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Next generation interferometers, such as the Square Kilometre Array, are set to obtain vast quantities of information about the kinematics of cold gas in galaxies. Given the volume of data produced by such facilities astronomers will need fast, reliable, tools to informatively filter and classify incoming data in real time. In this paper, we use machine learning techniques with a hydrodynamical simulation training set to predict the kinematic behaviour of cold gas in galaxies and test these models on both simulated and real interferometric data. Using the power of a convolutional autoencoder we embed kinematic features, unattainable by the human eye or standard tools, into a three-dimensional space and discriminate between disturbed and regularly rotating cold gas structures. Our simple binary classifier predicts the circularity of noiseless, simulated, galaxies with a recall of $85\%$ and performs as expected on observational CO and HI velocity maps, with a heuristic accuracy of $95\%$. The model output exhibits predictable behaviour when varying the level of noise added to the input data and we are able to explain the roles of all dimensions of our mapped space. Our models also allow fast predictions of input galaxies' position angles with a $1\sigma$ uncertainty range of $\pm17^{\circ}$ to $\pm23^{\circ}$ (for galaxies with inclinations of $82.5^{\circ}$ to $32.5^{\circ}$, respectively), which may be useful for initial parameterisation in kinematic modelling samplers. Machine learning models, such as the one outlined in this paper, may be adapted for SKA science usage in the near future., Comment: 15 pages, 11 figures

We introduce a domain switch for deep neural networks that enables to re-weight convolutional kernels for an input of a known domain. This technique is designed to address re-occurring tasks across multiple domains that are known at runtime and to incorporate them into a single, domain-spanning network. We evaluate this approach in three distinct tasks, namely combined cell nuclei analysis across different stains and fluorescence images, facial landmark detection in grayscale and thermal infrared images, and the BraTS challenge where we treat different recording institutions as domains. We found that conventional U-nets trained on multiple domains perform similar to domain-specific U-nets. Our method improves the results in facial landmark detection significantly, but no change is measured in the other two experiments compared to multi-domain U-nets.

Thermal infrared imaging is an emerging imaging modality allowing capturing heat radiation not detectable in the visible spectrum. In recent years, numerous applications of thermal infrared imaging for the processing of face images have been published. Many of these approaches only allow minimal head movement due to the lack of sufficiently robust face tracking and interpretation algorithms for thermal infrared images. To address this issue, we present a suite of interconnected algorithms for a number of typical facial image processing tasks face detection, head pose estimation, detection and tracking of facial landmarks and facial expression analysis. The modules can be used independently or in combination with each other. For combined use and as a demonstration of the versatility of our solution, we present a multiprocess solution based on a networking middleware that allows using all proposed algorithms to perform real-time face tracking and emotion recognition in thermal infrared images. The full code is made freely available on gitub under GNU license to allow incorporating our solutions into own projects.

A lightweight framework for fast prototyping and training of deep neural networks for medical image analysis.