets demonstrate the advantage of our UFO search solution over related baselines.