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The mechanisms that drive disk winds are a window into the physical processes that underlie the disk. Stellar-mass black holes are an ideal setting in which to explore these mechanisms, in part because their outbursts span a broad range in mass accretion rate. We performed a spectral analysis of the disk wind found in six Chandra/HETG observations of the black hole candidate 4U~1630$-$472, covering a range of luminosities over two distinct spectral states. We modeled both wind absorption and extended wind re-emission components using PION, a self-consistent photoionized absorption model. In all but one case, two photoionization zones were required in order to obtain acceptable fits. Two independent constraints on launching radii, obtained via the ionization parameter formalism and the dynamical broadening of the re-emission, helped characterize the geometry of the wind. The innermost wind components ($r \simeq {10}^{2-3}$ $GM/{c}^{2}$) tend towards small volume filling factors, high ionization, densities up to $n \simeq {10}^{15-16} {\text{cm}}^{-3}$, and outflow velocities of $\sim 0.003c$. These small launching radii and large densities require magnetic driving, as they are inconsistent with numerical and analytical treatments of thermally driven winds. Outer wind components ($r \simeq {10}^{5}$ $GM/{c}^{2}$) are significantly less ionized and have filling factors near unity. Their larger launching radii, lower densities ($n \simeq {10}^{12} {\text{cm}}^{-3}$), and outflow velocities ($\sim 0.0007c$) are nominally consistent with thermally driven winds. The overall wind structure suggests that these components may also be part of a broader MHD outflow and perhaps better described as magneto-thermal hybrid winds., Comment: 27 pages, 13 figures

The mechanisms that drive disk winds are a window into the physical processes that underlie the disk. Stellar-mass black holes are an ideal setting in which to explore these mechanisms, in part because their outbursts span a broad range in mass accretion rate. We performed a spectral analysis of the disk wind found in six Chandra/HETG observations of the black hole candidate 4U~1630$-$472, covering a range of luminosities over two distinct spectral states. We modeled both wind absorption and extended wind re-emission components using PION, a self-consistent photoionized absorption model. In all but one case, two photoionization zones were required in order to obtain acceptable fits. Two independent constraints on launching radii, obtained via the ionization parameter formalism and the dynamical broadening of the re-emission, helped characterize the geometry of the wind. The innermost wind components ($r \simeq {10}^{2-3}$ $GM/{c}^{2}$) tend towards small volume filling factors, high ionization, densities up to $n \simeq {10}^{15-16} {\text{cm}}^{-3}$, and outflow velocities of $\sim 0.003c$. These small launching radii and large densities require magnetic driving, as they are inconsistent with numerical and analytical treatments of thermally driven winds. Outer wind components ($r \simeq {10}^{5}$ $GM/{c}^{2}$) are significantly less ionized and have filling factors near unity. Their larger launching radii, lower densities ($n \simeq {10}^{12} {\text{cm}}^{-3}$), and outflow velocities ($\sim 0.0007c$) are nominally consistent with thermally driven winds. The overall wind structure suggests that these components may also be part of a broader MHD outflow and perhaps better described as magneto-thermal hybrid winds., Comment: 27 pages, 13 figures

This paper details the methodology and results of the EmotioNet challenge. This challenge is the first to test the ability of computer vision algorithms in the automatic analysis of a large number of images of facial expressions of emotion in the wild. The challenge was divided into two tracks. The first track tested the ability of current computer vision algorithms in the automatic detection of action units (AUs). Specifically, we tested the detection of 11 AUs. The second track tested the algorithms' ability to recognize emotion categories in images of facial expressions. Specifically, we tested the recognition of 16 basic and compound emotion categories. The results of the challenge suggest that current computer vision and machine learning algorithms are unable to reliably solve these two tasks. The limitations of current algorithms are more apparent when trying to recognize emotion. We also show that current algorithms are not affected by mild resolution changes, small occluders, gender or age, but that 3D pose is a major limiting factor on performance. We provide an in-depth discussion of the points that need special attention moving forward.